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WO2015026967A1 - Procédés d'utilisation de la détection d'une faible fraction fœtale - Google Patents

Procédés d'utilisation de la détection d'une faible fraction fœtale Download PDF

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WO2015026967A1
WO2015026967A1 PCT/US2014/051926 US2014051926W WO2015026967A1 WO 2015026967 A1 WO2015026967 A1 WO 2015026967A1 US 2014051926 W US2014051926 W US 2014051926W WO 2015026967 A1 WO2015026967 A1 WO 2015026967A1
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fetal fraction
population
fetal
fraction
dna
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Allison Ryan
Matthew Rabinowitz
Sallie MCADOO
Styrmir Sigurjonsson
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Natera Inc
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Natera Inc
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/15—Devices for taking samples of blood
    • A61B5/150007—Details
    • A61B5/150015—Source of blood
    • A61B5/150038—Source of blood for blood from umbilical cord
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • This invention is in the field of prenatal diagnostics. Background
  • a human being normally has two sets of 23 chromosomes in every somatic cell, with one copy coming from each parent.
  • Aneuploidy a state where a cell has the wrong number of chromosomes, is responsible for a significant percentage of children born with genetic conditions.
  • Detection of chromosomal abnormalities can identify individuals, including fetuses or embryos, with conditions such as Down syndrome, Edwards syndrome, Klinefelters syndrome, and Turner syndrome, among others. Since chromosomal abnormalities are generally undesirable, the detection of such a chromosomal abnormality in a fetus may provide the basis for the decision to terminate a pregnancy.
  • Prenatal diagnosis can alert physicians and parents to abnormalities in growing fetuses.
  • Some currently available methods such as amniocentesis and chorionic villus sampling (CVS), are able to diagnose genetic defects with high accuracy; however, they may carry a risk of spontaneous abortion.
  • Other methods can indirectly estimate a risk of certain genetic defects non-invasively, for example from hormone levels in maternal blood and/or from ultrasound data, however their accuracies are much lower.
  • cell-free fetal DNA and intact fetal cells can enter maternal blood circulation. This provides an opportunity to directly measure genetic information about a fetus, specifically the aneuploidy state of the fetus, in a manner which is non-invasive, for example from a maternal blood draw.
  • Methods of detecting genetic abnormalities in a fetus based on the analysis of cell free DNA in maternal can involve the step of determining the relative amounts of cell free fetal DNA and cell free maternal DNA, i.e., the fetal fraction, in the maternal blood for analysis.
  • One embodiment is a method of evaluating a fetus for genetic abnormalities (or the risk of genetic abnormalities), comprising, obtaining a blood sample from a pregnant human subject, measuring the fetal fraction of cell free fetal DNA in the blood sample, determining if the fetal fraction of cell free fetal DNA in the blood sample is within the lowest 1 percentile of a reference population, and generating a report indicating that an invasive genetic analysis procedure should be performed.
  • Another embodiment is a method of evaluating a fetus for genetic abnormalities (or the risk of genetic abnormalities), comprising,
  • obtaining a blood sample from a pregnant human subject measuring the fetal fraction of the cell free fetal DNA in the blood sample, determining if the ratio of cell free fetal DNA in the blood sample to maternal cell free DNA in the blood sample is in lowest 1 percentile of a reference population, and performing an invasive genetic test if the fetal fraction is in lowest 1 percentile of a reference population.
  • Another embodiment is a method of evaluating a fetus for genetic abnormalities (or the risk of genetic abnormalities), comprising,
  • obtaining a plurality blood samples from a test population of pregnant human subjects measuring the fetal fraction of the cell free fetal DNA in the blood samples, determining a population subset consisting of members of the test population that have fetal fraction that is in the lowest 1 percentile of the test population, and performing an invasive genetic test on at least one member of the population subset.
  • Another embodiment is a method of evaluating a fetus for genetic abnormalities (or the risk of genetic abnormalities) , comprising, obtaining a plurality blood samples from a test population of pregnant human subjects, measuring the fetal fraction of the cell free fetal DNA in the blood sample to maternal cell free DNA in the blood samples, determining a population subset consisting of members of the test population that have fetal fraction that is in the lowest 1 percentile of the test population, and performing a test for a detached placenta on at least one member of the population subset.
  • the reference population is matched for gestational age or maternal weight of both gestational age and maternal weight. In some embodiments the test population is matched for gestational age or maternal weight of both gestational age and maternal weight. Genetic abnormalities that can be detected include aneuploidy, such as trisomies.
  • a low fetal fraction is indicative of genetic abnormalities in the fetus. Applicants have also discovered that a low fetal fraction is indicative of a detached placenta.
  • Low fetal fraction is indicative of a wide range of genetic abnormalities in the fetus.
  • the lower the fetal fraction in the test subject the greater the likelihood that the fetus contains a genetic abnormality or that there is a non-genetic problem with the pregnancy, e.g., a detached placenta.
  • the fetal fraction is measured in the blood sample obtained from the patient (i.e. pregnant woman being tested) and subsequently compared with a reference population fetal fraction measurement, so as to provide for the determination of what percentile of the reference population the fetal fraction lies.
