Patent Description:
The present disclosure provides methods for detecting a false-positive diagnosis in non-invasive prenatal screening (NIPS) of fetal aneuploidies. The present methods are based on analyzing cell-free fetal DNA (cff DNA) found in a pregnant woman's circulation through the next generation sequencing (NGS) technology. Particularly, the present methods analyze the relative abundance of different fetal genomic fragments present in the maternal sample, which fragments can be aligned to particular chromosomal locations of the fetal genome. The relative abundance information is indicative as to whether a particular chromosome is overrepresented or underrepresented in a fetal genome as compared to normal individuals, and thus can be used to detect fetal aneuploidy. Additionally, methods for increasing the positive predictive values (PPV) of NIPS by excluding false-positive detections are also provided.

To avoid false positives, <CIT> uses a combined size- and count-based analysis of maternal plasma comprising both maternal and fetal cell-free DNA that allows to determine whether only the fetus, or only the mother, or both have an aberration in a subchromosomal region.

<CIT> discloses methods for detecting microamplifications or microdeletions in the genome of a fetus. To reduce the probability of false positives, the method requires consecutive aberrant regions to identify a microamplification or microdeletion.

The present invention generally relates to the field of non-invasive prenatal screening (NIPS), particularly NIPS using cell-free fetal DNA (cff DNA) found in maternal plasma. Due to biological and technical issues, current NIPS methods can produce false-positive results, prompting a medical practitioner to prescribe further diagnostic testing through invasive procedures, such as amniocentesis or a chorionic villus sampling (CVS), which carry a risk of procedure-related miscarriage and other complications. Rather than undergo such procedures, a significant number of women have terminated their pregnancies based on a NIPS report of high risk of fetal aneuploidy without additional testing. Thus, there exists a need in the field for the development of new NIPS methods, particularly those with improved positive predictive values.

The present invention relates to a method for for detecting a false-positive diagnosis of chromosomal aneuploidy in a fetus by a non-invasive prenatal screening (NIPS), comprising (a) sequencing cell-free DNA from a maternal test sample of a pregnant woman carrying the fetus to provide sequence reads; wherein the fetus has been diagnosed to be aneuploid of a chromosome of interest by the NIPS; (b) dividing the chromosome of interest into a plurality of bins, each bin having a chromosomal location; (c) aligning the sequence reads to one or more bins; (d) generating a raw bin read count; (e) calculating a bin-specific test parameter by scaling the raw bin read count with an autosomal total read count, and performing a GC correction of the scaled bin read count; (f) plotting the bin-specific test parameters versus the chromosomal locations of corresponding bins to produce an ideogram of the chromosome of interest; and (g) detecting false-positive diagnosis when the ideogram exhibits an increase of a bin-specific test parameter in at least one bin by at least <NUM> fold compared to remaining bins.

In some embodiments, an increase of bin-specific test parameter in at least one bin compared to remaining bins. Particularly, in some embodiments, the large-scale increase is at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds, at least <NUM> folds or at least <NUM> folds.

In some embodiments, the methods further comprise repeating steps (a) to (g) for a confirming chromosome other than the chromosome of interest. Particularly, in some embodiments, the confirming chromosome is one or more chromosomes of the species under examination.

In some embodiments, the bin-specific parameter is reflective of relative abundance of genetic material corresponding to the bin in a maternal test sample.

In some embodiments, the present methods improves a positive predictive value (PPV) of the NIPS to at least <NUM>% for human trisomy <NUM>, at least <NUM>% for human trisomy <NUM>, and/or at least <NUM>% for human trisomy <NUM>. In some embodiments, the PPV for trisomy <NUM> is improved to <NUM>% for human trisomy <NUM>, <NUM>% for human trisomy <NUM>, and/or <NUM>% for human trisomy <NUM>.

In some embodiments, the present methods improve a positive predictive value (PPV) of the NIPS by at least <NUM>% for human trisomy <NUM>, at least <NUM>% for human trisomy <NUM>, and/or at least <NUM>% for human trisomy <NUM>.

In another aspect, provided herein are methods for detecting false-positive diagnosis of chromosomal aneuploidy in a fetus by a non-invasive prenatal screening (NIPS) are provided. Particularly, the methods comprise (a) dividing a confirming chromosome into a plurality of bins, each bin having a chromosomal location; (b) calculating the bin-specific parameter for each bin of the reference chromosome (c) calculating a first sum of bin-specific test parameters for corresponding bins residing on a confirming chromosome; wherein the confirming chromosome is different from a chromosome of interest diagnosed to be aneuploid; (d) calculating a second sum of bin-specific test parameters for corresponding bins residing on one or more autosomes; (e) calculating a chromosome representation value for the confirming chromosome by dividing the first sum by the second sum; (f) comparing the chromosome representation value to a set of references to generate a chromosome-specific comparison result; (g) detecting false-positive diagnosis when the chromosome-specific comparison result achieves a pre-determined threshold. In some embodiments, the confirming chromosome is one or more chromosomes in a reference genome.

In some embodiments, the set of references comprises a plurality of chromosome representation values for the confirming chromosome obtained from a random sample of unaffected pregnancies.

In some embodiments, step (f) is performed by calculating a Z-score of said test chromosome representation value with respect to the set of references. In some embodiments, the threshold is achieved when the Z-score is greater than <NUM> or greater than <NUM>.

Further, in any of the embodiments above, the fetal aneuploidy can be a complete or partial chromosomal duplication or a chromosomal trisomy, such as trisomy <NUM>, trisomy <NUM> or trisomy <NUM> of the human genome. The reference genome can be a human reference genome and the fetus can be an aneuploid mosaic individual. Additionally, in any of the embodiments above, the method can further comprise first assessing a fetal fraction of the cell-free DNA in the maternal test sample before performing step (a). In some embodiments, the maternal test sample is excluded when the fetal fraction is less than <NUM>%.

The present disclosure provides methods for detecting a false-positive diagnosis in non-invasive prenatal screening (NIPS) of fetal aneuploidies. The present methods are based on analyzing cell-free fetal DNA (cff DNA) found in a pregnant woman's circulation through the next generation sequencing (NGS) technology. Particularly, the present methods analyze the relative abundance of different fetal genomic fragments present in the maternal sample, where the fragments can be aligned to particular chromosomal locations of the fetal genome. The relative abundance information is indicative as to whether a particular chromosome is overrepresented or underrepresented in a fetal genome as compared to normal individuals, and thus can be used to detect fetal aneuploidy. Additionally, methods for increasing the positive predictive values (PPV) of NIPS by excluding false-positive detections are also provided.

The term "karyotype" is well-recognized in the field and refers to an organized profile of an organism's chromosomes, indicating the copy numbers of each chromosome in the genome. Different species of organisms may have different numbers of chromosomes in their genome, and thus different karyotypes. For example, the normal human karyotypes contain <NUM> pairs of autosomal chromosomes (autosome) and one pair of sex chromosomes (allosomes). Normal karyotypes for female humans contain two X allosomes; and normal male humans have both an X and a Y allosomes.

The term "ploidy" refers to the number of sets of chromosomes contained in the species' genome. Particularly, a haploid species has a single set of chromosomes, each chromosome not being part of a pair. A diploid species has two homologous copies of each chromosome. By extension, a cell may be called haploid or diploid if its nucleus is haploid or diploid, and an organism may be called haploid or diploid if its somatic cells are haploid or diploid. Nearly all mammals, including human, are diploid organisms.

The terms "aneuploidy" and "aneuploid" are terms well recognized in the art and refer to the presence of an abnormal number of chromosomes in a cell of an organism, which differs from the usual karyotype for that species. For example, because a normal human cell has <NUM> chromosomes, including <NUM> pairs of autosomes and <NUM> pair of sex chromosomes, a human cell having <NUM> or <NUM> chromosomes instead of the usual <NUM> is aneuploid. Aneuploidy may result from an error in the cell division process, where the "daughter" cells formed have the wrong number of chromosomes. In some cases there is a missing chromosome (monosomy), while in others an extra (trisomy). Both monosomy and trisomy are common causes of genetic disorders in human, including certain birth defects and cancers. In human, apart from sex chromosome disorders, most cases of aneuploidy result in miscarriage. The most common autosomal trisomy among live birth is trisomy of chromosomes <NUM>, <NUM>, or <NUM>. For example, the Down Syndrome is a genetic disorder caused by the presence of all or part of a <NUM>rd copy of chromosome <NUM>.

The term "trisomy" refers to a type of aneuploidy in a diploid organism, where there is an extra copy (three copies) of a particular chromosome, instead of the normal two copies of a pair. The term "monosomy" also refers to a form of aneuploidy in a diploid organism, where there is one missing copy (only one copy) of a particular chromosome, rather than the normal two copies in a pair.

The term "mosaicism" or "mosaic" as used herein refers to the presence of two or more cell lines with different karyotypes in the same individual. For example, in some embodiments, a mosaic individual may have certain populations of aneuploid somatic cells, while the other cells have the normal karyotype.

The term "fetal aneuploidy" as used herein refers to aneuploidy in a fetus in gestation. Diagnosis of such disorder may be through invasive or non-invasive methods.

