US20160053301A1 - Methods for quantitative genetic analysis of cell free dna - Google Patents

Methods for quantitative genetic analysis of cell free dna Download PDF

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US20160053301A1
US20160053301A1 US14/466,741 US201414466741A US2016053301A1 US 20160053301 A1 US20160053301 A1 US 20160053301A1 US 201414466741 A US201414466741 A US 201414466741A US 2016053301 A1 US2016053301 A1 US 2016053301A1
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cfdna
genetic
library
dna
sequence
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Christopher K. Raymond
Lee P. Lim
Christopher D. Armour
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CLEARFORK BIOSCIENCE Inc
Resolution Bioscience Inc
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Resolution Bioscience Inc
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    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING 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
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6806Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10Sequence alignment; Homology search
    • CCHEMISTRY; METALLURGY
    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING 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
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869Methods for sequencing
    • G06F19/20
    • G06F19/22
    • G06F19/28
    • G06F19/345
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    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10Ploidy or copy number detection
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

The invention provides a method for genetic analysis in individuals that reveals both the genetic sequences and chromosomal copy number of targeted and specific genomic loci in a single assay. The present invention further provides methods for the sensitive and specific detection of target gene sequences and gene expression profiles.

Description

    STATEMENT REGARDING SEQUENCE LISTING
  • The Sequence Listing associated with this application is provided in text format in lieu of a paper copy, and is hereby incorporated by reference into the specification. The name of the text file containing the Sequence Listing is CLFK00200US_ST25.txt. The text file is 117 KB, was created on Aug. 22, 2014, and is being submitted electronically via EFS-Web.
  • BACKGROUND
  • 1. Technical Field
  • The invention relates generally to compositions and methods for the quantitative genetic analysis of cell free DNA (cfDNA). In particular, the present invention relates to improved targeted sequence capture compositions and methods for the genetic characterization and analysis of cfDNA.
  • 2. Description of the Related Art
  • It is becoming increasing clear that most, if not all, of the most common human cancers are diseases of the human genome. The emerging picture is that somatic mutations accumulate during an individual's lifetime, some of which increase the probability that the cell in which they are harbored can develop into a tumor (Vogelstein et al., Science 339(6127): 1546-1558 (2013)). With just the wrong combination of accumulated mutational events, a precancerous growth loses constraints that keep uncontrolled proliferation in check and the resulting cell mass becomes a cancer. The constellations of mutations that are necessary and sufficient to cause cancer are often collectively referred to as “driver mutations.” One of the themes that have emerged from recent and intensive molecular analysis is that cancer, once thought of as a single, tissue-specific disease, is in fact a group of related diseases, each with a unique molecular pathology. The human genome project laid the groundwork for genome-wide analysis of cancers.
  • For example, the introduction of next-generation sequencing technologies (2004-present) has accelerated the discovery pace of causal genomic factors that underlie the diagnosis of NSCLC, making it clear that NSCLC is really a family of related diseases, each of which may be responsive to a different targeted therapy.
  • The art lacks reliable and robust molecular analysis methods for the analysis of genetic diseases. Traditionally, molecular diagnostics have consisted of antibody-based tests (immunohistochemistry), in-situ hybridization with DNA probes (fluorescence in situ hybridization), and hybridization or PCR-based tests that query specific nucleotide sequences. Until recently, DNA sequencing as a molecular diagnostic tool has been generally limited to the coding exons of one or two genes. While DNA sequencing has been used in the diagnosis and treatment of solid cancers, one of the most significant drawbacks of these methods is that they require direct access to tumor tissues. Such material is often difficult to obtain from the initial biopsy used to diagnose the disease and virtually impossible to obtain in multiple repetitions over time. Similarly, biopsies are not possible in patients with inaccessible tumors and not practical in individuals suffering from metastatic disease.
  • Thus, the vast potential of molecular diagnostics for genetic diseases; fetal testing; paternity testing; predicting response to drug treatment; diagnosing or monitoring a medical condition; mendelian disorders; genetic mosaicism; pathogen screening; microbiome profiling; and organ transplant monitoring; has yet to be realized. To date, existing molecular diagnostics approaches lack efficient solutions to clone and amplify individual DNA molecules, as well as solutions to efficiently target sequencing to specific genomic loci, with sensitivity sufficient to discriminate true positive test results from false positive signals that arise during sample processing.
  • BRIEF SUMMARY
  • The invention relates generally to compositions and methods for improved compositions and methods for the genetic analysis of cfDNA.
  • In various embodiments, a method for genetic analysis of cell-free DNA (cfDNA) is provided, comprising: treating cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA; ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate cfDNA library clones; determining the number of genome equivalents in the cfDNA clone library; and performing a quantitative genetic analysis of one or more target genetic loci in the cfDNA library clones.
  • In a particular embodiment, the method further comprises isolating cfDNA from a biological sample of a subject.
  • In an additional embodiment, the cfDNA is isolated from a biological sample selected from the group consisting of: amniotic fluid, blood, plasma, serum, semen, lymphatic fluid, cerebral spinal fluid, ocular fluid, urine, saliva, stool, mucous, and sweat.
  • In a certain embodiment, the one or more adaptors comprise a plurality of adaptor species.
  • In a particular embodiment, the one or more adaptors each comprise a primer binding site for amplification of the cfDNA library.
  • In a further embodiment, the one or more adaptors each comprise one or more unique read codes.
  • In an additional embodiment, the one or more adaptors each comprise one or more sample codes for sample multiplexing.
  • In another embodiment, the one or more adaptors each comprise one or more sequences for DNA sequencing.
  • In a particular embodiment, qPCR is performed on the cfDNA clone library and a qPCR measurement is compared to standards of known genome equivalents to determine the genome equivalents of the cfDNA clone library.
  • In another particular embodiment, qPCR is performed with a primer that binds to an Alu sequence and a primer that binds to a sequence in an adaptor.
  • In a certain embodiment, the quantitative genetic analysis is performed on a plurality of genetic loci in the cfDNA library clones.
  • In a further embodiment, the quantitative genetic analysis is performed on a plurality of genetic loci in a plurality of cfDNA clone libraries.
  • In an additional embodiment, the quantitative genetic analysis comprises hybridizing one or more capture probes to a target genetic locus to form capture probe-cfDNA clone complexes.
  • In a particular embodiment, the quantitative genetic analysis comprises isolating the capture probe-cfDNA clone complexes.
  • In a certain embodiment, the quantitative genetic analysis comprises amplification of the cfDNA clone sequence in the isolated hybridized capture probe-cfDNA clone complexes.
  • In a further embodiment, the quantitative genetic analysis comprises DNA sequencing to generate a plurality of sequencing reads.
  • In another embodiment, the quantitative genetic analysis comprises bioinformatic analysis of the plurality of sequencing reads.
  • In a particular embodiment, bioinformatics analysis is used: to quantify the number of genome equivalents analyzed in the cfDNA clone library; to detect genetic variants in a target genetic locus; to detect mutations within a target genetic locus; to detect genetic fusions within a target genetic locus; and to measure copy number fluctuations within a target genetic locus.
  • In an additional embodiment, the subject does not have a genetic disease.
  • In a certain embodiment, the subject has not been diagnosed with a genetic disease.
  • In another certain embodiment, the subject has been diagnosed with a genetic disease.
  • In another embodiment, the quantitative genetic analysis is used to identify or detect one or more genetic lesions that cause or associated with the genetic disease.
  • In a certain embodiment, the genetic lesion comprises a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion.
  • In a particular embodiment, the genetic lesion comprises a genomic rearrangement that fuses the 3′ coding region of the ALK gene to another gene.
  • In a particular embodiment, the 3′ coding region of the ALK gene is fused to the EML4 gene.
  • In another embodiment, the genetic disease is cancer.
  • In a further embodiment, the subject is pregnant.
  • In an additional embodiment, the quantitative genetic analysis is used to identify or detect one or more genetic variants or genetic lesions of one or more target genetic loci in fetal cfDNA.
  • In a particular embodiment, the subject is a transplant recipient.
  • In an additional embodiment, the quantitative genetic analysis is used to identify or detect donor cfDNA in the subject.
  • In various embodiments, a method of predicting, diagnosing, or monitoring a genetic disease in a subject is provided, comprising: isolating or obtaining cfDNA from a biological sample of a subject; treating the cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA; ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate a cfDNA clone library; determining the number of genome equivalents in the cfDNA clone library; and performing a quantitative genetic analysis of one or more target genetic loci associated with the genetic disease in the cfDNA clone library, wherein the identification or detection of one or more genetic lesions in the one or more target genetic loci is prognostic for, diagnostic of, or monitors the progression of the genetic disease.
  • In an additional embodiment, the cfDNA is isolated from a biological sample selected from the group consisting of: amniotic fluid, blood, plasma, serum, semen, lymphatic fluid, cerebral spinal fluid, ocular fluid, urine, saliva, stool, mucous, and sweat.
  • In a certain embodiment, the genetic lesion comprises a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion.
  • In a particular embodiment, the genetic lesion comprises a genomic rearrangement that fuses the 3′ coding region of the ALK gene to another gene.
  • In a further embodiment, the 3′ coding region of the ALK gene is fused to the EML4 gene.
  • In a particular embodiment, the genetic disease is cancer.
  • In various embodiments, a companion diagnostic for a genetic disease is provided comprising: isolating or obtaining cfDNA from a biological sample of a subject; treating the cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA; ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate a cfDNA clone library; determining the number of genome equivalents in the cfDNA clone library; and performing a quantitative genetic analysis of one or more biomarkers associated with the genetic disease in the cfDNA clone library, wherein detection of, or failure to detect, at least one of the one or more biomarkers indicates whether the subject should be treated for the genetic disease.
  • In a particular embodiment, the cfDNA is isolated from a biological sample selected from the group consisting of: amniotic fluid, blood, plasma, serum, semen, lymphatic fluid, cerebral spinal fluid, ocular fluid, urine, saliva, stool, mucous, and sweat.
  • In an additional embodiment, the biomarker is a genetic lesion.