  • the fetal fraction is said to be in the lowest 1% of the reference population.
  • a fetal fraction in the lowest 1% of the reference population is said to be a low fetal fraction.
  • the fetal fraction is measured in the blood sample obtained from the patient (i.e. pregnant woman being tested) and subsequently compared against the larger test population from which fetal fraction is measured, so as to provide for the determination of what percentile of the test population the fetal fraction lies. It will be understood by the person of ordinary that by determining how often a lower fetal fraction value measured in the patient is found in the test population, will give the same result as determining how often a higher fetal fraction value measured in the patient is found in the test population.
  • fetal fraction refers to the relative quantity of cell free fetal DNA to cell free maternal DNA plus cell free fetal DNA. Fetal fraction is expressed herein in term of percent.
  • the subject methods may be used with a variety of reference populations.
  • the reference population is composed of individuals who are as closely matched with respect to the following parameters, gestational age, maternal weight. Such parameters are known to affect the level of fetal fraction. In general, fetal fraction increases with gestational age. Similarly, fetal faction decreases with increasing weight of the mother.
  • the reference population will be matched with test patient for gestational age of the fetus. In some embodiments, the reference population will be matched for the weight of the mother. In some embodiments, the reference population will be matched with test patient for gestational age of the fetus and the weight of the mother. In some embodiments, the reference population will be further matched for parameters such as maternal ages, ethnicity, and the like.
  • a patient carrying a 10 week fetus may be compared with a reference population of women carrying fetuses that 10-12 weeks old, or 9-11 weeks old, or 10-15 weeks old.
  • a patient weighing 70 kilograms may be compared a reference population of women that are 65-75 kilograms, or 70-80 kilograms, or 65-80 kilograms.
  • the size of the reference population may vary substantially. In accordance with basic principles of statistics, the larger the reference population, the more meaningful the indication of the condition that is being detected, i.e., genetic abnormalities or detached placenta.
  • the reference population should have at least 25 members, more preferably at least 50 members, more preferably at least 75 members, and even more preferably at least 100 members.
  • the subject methods may employ any of a variety of methods of measuring fetal fraction and is not limited to a specific method.
  • Example of such methods of determining fetal fraction include using a high throughput DNA sequencer to count alleles at large number of polymorphic genie loci and modeling the likely fetal fraction (see for example US patent application 13/300,235; PCT application PCT/11/61506; US patent application 13/110,685; US published patent application 2013/0165203A1; US published patent application 2012/0264121 Al).
  • a method calculating fetal fraction can be found in Sparks et al. American Journal of Obstetrics and Gynecology 319. el (April 2012).
  • Fetal fraction may be determine using a methylation assay (see US patents 7,754,428 B2; 7,901,884 B2; 8,166,382 B2) that assumes certain loci are methylated or preferentially methylated in the fetus, and those same loci are unmethylated or preferentially unmethylated in the mother.
  • a methylation assay see US patents 7,754,428 B2; 7,901,884 B2; 8,166,382 B2 that assumes certain loci are methylated or preferentially methylated in the fetus, and those same loci are unmethylated or preferentially unmethylated in the mother.
  • the fetal fraction data used to create the reference population is obtained by the same method as the method used to measure fetal fraction in the patient.
  • the method of determining fetal fraction used for the reference population is different from the method of determining fetal fraction used for the individual patient.
  • fetal fraction is determined in essentially the same manner for all constituents of the test population.
  • the test population comprises a set of patients that are being co-analyzed in some way, e.g., sam laboratory, same protocol, same study, and the like, and thus does not require comparison to a set of a priori generated fetal fraction data.
  • the larger the test population the more meaningful results.
  • a test population preferably comprises at least 100 members. The subject methods may be used with a variety of reference populations.
  • the test population is composed of individuals who are as closely matched with respect to the following parameters, gestational age, maternal weight. Such parameters are known to affect the level of fetal fraction. In general, fetal fraction increases with gestational age. Similarly, fetal faction decreases with increasing weight of the mother.
  • the test population will be matched for gestational age of the fetus. In some embodiments, the test population will be matched for the weight of the mother. In some embodiments, the test population will be matched for gestational age of the fetus and the weight of the mother. In some embodiments, the test population will be further matched for parameters such as maternal ages, ethnicity, and the like.
  • test population of sufficient size may be necessary to expand the range of the parameter of interest rather than relying on a perfectly matched test population.
  • a test population may consist of subjects with fetuses that are 10-12 weeks old, or 9-11 weeks old, or 10- 15 weeks old.
  • test population of make consist of women that weigh 65-75 kilograms, or 70-80 kilograms, or 65-80 kilograms.
  • the detection of fetal fraction that is in the lowest 1 percentile is indicative of an increased likelihood of genetic abnormalities in the fetus. In some embodiments the detection of fetal fraction that is in the lowest 1 percentile of the reference population is indicative of an increased likelihood of a detached placenta in the mother. In some embodiments, the lowest 0.5 percentile is indicative of an increased likelihood of genetic abnormalities in the fetus. In some embodiments the detection of fetal fraction that is in the lowest 0.5 percentile is indicative of an increased likelihood of a detached placenta in the mother.
