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Review
Implementation of whole genome massively parallel sequencing for noninvasive prenatal testing in laboratories Expert Rev. Mol. Diagn. 15(1), 111–124 (2015)
Djie Tjwan Thung, Lean Beulen, Jayne Hehir-Kwa and Brigitte H Faas* Radboud University Medical Center, Nijmegen, Netherlands *Author for correspondence: [email protected]
Noninvasive prenatal testing (NIPT) for fetal aneuploidies using cell-free fetal DNA in maternal plasma has revolutionized the field of prenatal care and methods using massively parallel sequencing are now being implemented almost worldwide. Substantial progress has been made from initially testing for (an)euploidies of chromosomes 13, 18 and 21, to testing for sex chromosome (an)euploidies, additional autosomal aneuploidies as well as partial deletions and duplications genome-wide. Although NIPT is associated with significantly reduced risks for the fetus in comparison to existing invasive prenatal diagnostic methods, it presents several implementation challenges. Here, we review key issues potentially influencing NIPT and illustrate them using both data from literature and in-house data. KEYWORDS: depth of coverage • NGS • NIPT • noninvasive • prenatal • whole genome sequencing
Prenatal diagnosis of fetal genetic aberrations requires an invasive diagnostic procedure, specifically chorionic villus sampling or amniocentesis, to obtain fetal material for genetic testing by rapid aneuploidy detection, karyotyping or array analysis. Although the safety of these invasive procedures has improved since their introduction, these tests carry a risk of procedure-related fetal loss and are therefore reserved for pregnancies considered to be at high risk for fetal genetic aberrations. The discovery of the presence of circulating cell-free fetal DNA (ccffDNA) in the plasma of pregnant women has opened up possibilities for noninvasive prenatal diagnostics [1]. DNA of fetal origin is believed to be present in the maternal circulation as a result of apoptosis of placental cells, is highly fragmented and ranges approximately between 6 and 20% of the total DNA circulating in maternal plasma [2–6]. The first applications of ccffDNA described and subsequently implemented in prenatal care were the noninvasive determination of the fetal Rhesus D genotype in Rhesus D-negative women [7–10] and of the fetal gender in women informahealthcare.com
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carrying a sex-linked recessive genetic disorder [9,11,12]. These analyses dealt with paternally inherited alleles present only in the fetus, which allowed selective targeting and analysis by PCR. Unfortunately, noninvasive detection of fetal genetic aberrations such as aneuploidies and recessive genetic disorders from ccffDNA is more complicated. As half of the fetal DNA is maternally inherited, the chromosome or region of interest is also carried by the mother, resulting in only a quantitative difference between the maternal and fetal contribution. Furthermore, as fetal DNA represents only a minor fraction of total circulating cell-free DNA in maternal plasma, this quantitative difference is substantially diluted by the maternal DNA contribution. Although separation of fetal and maternal DNA was attempted on the basis of size [13,14] or methylation profile [15,16], no technology with satisfactory yield and specificity has been developed so far. Therefore, for noninvasive detection of fetal genetic aberrations it is essential to have a method that can accurately determine a very small decrease or increase in the total amount of a specific chromosome or region of interest.
2015 Informa UK Ltd
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Thung, Beulen, Hehir-Kwa & Faas
Using massively parallel sequencing (MPS), it is possible to sequence millions to billions of DNA fragments in maternal plasma from multiple samples simultaneously. Most sequences (‘reads’) are unique enough to allow alignment (‘mapping’) to a specific physical location within the human genome. With MPS, even relatively small changes in the representation of chromosomes can be detected with statistically significant power [17,18]. Several studies have reported on the clinical validation of analyzing ccffDNA in maternal plasma for the detection of fetal aneuploidies and by now, in the clinical setting, three different approaches are used: whole genome MPS, sequencing all fragments in the plasma, followed by counting statistics [19–31]; targeted MPS, sequencing selected regions only, followed by analysis based on counting statistics [32–36]; and targeted MPS, sequencing selected regions all containing SNPs, followed by investigation of SNP allelic ratios [37–41]. Using whole genome MPS, a random subset of DNA fragments from maternal plasma is sequenced. With targeted MPS, the sample is first enriched for selected regions, such as regions on the chromosomes 13, 18, 21, X and Y, before sequencing. This allows a significant reduction in the number of reads required, thereby increasing throughput and limiting costs. In both whole genome and targeted MPS, the ratio of circulating DNA (cfDNA) fragments from particular chromosomes or regions of interest can be compared with reference values obtained from a disomic reference chromosome. A statistically significant deviation in the number of counts, expressed as, for example, a z-score or normalized chromosome value, indicates aneuploidy for that chromosome or region of interest. Generally speaking, the higher the number of sequenced reads is, expressed in coverage or sequencing depth, the greater the resolution is for detecting aberrations. In contrast to this so-called quantitative approach using counting statistics, targeted sequencing in combination with SNP-based methods determines chromosomal copy numbers by looking for specific patterns in allelic measurements. Several thousands of SNPs on chromosomes 13, 18, 21, X and Y are selectively amplified and sequenced. A maximum likelihood statistical method incorporating maternal genotype information and recombination frequencies is subsequently applied to determine the chromosomal count of the chromosomes analyzed in each sample. As this method uses allele distributions, it does not require reference values. Although studies validating and implementing MPS of ccffDNA to detect chromosomal aneuploidy have shown this to be highly sensitive and specific, it has become clear that discordant results between noninvasive prenatal testing (NIPT) and true fetal genotype are unavoidable. A large number of discordant cases have underlying biological reasons, including confined placental mosaicism (CPM), maternal mosaicism, co-twin demise or maternal malignancy. Confirmation of a positive NIPT result is always required and therefore, for this application, the term NIPT is preferred over the use of noninvasive prenatal diagnosis (NIPD). In this review, a number of practical issues encountered during the implementation of NIPT for the detection of fetal 112