  • In a particular embodiment, the genetic lesion comprises a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion.
  • In an additional embodiment, the genetic lesion comprises a genomic rearrangement that fuses the 3′ coding region of the ALK gene to another gene.
  • In a further embodiment, the 3′ coding region of the ALK gene is fused to the EML4 gene.
  • In a certain embodiment, the genetic disease is cancer.
  • BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
  • FIG. 1 shows the expected versus observed variant frequencies as a function of admix dilution in the absence of unique read filtering. In the absence of unique read filtering, random base changes at these four selected positions occurred with measurable, non-zero frequencies; thus, demonstrating a lack of sensitivity to detect the particular single nucleotide variants (SNV).
  • FIG. 2 shows that unique read filtering performed on the data generated in FIG. 1. The left hand panel shows the data from FIG. 1 on the BRAF I326T SNV without unique read filtering. The right hand panel shows that using unique read filtering of the same data increased the assay sensitivity and allowed the discrimination of true signal from error-prone noise.
  • FIG. 3 shows capture probe performance as a function of length and wash temperature. The y-axis shows the total number of reads associated with each capture probe. The bars in the bar chart are broken into two categories, where open bars correspond to on-target reads that align to the intended capture probe targets and solid bars show off-target reads that are associated with a capture probe but that map to unintended regions of the genome. Overall, the 40-mer and 60-mer capture probes perform substantially the same with 44° C. and 47° C. washes. With the 50° C. wash, the 40-mer capture probes perform erratically. These data validate the use of 40-mer capture probes at wash temperatures in the range of about 44° C. to about 47° C.
  • FIG. 4 shows a schematic for the targeted and oriented sequencing of intron 19 of the ALK gene. A) In the “wild-type” reference sequence, antisense-oriented ALK capture probes identify sequences from intron 19. B) In the case of pathogenic fusion genes, some ALK capture probes will identify junction sequences associated with the gene fusion event.
  • FIG. 5 shows a schematic for high density capture probe placement for complete sequencing of target regions. Each capture probe captures a collection of sequences that provide cumulative coverage at each base position. Here, coverage is represented by a line, and the amplitude of the line denotes depth of coverage derived from a particular capture probe. Overlapping coverage from adjacent capture probes provides complete sequencing of target regions in both possible directions. In addition, the head-to-head placement of opposite strand capture probes ensures that all capture probe binding sites are sequenced.
  • FIG. 6 shows a representative example of the size distribution of fragmented DNA used in library construction.
  • FIG. 7 shows the performance of high-density 40-mer capture probes in a representative experiment. The y-axis shows the total number of reads, which are broken out as on-target reads, off-target reads, and unmappable reads. The x-axis enumerates each of the 105 capture probes used in this experiment for sequence capture.
  • FIG. 8 shows a representative example of the cumulative coverage of a target region using high density 40-mer capture probes. Shown here is the cumulative coverage of TP53 coding exons.
  • FIG. 9A shows a representative example of the size distribution of cell-free DNA (cfDNA) libraries. The dominant band is consistent with a collection of 170±10 bp fragments ligated to 90 bp of adaptors. FIG. 9B shows a published gel image of cfDNA and a representative cfDNA library generated using the methods disclosed and/or contemplated herein. The qualitative “ladder” appearance is conserved in the library, but the library is shifted to higher mass by the addition of 90 bp of adaptor sequences. FIG. 9C shows a representative example of genomic, plasma-derived cfDNA libraries from Ovarian cancer patients (OvC) and “healthy donors” (HD).
  • FIG. 10 shows the unique read counts across eight cfDNA libraries derived from four plasma samples. Fragmentation (frag) prior to library construction with this sample 23407 increased the library yield by more than two-fold.
  • FIG. 11 shows the representative read coverage of cfDNA across a region of the TP53 gene. Twenty four 131 bp reads captured by the “TP53_NM000546_chr17:7579351:region3:280nt:41:80:r” capture probe (SEQ ID NO:201) were chosen at random and aligned using the BLAT algorithm within the UCSC genome browser. Twenty one reads map to the target region, and they do so in a pattern of overlapping coverage. The probe used to capture these reads is marked with an arrow.
  • FIG. 12 shows an overview of targeted DNA sequencing of the coding regions of the TP53 gene from a cfDNA genomic library. The coverage (horizontal axis) extends across all 10 coding regions and includes intronic regions involved in mRNA splicing. The sequencing depth (vertical axis) reaches a maximum of 4851 and is uniform across all coding exons.
  • FIG. 13 shows a plot of unique read counts versus qPCR estimated genome equivalents in an ACA2-based assay. qPCR measurements are shown on the X-axis versus read counts on the Y-axis. Perfect agreement between these measurements is shown as the diagonal. There is very poor, if any, correlation between measurements, especially at lower genomic inputs. These data show that the ACA2-based qPCR assay chronically underestimates library complexity and is inadequate for measuring genome equivalents.
  • FIG. 14 shows a schematic of the core elements of a qPCR genome equivalent measurement assay that couples an genomic repeat specific primer (e.g., Alu) and a long adaptor-specific primer. (A) Standard library amplification using a single, 25 nt primer named ACA2 (primer 1). (B) Longer, 58 nt versions of the ACA2 primer (primer 2) do not amplify genomic libraries because of stem-loop suppression. (C) Forward and reverse primers directed to a consensus human Alu repeat element (primers 3 and 4) recognize 1000′s of loci and readily amplify genomic DNA. (D) A single Alu primer alone, either forward or reverse (primer 3 or primer 4), coupled with the long ACA2 primer (primer 2) do not amplify genomic DNA. (E) The same primer pair as in (D) readily amplifies genomic cfDNA library clones that contain Alu sequences.
  • FIG. 15 shows proof-of-concept data for an Alu plus adaptor-based qPCR assay of genome equivalents. (A) Amplification of 10 pg of a standard genomic library with various PCR primers. The x-axis specifies PCR primers used for amplification and the Y-axis (log scale) indicates the PCR signal measured in units of fg/μL. The standard ACA2 primer produced a strong signal, as expected. The ACA2 long primer failed to produce signal owing to PCR suppression. The two Alu primer pairs both produced signal at 1% the amount of ACA2, suggesting that 1% of clones possess an amplifiable Alu sequence. The combination of any Alu primer with the long ACA2 primer also produced signal in ˜1% of clones. (B) Validation against 10 pg of genomic DNA (left four samples) or 10 pg of library DNA (right four clones). Alu primer pairs amplify comparable signal from genomic DNA or a genomic library. In contrast, primer pairs consisting of an Alu primer and a long ACA2 primer amplify genomic DNA poorly (L+A1F) or not at all (L+A1R). These same pairs exhibit amplification of library that slightly exceeds the signal from Alu primer pairs.
  • FIG. 16 shows a direct comparison of ACA2 primer qPCR assay with the Alu-ACA2 long-primer qPCR assay. The Alu ACA2 long-primer qPCR assay shows an 8-fold increase in detectable genome equivalents, which is more consistent with unique read counts derived from sequencing data.
  • FIG. 17 shows a representative example of adaptor structure and function for high sensitivity, quantitative genetic assays that provide accurate determinations of genome equivalents analyzed. (A) Fine structure of the adaptor ligation strand. Details relating to each numbered element are provided in Example 4. (B) The duplex formed between 45 nt ligation strands and 12 nt partner oligo strand creates a blunt-end ligation substrate compatible with end-repaired cfDNA fragments (solid bars). (C) Following ligation, the complement to the ligation strand is created by a DNA polymerase-mediated fill-in reaction.
  • FIG. 18 shows a representative example of the size distribution of two DNA samples (NA06994 & NCI-H2228) processed to mimic cfDNA.
  • FIG. 19 shows a representative example of the sensitivity of detection of the TP53 point mutation Q331* in tumor sample DNA (H2228) admixed with normal genomic DNA (N). The most sensitive detection corresponds to ˜1 mutant copy of TP53 among 1000 normal copies of the gene.
  • FIG. 20 shows the precise determination of the junction sequence for the EML4-ALK fusion gene harbored in cell line NCI-H2228 using the compositions and methods contemplated herein.
  • FIG. 21 shows the detection of the EML4-ALK fusion gene tumor sample DNA (H2228) admixed with normal genomic DNA (N). Because the fusion is present as a heterozygote in the NCI-H2228 cell line, the most sensitive detection corresponds to one gene fusion among ˜100 normal chromosomal copies of the ALK gene (50 genome equivalents).
  • FIG. 22 shows the detection of the MYCN gene amplification in admixtures of cell line NCI-H69 (H69) diluted into normal human DNA (N). The threshold value of two normal diploid copies is shown as a dashed red line.
  • FIG. 23 shows the DNA mutations detected in the TP53 gene of three different cancer patients. The canonical gene model is shown at the top of the figure. The peaks represent DNA sequence coverage (X-axis) and depth (Y-axis). Sequencing depth was >4000 genome equivalents for all sample analyzed. An expanded view of exon 7 below the gene model shows where all detected mutations were localized. The frequency of mutant detection in cfDNA (plasma), tumor tissue, and normal adjacent tissue is shown, where available (NA—not available). OVA1 and OVA2 are ovarian cancer patients; CRC406 and CRC407 are colorectal cancer patients. No mutations in TP53 were found in any of the OVA1 samples.
  • FIG. 24 shows the DNA sequencing of a larger, thirteen gene panel (boxed). The sequencing identified a KRAS mutation in cfDNA and tumor from ovarian cancer patient OVA1.
  • FIG. 25 shows the DNA sequencing of a larger, twelve gene panel. The sequencing identified an ERBB2 gene amplification in the plasma of colorectal cancer patient CRC407.