  • the detection of fetal fraction that is in the lowest 1 percentile of the test population is indicative of an increased likelihood of genetic abnormalities in the fetus. In some embodiments the detection of fetal fraction that is in the lowest 1 percentile is indicative of an increased likelihood of a detached placenta in the mother. In some embodiments, the lowest 0.5 percentile is indicative of an increased likelihood of genetic abnormalities in the fetus. In some embodiments the detection of fetal fraction that is in the lowest 0.5 percentile is indicative of an increased likelihood of a detached placenta in the mother.
  • the subject methods may be used to test for increased risk of a wide variety of genetic abnormalities that produce low fetal fraction.
  • Such abnormalities include, aneuploidy, deletions, translocations, insertions, and point mutations.
  • Exemplary of such genetic abnormalaities are trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), trisomy 13 (Patau Syndrome), 45,X (Turner syndrome), an unbalanced translocation on chromosome 10.
  • blood samples from pregnant women where the fetal fraction in the maternal plasma is abnormally low is predictive of a fetal abnormality, and appropriate follow up measures may be taken, for example, an invasive genetic testing procedure such as chrionic villus biopsy or amniocentesis, thereby providing for the possibility of diagnosing an abnormal karyotype or other abnormality in the fetus.
  • abnormally low may mean in the lowest half percentile for fetal fraction, the lowest percentile for fetal fraction, the lowest two percentiles for fetal fraction, the lowest three percentiles for fetal fraction, the lowest four percentiles for fetal fraction, or the lowest five percentiles for fetal fraction.
  • the percentiles may be determined using fetal fraction distribution for all samples in a data set. In one embodiment , the percentiles may be determined using fetal fraction distribution for only euploid samples in a data set. In one embodiment the percentiles may be determined using fetal fraction distributions that are adjusted for maternal weight, gestational age, any other factor correlated with fetal fraction, or a combination thereof.
  • the fetal abnormality may include trisomy 13, trisomy 18, triploidy, detatched placenta, a trisomy, other whole chromosome abnormalities, an unbalanced translocation, a microdeletion of up to 20 Mb, a microduplication of up to 20 Mb, a genetic defect, or combination thereof.
  • the fetal abnormality may indicate the presence of a condition that is threatening to the health of the mother, such as pre-eclampsia.
  • this may entail making a measurement of the mixed sample to determine the fraction of fetal DNA in the mixture; this estimation of the fetal fraction may be done with sequencing, it may be done with TaqMan, it may be done with qPCR, it may be done with SNP arrays, it may be done with any method that can distinguish different alleles at a given loci.
  • the estimation for fetal fraction may be done using a methylation assay (see US patents 7,754,428 B2; 7,901,884 B2; 8,166,382 B2) that assumes certain loci are methylated or preferentially methylated in the fetus, and those same loci are unmethylated or preferentially unmethylated in the mother.
  • the method can measure the degree of methylation at those loci and infer the percent fetal DNA that must be in the sample.
  • the need for a fetal fraction estimate may be eliminated by including hypotheses that cover all or a selected set of fetal fractions in the set of hypotheses that are considered when comparing to the actual measured data. After the fraction fetal DNA in the mixture has been determined, the number of sequences to be read for each sample may be determined.
  • SNPs tend to be dimorphic, that is, one of two possible alleles tend to be observed in the population.
  • Each person typically has two copies of each chromosome, and therefore, two copies of each allele. There are examples where this is not true, for example, an individual with Down syndrome has three copies of chromosome 21, and a male will have one copy of each of chromosome X and Y. If an individual has two copies of the A allele, or two copies of the B allele, the homozygous at that allele, and this may be written as A A or BB.
  • AAAAB may be used to refer to the parental context at a given SNP where the mother is AA - homozygous, and the fetus is AB - heterozygous.
  • the fetal fraction may be estimated by targeted sequencing, wherein a plurality of single nucleotide polymorphisms are preferentially enriched and/or selectively amplified.
  • the fetal fraction can be most easily estimated by looking at those loci where the mother is homozygous and the fetus is heterozygous, that is, the parental context (AAIAB), which is equivalent by symmetry to the context (BBIAB).
  • AAIAB parental context
  • BBIAB symmetry to the context
  • those sequencing reads that correspond to SNPs where the mother is homozygous and the fetus is heterozygous are quantified for each allele for each SNP.
  • fetal fraction X to mean that X% of the DNA in the sample is of fetal origin, and 100% - X% of the DNA in the sample is of maternal origin.
  • the parental context is (AAIAB)
  • the fetal fraction, X 2F.
  • the fraction of reads from the A allele is expected to be 50% + 1 ⁇ 2 X%
  • the average percent of reads mapping to the A allele is G
  • the fetal fraction is about 2 x (G-50%).
  • this sequence data may be measured on a high throughput sequencer.
  • the sequence data may be measured on DNA that originated from free floating DNA isolated from maternal blood, wherein the free floating DNA comprises some DNA of maternal origin, and some DNA of fetal / placental origin.