(partial) aneuploidies using (whole genome) MPS-based techniques are highlighted and illustrated with data from literature and examples from our own experience. Specific attention will be given to data analysis, as it is becoming increasingly evident that bioinformatics play a key role in NIPT. Practical issues
Most practical issues encountered during the technical implementation of whole genome MPS for NIPT are independent of the MPS approach (whole genome vs targeted) and/or platform. FIGURE 1 schematically represents the steps of the NIPT MPS procedure, and several issues of these steps will be discussed and illustrated with data from literature and our own data below. Sample collection & processing
The reliability of NIPT depends on a sufficient percentage of ccffDNA being present in the maternal plasma (for ‘fetal fraction’ (FF) in more detail, see the ‘Fetal fraction’ section). Transport and process conditions have been studied extensively by several groups to determine preservation of the FF after blood collection [42–45]. The FF decreases when plasma and blood cells are not separated in time due to the release of cellfree maternal DNA through maternal white cell lysis. Studies on transport and process conditions have therefore mainly focused on limiting this effect. Originally, blood was collected in EDTA-anticoagulant tubes and plasma was separated from the maternal blood cells within 6 h after venipuncture to limit the effect of white cell lysis on the FF. As a processing time of 6 h after venipuncture is not always practical in routine prenatal care, subsequent investigations studied longer processing times and showed that more than 6 h can elapse between venipuncture and the separation of plasma from maternal blood cells when using EDTA-anticoagulant tubes [43,46–48]. When measured directly after DNA isolation (for more details, see the ‘Fetal fraction’ section), the absolute amount of ccffDNA remains stable when processed 24 h after venipuncture, but the FF (which is the relative proportion compared to the maternal fraction) decreases due to the increased amount of maternal cfDNA [42,43,45]. In our own study [48], we showed that when processing blood 24 h after venipuncture, fetal T21 could still reliably be detected when using whole genome MPS. Although the FF on the DNA isolate had decreased compared to a simultaneously drawn sample processed within 6 h, the FF in the actual sequenced fraction remained stable, probably as a result of a size selection step in the library preparation protocol, which enriches for shorter DNA fragments. It has been shown that the additionally released maternal DNA fragments are on average longer than fetal DNA fragments [43] and these are thus removed by the size selection step. However, when processing after 24 h, larger deviation in z-scores of several chromosomes was noticed, which might be explained by a lower DNA quality [48]. Therefore, processing times beyond 24 h are to be avoided when using EDTA anticoagulant tubes. Alternatively, studies have investigated the use of specific agents to stabilize the maternal blood cells and prevent them Expert Rev. Mol. Diagn. 15(1), (2015)
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Figure 1. Potential noninvasive prenatal testing workflow including challenges encountered during the laboratory implementation: (A) samples are collected and processed, (B) the presence of fetal DNA (or the fetal fraction) can be determined either from the DNA isolate (depicted here) or from the sequencing data, but is currently limited by the accuracy of the method, type of sequencing and/or gender of the fetus (C) because of the high throughput of noninvasive prenatal testing, laboratory steps such as DNA isolation and library preparation can be automated and (D) methods for tracking samples are key for ensuring quality. (E) Commonly lowcoverage whole genome sequencing is performed allowing either (F) targeted or whole genome analysis. (G) Data analysis takes into account systematic biases to identify whole or partial chromosome aberrations. However, (H) discordant findings are known to occur for a number of biological reasons such as confined placental mosaicism.
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from lysing, allowing for a longer time period between venipuncture and separation of plasma from cells [42,45]. Dhallan et al., for example, found a higher FF when combining the use of formaldehyde as a stabilizer with a gentle centrifugation step [49]. Others, however, could not confirm their findings [50]. Wong et al. showed that when using Streck BCT stabilizing tubes, maternal blood can be stored for up to 7 days at ambient temperature without affecting the FF or DNA quality. However, Streck stabilizing tubes are relatively expensive and not readily available to all caregivers (8.4 US$ per tube; Cell-Free DNATM BCT, Streck) in comparison to standard EDTA-anticoagulant tubes (0.13 US$ per tube; K2-EDTA BD Vacutainer). As both EDTA anticoagulant tubes with processing times within 24 h and Streck BCT tubes are suitable for NIPT, in daily practice, NIPT-offering laboratories use either of these depending on their own transport and laboratory logistics.
A key determinant of the reliability of NIPT is the FF within the sample. When the FF is insufficient, fetal aneuploidies may be masked by an overwhelming amount of maternal cfDNA. Several studies have suggested an FF of 6–20% to be generally present [4–6,20]. Typically, an FF of approximately 4% is regarded to be the minimal fraction needed for a reliable NIPT result [34,51], although the minimal FF for a reliable result strongly depends on the sequencing depth [52].
chromosome in case of an aneuploidy [6,18]; (ii) the number of reads in benign or pathogenic copy number variations (CNVs) [55] or (iii) SNP genotyping data in case of targeted sequencing [35], only method iii being applicable to any pregnancy. As most of the methods are either complex or not applicable to all pregnancies, determination of the FF still remains a challenge to many NIPT-offering laboratories, especially those offering whole genome NIPT. In contrast a less complex and recent novel method to determine the FF based on the size differences between the fetal and the maternal DNA was reported by Yu et al. [56]. They hypothesized that maternal plasma samples with a higher FF would contain a higher proportion of short DNA fragments. In a proofof-principle study, they indeed showed a positive correlation between the proportion of short DNA fragments present in the plasma and the FF, as determined using the proportion of sequences aligned to the Y-chromosome. The median difference between the values was 2.3%. They showed that the FF could be measured not only by fragment size information obtained from paired-end sequencing but also by fragment length information obtained from electrophoresis of sequencing libraries. Even though the method only gives an estimation of the FF and needs further validation, the method is very simple and can be applied at virtually no additional costs, as electrophoresis of sequencing libraries is already incorporated in (most) protocols.