  • DETAILED DESCRIPTION A. Overview
  • The present invention contemplates, in part, compositions and methods for the quantitative genetic analysis of the genetic state of an individual using cell-free DNA (cfDNA). As used herein, the term “genetic state” refers to the sequence of one or more target genome sequences in the genome in relation to a non-causal normal sequence or in relation to a sequence that is causal for a genetic condition or disease. In one embodiment, analyzing the genetic state refers to identifying, quantifying, or monitoring a genetic variant in a target genetic locus, wherein the variant varies with respect to a reference sequence (e.g., a normal or mutated sequence). The present inventors have provided solutions to the molecular diagnostic problems of genetic conditions or diseases associated with lack of sensitivity to discriminate true positives from false positives, inefficient cloning and amplification of individual DNA molecules, and inefficient targeted sequencing to specific genomic loci. The solutions contemplated herein comprise compositions and methods for reliable and robust quantitative genetic analysis with sensitivity sufficient to discriminate true positive test results from false positive signals that arise during sample processing.
  • Next-generation sequencing technology has afforded the opportunity to add broad genomic surveys to molecular diagnosis in a variety of scenarios including cancers, fetal diagnostics, paternity testing, pathogen screening and organ transplant monitoring. In the context of genetic diseases, next-generation sequencing information is being used in a clinical setting to identify mutations within genes that are likely to alter gene function, to identify the gain or loss of genetic material within cells, and to identify genomic rearrangements that are not found in normal, healthy cells. The results of these broad diagnostic surveys are often used to guide patient treatment.
  • However, the potential benefits of DNA sequencing in diagnosis and treatment of the genetic state of an individual or genetic conditions or diseases is outweighed by the need to directly access affected tissues to obtain samples. Such material is often difficult to obtain from the initial biopsy used to diagnose the disease and virtually impossible to obtain in multiple repetitions over time. Similarly, in cancer patients, biopsies are not possible in patients with inaccessible tumors and not practical in individuals suffering from metastatic disease. In contrast, the present inventors' approach derives from the fact that all tissues require access to the vasculature to survive and as a consequence these masses deposit DNA into bodily fluids. One major depot of bodily fluid in which the DNA of diseased cells is found is the plasma of human blood.
  • In contrast to testing methods that rely on shallow, genome-wide sequence coverage, molecular diagnostics contemplated herein for the genetic state of an individual; genetic diseases; mendelian disorders; genetic mosaicism; fetal testing; paternity testing; predicting response to drug treatment; diagnosing or monitoring a medical condition; pathogen screening; microbiome profiling; and organ transplant monitoring leverage the availability of cfDNA to provide deep sequence coverage of select target genes. In addition, the cfDNA-based cancer diagnostics contemplated herein possess the ability to detect a variety of genetic changes including somatic sequence variations that alter protein function, large-scale chromosomal rearrangements that create chimeric gene fusions, and copy number variation that includes loss or gain of gene copies. Using the contemplated compositions and methods, these changes are detectable and quantifiable in the face of significant dilution, or admixture, of normal sequences within cfDNA that are contributed by the normal turnover processes that happen within healthy tissues. The compositions and methods contemplated herein also successfully address the major challenges associated with detecting rare genetic changes causal of disease; namely, that cfDNA is highly fragmented, that cfDNA levels vary substantially between different individuals, and that the degree of admixture of diseased versus normal sequences is highly variable among patients, even within individuals suffering from the same molecular disease and stage.
  • In various embodiments, compositions and methods for genetic analysis of comprise interrogating the DNA fraction within biological fluid samples and stool samples. The methods contemplated herein provide a novel comprehensive framework address molecular genetic analysis using cfDNA available from a variety of biological sources. Cloning of purified cfDNA introduces tagged cfDNA sequences that inform downstream analysis and enable amplification of the resulting clone libraries. Hybrid capture with target specific oligonucleotides is used to retrieve specific sequences for subsequent analysis. Independent measurements of the number of genomes present in the library are applied to each sample, and these assays provide a means to estimate the assay's sensitivity. The assays contemplated herein provide reliable, reproducible, and robust methods for the analysis, detection, diagnosis, or monitoring of genetic states, conditions, or disease.
  • The practice of particular embodiments of the invention will employ, unless indicated specifically to the contrary, conventional methods of chemistry, biochemistry, organic chemistry, molecular biology, microbiology, recombinant DNA techniques, genetics, immunology, and cell biology that are within the skill of the art, many of which are described below for the purpose of illustration. Such techniques are explained fully in the literature. See, e.g., Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001); Sambrook, et al., Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Maniatis et al., Molecular Cloning: A Laboratory Manual (1982); Ausubel et al., Current Protocols in Molecular Biology (John Wiley and Sons, updated July 2008); Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience; Glover, DNA Cloning: A Practical Approach, vol. I & II (IRL Press, Oxford, 1985); Anand, Techniques for the Analysis of Complex Genomes, (Academic Press, New York, 1992); Transcription and Translation (B. Hames & S. Higgins, Eds., 1984); Perbal, A Practical Guide to Molecular Cloning (1984); and Harlow and Lane, Antibodies, (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1998).
  • B. Definitions
  • Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred embodiments of compositions, methods and materials are described herein. For the purposes of the present invention, the following terms are defined below.
  • The articles “a,” “an,” and “the” are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.
  • The use of the alternative (e.g., “or”) should be understood to mean either one, both, or any combination thereof of the alternatives.
  • The term “and/or” should be understood to mean either one, or both of the alternatives.
  • As used herein, the term “about” or “approximately” refers to a quantity, level, value, number, frequency, percentage, dimension, size, amount, weight or length that varies by as much as 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% or 1% to a reference quantity, level, value, number, frequency, percentage, dimension, size, amount, weight or length. In one embodiment, the term “about” or “approximately” refers a range of quantity, level, value, number, frequency, percentage, dimension, size, amount, weight or length±15%, ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, or ±1% about a reference quantity, level, value, number, frequency, percentage, dimension, size, amount, weight or length.
  • Throughout this specification, unless the context requires otherwise, the words “comprise”, “comprises” and “comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. In particular embodiments, the terms “include,” “has,” “contains,” and “comprise” are used synonymously.
  • By “consisting of” is meant including, and limited to, whatever follows the phrase “consisting of.” Thus, the phrase “consisting of” indicates that the listed elements are required or mandatory, and that no other elements may be present.
  • By “consisting essentially of” is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase “consisting essentially of” indicates that the listed elements are required or mandatory, but that no other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements.
  • Reference throughout this specification to “one embodiment,” “an embodiment,” “a particular embodiment,” “a related embodiment,” “a certain embodiment,” “an additional embodiment,” or “a further embodiment” or combinations thereof means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the foregoing phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
  • As used herein, the term “isolated” means material that is substantially or essentially free from components that normally accompany it in its native state. In particular embodiments, the term “obtained” or “derived” is used synonymously with isolated.
  • As used herein, the term “DNA” refers to deoxyribonucleic acid. In various embodiments, the term DNA refers to genomic DNA, recombinant DNA, synthetic DNA, or cDNA. In one embodiment, DNA refers to genomic DNA or cDNA. In particular embodiments, the DNA comprises a “target region.” DNA libraries contemplated herein include genomic DNA libraries and cDNA libraries constructed from RNA, e.g., an RNA expression library. In various embodiments, the DNA libraries comprise one or more additional DNA sequences and/or tags.
  • A “target genetic locus” or “DNA target region” refers to a region of interest within a DNA sequence. In various embodiments, targeted genetic analyses are performed on the target genetic locus. In particular embodiments, the DNA target region is a region of a gene that is associated with a particular genetic state, genetic condition, genetic diseases; fetal testing; genetic mosaicism, paternity testing; predicting response to drug treatment; diagnosing or monitoring a medical condition; microbiome profiling; pathogen screening; or organ transplant monitoring.
  • As used herein, the terms “circulating DNA,” “circulating cell-free DNA” and “cell-free DNA” are often used interchangeably and refer to DNA that is extracellular DNA, DNA that has been extruded from cells, or DNA that has been released from necrotic or apoptotic cells.
  • A “subject,” “individual,” or “patient” as used herein, includes any animal that exhibits a symptom of a condition that can be detected or identified with compositions contemplated herein. Suitable subjects include laboratory animals (such as mouse, rat, rabbit, or guinea pig), farm animals (such as horses, cows, sheep, pigs), and domestic animals or pets (such as a cat or dog). In particular embodiments, the subject is a mammal. In certain embodiments, the subject is a non-human primate and, in preferred embodiments, the subject is a human.
  • C. Methods of Genetic Analysis of Cell Free DNA
  • In various embodiments, a method for genetic analysis of cfDNA is provided.
  • In particular embodiments, a method for genetic analysis of cfDNA comprises: generating and amplifying a cfDNA library, determining the number of genome equivalents in the cfDNA library; and performing a quantitative genetic analysis of one or more genomic target loci.
  • A method for genetic analysis of cfDNA comprises treating cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA and ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate cfDNA library clones; determining the number of genome equivalents of cfDNA library clones; and performing a quantitative genetic analysis of one or more target genetic loci in the cfDNA library clones.
  • 1. Generating a cfDNA Library
  • In particular embodiments, methods of genetic analysis contemplated herein comprise generating a cfDNA library comprising treating cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA and ligating one or more adaptors to each end of the end-repaired cfDNA to generate the cfDNA library.
  • (a) Cell-Free DNA (cfDNA)
  • The methods and compositions contemplated herein are designed to efficiently analyze, detect, diagnose, and/or monitor genetic states, genetic conditions, genetic diseases, genetic mosaicism, fetal diagnostics, paternity testing, microbiome profiling, pathogen screening, and organ transplant monitoring using cell-free DNA (cfDNA) as an analyte. The size distribution of cfDNA ranges from about 150 bp to about 180 bp fragments. Fragmentation may be the result of endonucleolytic and/or exonucleolytic activity and presents a formidable challenge to the accurate, reliable, and robust analysis of cfDNA. Another challenge for analyzing cfDNA is its short half-life in the blood stream, on the order of about 15 minutes. Without wishing to be bound to any particular theory, the present invention contemplates, in part, that analysis of cfDNA is like a “liquid biopsy” and is a real-time snapshot of current biological processes.
  • Moreover, because cfDNA is not found within cells and may be obtained from a number of suitable sources including, but not limited to, biological fluids and stool samples, it is not subject to the existing limitations that plague next generation sequencing analysis, such as direct access to the tissues being analyzed.