  • the fraction of fetal DNA (“fetal fraction") or the percentage of fetal DNA in the mixture can be measured by another method, and is assumed to be known in determining the ploidy state of the fetus.
  • the fetal fraction can be calculated using only the genotyping measurements made on the maternal blood sample itself, which is a mixture of fetal and maternal DNA.
  • the fraction may be calculated also using the measured or otherwise known genotype of the mother and/or the measured or otherwise known genotype of the father.
  • ploidy state of the fetus can be determined solely based on the calculated fraction of fetal DNA for the chromosome in question compared to the calculated fraction of fetal DNA for the reference chromosome assumed disomic.
  • N SNPs for which we have:
  • D (M,F,SM,SF,S).
  • priorprob(H) is the prior probability assigned to each hypothesis H, based on model design and prior knowledge.
  • the copy number hypotheses that may be considered are:
  • o Maternal H21_matched (two identical copies from mother, one copy from father), H21_unmatched (BOTH copies from mother, one copy from father)
  • H12_matched one copy from mother , two identical copies from father
  • H12_unmatched one copy from mother , both copies from father
  • nullsomy H00
  • uniparental disomy H20 and H02
  • tetrasomy H04, H13, H22, H31 and H40
  • each trisomy whether the origin was mitotis, meiosis I, or meiosis II, would be one of the matched or unmatched trisomies. Due to crossovers, true trisomy is usually a combination of the two.
  • a method to derive hypothesis likelihoods for simple hypotheses is described.
  • a method to derive hypothesis likelihoods for composite hypotheses is described, combining individual SNP likelihood with crossovers.
  • LIK(DIH) may be determined for simple hypotheses, as follows.
  • the log likelihood of hypothesis H on a whole chromosome may be calculated as the sum of log likelihoods of individual SNPs, assuming known or derived child fraction cf. In an embodiment it i data.
  • the Log Likelihood may be determined on a per SNP basis.
  • SNP i assuming fetal ploidy hypothesis H and percent fetal DNA cf, log likelihood of observed data D is defined as: f, c, H, cf, i)P(c
  • Genotype prior frequency p(mli) is the general prior probability of mother genotype m on SNP i, based on the known population frequency at SNP I, denoted pAi.
  • p(fli) Father genotype probability, p(fli), may be determined in an analogous fashion.
  • m, f, c, H, i, cf) is the probability of given data D on SNP i, given true mother genotype m, true father genotype f, true child genotype c, hypothesis H and child fraction cf. It can be broken down into the probability of mother, father and child data as follows:
  • m, f, c, H, cf, i) P(SM
  • Mother SNP array data likelihood Probability of mother SNP array genotype data m; at SNP i compared to true genotype m, assuming SNP array genotypes are correct, is simply
  • the method involves building a joint distribution model for the expected allele counts at a plurality of polymorphic loci on the chromosome for each ploidy hypothesis; one method to accomplish such an end is described here.
  • m, c, H, cf, i) is the probability of free fetal DNA sequence data on SNP i, given true mother genotype m, true child genotype c, child copy number hypothesis H, and assuming child fraction cf. It is in fact the probability of sequence data S on SNP I, given the true probability of A content on SNP i ⁇ ( ⁇ , c, cf, H)
  • #A(g) number of A's in genotype g
  • n m 2 is somy of mother
  • n c is ploidy of the child under hypothesis H (1 for monosomy, 2 for disomy, 3 for trisomy).
  • the method involves building a joint distribution model for the expected allele counts at the plurality of polymorphic loci on the chromosome for each ploidy hypothesis; one method to accomplish such an end is described here.
  • trisomy is usually not purely matched or unmatched, due to crossovers, so in this section results for composite hypotheses H21 (maternal trisomy) and H12 (paternal trisomy) are derived, which combine matched and unmatched trisomy, accounting for possible crossovers.
  • trisomy In the case of trisomy, if there were no crossovers, trisomy would be simply matched or unmatched trisomy. Matched trisomy is where child inherits two copies of the identical chromosome segment from one parent. Unmatched trisomy is where child inherits one copy of each homologous chromosome segment from the parent. Due to crossovers, some segments of a chromosome may have matched trisomy, and other parts may have unmatched trisomy. Described in this section is how to build a joint distribution model for the heterozygosity rates for a set of alleles; that is, for the expected allele counts at a number of loci for one or more hypotheses.
  • Hm, i) is the fit for matched hypothesis H m
  • Hu, i) is the fit for unmatched hypothesis H u
  • pc(i) probability of crossover between SNPs i-1 andi.
  • E, 1: i) LIK(D
  • E, 1: 2) LIK(D
  • the child fraction may be determined.
  • the child fraction may refer to the proportion of sequences in a mixture of DNA that originate from the child.
  • the child fraction may refer to the proportion of sequences in the maternal plasma that originate from the fetus or the portion of the placenta with fetal genotype. It may refer to the child fraction in a sample of DNA that has been prepared from the maternal plasma, and may be enriched in fetal DNA.