Quantification of fetal fraction
Factors affecting fetal fraction
Different approaches have been described to determine the FF, either from the DNA isolate or from the sequencing data.
As mentioned in the Sample collection & processing, in vitro the FF can be influenced by the time interval between blood draw and separation of plasma from blood cells. In addition, there are a number of biological factors that affect the FF, such as maternal weight [4,32,36,57–59]. The most probable explanation for this correlation is the process of active remodeling of adipose tissue in obese women, resulting in an increased release of maternal cfDNA into the circulation [60]. As Ashoor et al. [4] showed, the median FF at 11–13 weeks gestation (singleton pregnancies) was 10.0%, but this decreased with maternal weight from 11.7% at 60 kg to 3.9% at 160 kg. The estimated proportion of women with FF below 4% increased with maternal weight from 0.7% at 60 kg to 7.1% at 100 kg and 51.1% at 160 kg. Rava et al. confirmed similarly an inverse correlation between FF and BMI [6], but found this relation to be only weak and noted that women with low BMI can also have a low FF. In a large-scale study, Wang et al. [57] also found this inverse correlation between maternal weight and FF and furthermore showed a correlation between gestational age and FF. According to their data, the FF increased incrementally between 10 and 21 weeks of gestation and over this gestational age window, an overall 1% increase in total FF was anticipated. However, after 21 weeks of gestation, a more rapid weekly increase in FF was found (1%). A clear relationship with gestational age has also been reported by others [6,58,59]. Another important biological factor influencing the FF is the genetic status of the fetus and several authors have already
Fetal fraction
DNA isolate
The FF can be determined by using differentially methylated markers between the fetus and the mother, such as the RASSF1A promoter region [53,54]. However, methods relying on methylation differences require methylation-specific restriction enzyme digestion or bisulfite conversion and are therefore less suitable for quantification. Furthermore, the FF can also be directly determined from the isolated DNA by quantifying the amount of Y-chromosome-specific sequences, but this can only be applied in case of a male fetus. However, an accurate measurement is difficult due to the total amount of DNA in plasma being very low (estimated 103–104 GE/ml plasma). Therefore, most methods on DNA isolates show a relatively high variation. Sequencing data
Alternatively, the FF can be measured after sample preparation or from the sequencing data. This may reflect more closely the FF used during sequencing, also because some library preparation protocols include a size selection step to remove longer (maternal) DNA fragments [48]. As a result, the sample used for actual sequencing might contain a slightly higher FF than the DNA isolate. Methods that calculate the FF from the sequencing data either use (i) the number of reads from the X- or Y-chromosome if the fetus is male, or from the aneuploid 114
Expert Rev. Mol. Diagn. 15(1), (2015)
Whole genome MPS for NIPT
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shown that FF is increased in case of a fetal T21 and decreased in case of fetal T13, T18 or monosomy X [4,6,32,58]. Despite the evidence that FF is mainly affected by gestational age and fetal genetic status and to a lesser extent by maternal weight, the majority of clinical NIPT samples obtained at at least 10 weeks of gestation contain sufficient fetal DNA for a reliable result [57]. Automation
Both the throughput possibilities per next generation sequencing platform and the numbers of clinical NIPT samples are rapidly increasing. Hence, reduction of manual operations is recommended to avoid sample swaps and reduce operator-to-operator variability, operator time and costs. Reduction of manual handlings can be achieved by automating DNA isolation and/or library preparation procedures. For DNA isolation, automated systems, such as the QiaSymphony from QIAGEN and the Chemagic Star from Perkin Elmer, are available, but systems like these are still not widely used and often DNA is extracted manually. Regarding automation of library preparation, Jensen et al. [61] compared manual sequencing library preparation methods with semiautomated methods and indeed showed that semiautomated methods can replace manual methods with a fourfold increase in throughput and a fourfold decrease in labor, without sacrificing library yield or quality. The use of robots usually leads to loss of material during the procedure, and as the input material for NIPT already contains such low amounts of DNA, thorough optimization is required. Furthermore, changes in laboratory methods, for example, through automation, can result in technical variability that can affect data analysis. For instance, we found that when using a reference-pool-based NIPT method (explained in the ‘Data analysis’ section), a test sample could only be analyzed against a reference pool of samples that underwent the same DNA isolation protocol. If not, technical variability between samples obscured aberrations or even lead to falsepositive results. We noticed this when extracting cell-free DNA from 16 different plasma samples using either a manual extraction method (QIAGEN Circulating Nucleic Acid kit: column-based method) or an automated extraction method (Perkin Elmer robot: bead-based method). The two sets of DNA samples were sequenced on the same slide, but the different extraction methods resulted in distinct patterns. Firstly, the two extraction methods can almost perfectly be separated in two clusters using principal component analysis on the read counts, suggesting a clear difference in read count behavior (for more details, see [SUPPLEMENTARY DATA & SUPPLEMENTARY FIGURE 1] (Supplementary material can be found online at www.informahealthcare.com/suppl/10.1586/ 14737159.2015.973857)). Secondly and more importantly, when analyzing both sets of 16 samples using a reference pool of manually extracted samples, the manual set exhibit no autosomal calls bigger than 20 Mb in the presumably euploid samples (as determined by karyotyping, SNP array or QFPCR), while the samples on the Perkin Elmer robot contain informahealthcare.com
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15 of such calls, which most probably all represent false positives (see SUPPLEMENTARY DATA). False positives can be reduced to zero for the robot-isolated samples by using an appropriate reference pool containing samples with the same extraction method (see SUPPLEMENTARY DATA). Sample tracking