  • Illustrative examples of biological fluids that are suitable sources from which to isolate cfDNA in particular embodiments include, but are not limited to amniotic fluid, blood, plasma, serum, semen, lymphatic fluid, cerebral spinal fluid, ocular fluid, urine, saliva, mucous, and sweat.
  • In particular embodiments, the biological fluid is blood or blood plasma.
  • In certain embodiments, commercially available kits and other methods known to the skilled artisan can used to isolate cfDNA directly from the biological fluids of a patient or from a previously obtained and optionally stabilized biological sample, e.g., by freezing and/or addition of enzyme chelating agents including, but not limited to EDTA, EGTA, or other chelating agents specific for divalent cations.
  • (b) Generating End-Repaired cfDNA
  • In particular embodiments, generating a cfDNA library comprises the end-repair of isolated cfDNA. The fragmented cfDNA is processed by end-repair enzymes to generate end-repaired cfDNA with blunt ends, 5′-overhangs, or 3′-overhangs. In some embodiments, the end-repair enzymes can yield for example. In some embodiments, the end-repaired cfDNA contains blunt ends. In some embodiments, the end-repaired cfDNA is processed to contain blunt ends. In some embodiments, the blunt ends of the end-repaired cfDNA are further modified to contain a single base pair overhang. In some embodiments, end-repaired cfDNA containing blunt ends can be further processed to contain adenine (A)/thymine (T) overhang. In some embodiments, end-repaired cfDNA containing blunt ends can be further processed to contain adenine (A)/thymine (T) overhang as the single base pair overhang. In some embodiments, the end-repaired cfDNA has non-templated 3′ overhangs. In some embodiments, the end-repaired cfDNA is processed to contain 3′ overhangs. In some embodiments, the end-repaired cfDNA is processed with terminal transferase (TdT) to contain 3′ overhangs. In some embodiments, a G-tail can be added by TdT. In some embodiments, the end-repaired cfDNA is processed to contain overhang ends using partial digestion with any known restriction enzymes (e.g., with the enzyme Sau3A, and the like.
  • (c) Ligating Adaptor Molecules to End-Repaired cfDNA
  • In particular embodiments, generating a cfDNA library comprises ligating one or more adaptors to each end of the end-repaired cfDNA. The present invention contemplates, in part, an adaptor module designed to accommodate large numbers of genome equivalents in cfDNA libraries. Adaptor modules are configured to measure the number of genome equivalents present in cfDNA libraries, and, by extension, the sensitivity of sequencing assays used to identify sequence mutations.
  • As used herein, the term “adaptor module” refers to a polynucleotide comprising at least five elements: (i) a first element comprising a PCR primer binding site for the single-primer library amplification; (ii) a second element comprising a 5 nucleotide read code that acts to uniquely identified each sequencing read; (iii) a third element comprising a 3 nucleotide sample code to identify different samples and enable sample multiplexing within a sequencing run; (iv) a fourth element comprising a 12 nucleotide anchor sequence that enables calibration of proper base calls in sequencing reads and acts as an anchor for hybridization to a partner oligonucleotide; and (v) a fifth element comprising the two 3′ terminal nucleotides of Element 4 (FIG. 17 and Tables 12-16). The adaptor module is hybridized to a partner oligonucleotide that is complementary to Element 4 to form an adaptor suitable for ligating to the ends of cfDNA, optionally end-repaired blunt-ended cfDNA.
  • In particular embodiments, an adaptor module comprises one or more PCR primer sequences, one or more read codes, one or more sample codes, one or more anchor sequences, and two or more 3′ nucleotides that are efficient ligation substrates. In additional embodiments, the adaptor module further comprises one or more sequencing primer binding sites.
  • In particular embodiments, an adaptor module comprises a first element that comprises one or more PCR primer binding sequences for single-primer amplification of a cfDNA library. In one embodiment, the PCR primer binding sequence is about 12 to about 40 nucleotides, about 18 to about 40 nucleotides, about 20 to about 35 nucleotides, or about 20 to about 30 nucleotides. In another embodiment, the PCR primer binding sequence is about 12 nucleotides, about 13 nucleotides, about 14 nucleotides, about 15 nucleotides, about 16 nucleotides, about 17 nucleotides, about 18 nucleotides, about 19 nucleotides, about 20 nucleotides, about 21 nucleotides, about 22 nucleotides, about 23 nucleotides, about 24 nucleotides, about 25 nucleotides, about 26 nucleotides, about 27 nucleotides, about 28 nucleotides, about 29 nucleotides, about 30 nucleotides, about 31 nucleotides, about 32 nucleotides, about 33 nucleotides, about 34 nucleotides, about 35 nucleotides, about 36 nucleotides, about 37 nucleotides, about 38 nucleotides, about 39 nucleotides, or about 40 nucleotides or more.
  • In one embodiment, the PCR primer binding sequence is about 25 nucleotides.
  • In particular embodiments, an adaptor module comprises a second element that comprises one or more read code sequences. As used herein, the term “read code” refers to a polynucleotide that is used to identify unique sequencing reads. In one embodiment, the read code is a random sequence of nucleotides. In one embodiment, the read code is about 1 nucleotide, about 2 nucleotides, about 3 nucleotides, about 4 nucleotides, about 5 nucleotides, about 6 nucleotides, about 7 nucleotides, about 8 nucleotides, about 9 nucleotides, about 10 nucleotides, or more.
  • By way of a non-limiting example, a 5 nucleotide codes consists of 256 possible unique sequences where each code chosen is 2 nucleotides different from every other code in the set. This feature enables unique and distinct reads to be differentiated from reads that appear to be unique owing to a sequencing error in the code region. In particular embodiments, codes that have been empirically determined to interfere with adaptor function, owing to particular sequence combinations, may be excluded from use, e.g., seven codes of the 256 had an overrepresentation of G nucleotides and were excluded.
  • In other embodiments, each read code of 5, 6, 7, 8, 9, 10 or more nucleotides may differ by 2, 3, 4, or 5 nucleotides from every other read code.
  • In one embodiment, the read code is about 5 nucleotides and differs from every other read code by 2 nucleotides.
  • In particular embodiments, an adaptor module comprises a third element that comprises one or more sample code sequences. As used herein, the term “sample code” refers to a polynucleotide that is used to identify the sample. The sample code is also useful in establishing multiplex sequencing reactions because each sample code is unique to the sample and thus, can be used to identify a read from a particular sample within a multiplexed sequencing reaction.
  • In one embodiment, the sample code comprises sequence that is about 1, about 2 nucleotides, about 3 nucleotides, about 4 nucleotides, or about 5 nucleotides, or more. In another embodiment, each sample code of 2, 3, 4, 5 or more nucleotides may differ from every other sample code by 2, 3, 4, or 5 nucleotides.
  • In one embodiment, the sample code is about three nucleotides and differs from every other sample code used in other samples by two nucleotides.
  • In particular embodiments, an adaptor module comprises a fourth element that comprises one or more anchor sequences. As used herein, an “anchor sequence” refers to a nucleotide sequence of at least 8 nucleotides, at least 10 nucleotides, at least 12 nucleotides, at least 14 nucleotides, or at least 16 nucleotides that hybridizes to a partner oligonucleotide and that comprises the following three properties: (1) each anchor sequence is part of a family of four anchor sequences that collectively represent each of the four possible DNA bases at each site within extension; this feature, balanced base representation, is useful to calibrate proper base calling in sequencing reads in particular embodiments; (2) each anchor sequence is composed of only two of four possible bases, and these are specifically chosen to be either and equal number of A+C or an equal number of G+T; an anchor sequence formed from only two bases reduces the possibility that the anchor sequence will participate in secondary structure formation that would preclude proper adaptor function; and (3) because each anchor sequence is composed of equal numbers of A+C or G+T, each anchor sequence shares roughly the same melting temperature and duplex stability as every other anchor sequence in a set of four.
  • In particular embodiments, an adaptor module comprises a fifth element that is comprised of the two 3′ terminal nucleotides of Element 4. These two bases at the 3′ end of each anchor are chosen based on an empirical determination that shows that these two nucleotides are efficient substrates for ligation to the cfDNA. In particular embodiments, Element 5 comprises the sequences selected from the group consisting of: AA, CC, TT and GG. In particular embodiments, Element 5 does not comprise the dinucleotide combination CG or TG as the inventors have determined that these combinations are not efficient ligation substrates.
  • In particular embodiments, a ligation step comprises ligating an adaptor module to the end-repaired cfDNA to generate a “tagged” cfDNA library. In some embodiments, a single adaptor module is employed. In some embodiments, two, three, four or five adaptor modules are employed. In some embodiments, an adaptor module of identical sequence is ligated to each end of the fragmented end-repaired DNA.
  • In one embodiment, a plurality of adaptor species is ligated to an end-repaired cfDNA library. Each of the plurality of adaptors may comprise one or more primer binding site for amplification of the cfDNA library, one or more read code sequences, one or more sequences for sample multiplexing, and one or more sequences for DNA sequencing.
  • Ligation of one or more adaptors contemplated herein may be carried out by methods known to those of ordinary skill in the art. In particular embodiments, one or more adaptors contemplated herein are ligated to end-repaired cfDNA that comprises blunt ends. In certain embodiments, one or more adaptors contemplated herein are ligated to end-repaired cfDNA that comprises complementary ends appropriate for the ligation method employed. In certain embodiments, one or more adaptors contemplated herein are ligated to end-repaired cfDNA that comprises a 3′ overhang.
  • 2. cfDNA Library Amplification
  • In particular embodiments, methods of genetic analysis contemplated herein comprise amplification of a cfDNA library to generate a cfDNA clone library or a library of cfDNA clones. Each molecule of the cfDNA library comprises an adaptor ligated to each end of an end-repaired cfDNA, and each adaptor comprises one or more PCR primer binding sites. In one embodiment, different adaptors are ligated to different ends of the end-repaired cfDNA.