  • One purpose of determining the child fraction in a sample of DNA is for use in an algorithm that can make ploidy calls on the fetus, therefore, the child fraction could refer to whatever sample of DNA was analyzed by sequencing for the purpose of non-invasive prenatal diagnosis.
  • any set of chromosomes It is possible to use any set of chromosomes. It is also possible to derive child fraction without assuming euploidy on the reference chromosomes. Using this method it is possible to determine the child fraction for any of the following situations: (1) one has array data on the parents and shotgun sequencing data on the maternal plasma; (2) one has array data on the parents and targeted sequencing data on the maternal plasma; (3) one has targeted sequencing data on both the parents and maternal plasma; (4) one has targeted sequencing data on both the mother and the maternal plasma fraction; (5) one has targeted sequencing data on the maternal plasma fraction; (6) other combinations of parental and child fraction measurements.
  • the informatics method may incorporate data dropouts; this may result in ploidy determinations of higher accuracy.
  • the probability of getting an A is a direct function of the true mother genotype, the true child genotype, the fraction of the child in the mixture, and the child copy number.
  • mother or child alleles can drop out, for example instead of measuring true child AB in the mixture, it may be the case that only sequences mapping to allele A are measured.
  • One may denote the parent dropout rate for genomic illumina data d pg , parent dropout rate for sequence data d ps and child dropout rate for sequence data d cs .
  • the mother dropout rate may be assumed to be zero, and child dropout rates are relatively low; in this case, the results are not severely affected by dropouts.
  • the possibility of allele dropouts may be sufficiently large that they result in a significant effect of the predicted ploidy call. For such a case, allele dropouts have been incorporated into the algorithm here:
  • ⁇ ( ⁇ £ ⁇ , c d , cf, H), i) is as defined in the section on free floating data likelihood.
  • p(m d ⁇ m) is the probability of observed mother genotype m d , given true mother genotype m, assuming dropout rate d ps
  • the informatics method may incorporate random and consistent bias.
  • the informatics method may incorporate random bias.
  • q the probability of getting an A on this SNP is equal to q, which is a bit different than p as defined above. How much different p is from q depends on the accuracy of the measurement process and number of other factors and can be quantified by standard deviations of q away from p.
  • the maternal plasma DNA sequence data (S) probability assuming true mother genotype (m), true child genotype (c), child fraction (cf), assuming child hypothesis H, given free floating DNA sequence A count on SNP i (a;) and free floating sequence B count on SNP i (bi) may be calculated as
  • N may be made to be a constant irrespective of the depth of read a;+bi, or a function of a;+bi, making bias smaller for larger depths of read.
  • the informatics method may incorporate consistent per-SNP bias. Due to artifacts of the sequencing process, some SNPs may have consistently lower or higher counts irrespective of the true amount of A content. Suppose that SNP i consistently adds a bias of Wi percent to the number of A counts. In some embodiments, this bias can be estimated from the set of training data derived under same conditions, and added back in to the parent sequence data estimate as:
  • P(SMIm,i) Pxim(ami) where Xlm ⁇ BetaBinom(p m (A)+ Wi, ami+bmi,s) and with the free floating DNA sequence data probability estimate as:
  • the method may be written to specifically take into account additional noise, differential sample quality, differential SNP quality, and random sampling bias.
  • additional noise differential sample quality
  • differential SNP quality differential SNP quality
  • random sampling bias random sampling bias
  • Ni molecules are sampled; usually Ni ⁇ No/2 molecules and random sampling bias is introduced due to sampling.
  • the amplified sample may contain a number of molecules N 2 where N 2 » Ni.
  • This sampling bias is included in the model by using a Beta-Binomial (BB) distribution instead of using a simple Binomial distribution model.
  • Parameter N of the Beta-Binomial distribution may be estimated later on per sample basis from training data after adjusting for leakage and amplification bias, on SNPs with 0 ⁇ p ⁇ l. Leakage is the tendency for a SNP to be read incorrectly.
  • the amplification step will amplify any allelic bias, thus amplification bias introduced due to possible uneven amplification.
  • the bias parameter, b is centered at 0, and indicates how much more or less the A allele get amplified as opposed to the B allele on a particular SNP.
  • the parameter b may differ from SNP to SNP.
  • Bias parameter b may be estimated on per SNP basis, for example from training data.
  • the sequencing step involves sequencing a sample of amplified molecules.
  • leakage is the situation where a SNP is read incorrectly. Leakage may result from any number of problems, and may result in a SNP being read not as the correct allele A, but as another allele B found at that locus or as an allele C or D not typically found at that locus.
  • the sequencing measures the sequence data of a number of DNA molecules from an amplified sample of size N 3 , where N 3 ⁇ N 2 .
  • N 3 may be in the range of 20,000 to 100,000; 100,000 to 500,000; 500,000 to 4,000,000; 4,000,000 to 20,000,000; or 20,000,000 to 100,000,000.
  • Each molecule sampled has a probability p g of being read correctly, in which case it will show up correctly as allele A.