Sample tracking and detection of sample swaps are an essential part of workflows within diagnostic laboratories. NIPT strategies using MPS multiplex samples within a single run and link the obtained sequencing data to the correct sample by ligating a unique molecular barcode to each sample during library preparation. After the ligation of this barcode, the possibility of sample swaps is reduced considerably with the exception of data analysis errors, such as demultiplexing the sequencing reads. However, guarding against sample swaps and incorporating procedures, able to detect sample swaps before the ligation of the unique barcodes, remains a challenge. Postnatal exome sequencing uses a laboratory procedure comparable to the NIPT-MPS procedure, but with a higher sequencing depth. As a result, SNP genotype information can be obtained directly from the sequencing data and compared with SNP information obtained from the sample before processing. However, the generally low-coverage sequencing used for whole genome NIPT in combination with the mixture of maternal and fetal DNA make direct extraction of SNP genotypes from the sequence data impossible and panels of 12 and 24 SNPs cannot be reliably used on samples sequenced with an average depth of 0.1–0.5x. For instance, in such low-coverage data, we have observed that in 20 out of 32 samples at most three of the 24 SNPs of a 24-SNP panel are covered at least 1x, the highest coverage for a single SNP being only 4x in all 32 samples. Thus, NIPT methods including an SNP check to detect sample swaps require sequencing to a medium or high depth. Alternatively, SNP checks can be performed on DNA from the buffy coat or plasma and the sequencing library, as reported by Porreco et al. [62]., but this check does not cover the whole procedure. Another option for sample tracking is performing a gender check before and after sequencing. This, however, necessitates the incorporation of an additional PCR test before sequencing, and it does not rule out sample switching between samples from fetuses with the same sex. Having each critical step controlled by a second operator, although still error prone, is the most widely used option. The Specific Constitutional Cytogenetic Guidelines, published by the European Cytogenetics Association in 2012 [63], recommend that invasively obtained material for prenatal testing should be handled with extreme care and processed in duplicate where possible to avoid sample contamination and loss of material due to failure of equipment. Although the maternal blood collected for NIPT is obtained noninvasively, the result of an NIPT test can have a high impact on future parents and the pregnancy outcome. Hence, extreme precautions should be taken to avoid and detect sample swaps. 115
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Scope of testing & analysis Chromosomes 13, 18, 21 & sex chromosomes
The initial aim of NIPT was detection of the most common fetal aneuploidies, that is, T13, T18 and T21. Several largescale studies have shown that this is now feasible with high sensitivity and specificity, both within high risk and the general population of pregnant women [25,30,62,64,65]. In general, specificity and sensitivity for T21 are almost comparable to those of diagnostic tests (>99%), whereas sensitivities of T13 and T18 are reported to be lower (>80 and >97%, respectively), depending on the technique used [66]. Since the commercial launch of NIPT in 2011, clinical testing for sex chromosomes and sex chromosomal aneuploidies has also become available, with sensitivities and specificities for sex chromosomal aneuploidies being lower than those for T18 and T21 (male >99%, female >97%, monosomy X >90%, sex chromosome trisomies 67–100%, depending on the technique) [66]. Despite the lower accuracy, most commercially available tests include sex chromosomal analysis or offer it as a separate test, even though noninvasively testing for sex and sex chromosomal aberrations goes along with a number of ethical issues [67]. Partial chromosomal aberrations
Theoretically, whole genome sequencing allows for the detection of fetal partial chromosomal aberrations in addition to the common aneuploidies. However, the detection of partial duplications and deletions is highly dependent on the sequencing depth used. The first report on the detection of a fetal microdeletion was the paper by Peters et al. in 2011 [68], in which they described the detection of a 4.2-Mb fetal microdeletion on chr12 using high-coverage whole genome sequencing. Subsequently, fetal 22q11.2 microdeletions (~3 Mb in size) using a genomic coverage of fourfold were reported [69], as well as other submicroscopic aberrations [23,24]. In contrast, a sequencing depth of 30–60x increases the detection resolution to 300 kb genome wide [55]. Alternatively, using a targeted sequencing approach, capturing only an 8-Mb region of the genome, resolutions of 100 kb for fetal microdeletions can be achieved [70]. But due to cost and throughput limitations, current clinical whole genome applications of NIPT use low-coverage data (0.1–0.5x). Despite this limitation, several algorithms have been developed for the identification of partial aberrations [71–74] and (micro)deletions and duplications as small as 6 Mb have been described [73]. Unfortunately, the development of partial aberration detection via NIPT is hampered by the limited availability of validation samples. Nonetheless, both Sequenom Inc. and Natera Inc. recently announced the commercial availability of NIPT tests for the determination of a number of fetal microdeletions, including the 22q11.12 microdeletion. Data analysis
After sequencing and demultiplexing of samples, bioinformatic and statistical analysis follow to identify chromosomal aberrations (FIGURE 2). 116
The most widely used methods for the detection of aberrations in whole genome MPS are based on counting the number of reads aligned to the reference genome [17,18]. These methods rely on the ability to correct for systematic biases and need sufficient numbers of reads for the detection of subtle differences. Chromosomal aneuploidies are identified by a relative increase (trisomy) or decrease (monosomy) in the number of aligned reads to the chromosome of interest. Theoretically, in a sample with an FF of 10% and a fetal T21, a 1.05-fold increase of aligned chr21 reads should be present, when compared to another sample with the same FF but no aneuploidy, as defined by: relative read increase =
((1 − fetal fraction) ⋅ 2)+(fetal fractiion ⋅ fetal ploidy of chr) 2
As whole genome NIPT is usually performed with low genomic coverage (
Review
Implementation of whole genome massively parallel sequencing for noninvasive prenatal testing in laboratories Expert Rev. Mol. Diagn. 15(1), 111–124 (2015)
Djie Tjwan Thung, Lean Beulen, Jayne Hehir-Kwa and Brigitte H Faas* Radboud University Medical Center, Nijmegen, Netherlands *Author for correspondence: [email protected]