  • In a preferred embodiment, the same adaptor is ligated to both ends of the cfDNA. Ligation of the same adaptor to both ends of end-repaired cfDNA allows for PCR amplification with a single primer sequence. In particular embodiments, a portion of the adaptor ligated-cfDNA library will be amplified using standard PCR techniques with a single primer sequence driving amplification. In one embodiment, the single primer sequence is about 25 nucleotides, optionally with a projected Tm of ≧55° C. under standard ionic strength conditions.
  • In particular embodiments, picograms of the initial cfDNA library are amplified into micrograms of cfDNA clones, implying a 10,000-fold amplification. The amount of amplified product can be measured using methods known in the art, e.g., quantification on a Qubit 2.0 or Nanodrop instrument.
  • 3. Determining the Number of Genome Equivalents
  • In various embodiments, a method for genetic analysis of cfDNA comprises determining the number of genome equivalents in the cfDNA clone library. As used herein, the term “genome equivalent” refers to the number of genome copies in each library. An important challenge met by the compositions and methods contemplated herein is achieving sufficient assay sensitivity to detect and analysis rare genetic mutations or differences in genetic sequence. To determine assay sensitivity value on a sample-by-sample basis, the numbers of different and distinct sequences that are present in each sample are measured, by measuring the number of genome equivalents that are present in a sequencing library. To establish sensitivity, the number of genome equivalents must be measured for each sample library.
  • The number of genome equivalents can be determined by qPCR assay or by using bioinformatics-based counting after sequencing is performed. In the process flow of clinical samples, qPCR measurement of genome equivalents is used as a QC step for cfDNA libraries. It establishes an expectation for assay sensitivity prior to sequence analysis and allows a sample to be excluded from analysis if its corresponding cfDNA clone library lacks the required depth of genome equivalents. Ultimately, the bioinformatics-based counting of genome equivalents is also used to identify the genome equivalents—and hence the assay sensitivity and false negative estimates—for each given cfDNA clone library.
  • The empirical qPCR assay and statistical counting assays should be well correlated. In cases where sequencing fails to reveal the sequence depth in a cfDNA clone library, reprocessing of the cfDNA clone library and/or additional sequencing may be required.
  • In one embodiment, the genome equivalents in a cfDNA clone library are determined using a quantitative PCR (qPCR) assay. In a particular embodiment, a standard library of known concentration is used to construct a standard curve and the measurements from the qPCR assay are fit to the resulting standard curve and a value for genome equivalents is derived from the fit. Surprisingly, the present inventors have discovered that a qPCR “repeat-based” assay comprising one primer that specifically hybridizes to a common sequence in the genome, e.g., a repeat sequence, and another primer that binds to the primer binding site in the adaptor, measured an 8-fold increase in genome equivalents compared to methods using just the adaptor specific primer (present on both ends of the cfDNA clone). The number of genome equivalents measured by the repeat-based assays provides a more consistent library-to-library performance and a better alignment between qPCR estimates of genome equivalents and bioinformatically counted tag equivalents in sequencing runs.
  • Illustrative examples of repeats suitable for use in the repeat-based genome equivalent assays contemplated herein include, but not limited to: short interspersed nuclear elements (SINEs), e.g., Alu repeats; long interspersed nuclear elements (LINEs), e.g., LINE1, LINE2, LINE3; microsatellite repeat elements, e.g., short tandem repeats (STRs), simple sequence repeats (SSRs); and mammalian-wide interspersed repeats (MIRs).
  • In one embodiment, the repeat is an Alu repeat.
  • 4. Quantitative Genetic Analysis
  • In various embodiments, a method for genetic analysis of cfDNA comprises quantitative genetic analysis of one or more target genetic loci of the cfDNA library clones. Quantitative genetic analysis comprises one or more of, or all of, the following steps: capturing cfDNA clones comprising a target genetic locus; amplification of the captured targeted genetic locus; sequencing of the amplified captured targeted genetic locus; and bioinformatic analysis of the resulting sequence reads.
  • (a) Capture of Target Genetic Locus
  • The present invention contemplates, in part, a capture probe module designed to retain the efficiency and reliability of larger probes but that minimizes uninformative sequence generation in a cfDNA clone library. A “capture probe module” refers to a polynucleotide that comprises a capture probe sequence and a tail sequence. In particular embodiments, the capture probe module sequence or a portion thereof serves as a primer binding site for one or more sequencing primers.
  • In particular embodiments, a capture probe module comprises a capture probe. As used herein a “capture probe” refers to a region capable of hybridizing to a specific DNA target region. Because the average size of cfDNA is about 150 to about 170 bp and is highly fragmented the compositions and methods contemplated herein comprise the use of high density and relatively short capture probes to interrogate DNA target regions of interest.
  • One particular concern with using high density capture probes is that generally capture probes are designed using specific “sequence rules.” For example, regions of redundant sequence or that exhibit extreme base composition biases are generally excluded in designing capture probes. However, the present inventors have discovered that the lack of flexibility in capture probe design rules does not substantially impact probe performance. In contrast, capture probes chosen strictly by positional constraint provided on-target sequence information; exhibit very little off-target and unmappable read capture; and yield uniform, useful, on-target reads with only few exceptions. Moreover, the high redundancy at close probe spacing more than compensates for occasional poor-performing capture probes.
  • In particular embodiments, a target region is targeted by a plurality of capture probes, wherein any two or more capture probes are designed to bind to the target region within 10 nucleotides of each other, within 15 nucleotides of each other, within 20 nucleotides of each other, within 25 nucleotides of each other, within 30 nucleotides of each other, within 35 nucleotides of each other, within 40 nucleotides of each other, within 45 nucleotides of each other, or within 50 nucleotides or more of each other, as well as all intervening nucleotide lengths.
  • In one embodiment, the capture probe is about 25 nucleotides, about 26 nucleotides, about 27 nucleotides, about 28 nucleotides, about 29 nucleotides, about 30 nucleotides, about 31 nucleotides, about 32 nucleotides, about 33 nucleotides, about 34 nucleotides, about 35 nucleotides, about 36 nucleotides, about 37 nucleotides, about 38 nucleotides, about 39 nucleotides, about 40 nucleotides, about 41 nucleotides, about 42 nucleotides, about 43 nucleotides, about 44 nucleotides, or about 45 nucleotides.
  • In one embodiment, the capture probe is about 100 nucleotides, about 200 nucleotides, about 300 nucleotides, about 400 nucleotides, or about 100 nucleotides. In another embodiment, the capture probe is from about 100 nucleotides to about 500 nucleotides, about 200 nucleotides to about 500 nucleotides, about 300 nucleotides to about 500 nucleotides, or about 400 nucleotides to about 500 nucleotides, or any intervening range thereof.
  • In a particular embodiment, the capture probe is not 60 nucleotides.
  • In another embodiment, the capture probe is substantially smaller than 60 nucleotides but hybridizes comparably, as well as, or better than a 60 nucleotide capture probe targeting the same DNA target region.
  • In a certain embodiment, the capture probe is 40 nucleotides.
  • In certain embodiments, a capture probe module comprises a tail sequence. As used herein, the term “tail sequence” refers to a polynucleotide at the 5′ end of the capture probe module, which in particular embodiments can serve as a primer binding site. In particular embodiments, a sequencing primer binds to the primer binding site in the tail region.
  • In particular embodiments, the tail sequence is about 5 to about 100 nucleotides, about 10 to about 100 nucleotides, about 5 to about 75 nucleotides, about 5 to about 50 nucleotides, about 5 to about 25 nucleotides, or about 5 to about 20 nucleotides. In certain embodiments, the third region is from about 10 to about 50 nucleotides, about 15 to about 40 nucleotides, about 20 to about 30 nucleotides or about 20 nucleotides, or any intervening number of nucleotides.
  • In particular embodiments, the tail sequence is about 30 nucleotides, about 31 nucleotides, about 32 nucleotides, about 33 nucleotides, about 34 nucleotides, about 35 nucleotides, about 36 nucleotides, about 37 nucleotides, about 38 nucleotides, about 39 nucleotides, or about 40 nucleotides.
  • In various embodiments, the capture probe module comprises a specific member of a binding pair to enable isolation and/or purification of one or more captured fragments of a tagged and or amplified cfDNA library that hybridizes to the capture probe. In particular embodiments, the capture probe module is conjugate to biotin or another suitable hapten, e.g., dinitrophenol, digoxigenin.
  • In various embodiments, the capture probe module is hybridized to a tagged and optionally amplified cfDNA library to form a complex. In some embodiments, the multifunctional capture probe module substantially hybridizes to a specific genomic target region in the cfDNA library.
  • Hybridization or hybridizing conditions can include any reaction conditions where two nucleotide sequences form a stable complex; for example, the tagged cfDNA library and capture probe module forming a stable tagged cfDNA library-capture probe module complex. Such reaction conditions are well known in the art and those of skill in the art will appreciated that such conditions can be modified as appropriate, e.g., decreased annealing temperatures with shorter length capture probes, and within the scope of the present invention. Substantial hybridization can occur when the second region of the capture probe complex exhibits 100%, 99%, 98%, 97%, 96%, 95%, 94%, 93%, 92% 91%, 90%, 89%, 88%, 85%, 80%, 75%, or 70% sequence identity, homology or complementarity to a region of the tagged cfDNA library.
  • In particular embodiments, the capture probe is about 40 nucleotides and has an optimal annealing temperature of about 44° C. o about 47° C.
  • In certain embodiments, the methods contemplated herein comprise isolating a tagged cfDNA library-capture probe module complex. In particular embodiments, methods for isolating DNA complexes are well known to those skilled in the art and any methods deemed appropriate by one of skill in the art can be employed with the methods of the present invention (Ausubel et al., Current Protocols in Molecular Biology, 2007-2012). In particular embodiments, the complexes are isolated using biotin-streptavidin isolation techniques.
  • In particular embodiments, removal of the single stranded 3′-ends from the isolated tagged cfDNA library-capture probe module complex is contemplated. In certain embodiments, the methods comprise 3′-5′ exonuclease enzymatic processing of the isolated tagged DNA library-multifunctional capture probe module complex to remove the single stranded 3′ ends.