  • the sample will be incorrectly read as an allele unrelated to the original molecule with probability l-p g , and will look like allele A with probability p r , allele B with probabililty p m or allele C or allele D with probability p 0 , where Parameters p g , p r , p m , p 0 are estimated on per SNP basis from the training data.
  • p the true amount of reference DNA
  • b per SNP bias
  • p g the probability of a correct read
  • p r the probability of read being read incorrectly but serendipitously looking like the correct allele, in case of a bad read, as described above, and:
  • the method uses a Beta-Binomial distribution instead of a simple binomial distribution; this takes care of the random sampling bias.
  • Parameter N of the Beta- Binomial distribution is estimated on per sample basis on an as needed basis.
  • bias correction F(p,b), H(p,b), instead of just p takes care of the amplification bias.
  • Parameter b of the bias is estimated on per SNP basis from training data ahead of time.
  • the method uses leakage correction L(p,p r ,p g ), instead of just p; this takes care of the leakage bias, i.e. varying SNP and sample quality.
  • parameters p g , p r , p 0 are estimated on per SNP basis from the training data ahead of time.
  • the parameters p g , p r , p 0 may be updated with the current sample on the go, to account for varying sample quality.
  • the model described herein is quite general and can account for both differential sample quality and differential SNP quality. Different samples and SNPs are treated differently, as exemplified by the fact that some embodiments use Beta-Binomial distributions whose mean and variance are a function of the original amount of DNA, as well as sample and SNP quality.
  • a set of 197 blood samples were collected from pregnant women determined to be at a high risk of chromosomal abnormalities due to at least one of the following: (1) a risk of greater than 1/100 risk of aneuploidy according to first trimester serum screen, (2) an ultrasound abnormality indicative of a chromosomal abnormality or (3) a maternal age of 39 or greater.
  • a paternal blood sample was also collected for each maternal sample.
  • Maternal venous blood samples (20 mL in Streck cell-free DNA BCTTM tubes) were obtained before chorionic villus sampling from the 205 singleton pregnancies. The patients gave written informed consent to provide samples for research into early prediction of pregnancy complications according to an IRB approved protocol.
  • Pregnant couples were enrolled at selected prenatal care centers under Institutional Review Board-approved protocols pursuant to local laws. Women were at least 18 years of age, had a GA of at least 9 weeks, singleton pregnancies, and signed an informed consent. A total of 205 maternal blood samples were drawn, and paternal genetic samples were collected (blood or buccal). The cohort included eighteen T21 (Down syndrome), three T18 (Edwards syndrome), two T13 (Patau Syndrome), one 45,X (Turner syndrome), three samples with triploidy, one sample with an unbalanced translocation on chromosome 10, and 170 samples from women with euploid pregnancies. In total, 28 of the 197 (14.2%) samples were abnormal.
  • the samples were prepared in the following way: up to 20 mL of maternal blood were centrifuged to isolate the buffy coat and the plasma.
  • the genomic DNA in the maternal sample was prepared from the buffy coat and paternal DNA was prepared from a blood sample or saliva sample.
  • Cell-free DNA in the maternal plasma was isolated using the QIAGEN CIRCULATING NUCLEIC ACID kit and eluted in 50 uL TE buffer according to manufacturer's instructions. Universal ligation adapters were appended to the end of each molecule of 40 uL of purified plasma DNA and libraries were amplified for 9 cycles using adaptor specific primers. Libraries were purified with AGENCOURT AMPURE beads and eluted in 50 ul DNA suspension buffer.
  • 6 ul of the DNA was amplified with 15 cycles of STAR 1 (95 °C for 10 min for initial polymerase activation, then 15 cycles of 96°C for 30s; 65°C for 1 min; 58°C for 6 min; 60°C for 8 min; 65°C for 4 min and 72°C for 30s; and a final extension at 72°C for 2 min) using 7.5 nM primer concentration of 19,488 target- specific tagged reverse primers and one library adaptor specific forward primer at 500 nM.
  • STAR 1 95 °C for 10 min for initial polymerase activation, then 15 cycles of 96°C for 30s; 65°C for 1 min; 58°C for 6 min; 60°C for 8 min; 65°C for 4 min and 72°C for 30s; and a final extension at 72°C for 2 min
  • STAR 1 95 °C for 10 min for initial polymerase activation, then 15 cycles of 96°C for 30s; 65°C for 1 min; 58°C for 6 min
  • the hemi-nested PCR protocol involved a second amplification of a dilution of the STAR 1 product for 15 cycles (STAR 2) (95 °C for 10 min for initial polymerase activation, then 15 cycles of 95°C for 30s; 65°C for 1 min; 60°C for 5 min; 65°C for 5 min and 72°C for 30s; and a final extension at 72°C for 2 min) using reverse tag concentration of 1000 nM, and a concentration of 20 nM for each of 19,488 target- specific forward primers.
  • 19,488 primers were used in the single-well reactions; the primers were designed to target SNPs found on chromosomes 1, 2, 13, 18, 21, X and Y.
  • the amplicons were then sequenced using an ILLUMINA GAIIX sequencer.