The terms "non-invasive prenatal testing (NIPT)" and "non-invasive prenatal screening (NIPS)" are used interchangeably herein and refer to maternal sample tests for fetal aneuploidy, such as selected chromosome trisomies, based on detecting cell-free fetal DNA presented in a maternal sample, such as a maternal blood sample.

The term "invasive prenatal examination" as used herein refers to methods for prenatal examination on a fetus via a probe or probes placed inside the fetus-containing space of a pregnant women's body or a maternal tissue directly connecting to the fetus, such as the uterus, placenta, or umbilical cord. Invasive prenatal examinations that have been contemplated to be used in connection with the present disclosure include but are not limited to amniocentesis and chorionic villus sampling.

The term "chromosomal duplication" as used herein refers to duplication of an entire chromosome or a portion of a chromosome. Depending on the contexts, the term "complete chromosomal duplication" may refer to the duplication of a whole chromosome, and the term "partial chromosomal duplication" may refer to duplication of a portion of a chromosome. For example, in some embodiments, a partial chromosomal duplication refers to the existence of duplicated genetic material corresponding to more than <NUM>%, <NUM>% <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, or <NUM>% of a particular chromosome in a genome. In other embodiments, a partial chromosomal duplication refers to the duplication of hundreds of kilo base pairs to tens of mega base pairs of genetic materials of a particular chromosome in a genome.

The term "chromosomal deletion" as used herein refers to the loss of an entire chromosome or a portion of a chromosome. In a case of partial chromosomal deletion, the genome may lose more than <NUM>%, <NUM>% <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, or <NUM>% of a particular chromosome. Partial chromosomal deletion may refer to the loss of hundreds of kilo base pairs to tens of mega base pairs of genetic materials of a particular chromosome in a genome.

Chromosomal duplication or deletion may arise as the product of various types of errors in DNA replication or repair machinery, as well as through fortuitous capture of genetic elements by the chromosome. As used herein, duplicated or deleted regions of a chromosome may or may not contain any gene.

"Gene" as used herein refers to a DNA sequence that comprises regulatory and coding sequences necessary for the production of an RNA, which may have a non-coding function (e.g., a ribosomal or transfer RNA) or which may include a polypeptide or a polypeptide precursor. The RNA or polypeptide may be encoded by a full length coding sequence or by any portion of the coding sequence so long as the desired activity or function is retained. Although a sequence of the nucleic acids may be shown in the form of DNA, a person of ordinary skill in the art recognizes that the corresponding RNA sequence will have a similar sequence with the thymine being replaced by uracil, i.e., "T" is replaced with "U.

The term "chromosome variation" as used herein refers to the phenomenon that chromosomes vary slightly in composition and size among individuals of a species. For example, copy number variation refers to the observed phenomenon that sections of a species' genome are repeated and the number of repeats in the genome varies among individuals in the population. Additionally, microduplication and microdeletion refer to chromosome variations in which a small amount of genetic material on a chromosome is abnormally copied or deleted in an individual's genome. Further, chromosomal duplication or deletion may occur over an extended span of genomic region. Depending on the context, chromosome variations may or may not produce observable phenotypic abnormality in the individuals. Thus, chromosomes may vary in composition and size among different individuals of a species due to inherited or de novo chromosome variations.

The terms "cell-free DNA (cfDNA)" as used herein refers to any free-floating DNA existing in a sample, such as the blood plasma of a pregnant patient. Cell-free DNA found in a pregnant woman's blood may contain DNA both originated from the mother and the fetus. The term "cell-free fetal DNA (cffDNA)" as used herein refers to fetal DNA circulating freely in the maternal system, such as in the mother's bloodstream. Through various mechanisms, cffDNA may, for example, originate from the trophoblasts making up the placenta. In some cases, the fetal DNA may be fragmented and make its way into the maternal bloodstream via shedding of the placental micro-particles into the maternal bloodstream. In some cases, cffDNA can first be observed in maternal blood as early as <NUM> weeks gestation, and increases in the amount as the pregnancy progresses. The cffDNA may be sampled by venipuncture on the mother and provides the basis for non-invasive prenatal diagnosis and testing.

The term "fetal fraction (ff)" as used herein refers to the percentage of cell-free DNA found in a pregnant mother's test sample that originates from the fetus. For example, if <NUM>% of cell-free DNA found in a mother's blood sample is of a fetal origin, the fetal fraction (ff) is determined to be <NUM>%. In some embodiments, fetal fraction is used as a parameter for sample quality and for determining whether a maternal sample should be included in the analysis. Particularly, in some embodiments, when the fetal fraction of a sample is below a pre-determined threshold, the maternal sample is excluded. The threshold may range between about <NUM>% to about <NUM>%. In some embodiments, the threshold is <NUM>%.

"Next generation sequencing (NGS)" as used herein, refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput parallel fashion (e.g., greater than <NUM><NUM>, <NUM><NUM>, <NUM><NUM> or more molecules are sequenced simultaneously). The relative abundance of the nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences in the data generated by the sequencing experiment. Next generation sequencing methods are known in the art, and are described, e.g., in <NPL>).

As used herein, the term "library" refers to a collection of nucleic acid sequences, e.g., a collection of nucleic acids derived from whole genomic, sub-genomic fragments, cDNA, cDNA fragments, RNA, RNA fragments, or a combination thereof. A portion or all of the library nucleic acid sequences may comprise an adapter sequence. The adapter sequence can be located at one or both ends. The adapter sequence can be useful, e.g., for a sequencing method (e.g., an NGS method), for amplification, for reverse transcription, or for cloning into a vector.

The term "sequencing bin" or simply "bin" is well-recognized in the field and refers to a chromosomal region which has a characteristic DNA sequence known to be unique to that chromosomal region. A bin thus has a chromosomal location corresponding to the particular region on a chromosome. In various embodiments, a bin may be <NUM> kilo base pairs (kbp), <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp, <NUM> kbp or <NUM> kbp long.

A "sequence read" or simply "read" as used herein refers to sequence information of a nucleic acid fragment obtained through a sequencing assay, such as a next generation sequencing (NGS) assay. Thus, if a sequence read aligns with the characteristic sequence of a bin, the sequence read can be unambiguously mapped to the bin and its specific chromosomal location. The term "bin read count" or simply "bin count" refers to the total number of reads mapped to a bin. According to the present invention, a raw bin read count is generated that is then scaled with an autosomal total read count.

The term "reference genome" refers to a nucleic acid sequence database, assembled as a representative example of a species' partial or complete set of genetic constitution, such as DNA sequences of particular chromosomes contained in the species' genome. For example, human reference genome is maintained and improved by the Genome Reference Consortium (GRC). The GRC continues to improve reference genomes by building new sequence alignments that contain fewer gaps in the genome. For example, the human reference genome GRCh38 is the twentieth version of human reference genome released by the GRC.

The term "Z-score" refers to a numerical measurement of a relationship between the value in question (the sample value) and the data set to which the data point belongs. Particularly, the Z-score measures the difference between the sample value and the centrality of distribution in terms of the spread of the distribution of the set of data points. The centrality of distribution can be measured as the median or mean value of the data set. The spread of the distribution may be measured as the standard deviation or median absolute deviation of the data set. More particularly, a Z-score indicates how many median absolute deviations above or below the median the sample value is. Particularly, Z-score can be calculated by z = (X - µ) / σ, where X represents the sample value; µ represents the median; and σ represents the median absolute deviation of the data set. Thus, a z-value equals to zero indicates that the sample value is identical to the median. A positive z-value indicates that the sample value is greater than the median, and a negative Z-score indicates that the sample value is less than the median.

The term "ideogram" as used herein refers to a schematic representation of one or more chromosomes. An ideogram can show, among others, the relative sizes of the chromosomes and their banding patterns, which may appear when a tightly coiled chromosomal region is stained and viewed under a microscope. As used herein, an ideogram can also show mapping of characteristic DNA sequences, including but not limited to, known gene sequences, marker sequences, bin sequences, to a particular chromosomal location. In some embodiments, mapping of a characteristic DNA sequence to a chromosomal location is associated with a value assigned to that chromosomal location. In some embodiments, such value can be a bin read count or a Z-score.

"Positive predictive value (PPV)" of a test for a disorder is proportional to the test's specificity and the prevalence of the disorder in the population. For example, a test with <NUM>% sensitivity and <NUM>% specificity (false-positive rate of <NUM>%) for a disorder with a prevalence of <NUM>:<NUM> (<NUM>%) will have a PPV of only <NUM>%, since for every <NUM> tests there will be approximately <NUM> true-positive and <NUM> false-positive result. In some embodiments, the prevalence of trisomies <NUM> is set to be <NUM>:<NUM>, trisomy <NUM> is <NUM>:<NUM>, and trisomy <NUM> is <NUM>: <NUM>.

Certain invasive procedures for detecting fetal aneuploidies carry a risk of procedure-related miscarriage and other complications. See for example <NPL> and <NPL>.

Accordingly, in one aspect of the present disclosure, provided are methods for noninvasive prenatal screening using cell-free fetal DNA contained in a maternal test sample. For non-invasive prenatal testing, the maternal test sample can be obtained from a pregnant woman without physically invading the fetus-containing space of the body or any maternal tissue directly connecting to the fetus. Exemplary embodiments of a maternal test sample include whole blood samples, plasma samples, tissue samples, urine samples, saliva samples, hair samples, feces and other types of biological specimens that can be non-invasively collected from the pregnant woman.