  • In certain other embodiments, the methods comprise performing 5′-3′ DNA polymerase extension of multifunctional capture probe utilizing the isolated tagged DNA library fragments as template.
  • In certain other embodiments, the methods comprise creating a hybrid capture probe-isolated tagged cfDNA target molecule through the concerted action of a 5′ FLAP endonuclease, DNA polymerization and nick closure by a DNA ligase.
  • A variety of enzymes can be employed for the 3′-5′ exonuclease enzymatic processing of the isolated tagged cfDNA library-multifunctional capture probe module complex. Illustrative examples of suitable enzymes, which exhibit 3′-5′ exonuclease enzymatic activity, that can be employed in particular embodiments include, but are not limited to: T4 or Exonucleases I, III, V (see also, Shevelev I V, Hübscher U., “The 3′ 5′ exonucleases,” Nat Rev Mol Cell Biol. 3(5):364-76 (2002)). In particular embodiments, the enzyme comprising 3′-5′ exonuclease activity is T4 polymerase. In particular embodiments, an enzyme which exhibits 3′-5′ exonuclease enzymatic activity and is capable of primer template extension can be employed, including for example T4 or Exonucleases I, III, V. Id.
  • In some embodiments, the methods contemplated herein comprise performing sequencing and/or PCR on the 3′-5′ exonuclease enzymatically processed complex discussed supra and elsewhere herein. In particular embodiments, a tail portion of a capture probe molecule is copied in order to generate a hybrid nucleic acid molecule. In one embodiment, the hybrid nucleic acid molecule generated comprises the target region capable of hybridizing to the capture probe module and the complement of the capture probe module tail sequence.
  • In a particular embodiment, genetic analysis comprises a) hybridizing one or more capture probe modules to one or more target genetic loci in a plurality of cfDNA library clones to form one or more capture probe module-cfDNA library clone complexes; b) isolating the one or more capture probe module-cfDNA library clone complexes from a); c) enzymatically processing the one or more isolated capture probe module-cfDNA library clone complexes from step b); d) performing PCR on the enzymatically processed complex from c) wherein the tail portion of the capture probe molecule is copied in order to generate amplified hybrid nucleic acid molecules, wherein the amplified hybrid nucleic acid molecules comprise a target sequence in the target genomic locus capable of hybridizing to the capture probe and the complement of the capture probe module tail sequence; and e) performing quantitative genetic analysis on the amplified hybrid nucleic acid molecules from d).
  • In a particular embodiment, methods for determining copy number of a specific target genetic locus are contemplated comprising: a) hybridizing one or more capture probe modules to one or more target genetic loci in a plurality of cfDNA library clones to form one or more capture probe module-cfDNA library clone complexes; b) isolating the one or more capture probe module-cfDNA library clone complexes from a); c) enzymatically processing the one or more isolated capture probe module-cfDNA library clone complexes from step b); d) performing PCR on the enzymatically processed complex from c) wherein the tail portion of the capture probe molecule is copied in order to generate amplified hybrid nucleic acid molecules, wherein the amplified hybrid nucleic acid molecules comprise a target sequence in the target genetic locus capable of hybridizing to the capture probe and the complement of the capture probe module tail sequence; e) performing PCR amplification of the amplified hybrid nucleic acid molecules in d); and f) quantitating the PCR reaction in e), wherein the quantitation allows for a determination of copy number of the specific target region.
  • In one embodiment, the enzymatic processing of step c) comprises performing 3′-5′ exonuclease enzymatic processing on the one or more capture probe module-cfDNA library clone complexes from b) using an enzyme with 3′-5′ exonuclease activity to remove the single stranded 3′ ends; creating one or more hybrid capture probe module-cfDNA library clone molecules through the concerted action of a 5′ FLAP endonuclease, DNA polymerization and nick closure by a DNA ligase; or performing 5′-3′ DNA polymerase extension of the capture probe using the isolated cfDNA clone in the complex as a template.
  • In one embodiment, the enzymatic processing of step c) comprises performing 5′-3′ DNA polymerase extension of the capture probe using the isolated cfDNA clone in the complex as a template.
  • In particular embodiments, PCR can be performed using any standard PCR reaction conditions well known to those of skill in the art. In certain embodiments, the PCR reaction in e) employs two PCR primers. In one embodiment, the PCR reaction in e) employs a first PCR primer that hybridizes to a repeat within the target genetic locus. In a particular embodiment, the PCR reaction in e) employs a second PCR primer that hybridizes to the hybrid nucleic acid molecules at the target genetic locus/tail junction. In certain embodiments, the PCR reaction in e) employs a first PCR primer that hybridizes to the target genetic locus and a second PCR primer hybridizes to the amplified hybrid nucleic acid molecules at the target genetic locus/tail junction. In particular embodiments, the second primer hybridizes to the target genetic locus/tail junction such that at least one or more nucleotides of the primer hybridize to the target genetic locus and at least one or more nucleotides of the primer hybridize to the tail sequence.
  • In certain embodiments, the amplified hybrid nucleic acid molecules obtained from step e) are sequenced and the sequences aligned horizontally, i.e., aligned to one another but not aligned to a reference sequence. In particular embodiments, steps a) through e) are repeated one or more times with one or more capture probe modules. The capture probe modules can be the same or different and designed to target either cfDNA strand of a target genetic locus. In some embodiments, when the capture probes are different, they hybridize at overlapping or adjacent target sequences within a target genetic locus in the tagged cfDNA clone library. In one embodiment, a high density capture probe strategy is used wherein a plurality of capture probes hybridize to a target genetic locus, and wherein each of the plurality of capture probes hybridizes to the target genetic locus within about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, bp of any other capture probe that hybridizes to the target genetic locus in a tagged cfDNA clone library, including all intervening distances.
  • In some embodiments, the method can be performed using two capture probe modules per target genetic locus, wherein one hybridizes to the “Watson” strand (non-coding or template strand) upstream of the target region and one hybridizes to the “Crick” strand (coding or non-template strand) downstream of the target region.
  • In particular embodiments, the methods contemplated herein can further be performed multiple times with any number of capture probe modules, for example 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more capture probe modules per target genetic locus any number of which hybridize to the Watson or Crick strand in any combination. In some embodiments, the sequences obtained can be aligned to one another in order to identify any of a number of differences.
  • In certain embodiments, a plurality of target genetic loci are interrogated, e.g., 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 10000, 50000, 100000, 500000 or more in a single reaction, using one or more capture probe modules.
  • (b) Sequencing
  • In particular embodiments, the quantitative genetic analysis comprises sequencing a plurality of hybrid nucleic acid molecules, as discussed elsewhere herein, supra, to generate sufficient sequencing depths to obtain a plurality of unique sequencing reads. A unique read is defined as the single consensus read from a “family” of reads that all share the same read code and sequence start point within cfDNA. Each capture probe yields a set of unique reads that are computationally distilled from total reads by grouping into families. The unique reads for a given sample are then computed as the average of all the unique reads observed on a probe-by-probe basis. Cases where there is an obvious copy number change are excluded from the data set used to compute the average. Unique reads are important because each unique read must be derived from a unique cfDNA clone. Each unique read represents the input and analysis of a haploid equivalent of genomic DNA. The sum of unique reads is the sum of haploid genomes analyzed. The number of genomes analyzed, in turn, defines the sensitivity of the sequencing assay. By way of a non-limiting example, if the average unique read count is 100 genome equivalents, then that particular assay has a sensitivity of being able to detect one mutant read in 100, or 1%. Any observation less than this is not defensible.
  • In particular embodiments, the quantitative genetic analysis comprises multiplex sequencing of hybrid nucleic acid molecules derived from a plurality of samples.
  • In various embodiments, the quantitative genetic analysis comprises obtaining one or more or a plurality of tagged DNA library clones, each clone comprising a first DNA sequence and a second DNA sequence, wherein the first DNA sequence comprises a sequence in a targeted genetic locus and the second DNA sequence comprises a capture probe sequence; performing a paired end sequencing reaction on the one or more clones and obtaining one or more sequencing reads or performing a sequencing reaction on the one or more clones in which a single long sequencing read of greater than about 100, 200, 300, 400, 500 or more nucleotides is obtained, wherein the read is sufficient to identify both the first DNA sequence and the second DNA sequence; and ordering or clustering the sequencing reads of the one or more clones according to the probe sequences of the sequencing reads.
  • (c) Bioinformatics Analysis
  • In various embodiments, the quantitative genetic analysis further comprises bioinformatic analysis of the sequencing reads. Bioinformatic analysis excludes any purely mental analysis performed in the absence of a composition or method for sequencing. In certain embodiments, bioinformatics analysis includes, but is not limited to: sequence alignments; genome equivalents analysis; single nucleotide variant (SNV) analysis; gene copy number variation (CNV) analysis; and detection of genetic lesions. In particular embodiments, bioinformatics analysis is useful to quantify the number of genome equivalents analyzed in the cfDNA clone library; to detect the genetic state of a target genetic locus; to detect genetic lesions in a target genetic locus; and to measure copy number fluctuations within a target genetic locus.
  • Sequence alignments may be performed between the sequence reads and one or more human reference DNA sequences. In particular embodiments, sequencing alignments can be used to detect genetic lesions in a target genetic locus including, but not limited to detection of a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion. Detection of genetic lesions that are causal or prognostic indicators may be useful in the diagnosis, prognosis, treatment, and/or monitoring of a particular genetic condition or disease.
  • Also contemplated herein, are methods for sequence alignment analysis that can be performed without the need for alignment to a reference sequence, referred to herein as horizontal sequence analysis. Such analysis can be performed on any sequences generated by the methods contemplated herein or any other methods. In particular embodiments, the sequence analysis comprises performing sequence alignments on the reads obtained by the methods contemplated herein.