Noninvasive prenatal testing (NIPT) for fetal aneuploidies using cell-free fetal DNA in maternal plasma has revolutionized the field of prenatal care and methods using massively parallel sequencing are now being implemented almost worldwide. Substantial progress has been made from initially testing for (an)euploidies of chromosomes 13, 18 and 21, to testing for sex chromosome (an)euploidies, additional autosomal aneuploidies as well as partial deletions and duplications genome-wide. Although NIPT is associated with significantly reduced risks for the fetus in comparison to existing invasive prenatal diagnostic methods, it presents several implementation challenges. Here, we review key issues potentially influencing NIPT and illustrate them using both data from literature and in-house data. KEYWORDS: depth of coverage • NGS • NIPT • noninvasive • prenatal • whole genome sequencing
Prenatal diagnosis of fetal genetic aberrations requires an invasive diagnostic procedure, specifically chorionic villus sampling or amniocentesis, to obtain fetal material for genetic testing by rapid aneuploidy detection, karyotyping or array analysis. Although the safety of these invasive procedures has improved since their introduction, these tests carry a risk of procedure-related fetal loss and are therefore reserved for pregnancies considered to be at high risk for fetal genetic aberrations. The discovery of the presence of circulating cell-free fetal DNA (ccffDNA) in the plasma of pregnant women has opened up possibilities for noninvasive prenatal diagnostics [1]. DNA of fetal origin is believed to be present in the maternal circulation as a result of apoptosis of placental cells, is highly fragmented and ranges approximately between 6 and 20% of the total DNA circulating in maternal plasma [2–6]. The first applications of ccffDNA described and subsequently implemented in prenatal care were the noninvasive determination of the fetal Rhesus D genotype in Rhesus D-negative women [7–10] and of the fetal gender in women informahealthcare.com
10.1586/14737159.2015.973857
carrying a sex-linked recessive genetic disorder [9,11,12]. These analyses dealt with paternally inherited alleles present only in the fetus, which allowed selective targeting and analysis by PCR. Unfortunately, noninvasive detection of fetal genetic aberrations such as aneuploidies and recessive genetic disorders from ccffDNA is more complicated. As half of the fetal DNA is maternally inherited, the chromosome or region of interest is also carried by the mother, resulting in only a quantitative difference between the maternal and fetal contribution. Furthermore, as fetal DNA represents only a minor fraction of total circulating cell-free DNA in maternal plasma, this quantitative difference is substantially diluted by the maternal DNA contribution. Although separation of fetal and maternal DNA was attempted on the basis of size [13,14] or methylation profile [15,16], no technology with satisfactory yield and specificity has been developed so far. Therefore, for noninvasive detection of fetal genetic aberrations it is essential to have a method that can accurately determine a very small decrease or increase in the total amount of a specific chromosome or region of interest.
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Using massively parallel sequencing (MPS), it is possible to sequence millions to billions of DNA fragments in maternal plasma from multiple samples simultaneously. Most sequences (‘reads’) are unique enough to allow alignment (‘mapping’) to a specific physical location within the human genome. With MPS, even relatively small changes in the representation of chromosomes can be detected with statistically significant power [17,18]. Several studies have reported on the clinical validation of analyzing ccffDNA in maternal plasma for the detection of fetal aneuploidies and by now, in the clinical setting, three different approaches are used: whole genome MPS, sequencing all fragments in the plasma, followed by counting statistics [19–31]; targeted MPS, sequencing selected regions only, followed by analysis based on counting statistics [32–36]; and targeted MPS, sequencing selected regions all containing SNPs, followed by investigation of SNP allelic ratios [37–41]. Using whole genome MPS, a random subset of DNA fragments from maternal plasma is sequenced. With targeted MPS, the sample is first enriched for selected regions, such as regions on the chromosomes 13, 18, 21, X and Y, before sequencing. This allows a significant reduction in the number of reads required, thereby increasing throughput and limiting costs. In both whole genome and targeted MPS, the ratio of circulating DNA (cfDNA) fragments from particular chromosomes or regions of interest can be compared with reference values obtained from a disomic reference chromosome. A statistically significant deviation in the number of counts, expressed as, for example, a z-score or normalized chromosome value, indicates aneuploidy for that chromosome or region of interest. Generally speaking, the higher the number of sequenced reads is, expressed in coverage or sequencing depth, the greater the resolution is for detecting aberrations. In contrast to this so-called quantitative approach using counting statistics, targeted sequencing in combination with SNP-based methods determines chromosomal copy numbers by looking for specific patterns in allelic measurements. Several thousands of SNPs on chromosomes 13, 18, 21, X and Y are selectively amplified and sequenced. A maximum likelihood statistical method incorporating maternal genotype information and recombination frequencies is subsequently applied to determine the chromosomal count of the chromosomes analyzed in each sample. As this method uses allele distributions, it does not require reference values. Although studies validating and implementing MPS of ccffDNA to detect chromosomal aneuploidy have shown this to be highly sensitive and specific, it has become clear that discordant results between noninvasive prenatal testing (NIPT) and true fetal genotype are unavoidable. A large number of discordant cases have underlying biological reasons, including confined placental mosaicism (CPM), maternal mosaicism, co-twin demise or maternal malignancy. Confirmation of a positive NIPT result is always required and therefore, for this application, the term NIPT is preferred over the use of noninvasive prenatal diagnosis (NIPD). In this review, a number of practical issues encountered during the implementation of NIPT for the detection of fetal 112
(partial) aneuploidies using (whole genome) MPS-based techniques are highlighted and illustrated with data from literature and examples from our own experience. Specific attention will be given to data analysis, as it is becoming increasingly evident that bioinformatics play a key role in NIPT. Practical issues