Particularly, the maternal test sample also contains a sufficient amount of cell-free fetal DNA, such that information of the fetal genome can be analyzed according to the methods provided herein. In certain embodiments, the maternal test sample can also contain cell-free DNA that originates from the maternal genome. For example, circulating cell-free DNA in the plasma of pregnant woman may be a mixture of placental fetal DNA and maternal DNA. In some embodiments, the cell-free fetal DNA is present in a wide background of maternally-originated DNAs. Thus, alternations in the amount of genetic material attributable to the fetal genome may be diluted by maternal contributions. Accordingly, in some embodiments, the maternal test sample is evaluated for the fetal fraction of cell-free DNA. Preferably, the fetal fraction is sufficient such that genetic composition of the fetal genome can be analyzed according to the methods provided herein.

In some embodiments, the fetal fraction of cell-free DNA contained in a maternal sample is measured. Samples having a fetal fraction below a certain threshold can be excluded from analysis. In some embodiments, maternal test samples of less than <NUM>% fetal fraction are excluded. In some embodiments, maternal test samples of less than <NUM>% fetal fraction are excluded. In some embodiments, maternal test samples of less than <NUM>% fetal fraction are excluded. In some embodiments, maternal test samples of less than <NUM>% fetal fraction are excluded.

Various methods can be used to quantify cell-free fetal DNA and to establish the fetal fraction of a sample. For example, in some embodiments, for male-bearing pregnancies, the presence of Y chromosome-specific sequences, such as SRY, can be quantified to establish the fetal fraction of cell-free DNA in a maternal sample. In other embodiments, for male or female-bearing pregnancies, paternally-inherited fetal single nucleotide polymorphism (SNP) alleles can be quantitated to establish the fetal fraction. In other embodiments, for male or female-bearing pregnancies, different methylation characteristics of fetal DNA and maternal DNA can be distinguished and respectively quantitated to establish the fetal fraction. In various embodiments, DNA quantitation techniques such as real-time polymerase chain reaction (RT-PCR) can be used.

In some embodiments, establishing the fetal fraction can be based on next-generation sequencing (NGS) data. Particularly, in some embodiments, total cell-free DNA in a maternal test sample is sequenced via next-generation sequencing technology to generate a plurality of DNA sequence reads. Then, the sequence reads are aligned to various bins residing on one or more chromosomes of a reference genome.

In some embodiments, for male-bearing pregnancies, fetal fraction can be calculated as: <MAT> where N<NUM>/N is average bin read count for chromosome X normalized to the average bin read count for all autosomes.

In some embodiments, male fetal fraction is estimated based on X chromosome underrepresentation in the test sample. Particularly, in some embodiments, NGS data are processed by the published Reliable Accurate Prenatal non-Invasive Diagnosis R package (RAPIDR). In other embodiments, X chromosome underrepresentation is estimated using a non-pregnant female as the two X chromosome copy reference, a male as the single X chromosome copy reference or pregnant samples of known fetal fractions as standard controls.

In some embodiments, male fetal fraction is estimated based on Y chromosome overrepresentation in a sample. Particularly, in some embodiments, Y chromosome overrepresentation is estimated using a non-pregnant female as the Y chromosome absence (<NUM>% Y) reference, a male as the Y chromosome presence (<NUM>% Y) reference and known pregnant samples of known fetal fractions as standard controls.

In some embodiments, for female-bearing pregnancies, fetal fraction is estimated using a regularized regression model. For example, in some embodiments, male fetal fractions are estimated for a training set of multiple male-bearing pregnancies. The estimated male fetal fractions are used to model fetal fraction as a function of a sample's bin counts normalized by the sample's total read count before GC-bias correction The model is then used to estimate fetal fraction for female-bearing pregnancies. In some embodiments, bins residing on chromosomes <NUM>, <NUM>, <NUM>, X or Y chromosomes are excluded from the modeling process. In some embodiments, the model is a regularized linear regression model. Particularly, in some embodiments, the model is a Lasso and Elastic-Net Regularized Generalized Linear Model (GLMNET). In some embodiments, ten-fold cross-validation using an alpha parameter of <NUM> is used to select the lambda parameter having the minimum cross-validated error for use in building the final model.

Further in some embodiments, estimation of fetal fraction is based on more than one of the methods as described above. For example, in some embodiments, fetal fraction estimates given by two or more different methods are averaged to produce the final estimate of fetal fraction in a sample.

For the purpose of the present invention cell-free DNA contained in a maternal sample is analyzed for the detection of fetal aneuploidy. Particularly, in some embodiments, the maternal test sample is non-invasively collected from a pregnant woman. In some embodiments, the pregnant woman has been previously determined to be at high risk of producing an aneuploid progeny. In some embodiments, the pregnancy has been previously determined to be at high risk of being aneuploid. In some embodiments, individuals or pregnancies deemed to be at high risk include women aged <NUM> or above, with ultrasonographic findings suggesting an increased risk of fetal aneuploidy, having previous pregnancy affected by aneuploidy or parental balanced Robertsonian translocation associated with trisomy <NUM>, <NUM>, and those screened positive for high risk aneuploidy by conventional first or second trimester screening tests. In some embodiments, the present methods are used to detect fetal aneuploidy for singlet or twin pregnancies.

In some embodiments, cell-free DNA from a maternal test sample obtained from a pregnant woman is sequenced with next generation sequencing technique. Particularly, in some embodiments, shotgun (genome wide) massively parallel sequencing (s-MPS) is used. In some embodiments, s-MPS relies on identification and counting of large numbers of DNA fragments in maternal specimens. MPS is used to simultaneously sequence millions of genome-wide fetal and maternal fragments and informative sequences are mapped to discrete locations on all chromosomes. Thus, for example, if fetal trisomy is present, there will be a relative excess of counts for a given chromosome and with a monosomy deficit.

In some embodiments, nucleic acid fragments contained in the maternal test sample are sequenced to produce a plurality of sequence reads. In some embodiments, the plurality of sequence reads are aligned to one or more bins of a reference genome, each bin is residing on a chromosome of the reference genome and having a chromosomal location. A raw bin read count is calculated for each bin by counting the total number of sequence reads mapped to the bin.

The raw bin read count may be normalized to remove artifacts such as individual sample variations, GC-sequencing biases, and other artifacts due to chromosome's high-order structures, etc. For example, a normalized bin read count can be obtained by processing a corresponding raw bin read count via one or more normalization steps as described below.

Particularly, in some embodiments, a raw bin read count can be scaled by dividing the raw bin read count by the sum of autosomal bin read counts of the sample and the scaled bin read count is further corrected by subtracting sequencing bias caused by varying GC-content across the genome. The result may be centered at the median of the scaled autosomal bin read counts. The corrected bin read counts may be further scaled by multiplying the total number of bins in the assay.

In some embodiments, samples from a reference population of presumably unaffected pregnancies are also obtained and analyzed. Particularly, for each reference sample, raw bin counts are obtained and processed as described above. Thus, the reference population provides a set of reference bin read counts for each bin that is analyzed for the sample in question. A median value may be calculated based on the set of reference bin read counts for each bin.

Bin read counts of the sample in question may also be scaled (divided) by the median reference bin read count of the corresponding bin. The result may be centered around <NUM> and further corrected by subtracting the median of the sample's autosomal bin read counts.

High order artifacts may be corrected as defined by a regression of normalized bin read counts of the sample versus the first ten principal components among the normalized bin read counts determined from a reference population of presumably unaffected samples.

In some embodiments, the relative abundance of genetic materials originating from individual fetal chromosomes in the sample are determined for the detection of fetal aneuploidy. Particularly, in some embodiments, representation of one or more chromosome of interest in a sample is calculated. The level of representation of individual chromosomes is reflective of the relative abundance of the individual chromosomes present in the sample fetal genome. Particularly, in some embodiments, chromosomal representation of a particular chromosome of interest chrRepi can be calculated as <MAT> where chrTotalRCi denotes the sum of bin-specific parameters of bins residing on a chromosome of interest, and ∑j=<NUM>. <NUM> chrTotalRCj denotes the sum of bin-specific parameters of bins residing on all autosomes of the reference genome.

In some embodiments, to determine whether a particular chromosome of interest is overrepresented or underrepresented in a sample, the chromosomal representation value of the sample is compared to a reference indicative of the normal representation. Particularly, in some embodiments, representation of the chromosome of interest is determined for samples collected from a reference population of presumably unaffected pregnancies. The chromosomal representation value of the sample in question is then compared to the set of chromosomal representation values determined from the reference population. Particularly, in some embodiments, a chromosome-specific Z-score indicative of the relationship between the sample chromosomal representation and the set of reference chromosomal representation values is calculated as: <MAT> where X is the sample chromosomal representation; µ is the median value of the set of reference chromosomal representations; and σ is the median absolute deviation (MAD) of the set of reference chromosomal representations.