  • In one embodiment, the genome equivalents in a cfDNA clone library are determined using bioinformatics-based counting after sequencing is performed. Each sequencing read is associated with a particular capture probe, and the collection of reads assigned to each capture probe is parsed into groups. Within a group, sets of individual reads share the same read code and the same DNA sequence start position within genomic sequence. These individual reads are grouped into a “family” and a single consensus representative of this family is carried forward as a “unique read.” All of the individual reads that constituted a family are derived from a single ligation event and thus, they are amplification-derived “siblings” of one another. Each unique read is considered a unique ligation event and the sum of unique reads is considered equivalent to the number of genome equivalents analyzed.
  • As the number of unique clones approaches the total number of possible sequence combinations, probability dictates that the same code and start site combinations will be created by independent events and that these independent events will be inappropriately grouped within single families. The net result will be an underestimate of genome equivalents analyzed, and rare mutant reads may be discarded as sequencing errors because they overlap with wild-type reads bearing the same identifiers.
  • In particular embodiments, to provide an accurate analysis for cfDNA clone libraries, the number of genome equivalents analyzed is about 1/10, about 1/12, about 1/14, about 1/16, about 1/18, about 1/20, about 1/25 or less the number of possible unique clones. It should be understood that the procedure outlined above is merely illustrative and not limiting.
  • In some embodiments, the number of genome equivalents to be analyzed may need to be increased. To expand the depth of genome equivalents, at least two solutions are contemplated. The first solution is to use more than one adaptor set per sample. By combining adaptors, it is possible to multiplicatively expand the total number of possible clones and therefore, expand the comfortable limits of genomic input. The second solution is to expand the read code by 1, 2, 3, 4, or 5 or more bases. The number of possible read codes that differ by at least 2 bases from every other read code scales as 4(n−1) where n is the number of bases within a read code. Thus, in a non-limiting example, if a read code is 5 nucleotides and 4(5−1)=256; therefore, the inclusion of additional bases expands the available repertoire by a factor of four for each additional base.
  • In one embodiment, quantitative genetic analysis comprises bioinformatic analysis of sequencing reads to identify rare single nucleotide variants (SNV).
  • Next-generation sequencing has an inherent error rate of roughly 0.02-0.02%, meaning that anywhere from 1/200 to 1/500 base calls are incorrect. To detect variants and other mutations that occur at frequencies lower than this, for example at frequencies of 1 per 1000 sequences, it is necessary to invoke molecular annotation strategies. By way of a non-limiting example, analysis of 5000 unique molecules using targeted sequence capture technology would generate—at sufficient sequencing depths of >50,000 reads—a collection of 5000 unique reads, with each unique read belonging to a “family” of reads that all possess the same read code. A SNV that occurs within a family is a candidate for being a rare variant. When this same variant is observed in more than one family, it becomes a very strong candidate for being a rare variant that exists within the starting sample. In contrast, variants that occur sporadically within families are likely to be sequencing errors and variants that occur within one and only one family are either rare or the result of a base alteration that occurred ex vivo (e.g., oxidation of a DNA base or PCR-introduced errors).
  • In one embodiment, the methods of detecting SNVs comprise introducing 10-fold more genomic input (genomes or genome equivalents) as the desired target sensitivity of the assay. In one non-limiting example, if the desired sensitivity is 2% (2 in 100), then the experimental target is an input of 2000 genomes.
  • In particular embodiments, bioinformatics analysis of sequencing data is used to detect or identify SNV associated with a genetic state, condition or disease, genetic mosaicism, fetal testing, paternity testing, predicting response to drug treatment, diagnosing or monitoring a medical condition, microbiome profiling, pathogen screening, and monitoring organ transplants.
  • In various embodiments, a method for copy number determination analysis is provided comprising obtaining one or more or a plurality of clones, each clone comprising a first DNA sequence and a second DNA sequence, wherein the first DNA sequence comprises a sequence in a targeted genetic locus and the second DNA sequence comprises a capture probe sequence. In related embodiments, a paired end sequencing reaction on the one or more clones is performed and one or more sequencing reads are obtained. In another embodiment, a sequencing reaction on the one or more clones is performed in which a single long sequencing read of greater than about 100 nucleotides is obtained, wherein the read is sufficient to identify both the first DNA sequence and the second DNA sequence. The sequencing reads of the one or more clones can be ordered or clustered according to the probe sequence of the sequencing reads.
  • Copy number analyses include, but are not limited to analyses, that examine the number of copies of a particular gene or mutation that occurs in a given genomic DNA sample and can further include quantitative determination of the number of copies of a given gene or sequence differences in a given sample. In particular embodiments, copy number analysis is used to detect or identify gene amplification associated with genetic states, conditions, or diseases, fetal testing, genetic mosaicism, paternity testing, predicting response to drug treatment, diagnosing or monitoring a medical condition, microbiome profiling, pathogen screening, and monitoring organ transplants.
  • In particular embodiments, bioinformatics analysis of sequencing data is used to detect or identify one or more sequences or genetic lesions in a target locus including, but not limited to detection of a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion. Detection of genetic lesions that are causal or prognostic indicators may be useful in the diagnosis, prognosis, treatment, and/or monitoring of a particular genetic condition or disease. In one embodiment, genetic lesions are associated with genetic states, conditions, or diseases, fetal testing, genetic mosaicism, paternity testing, predicting response to drug treatment, diagnosing or monitoring a medical condition, microbiome profiling, pathogen screening, and monitoring organ transplants.
  • D. Clinical Applications of Quantitative Genetic Analysis
  • In various embodiments, the present invention contemplates a method of detecting, identifying, predicting, diagnosing, or monitoring a condition or disease in a subject.
  • In particular embodiments, a method of detecting, identifying, predicting, diagnosing, or monitoring a genetic state, condition or disease in a subject comprises performing a quantitative genetic analysis of one or more target genetic loci in a cfDNA clone library to detect or identify a change in the sequence at the one or more target genetic loci.
  • In one embodiment, a method of detecting, identifying, predicting, diagnosing, or monitoring a genetic state, condition or disease comprises isolating or obtaining cfDNA from a biological sample of a subject; treating the cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA; ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate a cfDNA clone library; determining the number of genome equivalents in the cfDNA clone library; and performing a quantitative genetic analysis of one or more target genetic loci in a cfDNA clone library to detect or identify a change in the sequence at the one or more target genetic loci.
  • In particular embodiments, a method of detecting, identifying, predicting, diagnosing, or monitoring a genetic state, or genetic condition or disease selected from the group consisting of: genetic diseases; genetic mosaicism; fetal testing; paternity testing; paternity testing; predicting response to drug treatment; diagnosing or monitoring a medical condition; microbiome profiling; pathogen screening; and organ transplant monitoring comprising isolating or obtaining cfDNA from a biological sample of a subject; treating the cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA; ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate a cfDNA clone library; determining the number of genome equivalents in the cfDNA clone library; and performing a quantitative genetic analysis of one or more target genetic loci in a cfDNA clone library to detect or identify a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion in the sequence at the one or more target genetic loci.
  • Illustrative examples of genetic diseases that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include, but are not limited to cancer, Alzheimer's disease (APOE1), Charcot-Marie-Tooth disease, Leber hereditary optic neuropathy (LHON), Angelman syndrome (UBE3A, ubiquitin-protein ligase E3A), Prader-Willi syndrome (region in chromosome 15), β-Thalassaemia (HBB, β-Globin), Gaucher disease (type I) (GBA, Glucocerebrosidase), Cystic fibrosis (CFTR Epithelial chloride channel), Sickle cell disease (HBB, β-Globin), Tay-Sachs disease (HEXA, Hexosaminidase A), Phenylketonuria (PAH, Phenylalanine hydrolyase), Familial hypercholesterolaemia (LDLR, Low density lipoprotein receptor), Adult polycystic kidney disease (PKD1, Polycystin), Huntington disease (HDD, Huntingtin), Neurofibromatosis type I (NF1, NF1 tumour suppressor gene), Myotonic dystrophy (DM, Myotonin), Tuberous sclerosis (TSC1, Tuberin), Achondroplasia (FGFR3, Fibroblast growth factor receptor), Fragile X syndrome (FMR1, RNA-binding protein), Duchenne muscular dystrophy (DMD, Dystrophin), Haemophilia A (F8C, Blood coagulation factor VIII), Lesch-Nyhan syndrome (HPRT1, Hypoxanthine guanine ribosyltransferase 1), and Adrenoleukodystrophy (ABCD1).
  • Illustrative examples of cancers that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include, but are not limited to: B cell cancer, e.g., multiple myeloma, melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.
  • In one embodiment, the genetic lesion is a lesion annotated in the Cosmic database (the lesions and sequence data can be downloaded from cancer.sanger.ac.uk/cosmic/census) or a lesion annotated in the Cancer Genome Atlas (the lesions and sequence data can be downloaded from tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp).
  • Illustrative examples of genes that harbor one or more genetic lesions associated with cancer that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include, but are not limited to ABCB1, ABCC2, ABCC4, ABCG2, ABL1, ABL2, AKT1, AKT2, AKT3, ALDH4A1, ALK, APC, AR, ARAF, ARFRP1, ARID1A, ATM, ATR, AURKA, AURKB, BCL2, BCL2A1, BCL2L1, BCL2L2, BCL6, BRAF, BRCA1, BRCA2, Clorf144, CARD11, CBL, CCND1, CCND2, CCND3, CCNE1, CDH1, CDH2, CDH2O, CDH5, CDK4, CDK6, CDK8, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CRKL, CRLF2, CTNNB1, CYP1B1, CYP2C19, CYP2C8, CYP2D6, CYP3A4, CYP3A5, DNMT3A, DOT1L, DPYD, EGFR, EPHA3, EPHA5, EPHA6, EPHA7, EPHB1, EPHB4, EPHB6, EPHX1, ERBB2, ERBB3, ERBB4, ERCC2, ERG, ESR1, ESR2, ETV1, ETV4, ETV5, ETV6, EWSR1, EZH2, FANCA, FBXW7, FCGR3A, FGFR1, FGFR2, FGFR3, FGFR4, FLT1, FLT3, FLT4, FOXP4, GATA1, GNA11, GNAQ, GNAS, GPR124, GSTP1, GUCY1A2, HOXA3, HRAS, HSP90AA1, IDH1, IDH2, IGF1R, IGF2R, IKBKE, IKZF1, INHBA, IRS2, ITPA, JAK1, JAK2, JAK3, JUN, KDR, KIT, KRAS, LRP1B, LRP2, LTK, MAN1B1, MAP2K1, MAP2K2, MAP2K4, MCL1, MDM2, MDM4, MEN1, MET, MITF, MLH1, MLL, MPL, MRE11A, MSH2, MSH6, MTHFR, MTOR, MUTYH, MYC, MYCL1, MYCN, NF1, NF2, NKX2-1, NOTCH1, NPM1, NQO1, NRAS, NRP2, NTRK1, NTRK3, PAK3, PAX5, PDGFRA, PDGFRB, PIK3CA, PIK3R1, PKHD1, PLCG1, PRKDC, PTCH1, PTEN, PTPN11, PTPRD, RAF1, RARA, RB1, RET, RICTOR, RPTOR, RUNX1, SLC19A1, SLC22A2, SLCO1B3, SMAD2, SMAD3, SMAD4, SMARCA4, SMARCB1, SMO, SOD2, SOX10, SOX2, SRC, STK11, SULT1A1, TBX22, TET2, TGFBR2, TMPRSS2, TNFRSF14, TOP1, TP53, TPMT, TSC1, TSC2, TYMS, UGT1A1, UMPS, USP9X, VHL, and WT1.