Most practical issues encountered during the technical implementation of whole genome MPS for NIPT are independent of the MPS approach (whole genome vs targeted) and/or platform. FIGURE 1 schematically represents the steps of the NIPT MPS procedure, and several issues of these steps will be discussed and illustrated with data from literature and our own data below. Sample collection & processing
The reliability of NIPT depends on a sufficient percentage of ccffDNA being present in the maternal plasma (for ‘fetal fraction’ (FF) in more detail, see the ‘Fetal fraction’ section). Transport and process conditions have been studied extensively by several groups to determine preservation of the FF after blood collection [42–45]. The FF decreases when plasma and blood cells are not separated in time due to the release of cellfree maternal DNA through maternal white cell lysis. Studies on transport and process conditions have therefore mainly focused on limiting this effect. Originally, blood was collected in EDTA-anticoagulant tubes and plasma was separated from the maternal blood cells within 6 h after venipuncture to limit the effect of white cell lysis on the FF. As a processing time of 6 h after venipuncture is not always practical in routine prenatal care, subsequent investigations studied longer processing times and showed that more than 6 h can elapse between venipuncture and the separation of plasma from maternal blood cells when using EDTA-anticoagulant tubes [43,46–48]. When measured directly after DNA isolation (for more details, see the ‘Fetal fraction’ section), the absolute amount of ccffDNA remains stable when processed 24 h after venipuncture, but the FF (which is the relative proportion compared to the maternal fraction) decreases due to the increased amount of maternal cfDNA [42,43,45]. In our own study [48], we showed that when processing blood 24 h after venipuncture, fetal T21 could still reliably be detected when using whole genome MPS. Although the FF on the DNA isolate had decreased compared to a simultaneously drawn sample processed within 6 h, the FF in the actual sequenced fraction remained stable, probably as a result of a size selection step in the library preparation protocol, which enriches for shorter DNA fragments. It has been shown that the additionally released maternal DNA fragments are on average longer than fetal DNA fragments [43] and these are thus removed by the size selection step. However, when processing after 24 h, larger deviation in z-scores of several chromosomes was noticed, which might be explained by a lower DNA quality [48]. Therefore, processing times beyond 24 h are to be avoided when using EDTA anticoagulant tubes. Alternatively, studies have investigated the use of specific agents to stabilize the maternal blood cells and prevent them Expert Rev. Mol. Diagn. 15(1), (2015)
Whole genome MPS for NIPT
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B 1.6 1.4 1.2 1.0 0.8
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Figure 1. Potential noninvasive prenatal testing workflow including challenges encountered during the laboratory implementation: (A) samples are collected and processed, (B) the presence of fetal DNA (or the fetal fraction) can be determined either from the DNA isolate (depicted here) or from the sequencing data, but is currently limited by the accuracy of the method, type of sequencing and/or gender of the fetus (C) because of the high throughput of noninvasive prenatal testing, laboratory steps such as DNA isolation and library preparation can be automated and (D) methods for tracking samples are key for ensuring quality. (E) Commonly lowcoverage whole genome sequencing is performed allowing either (F) targeted or whole genome analysis. (G) Data analysis takes into account systematic biases to identify whole or partial chromosome aberrations. However, (H) discordant findings are known to occur for a number of biological reasons such as confined placental mosaicism.
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from lysing, allowing for a longer time period between venipuncture and separation of plasma from cells [42,45]. Dhallan et al., for example, found a higher FF when combining the use of formaldehyde as a stabilizer with a gentle centrifugation step [49]. Others, however, could not confirm their findings [50]. Wong et al. showed that when using Streck BCT stabilizing tubes, maternal blood can be stored for up to 7 days at ambient temperature without affecting the FF or DNA quality. However, Streck stabilizing tubes are relatively expensive and not readily available to all caregivers (8.4 US$ per tube; Cell-Free DNATM BCT, Streck) in comparison to standard EDTA-anticoagulant tubes (0.13 US$ per tube; K2-EDTA BD Vacutainer). As both EDTA anticoagulant tubes with processing times within 24 h and Streck BCT tubes are suitable for NIPT, in daily practice, NIPT-offering laboratories use either of these depending on their own transport and laboratory logistics.
A key determinant of the reliability of NIPT is the FF within the sample. When the FF is insufficient, fetal aneuploidies may be masked by an overwhelming amount of maternal cfDNA. Several studies have suggested an FF of 6–20% to be generally present [4–6,20]. Typically, an FF of approximately 4% is regarded to be the minimal fraction needed for a reliable NIPT result [34,51], although the minimal FF for a reliable result strongly depends on the sequencing depth [52].
chromosome in case of an aneuploidy [6,18]; (ii) the number of reads in benign or pathogenic copy number variations (CNVs) [55] or (iii) SNP genotyping data in case of targeted sequencing [35], only method iii being applicable to any pregnancy. As most of the methods are either complex or not applicable to all pregnancies, determination of the FF still remains a challenge to many NIPT-offering laboratories, especially those offering whole genome NIPT. In contrast a less complex and recent novel method to determine the FF based on the size differences between the fetal and the maternal DNA was reported by Yu et al. [56]. They hypothesized that maternal plasma samples with a higher FF would contain a higher proportion of short DNA fragments. In a proofof-principle study, they indeed showed a positive correlation between the proportion of short DNA fragments present in the plasma and the FF, as determined using the proportion of sequences aligned to the Y-chromosome. The median difference between the values was 2.3%. They showed that the FF could be measured not only by fragment size information obtained from paired-end sequencing but also by fragment length information obtained from electrophoresis of sequencing libraries. Even though the method only gives an estimation of the FF and needs further validation, the method is very simple and can be applied at virtually no additional costs, as electrophoresis of sequencing libraries is already incorporated in (most) protocols.