The Z-score indicates how many standard deviations above or below the mean the sample value is. Accordingly, in some embodiments, a Z-score equals or close to zero indicates that the sample chromosomal presentation of the chromosomal of interest is identical or very similar to the mean chromosomal representation in unaffected pregnancies; a Z-score significantly greater than zero indicates that the chromosome of interest is overrepresented in the sample as compared to unaffected pregnancies; and a Z-score significantly lower than zero indicates that the chromosome of interest is underrepresented in the sample as compared to unaffected pregnancies. Particularly, in some embodiments, a Z-score > <NUM> indicates chromosome overrepresentation. In some embodiments, a Z-score > <NUM> indicates a high risk of fetal chromosomal trisomy. A Z-score > <NUM> but < <NUM> may suggest that further diagnostic tests for fetal aneuploidy are advisable for the pregnant patient, such as invasive prenatal diagnostic tests. In some embodiments, a Z-score ≥ <NUM> indicates chromosome overrepresentation. In some embodiments, a Z-score ≥ <NUM> indicates a high risk of fetal chromosomal trisomy. In some embodiments, a Z-score ≥ <NUM> suggests that further diagnostic tests for fetal aneuploidy are advisable for the pregnant patient, such as invasive prenatal diagnostic tests.

Cell-free DNA in a maternal test sample may contain a mixture of maternal and fetal DNA. Because surviving aneuploid individuals typically have obvious phenotypic abnormalities, phenotypically normal pregnant women are presumed to be euploid. Thus, abnormalities in chromosomal representation as suggested by the present data can be reasonably attributed to abnormalities in the fetal genome.

Accordingly, in some embodiments, the Z-score is used as an indicative parameter for detecting aneuploidy in the fetal genome. In various embodiments, the abnormality can be chromosomal trisomy or monosomy, or a complete or partial chromosomal duplication or deletion. In some embodiments, the fetus can be chromosomal mosaicism. The fetal genome can have one or more chromosome translocations.

Particularly, in some embodiments, a Z-score > <NUM> in indicates the presence of some kind of genetic abnormality in the fetal genome. In some embodiments, a Z-score > <NUM> indicates a high risk of fetal chromosomal trisomy. A Z-score > <NUM> but < <NUM> may suggests that further diagnostic tests for fetal aneuploidy are advisable for the pregnant patient, such as invasive prenatal diagnostic tests. In some embodiments, a Z-score ≥ <NUM> indicates the presence of some kind of aneuploidy in the fetal genome. In some embodiments, a Z-score ≥ <NUM> indicates a high risk of fetal chromosomal trisomy. In some embodiments, a Z-score ≥ <NUM> suggests that further diagnostic tests for fetal aneuploidy are advisable for the pregnant patient, such as invasive prenatal diagnostic tests.

Thus, it can be appreciated that the present methods provide an effective option for detecting a false-positive diagnosis of fetal aneuploidies, such as trisomy or monosomy for high risk pregnancies by a non-invasive prenatal screening. Particularly, fetal aneuploidies that can be detected with the present methods include but are not limited to human trisomy <NUM>, trisomy <NUM>, trisomy <NUM>, and sex chromosome abnormalities.

Particularly, in some embodiments, the present NIPS methods provide improved positive predictive values (PPVs) as compared to traditional methods, such as maternal serum screening or nuchal translucency testing. Particularly, in some embodiments, the PPV of the present method is improved to at least <NUM>% for trisomy <NUM>, at least <NUM>% for trisomy <NUM> and/or at least <NUM>% for trisomy <NUM>.

The present method of NIPS methods further take into consideration that chromosomes may vary in composition and size from person to person due to the presence of relatively minor variations in the individual's genome. These minor variations may or may not produce any observable phenotype in a pregnant individual, but might affect diagnosis of fetal aneuploidy through a non-invasive method.

Accordingly, the present methods provide a mechanism for distinguishing fetal aneuploidies from maternal chromosome variations, such as maternal copy number variations, microduplications, or microdeletions. Some maternal chromosome variations may be global, affecting multiple or all chromosomes in the maternal genome. Alternatively, some maternal chromosome variations may be local and relates to a particular chromosome in the maternal genome.

Particularly, maternal global copy number abnormalities may affect multiple chromosomes at the same time, while cases of a fetus having multiple chromosomal aneuploidies tend to be rare. Accordingly, the present methods provide a mechanism that serves to examine the fetal genome karyotype by examining multiple or all chromosomes in the fetal genome. Particularly, chromosomal representation values may be obtained for one or more chromosomes in a sample, and may be compared to corresponding reference values, such as expected normal values as estimated from presumably unaffected pregnancies. A chromosome-specific Z-score may be calculated for one or more chromosomes. Particularly, the one or more chromosomes under examination by the present methods may include at least one chromosome other than a chromosome of interest that has been previously diagnosed to be affected by aneuploidy. The one or more chromosomes under examination may include all chromosomes in the fetal genome.

The present methods can recognize maternal contribution and exclude a detection as false-positive, when the data suggest that multiple fetal chromosomes including or in addition to the chromosome of interest are simultaneously affected by aneuploidy. Particularly, a chromosome-specific Z-score may be calculated for each of the multiple chromosomes. The present methods may exclude detection as false-positive when multiple chromosome-specific Z-scores are greater than <NUM> in a sample. Particularly, the present methods may exclude a detection as false-positive when multiple chromosome-specific Z-scores are greater than <NUM> in a sample.

Certain maternal chromosome variations, such as microduplications or microdeletions, affect only to a limited region of a chromosome, while fetal aneuploidies usually affect an entire chromosome or a substantial portion thereof. Thus, additionally or alternatively, the present methods provide a mechanism that serves to distinguish fetal aneuploidies from maternal contribution by pinpointing the source of observed genetic variations to a discrete chromosomal region or regions.

Particularly, the present methods can detect aneuploidy of a chromosome of interest when an observed genetic variation is consistent across the entire chromosome or a substantial portion thereof. Additionally or alternatively, the present methods can exclude a detection of aneuploidy of a chromosome of interest as false-positive, when the observed genetic variation only originates from one or more regions that represent less than a substantial portion of the chromosome of interest.

Particularly, the present methods may analyze whether bin-specific test parameters of bins residing on the chromosome of interest are consistent across the entire or a substantial portion of the chromosome. An ideogram for the chromosome of interest may be generated by plotting the set of bin-specific test parameters versus the corresponding bins' chromosomal location. The present methods may detect aneuploidy of a chromosome of interest if the ideogram exhibit consistent bin-specific test parameters across the entire chromosome of interest of a substantial portion thereof.

The substantial portion of the chromosome of interest may may represent about more than about <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>% , <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, or <NUM>% of a chromosome of interest. Consistency may indicate that difference, if any, among the set of bin-specific test parameters is statistically insignificant. Alternatively, consistency may indicate that any difference among the set of bin-specific test parameters is less than <NUM>%, <NUM>% or <NUM>%. Whether a set of bin-specific test parameters is consistent may be determined as follows: (a) defining a residual as the difference between a bin-specific test parameter for a particular bin and the mean or median of all bin-specific test parameters for a chromosome of interest; and (b) calculating a standard deviation of such residues. Particularly, if a standard deviation of such residues is less than <NUM>, then the set of bin-specific test parameters is determined to be consistent. If all the residues are within <NUM>, <NUM> or <NUM> folds of the standard deviation, the set of bin-specific test parameters is determined to be consistent. If all the residues are within ±<NUM>, ±<NUM> or ±<NUM> unit away from the mean or median, the set of bin-specific test parameters is determined to be consistent.

Maternal microduplication or microdeletion may be detected, when the ideogram exhibits a large-scale difference of bin-specific test parameter in a small chromosomal region as compared to remaining regions of the chromosome. Particularly, the large-scale difference means at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold, at least <NUM> fold or at least <NUM> fold of increase or decrease of bin-specific test parameters of certain region compared to those of other regions. A large -scale difference is defined as follows: (a) defining a residual as the difference between a bin-specific test parameter for a particular bin and the mean or median of all bin-specific test parameters for the chromosome of interest; and (b) calculating a standard deviation of such residues. Particularly, a residue greater than <NUM>, <NUM> or <NUM> folds of the standard deviation is defined to be a large-scale difference. A residue more than ±<NUM>, ±<NUM> or ±<NUM> unit away from the mean or median is defined to be a large-scale difference. Maternal contribution is detected when the ideogram exhibits a large-scale difference of bin-specific test parameter in at least one bin of the chromosome.

Fetal aneuploidy may be confirmed when the ideogram exhibits a small-scale increase in bin-specific test parameters compared to the normal value. Particularly, the normal value may be estimated based on a random set of unaffected pregnancies. A small-scale increase means that the bin-specific test parameter is increased less than <NUM> fold, less than <NUM> fold, less than <NUM> fold, less than <NUM> fold, less than <NUM> fold, or less than <NUM> fold compared to the normal value. Further, the observed small-scale increase may be consistent across the whole chromosome of interest, or a substantial portion thereof. The substantial portion of the chromosome of interest may represent about more than about <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, or <NUM>% of a chromosome of interest. Consistency may indicate that difference, if any, among the set of bin-specific test parameters is statistically insignificant. Alternatively, consistency may indicate that any difference among the set of bin-specific test parameters is less than <NUM>%, <NUM>% or <NUM>%. Whether a set of bin-specific test parameters is consistent may be determined as follows: (a) defining a residual as the difference between a bin-specific test parameter for a particular bin and the mean or median of all bin-specific test parameters for a chromosome of interest; and (b) calculating a standard deviation of such residues. Particularly, if a standard deviation of such residues is less than <NUM>, then the set of bin-specific test parameters is determined to be consistent. If all the residues are within <NUM>, <NUM> or <NUM> folds of the standard deviation, the set of bin-specific test parameters is determined to be consistent. If all the residues are within ±<NUM>, ±<NUM> or ±<NUM> unit away from the mean or median, the set of bin-specific test parameters is determined to be consistent.