  • approximately 10 million reads were generated by the sequencer, with 9.4-9.6 million reads mapping to the genome (94-96 %), and of those, 99.95 % mapped to targeted SNPs with a mean depth of read of 460 and a median depth of read of 350.
  • 10M reads / 19,488 targets 513 reads/target.
  • the number of sequencing reads can be increased to increase the number of targeted SNPs that are amplified and sequenced.
  • Genome sequence alignment was performed using a proprietary algorithm adapted from the Novoalign (Novocraft, Selangor, Malaysia) commercial software package.
  • a chromosome copy number classification algorithm was implemented inMATLAB (MathWorks, Natick, MA, USA) leveraging a proprietary statistical algorithm termed Parental SupportTM (PS).
  • PS Parental SupportTM
  • the technique uses parental genotypes, data from the Hapmap Database, and the observed number of sequence reads associated with each of the relevant alleles at SNP loci.
  • PS uses measured parental genotypes and crossover frequency data, to create, in silico, billions of possible monosomic, disomic, and trisomic fetal genotypes at measured loci, each considered as a separate hypothesis.
  • PS uses a data model that predicts what the sequencing data is expected to look like for a plasma sample containing different fetal cfDNA fractions for each hypothetical fetal genotype.
  • Bayesian statistics are used to determine the relative likelihood of each hypothesis given the data, and likelihoods are summed for each copy number hypothesis family: monosomy, disomy, or trisomy.
  • the hypothesis with the maximum likelihood is selected as the copy number and fetal fraction, and the absolute likelihood of the call is the calculated accuracy, analogous to a test-specific risk score.
  • fetal fraction percentile was calculated, given the maternal weight and gestational age for that sample.
  • Six of the samples were found to have MW and GA adjusted fetal fractions that were in the lowest half -percentile. Of those six samples, one was from a trisomy 21 pregnancy, three were from a triploid pregnancies, and one was from a pregnancy with a fetus with an unbalanced translocation on chromosome 10.
  • the follow up may be an invasive procedure such as a chrionic villus biopsy or amniocentesis.
  • a number of samples were flagged as having abnormally low fetal fraction. Specifically, they were chosen as being in the lowest half-percentile for fetal fraction, and also from cases with maternal weight below 150 lbs., and gestational age at 14 weeks or below.
  • follow up was obtained from physicians on 11 of these samples. Four were found to be abnormal, and seven were found to be normal. Two were found have triploid karyotype, one was found to have an ultrasound consistent with trisomy 18 or triploidy, one had a detached placenta. Thus, 4/11 , or 36.4% of cases with abnormally low fetal fraction involved fetal abnormality. As a whole, the data contains about 2% of women with fetal abnormality, therefore, among the women with abnormally low fetal fraction, the risk of a fetal abnormality was about 20 times higher, as compared to the entire cohort.
  • Blood was drawn from 66986 pregnant patients. The blood was subjected to Panorama screening for the detecting of chromosomal abnormalities, i.e., trisomy 21, trisomy 18, trisomy 13, and X monosomy. Ploidy calls were made on 62333 of the samples. 1113 patients were identified as having a high risk of chromosomal abnormality. 60898 were identified as has having a low risk of a chromosomal abnormality. 269 were identified as either having twins or being triploid. 53 were identified as having a sex chromosome trisomy.
  • chromosomal abnormalities i.e., trisomy 21, trisomy 18, trisomy 13, and X monosomy.
  • Ploidy calls were made on 62333 of the samples. 1113 patients were identified as having a high risk of chromosomal abnormality. 60898 were identified as has having a low risk of a chromosomal abnormality. 269 were identified as either having twins or being triploid. 53
  • 4,653 samples did not generate a result, primarily due to the sample having a fraction of fetal DNA that was too low for the algorithm to make a determination; a second sample was requested from patients when the initial sample did not generate a result.
  • a cohort of 313 samples were identified as having a fetal fraction in the lowest one 1 % of the population, as adjusted for maternal weight and gestational age, and suitable for collection of follow-up information. Samples lacking accurate information for maternal weight or gestational ages were not considered in determining the lowest fetal fraction in 1% of the population.
  • the calculated odd ratio was 73.1 [(8/6)/(l, 113/60,898)] based on samples were there was post-test follow up to confirm the ploidy status of the low fetal fraction and normal fetal fraction samples.
  • the calculated odd ratio was 7.3 [((8+5)/(6+14+77))/(l,113)/(60,898)] based on samples were there was confirmation either by post-test follow up to confirm the ploidy status of the low fetal fraction and normal fetal fraction samples.
  • the odds ratio showed that samples with a fetal fraction in the 1st %ile were considerably more likely to be aneuploidy as compared to samples that had a fetal fraction that was not in the 1st ile.