Quantification of fetal fraction
Factors affecting fetal fraction
Different approaches have been described to determine the FF, either from the DNA isolate or from the sequencing data.
As mentioned in the Sample collection & processing, in vitro the FF can be influenced by the time interval between blood draw and separation of plasma from blood cells. In addition, there are a number of biological factors that affect the FF, such as maternal weight [4,32,36,57–59]. The most probable explanation for this correlation is the process of active remodeling of adipose tissue in obese women, resulting in an increased release of maternal cfDNA into the circulation [60]. As Ashoor et al. [4] showed, the median FF at 11–13 weeks gestation (singleton pregnancies) was 10.0%, but this decreased with maternal weight from 11.7% at 60 kg to 3.9% at 160 kg. The estimated proportion of women with FF below 4% increased with maternal weight from 0.7% at 60 kg to 7.1% at 100 kg and 51.1% at 160 kg. Rava et al. confirmed similarly an inverse correlation between FF and BMI [6], but found this relation to be only weak and noted that women with low BMI can also have a low FF. In a large-scale study, Wang et al. [57] also found this inverse correlation between maternal weight and FF and furthermore showed a correlation between gestational age and FF. According to their data, the FF increased incrementally between 10 and 21 weeks of gestation and over this gestational age window, an overall 1% increase in total FF was anticipated. However, after 21 weeks of gestation, a more rapid weekly increase in FF was found (1%). A clear relationship with gestational age has also been reported by others [6,58,59]. Another important biological factor influencing the FF is the genetic status of the fetus and several authors have already
Fetal fraction
DNA isolate
The FF can be determined by using differentially methylated markers between the fetus and the mother, such as the RASSF1A promoter region [53,54]. However, methods relying on methylation differences require methylation-specific restriction enzyme digestion or bisulfite conversion and are therefore less suitable for quantification. Furthermore, the FF can also be directly determined from the isolated DNA by quantifying the amount of Y-chromosome-specific sequences, but this can only be applied in case of a male fetus. However, an accurate measurement is difficult due to the total amount of DNA in plasma being very low (estimated 103–104 GE/ml plasma). Therefore, most methods on DNA isolates show a relatively high variation. Sequencing data
Alternatively, the FF can be measured after sample preparation or from the sequencing data. This may reflect more closely the FF used during sequencing, also because some library preparation protocols include a size selection step to remove longer (maternal) DNA fragments [48]. As a result, the sample used for actual sequencing might contain a slightly higher FF than the DNA isolate. Methods that calculate the FF from the sequencing data either use (i) the number of reads from the X- or Y-chromosome if the fetus is male, or from the aneuploid 114
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Whole genome MPS for NIPT
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shown that FF is increased in case of a fetal T21 and decreased in case of fetal T13, T18 or monosomy X [4,6,32,58]. Despite the evidence that FF is mainly affected by gestational age and fetal genetic status and to a lesser extent by maternal weight, the majority of clinical NIPT samples obtained at at least 10 weeks of gestation contain sufficient fetal DNA for a reliable result [57]. Automation
Both the throughput possibilities per next generation sequencing platform and the numbers of clinical NIPT samples are rapidly increasing. Hence, reduction of manual operations is recommended to avoid sample swaps and reduce operator-to-operator variability, operator time and costs. Reduction of manual handlings can be achieved by automating DNA isolation and/or library preparation procedures. For DNA isolation, automated systems, such as the QiaSymphony from QIAGEN and the Chemagic Star from Perkin Elmer, are available, but systems like these are still not widely used and often DNA is extracted manually. Regarding automation of library preparation, Jensen et al. [61] compared manual sequencing library preparation methods with semiautomated methods and indeed showed that semiautomated methods can replace manual methods with a fourfold increase in throughput and a fourfold decrease in labor, without sacrificing library yield or quality. The use of robots usually leads to loss of material during the procedure, and as the input material for NIPT already contains such low amounts of DNA, thorough optimization is required. Furthermore, changes in laboratory methods, for example, through automation, can result in technical variability that can affect data analysis. For instance, we found that when using a reference-pool-based NIPT method (explained in the ‘Data analysis’ section), a test sample could only be analyzed against a reference pool of samples that underwent the same DNA isolation protocol. If not, technical variability between samples obscured aberrations or even lead to falsepositive results. We noticed this when extracting cell-free DNA from 16 different plasma samples using either a manual extraction method (QIAGEN Circulating Nucleic Acid kit: column-based method) or an automated extraction method (Perkin Elmer robot: bead-based method). The two sets of DNA samples were sequenced on the same slide, but the different extraction methods resulted in distinct patterns. Firstly, the two extraction methods can almost perfectly be separated in two clusters using principal component analysis on the read counts, suggesting a clear difference in read count behavior (for more details, see [SUPPLEMENTARY DATA & SUPPLEMENTARY FIGURE 1] (Supplementary material can be found online at www.informahealthcare.com/suppl/10.1586/ 14737159.2015.973857)). Secondly and more importantly, when analyzing both sets of 16 samples using a reference pool of manually extracted samples, the manual set exhibit no autosomal calls bigger than 20 Mb in the presumably euploid samples (as determined by karyotyping, SNP array or QFPCR), while the samples on the Perkin Elmer robot contain informahealthcare.com
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15 of such calls, which most probably all represent false positives (see SUPPLEMENTARY DATA). False positives can be reduced to zero for the robot-isolated samples by using an appropriate reference pool containing samples with the same extraction method (see SUPPLEMENTARY DATA). Sample tracking