According to some embodiments, provided herein are methods for improving the positive predictive value of a non-invasive prenatal test. Particularly, in some embodiments, a maternal test sample is obtained from a pregnant woman carrying a fetus that has been previously diagnosed to be aneuploid for one or more chromosome of interest. In some embodiments, cell-free DNA contained in the maternal test sample is sequenced to produce sequence reads. In some embodiments, the sequence reads are aligned to various bins residing on one or more chromosomes of a reference genome.

In some embodiments, a chromosome-specific Z-score is calculated for at least one confirming chromosome that is different from the chromosome of interest. In some embodiments, the method excludes the previous diagnosis as false positive, when the Z-score for the at least one confirming chromosome is greater than <NUM>. In some embodiments, the method excludes the previous diagnosis as false positive, when the Z-score for the at least one confirming chromosome is greater than <NUM>.

Additionally or alternatively, the set of bin-specific test parameters for corresponding bins that reside on a chromosome of interest may be analyzed to determine whether the set of bin-specific test parameters are consistent across the entire chromosome of interest or a substantial portion thereof. Particularly, an ideogram for the chromosome of interest may be constructed by plotting the set of bin-specific test parameters versus the corresponding bins' location on the chromosome. Accordingly, the present methods may exclude the previous diagnosis as false-positive if the ideogram exhibits that the bin-specific test parameters are not consistent across a substantial portion of the chromosome of interest.

Particularly, the substantial portion of the chromosome of interest may represents more than about <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>% , <NUM>%, <NUM>%, <NUM>%, <NUM>%, <NUM>%, or <NUM>% of a chromosome of interest. Consistency may indicate that difference, if any, among the set of bin-specific test parameters is statistically insignificant. Alternatively, consistency may indicate that any difference among the set of bin-specific test parameters is less than <NUM>%, <NUM>% or <NUM>%. Whether a set of bin-specific test parameters is consistent is determined as follows: (a) defining a residual as the difference between a bin-specific test parameter for a particular bin and the mean or median of all bin-specific test parameters for a chromosome of interest; and (b) calculating a standard deviation of such residues. If a standard deviation of such residues is less than <NUM>, then the set of bin-specific test parameters is determined to be consistent. If all the residues are within <NUM>, <NUM> or <NUM> folds of the standard deviation, the set of bin-specific test parameters is determined to be consistent. If all the residues are within ±<NUM>, ±<NUM> or ±<NUM> unit away from the mean or median, the set of bin-specific test parameters is determined to be consistent.

Thus, it can now be appreciated that the present disclosure provides methods for excluding a previously diagnosed fetal aneuploidy as false-positive, thus improving the positive predictive value (PPV) of the previous test. Particularly, in some embodiments, the previously test for fetal aneuploidy can be performed through the methods presently disclosed. In other embodiments, the previously test of fetal aneuploidy can be through other methods currently available in the field or to be developed in the future. Exemplary methods for detecting fetal aneuploidy that can be used in connection with the present methods include, but are not limited to, ultra-sonographic diagnosis, amniocentesis, and conventional first or second trimester screenings for biomarkers contained in maternal serum. In some embodiments, the present method can improve the positive predicative value of a NIPS method by at least <NUM>% for trisomy <NUM>, and particularly at least <NUM>%, for trisomy <NUM>. In some embodiments, the present method can improve the positive predicative value of a NIPS method by at least <NUM>%, and particularly at least <NUM>%, for trisomy <NUM>. In some embodiments, the present method can improve the positive predicative value of a NIPS method by at least <NUM>%, and particularly at least <NUM>% for trisomy <NUM>.

In some embodiments, next-generation sequencing (NGS) methods are used, including a number of different modern high-throughput sequencing technologies. In some embodiments, sequencing methods capable of generating large numbers of bin counts are preferred, since the fetal fraction of cell-free DNA is usually low, and the excess or deficit in the assigned DNA fragments is small. In some embodiments, shotgun (genome wide) massively parallel sequencing (s-MPS) is used. In some embodiments, s-MPS relies on identification and counting of large numbers of DNA fragments in maternal specimens. Particularly, in some embodiments, millions of genome-wide fetal and maternal fragments are simultaneously sequenced and informative sequences are mapped to discrete locations on all chromosomes. Thus, for example, if fetal trisomy is present, there will be a relative excess of counts for a given chromosome and with a monosomy deficit.

In some embodiments, high throughput, massively parallel sequencing employs sequencing-by-synthesis with reversible dye terminators. In other embodiments, sequencing is performed via sequencing-by-ligation. In yet other embodiments, sequencing is single molecule sequencing. Examples of Next Generation Sequencing techniques include, but are not limited to pyrosequencing, Reversible dye-terminator sequencing, SOLiD sequencing, Ion semiconductor sequencing, Helioscope single molecule sequencing etc..

The Ion Torrent™ (Life Technologies, Carlsbad, CA) amplicon sequencing system employs a flow-based approach that detects pH changes caused by the release of hydrogen ions during incorporation of unmodified nucleotides in DNA replication. For use with this system, a sequencing library is initially produced by generating DNA fragments flanked by sequencing adapters. In some embodiments, these fragments can be clonally amplified on particles by emulsion PCR. The particles with the amplified template are then placed in a silicon semiconductor sequencing chip. During replication, the chip is flooded with one nucleotide after another, and if a nucleotide complements the DNA molecule in a particular microwell of the chip, then it will be incorporated. A proton is naturally released when a nucleotide is incorporated by the polymerase in the DNA molecule, resulting in a detectable local change of pH. The pH of the solution then changes in that well and is detected by the ion sensor. If homopolymer repeats are present in the template sequence, multiple nucleotides will be incorporated in a single cycle. This leads to a corresponding number of released hydrogens and a proportionally higher electronic signal.

The 454TM GS FLX ™ sequencing system (Roche, Germany), employs a light-based detection methodology in a large-scale parallel pyrosequencing system. Pyrosequencing uses DNA polymerization, adding one nucleotide species at a time and detecting and quantifying the number of nucleotides added to a given location through the light emitted by the release of attached pyrophosphates. For use with the <NUM>™ system, adapter-ligated DNA fragments are fixed to small DNA-capture beads in a water-in-oil emulsion and amplified by PCR (emulsion PCR). Each DNA-bound bead is placed into a well on a picotiter plate and sequencing reagents are delivered across the wells of the plate. The four DNA nucleotides are added sequentially in a fixed order across the picotiter plate device during a sequencing run. During the nucleotide flow, millions of copies of DNA bound to each of the beads are sequenced in parallel. When a nucleotide complementary to the template strand is added to a well, the nucleotide is incorporated onto the existing DNA strand, generating a light signal that is recorded by a CCD camera in the instrument.

Sequencing technology based on reversible dye-terminators: DNA molecules are first attached to primers on a slide and amplified so that local clonal colonies are formed. Four types of reversible terminator bases (RT-bases) are added, and non-incorporated nucleotides are washed away. Unlike pyrosequencing, the DNA can only be extended one nucleotide at a time. A camera takes images of the fluorescently labeled nucleotides, then the dye along with the terminal <NUM>' blocker is chemically removed from the DNA, allowing the next cycle.

Helicos's single-molecule sequencing uses DNA fragments with added poly-A tail adapters, which are attached to the flow cell surface. At each cycle, DNA polymerase and a single species of fluorescently labeled nucleotide are added, resulting in template-dependent extension of the surface-immobilized primer-template duplexes. The reads are performed by the Helioscope sequencer. After acquisition of images tiling the full array, chemical cleavage and release of the fluorescent label permits the subsequent cycle of extension and imaging.

Sequencing by synthesis (SBS), like the "old style" dye-termination electrophoretic sequencing, relies on incorporation of nucleotides by a DNA polymerase to determine the base sequence. A DNA library with affixed adapters is denatured into single strands and grafted to a flow cell, followed by bridge amplification to form a high-density array of spots onto a glass chip. Reversible terminator methods use reversible versions of dye-terminators, adding one nucleotide at a time, detecting fluorescence at each position by repeated removal of the blocking group to allow polymerization of another nucleotide. The signal of nucleotide incorporation can vary with fluorescently labeled nucleotides, phosphate-driven light reactions and hydrogen ion sensing having all been used. Examples of SBS platforms include Illumina GA and HiSeq <NUM>. The MiSeq® personal sequencing system (Illumina, Inc. ) also employs sequencing by synthesis with reversible terminator chemistry.

In contrast to the sequencing by synthesis method, the sequencing by ligation method uses a DNA ligase to determine the target sequence. This sequencing method relies on enzymatic ligation of oligonucleotides that are adjacent through local complementarity on a template DNA strand. This technology employs a partition of all possible oligonucleotides of a fixed length, labeled according to the sequenced position. Oligonucleotides are annealed and ligated and the preferential ligation by DNA ligase for matching sequences results in a dinucleotide encoded color space signal at that position (through the release of a fluorescently labeled probe that corresponds to a known nucleotide at a known position along the oligo). This method is primarily used by Life Technologies' SOLiD™ sequencers. Before sequencing, the DNA is amplified by emulsion PCR. The resulting beads, each containing only copies of the same DNA molecule, are deposited on a solid planar substrate.