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Abstract

La présente invention concerne, selon un mode de réalisation, un procédé de recherche d'anomalies génétiques (ou d'un risque d'anomalies génétiques) chez un fœtus comprenant les étapes consistant à prélever un échantillon de sang chez une femme enceinte, à mesurer la fraction fœtale d'ADN fœtal cellulaire libre dans l'échantillon de sang, à déterminer si la fraction fœtale d'ADN fœtal cellulaire libre dans l'échantillon de sang se situe au sein du percentile le plus bas d'une population de référence et à générer un rapport indiquant qu'une procédure d'analyse génétique invasive devrait être mise en œuvre. Selon certains modes de réalisation, la population de référence est choisie de façon à correspondre à l'âge gestationnel et/ou au poids de la mère. Selon certains modes de réalisation, la population d'essai est choisie de façon à correspondre à l'âge gestationnel et/ou au poids de la mère. Parmi les anomalies génétiques susceptibles d'être détectées, on peut citer l'aneuploïdie.
PCT/US2014/051926 2013-08-20 2014-08-20 Procédés d'utilisation de la détection d'une faible fraction fœtale Ceased WO2015026967A1 (fr)

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Cited By (8)

* Cited by examiner, † Cited by third party
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BE1023274B1 (nl) * 2015-07-17 2017-01-19 Multiplicom Nv Schattingsmethode en systeem voor het schatten van een foetale fractie
EP3135770A1 (fr) * 2015-08-28 2017-03-01 Latvian Biomedical Research and Study Centre Ensemble d'oligonucléotides et procédé de détection de fraction d'adn foetal dans du plasma maternel
WO2017087206A1 (fr) * 2015-11-16 2017-05-26 Sequenom, Inc. Méthodes et procédés d'évaluation non invasive de variations génétiques
GB2564847A (en) * 2017-07-18 2019-01-30 Congenica Ltd Knowledgebase for non-invasive prenatal genetic screening and diagnosis
WO2019020180A1 (fr) * 2017-07-26 2019-01-31 Trisomytest, S.R.O. Procédé de détection prénatal non effractif d'aneuploïdie chromosomique fœtale à partir du sang maternel sur la base d'un réseau bayésien
EP3510174A4 (fr) * 2016-09-07 2020-02-19 Baylor College of Medicine Application clinique de technologies d'analyse de l'adn acellulaire à un diagnostic prénatal non effractif et à d'autres biopsies liquides
US11697849B2 (en) 2012-01-20 2023-07-11 Sequenom, Inc. Methods for non-invasive assessment of fetal genetic variations that factor experimental conditions
US11869630B2 (en) 2017-07-18 2024-01-09 Congenica Ltd. Screening system and method for determining a presence and an assessment score of cell-free DNA fragments

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US20120264121A1 (en) * 2011-04-12 2012-10-18 Verinata Health, Inc. Resolving genome fractions using polymorphism counts
US20120270212A1 (en) * 2010-05-18 2012-10-25 Gene Security Network Inc. Methods for Non-Invasive Prenatal Ploidy Calling
US20130190653A1 (en) * 2012-01-25 2013-07-25 Angel Gabriel Alvarez Ramos Device for blood collection from the placenta and the umbilical cord
US20130196862A1 (en) * 2010-05-18 2013-08-01 Natera, Inc. Informatics Enhanced Analysis of Fetal Samples Subject to Maternal Contamination

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120270212A1 (en) * 2010-05-18 2012-10-25 Gene Security Network Inc. Methods for Non-Invasive Prenatal Ploidy Calling
US20130196862A1 (en) * 2010-05-18 2013-08-01 Natera, Inc. Informatics Enhanced Analysis of Fetal Samples Subject to Maternal Contamination
US20120264121A1 (en) * 2011-04-12 2012-10-18 Verinata Health, Inc. Resolving genome fractions using polymorphism counts
US20130190653A1 (en) * 2012-01-25 2013-07-25 Angel Gabriel Alvarez Ramos Device for blood collection from the placenta and the umbilical cord

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11697849B2 (en) 2012-01-20 2023-07-11 Sequenom, Inc. Methods for non-invasive assessment of fetal genetic variations that factor experimental conditions
BE1023274B1 (nl) * 2015-07-17 2017-01-19 Multiplicom Nv Schattingsmethode en systeem voor het schatten van een foetale fractie
EP3135770A1 (fr) * 2015-08-28 2017-03-01 Latvian Biomedical Research and Study Centre Ensemble d'oligonucléotides et procédé de détection de fraction d'adn foetal dans du plasma maternel
WO2017087206A1 (fr) * 2015-11-16 2017-05-26 Sequenom, Inc. Méthodes et procédés d'évaluation non invasive de variations génétiques
EP3510174A4 (fr) * 2016-09-07 2020-02-19 Baylor College of Medicine Application clinique de technologies d'analyse de l'adn acellulaire à un diagnostic prénatal non effractif et à d'autres biopsies liquides
GB2564847A (en) * 2017-07-18 2019-01-30 Congenica Ltd Knowledgebase for non-invasive prenatal genetic screening and diagnosis
US11869630B2 (en) 2017-07-18 2024-01-09 Congenica Ltd. Screening system and method for determining a presence and an assessment score of cell-free DNA fragments
WO2019020180A1 (fr) * 2017-07-26 2019-01-31 Trisomytest, S.R.O. Procédé de détection prénatal non effractif d'aneuploïdie chromosomique fœtale à partir du sang maternel sur la base d'un réseau bayésien

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