Sample tracking and detection of sample swaps are an essential part of workflows within diagnostic laboratories. NIPT strategies using MPS multiplex samples within a single run and link the obtained sequencing data to the correct sample by ligating a unique molecular barcode to each sample during library preparation. After the ligation of this barcode, the possibility of sample swaps is reduced considerably with the exception of data analysis errors, such as demultiplexing the sequencing reads. However, guarding against sample swaps and incorporating procedures, able to detect sample swaps before the ligation of the unique barcodes, remains a challenge. Postnatal exome sequencing uses a laboratory procedure comparable to the NIPT-MPS procedure, but with a higher sequencing depth. As a result, SNP genotype information can be obtained directly from the sequencing data and compared with SNP information obtained from the sample before processing. However, the generally low-coverage sequencing used for whole genome NIPT in combination with the mixture of maternal and fetal DNA make direct extraction of SNP genotypes from the sequence data impossible and panels of 12 and 24 SNPs cannot be reliably used on samples sequenced with an average depth of 0.1–0.5x. For instance, in such low-coverage data, we have observed that in 20 out of 32 samples at most three of the 24 SNPs of a 24-SNP panel are covered at least 1x, the highest coverage for a single SNP being only 4x in all 32 samples. Thus, NIPT methods including an SNP check to detect sample swaps require sequencing to a medium or high depth. Alternatively, SNP checks can be performed on DNA from the buffy coat or plasma and the sequencing library, as reported by Porreco et al. [62]., but this check does not cover the whole procedure. Another option for sample tracking is performing a gender check before and after sequencing. This, however, necessitates the incorporation of an additional PCR test before sequencing, and it does not rule out sample switching between samples from fetuses with the same sex. Having each critical step controlled by a second operator, although still error prone, is the most widely used option. The Specific Constitutional Cytogenetic Guidelines, published by the European Cytogenetics Association in 2012 [63], recommend that invasively obtained material for prenatal testing should be handled with extreme care and processed in duplicate where possible to avoid sample contamination and loss of material due to failure of equipment. Although the maternal blood collected for NIPT is obtained noninvasively, the result of an NIPT test can have a high impact on future parents and the pregnancy outcome. Hence, extreme precautions should be taken to avoid and detect sample swaps. 115
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Scope of testing & analysis Chromosomes 13, 18, 21 & sex chromosomes
The initial aim of NIPT was detection of the most common fetal aneuploidies, that is, T13, T18 and T21. Several largescale studies have shown that this is now feasible with high sensitivity and specificity, both within high risk and the general population of pregnant women [25,30,62,64,65]. In general, specificity and sensitivity for T21 are almost comparable to those of diagnostic tests (>99%), whereas sensitivities of T13 and T18 are reported to be lower (>80 and >97%, respectively), depending on the technique used [66]. Since the commercial launch of NIPT in 2011, clinical testing for sex chromosomes and sex chromosomal aneuploidies has also become available, with sensitivities and specificities for sex chromosomal aneuploidies being lower than those for T18 and T21 (male >99%, female >97%, monosomy X >90%, sex chromosome trisomies 67–100%, depending on the technique) [66]. Despite the lower accuracy, most commercially available tests include sex chromosomal analysis or offer it as a separate test, even though noninvasively testing for sex and sex chromosomal aberrations goes along with a number of ethical issues [67]. Partial chromosomal aberrations
Theoretically, whole genome sequencing allows for the detection of fetal partial chromosomal aberrations in addition to the common aneuploidies. However, the detection of partial duplications and deletions is highly dependent on the sequencing depth used. The first report on the detection of a fetal microdeletion was the paper by Peters et al. in 2011 [68], in which they described the detection of a 4.2-Mb fetal microdeletion on chr12 using high-coverage whole genome sequencing. Subsequently, fetal 22q11.2 microdeletions (~3 Mb in size) using a genomic coverage of fourfold were reported [69], as well as other submicroscopic aberrations [23,24]. In contrast, a sequencing depth of 30–60x increases the detection resolution to 300 kb genome wide [55]. Alternatively, using a targeted sequencing approach, capturing only an 8-Mb region of the genome, resolutions of 100 kb for fetal microdeletions can be achieved [70]. But due to cost and throughput limitations, current clinical whole genome applications of NIPT use low-coverage data (0.1–0.5x). Despite this limitation, several algorithms have been developed for the identification of partial aberrations [71–74] and (micro)deletions and duplications as small as 6 Mb have been described [73]. Unfortunately, the development of partial aberration detection via NIPT is hampered by the limited availability of validation samples. Nonetheless, both Sequenom Inc. and Natera Inc. recently announced the commercial availability of NIPT tests for the determination of a number of fetal microdeletions, including the 22q11.12 microdeletion. Data analysis
After sequencing and demultiplexing of samples, bioinformatic and statistical analysis follow to identify chromosomal aberrations (FIGURE 2). 116
The most widely used methods for the detection of aberrations in whole genome MPS are based on counting the number of reads aligned to the reference genome [17,18]. These methods rely on the ability to correct for systematic biases and need sufficient numbers of reads for the detection of subtle differences. Chromosomal aneuploidies are identified by a relative increase (trisomy) or decrease (monosomy) in the number of aligned reads to the chromosome of interest. Theoretically, in a sample with an FF of 10% and a fetal T21, a 1.05-fold increase of aligned chr21 reads should be present, when compared to another sample with the same FF but no aneuploidy, as defined by: relative read increase =
((1 − fetal fraction) ⋅ 2)+(fetal fractiion ⋅ fetal ploidy of chr) 2
As whole genome NIPT is usually performed with low genomic coverage (