SMRT™ sequencing is based on the sequencing by synthesis approach. The DNA is synthesized in zero-mode wave-guides (ZMWs)-small well-like containers with the capturing tools located at the bottom of the well. The sequencing is performed with use of unmodified polymerase (attached to the ZMW bottom) and fluorescently labeled nucleotides flowing freely in the solution. The wells are constructed in a way that only the fluorescence occurring at the bottom of the well is detected. The fluorescent label is detached from the nucleotide at its incorporation into the DNA strand, leaving an unmodified DNA strand.

Certain sequencing methods produce result that is biased by the varying guanine-cytosine (G-C) base content of the sequence. Accordingly, in some embodiments, GC-sequencing biases are corrected during data processing. According to the present disclosure, various methods for correcting GC-sequencing biases can be used in connection with the present methods. An exemplary procedure is provided in the example section below. A skilled artisan would be able to identify other suitable methods, either readily available in the field or to be developed in the future.

The following sections describe materials and methods used for performing the present NIPS assay.

In one example, for assay development, verification, and validation studies, applicant obtained samples from pregnant women from Sequenom (San Diego, CA), Precision Medicine, and consented volunteers. For singleton pregnancies Applicants obtained <NUM>,<NUM> samples from Sequenom, <NUM> from Precision Medicine, and <NUM> from volunteers; Sequenom also provided samples from <NUM> twin gestations. The Sequenom samples were scheduled to be discarded and were de-identified before being sent to Applicants. The samples from Precision Medicine were consented using their protocols. Volunteers provided written informed consent via signed forms approved by the Western Institutional Review Board, which specifically reviewed and approved this study. The study was conducted according to the principles in the Declaration of Helsinki.

In one example, whole blood was collected in two, <NUM> Cell-Free DNA BCT blood collection tubes (Streck, Omaha, NE), and transported at room temperature. Blood tubes were processed within <NUM> days of draw. The plasma was isolated from each of these samples using a Tecan EVO <NUM> liquid handler (Tecan, Männedorf, Switzerland). The Tecan EVO <NUM> liquid handler performs the following activities: centrifuges the Streck blood tubes at <NUM> for <NUM> minutes at <NUM>,<NUM> x g, transfers the plasma to a <NUM> conical tube, centrifuges the <NUM> conical tube at <NUM> for <NUM> minutes at <NUM>,<NUM> x g, transfers plasma to a final <NUM> conical tube. The cell-free DNA (cfDNA) is then extracted from <NUM> of plasma using DynaMax chemistry (Thermo Fisher Scientific, Waltham, MA), following manufacturers recommendations, with the aid of a Kingfisher Flex Purification System (Thermo Fisher Scientific). cfDNA was made into sequencing ready libraries using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina® (New England BioLabs Inc, Ipswich, MA) following manufacturers recommendations. During PCR, a 10bp barcode is amplified onto each sample using the reverse PCR primer, all reactions shared a common forward primer. The universal forward primer sequence was:
<IMG>.

The reverse primer was:
CAAGCAGAAGACGGCATACGAGATXXXXXXXXXXGTGACTGGAGTTCAGACGT GTGCTCTTCCGATCT, where X denotes the <NUM> base barcode location. PCR was performed on a SimpliAmp Thermal Cycler (Thermo Fisher Scientific). PCR conditions were as follows: initial denaturation at <NUM> for <NUM> seconds, <NUM> cycles of denaturation at <NUM> for <NUM> seconds, annealing at <NUM> for <NUM> seconds and extension at <NUM> for <NUM> seconds, final extension at <NUM> for <NUM> minutes, and ends with a <NUM> hold. Following PCR, the products were purified using the Agencourt AMPure XP PCR Purification beads (Beckman Coulter, Brea, CA) following manufacturers recommendations. The AMPure bead to PCR product ratio was <NUM>: <NUM>. The cleaned-up PCR products were quantified using the Quant-It PicoGreen dsNDA Assay Kit (Thermo Fisher Scientific), following manufacturer's recommendations, and read on an Infinate <NUM> PRO Microplate Reader (Tecan). Samples were normalized to <NUM>, and pooled with <NUM> samples in each library. Library pools were denatured and further diluted to <NUM> pM. A <NUM>% PhiX Control (Illumina, San Diego, CA), was spiked into each pool. The pooled libraries were clonally amplified and bound to high output flow cells (Illumina) using the cBot system from Illumina. Sequencing was performed on a HiSeq2500 system by single read <NUM> cycles followed by <NUM> cycles to sequence the index. A minimum of <NUM> million reads were required for the bioinformatics process. Data were streamed from the HiSeq2500 system to an Isilon (EMC Isilon, Seattle, WA) server, where the data analysis pipeline was begun automatically.

Applicants used a read length of <NUM> base pairs in one direction at an average sequencing depth of <NUM>. All quality score "Q scores" were > <NUM>.

In some embodiments, fetal fractions (FF) were calculated based on X chromosome under representation or Y chromosome over representation using the following methods. a) Fetal fraction was estimated as <NUM> × (<NUM> - N<NUM> / N), where N<NUM>/N is average read count per bin for chromosome X normalized to the autosome bin average. b) Applicant used R package RAPIDR based on X chromosome under representation to estimate male FF based on X chromosome under representation. c) FF was estimated based on X chromosome under representation with non-pregnant female as two X chromosome copy reference, non-pregnant male as single X chromosome copy reference and known FF samples as standard controls. d) FF was estimated based on Y chromosome over representation with non-pregnant female as Y chromosome absence (<NUM>% Y) reference, non-pregnant male as Y chromosome presence (<NUM>% Y) reference and known FF samples as standard controls. For better male FF estimation the median value of these four calculations was used as our final male FF and such median of four FF is correlated very well with a set of known FF sample shaving R square = <NUM> with y-intercept =<NUM>.

For female fetuses, fetal fraction was estimated using a regularized regression model. Briefly, a training set of <NUM> samples from known male fetuses was used to model fetal fraction (estimated as described above) as a function of sample bin counts normalized by the sample total read count but uncorrected for GC content. Bins residing on chromosomes <NUM>, <NUM>, <NUM>, X or Y chromosomes were excluded from the modeling process. The model was a regularized linear regression model implemented with the R package "glmnet" (version <NUM>-<NUM>). Ten-fold cross-validation using an alpha parameter of <NUM> was used to select the lambda parameter having the minimum cross-validated error for use in building the final model which is subsequently used to estimate fetal fraction for female fetuses.

Fetal fractions were calculated for male fetuses using Y chromosome-specific sequences. For female fetuses Applicants developed a proprietary bioinformatics approach.

Certain genomic regions (e.g., Chromosomes <NUM> and <NUM>) are GC-rich relative to others, causing sequencing bias that may skew the percentage of counts mapped to those chromosomes. Therefore, in some embodiments, GC correction is performed to reduce variability due to sample differences with respect to the magnitude of relationship between GC content and observed read counts.

Particularly, GC content for regions corresponding to the genomic locations of sequenced bins were obtained from the HG19 reference genome materials at UCSC Genome Browser (https://genome. Then GC content was discretized by rounding GC content values to <NUM> decimal places such that multiple bins correspond to each unique value of GC content. The median of scaled (by total autosome read count) autosomal bin counts were determined at each unique level of GC content. Then, local polynomial regression (loess) is performed to estimate bin count as a smooth function of GC content. Finally, the GC normalized bin count is calculated as the median of the scaled autosomal read count plus the difference (residual) of the observed read count and the read count predicted by the loess regression model.

In some embodiments, a chromosome-specific Z-score is calculated for each chromosome of interest. Particularly, bin read count (RC) data were first scaled (divided) by its own sample autosomal total read counts. Then GC correction was performed using local polynomial regression fitting R loess function and hg19 data (see Example <NUM>). A pca model was applied to such normalized data to remove high order artifacts. Particularly, high order artifacts were subtracted as defined by a regression of normalized bin counts of sample vs. 1st <NUM> principal components among the normalized bin read counts determined from a reference population of presumably unaffected samples.

Then, a chromosome representations was calculated as the sum of normalized individual bin read counts that reside on the chromosome of interest scaled (divided) by the sum of all autosomal normalized individual bin read counts, and particularly <MAT>.

Then, each chromosome specific Z-score was calculated as <MAT>.

In some embodiments, an ideogram is generated for a chromosome of interest. Particularly, raw bin read count (RC) data were first scaled (divided) by its own sample's autosomal total read counts. Then GC correction was performed using local polynomial regression fitting R loess function and hg19 data (see Example <NUM>). Then the data was centered at the median of scaled autosomal bin read counts. Then, the data was further scaled (multiplied) by total number of bins. Each bin count was then scaled (divided) by median of corresponding normalized bin count from a reference population of presumably unaffected samples. The data was then centered around <NUM> and corrected (subtracted) by median of sample's normalized autosomal bin counts. High order artifacts were subtracted as defined by a regression of normalized bin counts of sample vs. <NUM>st <NUM> principal components among the normalized bin read counts determined from a reference population of presumably unaffected samples. Finally, the resulting normalized bin read counts were plotted versus the chromosomal location of corresponding bins to obtain the chromosomal ideogram.

Follow-up information was obtained for every positive NIPS result obtained through clinical testing at a reference laboratory. A genetic counseling team contacts the referring physician to determine the outcome of the pregnancy.

Once the performance parameters of the assay were established, a series of verification samples including known unaffected and known aneuploid pregnancies were tested. This series of <NUM>,<NUM> samples included trisomy <NUM> (n=<NUM>), trisomy <NUM> (n=<NUM>), and trisomy <NUM> (n=<NUM>). No unaffected pregnancy had a Z-score ><NUM> and no affected pregnancy had a Z-score <<NUM>. Following assay verification, a validation set comprising <NUM> samples was analyzed, including samples known to be positive for trisomy <NUM> (n=<NUM>), trisomy <NUM> (n=<NUM>), trisomy <NUM> (n=<NUM>), and XO (n=<NUM>). Once again, no unaffected pregnancy had a Z-score ><NUM> and no affected pregnancy had a Z-score <<NUM>.

Since there was no difference in performance between the verification and validation studies, the results were combined for analysis. The effects of GC correction were least for chromosome <NUM>, which has normal GC content, intermediate for chromosome <NUM>, known for having an intermediate increase and GC content, and greatest for chromosome <NUM>, which has the highest GC content (<FIG>). Using raw data, a Z-score threshold of <NUM> yielded absolute discrimination between the <NUM>,<NUM> unaffected pregnancies and the <NUM> trisomy <NUM> samples; no unaffected pregnancy had a Z-score ><NUM>, and no affected pregnancy had a Z-score <<NUM>. However, GC correction improved discrimination for chromosomes <NUM> and <NUM>: without GC correction, most trisomy <NUM> samples had Z-scores less than <NUM>; after GC correction, all trisomy <NUM> samples had Z-scores well over <NUM>. GC correction also allowed complete discrimination of trisomy <NUM> from unaffected pregnancies. Therefore, after GC correction and biostatistical smoothing, the assay provided <NUM>% discrimination between affected and unaffected pregnancies (<FIG>, right panel) demonstrates the combination of GC correction with statistical smoothing, which further improves assay performance.

Also analyzed was a series of <NUM> samples from twin gestations with known aneuploidy status as part of assay validation, including <NUM> trisomy <NUM>, <NUM> trisomy <NUM>, and <NUM> trisomy <NUM> samples. Following GC correction and smoothing, all samples with autosomal trisomies had Z-scores ><NUM> and all unaffected pregnancies had Z-scores <<NUM>. Overall, discrimination was greater in twin than singleton samples (data not shown), even though most twins would be expected to be discordant for autosomal trisomies.

As a final validation for trisomy detection, samples were obtained from <NUM> consented volunteer pregnant women and split the samples between our laboratory and Sequenom. Results were concordant in all cases. This series had <NUM> unaffected and <NUM> sample predicted to be from a woman carrying a fetus with trisomy <NUM> by both laboratories.

To assess the accuracy of the NIPS assay for fetal sex determination, <NUM> (<NUM> male) samples were tested over the course of <NUM> different assay setups. Fetal sex had been previously determined using the Sequenom Maternity21 Plus assay, but was not phenotypically confirmed. The current NIPS assay yielded concordant results in all but <NUM> sample, in which results indicated a male fetus when a female fetus was expected. Thus, overall accuracy was <NUM>% (<NUM>/<NUM>). However, the fetal fraction for this sample (<NUM>%) was below the <NUM>% threshold for reporting (not shown) and would have prompted a request for a new sample in clinical testing.

The above data indicate that the present NIPS assay is verified and validated for clinical implementation.

The following sections describes results of the present NIPS assay in exemplary clinical implementations. Particularly, samples beginning at the <NUM>th gestational week were accepted. Greater than <NUM>% of samples received are from between the <NUM>th and <NUM>th gestation week.

Based on the above validation and verification results, for clinical implementation a Z-score cutoff of ≤<NUM> was used for unaffected pregnancies and ><NUM> for affected pregnancies. Z-scores ><NUM> but < <NUM> prompted further examination. Review of the first <NUM>,<NUM> clinical samples revealed abnormal NIPS results in <NUM> (<NUM>%) (Table <NUM>). Overall positive rates were <NUM>% for trisomy <NUM>, <NUM>% for trisomy <NUM>, <NUM>% for trisomy <NUM>, and <NUM>% for sex aneuploidies. One sample was positive for the DiGeorge microdeletion and <NUM> cases had <NUM> abnormalities. Of the first <NUM>,<NUM> samples tested, results could not be reported in <NUM> (<NUM>%); the cause was low fetal fraction in <NUM> cases (<NUM>%) and uninformative DNA pattern, failure to meet quality metrics, or other technical issues in <NUM> samples (<NUM>%).

NIPS was performed using whole genome shotgun sequencing (a method that involves sequencing fragments of DNA that, in the aggregate, represent almost all of the genome). This allows the generation of a karyogram that graphically represents Z-scores throughout the entire genome. Thus a process was instituted in which, for every positive result obtained by NIPS, the karyogram of the affected chromosome was generated and examined. For a true positive result, sequence reads are increased throughout the entire chromosome. When a maternal microduplication is present, only a small region (i.e., the region that is duplicated) of the chromosome is represented through an increased number of sequence reads. The process was able to identify, in a series of <NUM>,<NUM> screened pregnant women, <NUM> women in whom maternal microduplications occurring on chromosomes <NUM>, <NUM>, and <NUM> yielded false positive results.

Until Applicant was confident that karyograms correctly predicted maternal microduplications, suspected microduplications were confirmed by microarray analysis (Affymetrix CytoScan® HD). Subsequently, maternal microarray analysis was performed at the discretion of the ordering physician. A genetic counselor contacted the physician with the report, which included a description of the suspected maternal microduplication and an offer of confirmatory microarray analysis (at no charge for under-insured patients).

For example, early during clinical testing <NUM> cases with intermediate Z-scores between <NUM> and <NUM> were encountered. One had a Z-score of <NUM> for trisomy <NUM> and another had a Z-score of <NUM> for trisomy <NUM>. "False-positive" NIPS results may be due to maternal microduplications and thus chromosomal ideograms were used to investigate whether these intermediate Z-scores represented maternal microduplications. <FIG> shows the ideogram for a typical NIPS result from a fetus confirmed to have trisomy <NUM>. In both of the cases, the ideograms clearly showed that the duplications were in a small portion of the affected chromosomes (<FIG>). With permission from the ordering physicians, microarray analysis was performed on the maternal buffy coat cells, which confirmed the maternal microduplication on chromosome <NUM> (<FIG>) and <NUM> (<FIG> and <FIG>). Henceforth, the ideogram was examined for each chromosome with an elevated Z-score before reporting an abnormal result, to ensure the entire chromosome is duplicated and the result is not due to a maternal microduplication.

Microarray analysis showed the presence of a maternal microduplication in all confirmatory tests carried out. The identification of maternal microduplications as a source of false positive results improved the PPV of our screen to <NUM>%, <NUM>% and <NUM>% for Trisomies <NUM>, <NUM> and <NUM>, respectively (Table <NUM>). True positives for Trisomy <NUM> were confirmed by karyotype and / or microarray analysis of amniocytes. Some true positives for Trisomies <NUM> and <NUM> were confirmed by the presence of characteristic sonographic abnormalities.

If there was no contact from delivering physician or neonatologist, it was assumed the delivery was unaffected. None of the maternal duplication births were Trisomies. There are no reported affected births with Trisomy <NUM> or <NUM>, leading to a NPV of <NUM>%. There was a single newborn with Trisomy <NUM>, leading to an NPV of ><NUM>%.

These results suggests that the present NIPS assay can distinguish maternal microduplications from true fetal trisomy, thus avoiding false-positive results caused by maternal duplications.

In one case, NIPS yielded a positive result for trisomy <NUM> with a Z-score of <NUM> but amniocentesis revealed a euploid fetus. The NIPS data for the entire genome were thus examined and revealed copy number changes at multiple chromosomes, reflected by elevated Z-scores for chromosomes <NUM>, <NUM>, and <NUM>, and negative Z-scores (< -<NUM>) for chromosomes <NUM>, <NUM>, and <NUM>. The mother had large fibroids. Uterine fibroids can shed DNA into the circulation, causing artificial copy number changes in NIPS analysis. Following this case, a procedure was instituted to examine the entire genome of positive NIPS cases to avoid reporting false-positive results due to global circulating aneuploidy. There have been a total of <NUM> samples with elevated Z-scores for chromosomes <NUM>, <NUM>, or <NUM> that have also had multiple copy number abnormalities on several other chromosomes. All microarray raw data has been uploaded to: http://www. gov/geo/query/acc. cgi?acc=GSE84810, Accession: GSE84810.

These results suggest that the present NIPS assay can distinguish maternal global copy number abnormalities from true fetal trisomy, thus avoiding false-positive results caused by maternal global copy number variations.

There was a single case of Down syndrome with a <NUM>:<NUM> Robertsonian translocation. This case had a highly elevated Z-score of <NUM>, which was not unexpected, since most chromosome <NUM> material was duplicated. Another patient had an intermediate Z-score (<NUM>) for chromosome <NUM>. A second sample, submitted after consulting the physician, had a Z-score of <NUM>, and a third had a Z-score of <NUM>. G banding analysis of amniocytes following amniocentesis revealed mosaic trisomy <NUM> with <NUM> trisomic cells and <NUM> euploid cells counted. A case with a highly elevated chromosome <NUM> Z-score (<NUM>) had amniocentesis demonstrating <NUM> trisomic cells and <NUM> euploid cells. A final mosaic case had a Z-score of <NUM> for chromosome <NUM>, and fetal mosaicism for Down syndrome was diagnosed by amniocentesis. The amniocyte karyotype was performed by another laboratory, and we could not obtain the ratio. The present NIPS assay detected a single mosaic fetus for trisomy <NUM> following a Z-score <NUM>. This sample had a trisomy:euploid cell ratio of <NUM>:<NUM>.

In all mosaic cases, the present NIPS analysis did not predict the mosaicism. The mosaicism was reported when follow up information on the high risk cases was obtained.

Because the percentage of trisomy mosaicism in amniocytes may not reflect the percentage in the chorion, it is difficult to estimate the analytical sensitivity of our assay for mosaic Down syndrome. However, these results suggest that the present NIPS assay can detect fetuses with as little as <NUM>% trisomic cells.

Maternal genetic variations can also affected sex chromosome aneuploidy screening. In one case positive for <NUM>,X (Turner syndrome), the estimated fetal fraction was ><NUM>% and amniocentesis revealed a euploid fetus. Maternal DNA analysis revealed maternal mosaicism for <NUM>,X. Three other cases showed a negative fetal fraction on NIPS; all <NUM> women were non-mosaic for <NUM>, XXX. Other than the single case of maternal mosaicism for Turner syndrome, all confirmed sex aneuploidies were correctly identified.

These results suggest that the present NIPS assay is able to detect fetal sex chromosome aneuploidy at least when the mother is non-mosaic.

Four sets of twins had elevated Z-scores for trisomy <NUM>. One pregnancy resulted in fetal demise of one twin without genetic testing. In <NUM> pregnancies, the diagnosis of Down syndrome was confirmed in <NUM> twin. In a third case, <NUM> twin had a teratoma and both had normal karyotypes. There was <NUM> twin gestation positive for trisomy <NUM>, which miscarried without genetic testing.

Information of whether these twin gestations were either monochorionic or dichorionic was not obtained in this study. Since <NUM>% of twin gestations are dichorionic, it was assumed that this was also the case with the present case. One might expect Z-scores for twins discordant for trisomies to be lower than from singletons, but this does not appear to be case. More data will be necessary before any conclusion can be drawn regarding the mechanism of circulating fetal DNA in twin gestations.

These results suggest that the present NIPS assay can yield similar results in twin pregnancy cases as singleton cases.

A study is performed to assess positive predictive values for previously available NIPS methods. Particularly, <NUM> consecutive specimens were analyzed, along with combined data from more recent publications, for a total of <NUM> samples in the combined data set (Table <NUM>). The cumulative PPVs were <NUM>% for trisomy <NUM>, <NUM>% for trisomy <NUM>, <NUM>% for trisomy <NUM>, and <NUM>% for sex chromosome aneuploidies. These numbers suggest that the PPV is not improving over the years for the previously available first-generation NIPS tests. Meck and colleagues recently reported similar PPV results in a series of <NUM> samples referred for invasive testing following NIPS (See <NPL>. ) These data suggest that to improve the PPV of NIPS for aneuploidies, the false-positive rate must be further decreased.

Confirmation of positive NIPS results for trisomy <NUM> was based on invasive testing. Sonographic findings for trisomy <NUM> confirmation were excluded because most "soft" findings lack specificity. Accepted were invasive testing and ultrasound evidence of abnormalities as confirmation of trisomy <NUM> and <NUM>, since there are clear sonographic findings in both disorders to confirm NIPS results.

In all, <NUM> pregnancy samples were positive for trisomy <NUM>, including <NUM> singleton and <NUM> twin pregnancies. Of these, <NUM> had successful follow-up; follow-up is pending in <NUM> patients; and <NUM> were lost to follow up. Forty-two (<NUM>%) of cases with follow-up had confirmation available by invasive testing or physical examination at delivery, including <NUM> twin gestations (Table <NUM>). The positive NIPS result was confirmed in all but <NUM> trisomy <NUM> case, and all twin gestations had <NUM> affected and <NUM> unaffected fetus. Thus, the PPV for trisomy <NUM> was <NUM>%. The single false-positive trisomy <NUM> NIPS result was associated with multiple maternal genetic abnormalities (described above) and would not have been reported positive using the new reporting criteria. Therefore, with the current practices in place, the PPV for trisomy <NUM> would have been <NUM>%. Because many pregnancies have no confirmation available, these data must be considered preliminary. In addition to confirmed cases, <NUM> pregnancies (<NUM> singletons, <NUM> twin) positive for trisomy <NUM> on NIPS suffered spontaneous abortion (Table <NUM>), consistent with an increased spontaneous abortion rate for aneuploid pregnancies. Eleven (<NUM>%) women with positive trisomy <NUM> NIPS results elected to terminate their pregnancies without confirmation by invasive testing, while <NUM> (<NUM>%) continued their pregnancies without invasive testing.

Of the <NUM> singleton and <NUM> twin pregnancy positive for trisomy <NUM>, <NUM> had successful follow-up. Direct (invasive testing) or indirect (suspected based on ultrasound findings) confirmation of positive results was available for <NUM> cases (<NUM>%). All but <NUM> were confirmed to have trisomy <NUM>, yielding a PPV of <NUM>% (Table <NUM>). One false-positive result involved the twin gestation in which <NUM> twin had a coccygeal mass thought to be a teratoma (described above). This case should have been excluded from NIPS given the frequent chromosomal abnormalities associated with neoplasias. Without this case, the PPV for trisomy <NUM> would have been <NUM>%. Four (<NUM>%) women with positive trisomy <NUM> NIPS results declined further testing and are continuing their pregnancies. There was only <NUM> spontaneous abortion among pregnancies positive for trisomy <NUM>.

Twenty-one samples were positive for trisomy <NUM>, including <NUM> (<NUM>%) with complete follow-up: <NUM> were confirmed positive based on invasive testing or suspected positive based on ultrasound findings, and <NUM> were false-positives. Thus, the PPV for trisomy <NUM> was <NUM>%. Of the <NUM> false-positive cases, <NUM> involved uterine fibroids (described above; the others remain unexplained. Placental material may be obtained to investigate the possibility of confined placental mosaicism. These cases could represent vanishing twins or confined placental mosaicism, since they had high Z-scores and no global abnormalities.

These results suggest that the positive predictive values of the present NIPS assay are at least <NUM>% for trisomy <NUM>, <NUM>% for trisomy <NUM> and <NUM>% for trisomy <NUM>, which are significantly improved as compared to conventional methods.

Of <NUM> samples positive for Turner syndrome (<NUM>,X) (Table <NUM>), <NUM> had available follow-up data; <NUM> was false-positive (PPV= <NUM>%). This was the case of maternal mosaicism for Turner syndrome described above. Using the present reporting rules, this case would have been reported as suspected maternal variation because the fetal fraction was ><NUM>%. Excluding this would lead to a theoretical <NUM>% PPV for Turner syndrome.

Of <NUM> cases positive for <NUM>,XXX, <NUM> have follow-up information; both were confirmed to have that karyotype. Two cases were positive for Klinefelter syndrome, and the single fetal genotype obtained confirmed the <NUM>,XXY karyotype. Only one sample was positive for <NUM>,XYY, but follow-up information was unavailable. Only one case involved microdeletion in the DiGeorge region of chromosome <NUM> (<FIG>). The DiGeorge-specific Z-score was -<NUM>. Amniocentesis confirmed the abnormality. Two samples had <NUM> abnormalities: <NUM> with trisomy <NUM> and Turner syndrome that miscarried and the other with high risk for both trisomy <NUM> and <NUM>, for which no follow-up data were received.

These results suggested that the present NIPS assay is able to detect sex chromosome aneuploidies and microdeletions, with a theoretical <NUM>% PPV for Turner syndrome.

The present technology is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations of individual aspects of the present technology. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

Claim 1:
A method for detecting a false-positive diagnosis of chromosomal aneuploidy in a fetus by a non-invasive prenatal screening (NIPS), comprising:
(a) sequencing cell-free DNA from a maternal test sample of a pregnant woman carrying the fetus to provide sequence reads; wherein the fetus has been diagnosed to be aneuploid of a chromosome of interest by the NIPS;
(b) dividing the chromosome of interest into a plurality of bins, each bin having a chromosomal location;
(c) aligning the sequence reads to one or more bins;
(d) generating a raw bin read count;
(e) calculating a bin-specific test parameter by scaling the raw bin read count with an autosomal total read count, and performing a GC correction of the scaled bin read count;
(f) plotting the bin-specific test parameters versus the chromosomal locations of corresponding bins to produce an ideogram of the chromosome of interest; and
(g) detecting false-positive diagnosis when the ideogram exhibits an increase of a bin-specific test parameter in at least one bin by at least <NUM> fold compared to remaining bins.