  • In particular embodiments, the genetic lesion comprises a nucleotide transition or transversion, a nucleotide insertion or deletion, a genomic rearrangement, a change in copy number, or a gene fusion.
  • In one embodiment, the genetic lesion is a gene fusion that fuses the 3′ coding region of the ALK gene to another gene.
  • In one embodiment, the genetic lesion is a gene fusion that fuses the 3′ coding region of the ALK gene to the EML4 gene.
  • Illustrative examples of conditions suitable for fetal testing that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include but are not limited to: Down Syndrome (Trisomy 21), Edwards Syndrome (Trisomy 18), Patau Syndrome (Trisomy 13), Klinefelter's Syndrome (XXY), Triple X syndrome, XYY syndrome, Trisomy 8, Trisomy 16, Turner Syndrome (XO), Robertsonian translocation, DiGeorge Syndrome and Wolf-Hirschhorn Syndrome.
  • Illustrative examples of alleles suitable for paternity testing that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include but are not limited to 16 or more of: D20S1082, D6S474, D12ATA63, D22S1045, D10S1248, D1S1677, D11S4463, D4S2364, D9S1122, D2S1776, D10S1425, D3S3053, D5S2500, D1S1627, D3S4529, D2S441, D175974, D6S1017, D4S2408, D9S2157, Amelogenin, D17S1301, D1GATA113, D18S853, D20S482, and D14S1434.
  • Illustrative examples of genes suitable for predicting the response to drug treatment that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include, but are not limited to, one or more of the following genes: ABCB1 (ATP-binding cassette, sub-family B (MDR/TAP), member 1), ACE (angiotensin I converting enzyme), ADH1A (alcohol dehydrogenase 1A (class I), alpha polypeptide), ADH1B (alcohol dehydrogenase IB (class I), beta polypeptide), ADH1C (alcohol dehydrogenase 1C (class I), gamma polypeptide), ADRB1 (adrenergic, beta-1-, receptor), ADRB2 (adrenergic, beta-2-, receptor, surface), AHR (aryl hydrocarbon receptor), ALDH1A1 (aldehyde dehydrogenase 1 family, member A1), ALOX5 (arachidonate 5-lipoxygenase), BRCA1 (breast cancer 1, early onset), COMT (catechol-O-methyltransferase), CYP2A6 (cytochrome P450, family 2, subfamily A, polypeptide 6), CYP2B6 (cytochrome P450, family 2, subfamily B, polypeptide 6), CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9), CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19), CYP2D6 (cytochrome P450, family 2, subfamily D, polypeptide 6), CYP2J2 (cytochrome P450, family 2, subfamily J, polypeptide 2), CYP3A4 (cytochrome P450, family 3, subfamily A, polypeptide 4), CYP3A5 (cytochrome P450, family 3, subfamily A, polypeptide 5), DPYD (dihydropyrimidine dehydrogenase), DRD2 (dopamine receptor D2), F5 (coagulation factor V), GSTP1 (glutathione S-transferase pi), HMGCR (3-hydroxy-3-methylglutaryl-Coenzyme A reductase), KCNH2 (potassium voltage-gated channel, subfamily H (eag-related), member 2), KCNJ11 (potassium inwardly-rectifying channel, subfamily J, member 11), MTHFR (5,10-methylenetetrahydrofolate reductase (NADPH)), NQO1 (NAD(P)H dehydrogenase, quinone 1), P2RY1 (purinergic receptor P2Y, G-protein coupled, 1), P2RY12 (purinergic receptor P2Y, G-protein coupled, 12), PTGIS (prostaglandin I2 (prostacyclin) synthase), SCN5A (sodium channel, voltage-gated, type V, alpha (long QT syndrome 3)), SLC19A1 (solute carrier family 19 (folate transporter), member 1), SLCO1B1 (solute carrier organic anion transporter family, member 1B1), SULT1A1 (sulfotransferase family, cytosolic, 1A, phenol-preferring, member 1), TPMT (thiopurine S-methyltransferase), TYMS (thymidylate synthetase), UGT1A1 (UDP glucuronosyltransferase 1 family, polypeptide A1), VDR (vitamin D (1,25-dihydroxyvitamin D3) receptor), VKORC1 (vitamin K epoxide reductase complex, subunit 1).
  • Illustrative examples of medical conditions that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include, but are not limited to: stroke, transient ischemic attack, traumatic brain injury, heart disease, heart attack, angina, atherosclerosis, and high blood pressure.
  • Illustrative examples of pathogens that can be screened for with the compositions and methods contemplated herein include, but are not limited to: bacteria fungi, and viruses.
  • Illustrative examples of bacterial species that can be screened for with the compositions and methods contemplated herein include, but are not limited to: a Mycobacterium spp., a Pneumococcus spp., an Escherichia spp., a Campylobacter spp., a Corynebacterium spp., a Clostridium spp., a Streptococcus spp., a Staphylococcus spp., a Pseudomonas spp., a Shigella spp., a Treponema spp., or a Salmonella spp.
  • Illustrative examples of fungal species that can be screened for with the compositions and methods contemplated herein include, but are not limited to: an Aspergillis spp., a Blastomyces spp., a Candida spp., a Coccicioides spp., a Cryptococcus spp., dermatophytes, a Tinea spp., a Trichophyton spp., a Microsporum spp., a Fusarium spp., a Histoplasma spp., a Mucoromycotina spp., a Pneumocystis spp., a Sporothrix spp., an Exserophilum spp., or a Cladosporium spp.
  • Illustrative examples of viruses that can be screened for with the compositions and methods contemplated herein include, but are not limited to: Influenza A such as H1N1, H1N2, H3N2 and H5N1 (bird flu), Influenza B, Influenza C virus, Hepatitis A virus, Hepatitis B virus, Hepatitis C virus, Hepatitis D virus, Hepatitis E virus, Rotavirus, any virus of the Norwalk virus group, enteric adenoviruses, parvovirus, Dengue fever virus, Monkey pox, Mononegavirales, Lyssavirus such as rabies virus, Lagos bat virus, Mokola virus, Duvenhage virus, European bat virus 1 & 2 and Australian bat virus, Ephemerovirus, Vesiculovirus, Vesicular Stomatitis Virus (VSV), Herpesviruses such as Herpes simplex virus types 1 and 2, varicella zoster, cytomegalovirus, Epstein-Bar virus (EBV), human herpesviruses (HHV), human herpesvirus type 6 and 8, Moloney murine leukemia virus (M-MuLV), Moloney murine sarcoma virus (MoMSV), Harvey murine sarcoma virus (HaMuSV), murine mammary tumor virus (MuMTV), gibbon ape leukemia virus (GaLV), feline leukemia virus (FLV), Spumavirus, Friend murine leukemia virus, Murine Stem Cell Virus (MSCV) and Rous Sarcoma Virus (RSV), HIV (human immunodeficiency virus; including HIV type 1, and HIV type 2), visna-maedi virus (VMV) virus, the caprine arthritis-encephalitis virus (CAEV), equine infectious anemia virus (EIAV), feline immunodeficiency virus (FIV), bovine immune deficiency virus (BIV), and simian immunodeficiency virus (SIV), papilloma virus, murine gammaherpesvirus, Arenaviruses such as Argentine hemorrhagic fever virus, Bolivian hemorrhagic fever virus, Sabia-associated hemorrhagic fever virus, Venezuelan hemorrhagic fever virus, Lassa fever virus, Machupo virus, Lymphocytic choriomeningitis virus (LCMV), Bunyaviridiae such as Crimean-Congo hemorrhagic fever virus, Hantavirus, hemorrhagic fever with renal syndrome causing virus, Rift Valley fever virus, Filoviridae (filovirus) including Ebola hemorrhagic fever and Marburg hemorrhagic fever, Flaviviridae including Kaysanur Forest disease virus, Omsk hemorrhagic fever virus, Tick-borne encephalitis causing virus and Paramyxoviridae such as Hendra virus and Nipah virus, variola major and variola minor (smallpox), alphaviruses such as Venezuelan equine encephalitis virus, eastern equine encephalitis virus, western equine encephalitis virus, SARS-associated coronavirus (SARS-CoV), West Nile virus, and any encephaliltis causing virus.
  • Illustrative examples of genes suitable for monitoring an organ transplant in a transplant recipient that can be detected, identified, predicted, diagnosed, or monitored with the compositions and methods contemplated herein include, but are not limited to, one or more of the following genes: HLA-A, HLA-B, HLA-C, HLA-DR, HLA-DP, and HLA-DQ.
  • In particular embodiments, a bioinformatic analysis is used to quantify the number of genome equivalents analyzed in the cfDNA clone library; detect genetic variants in a target genetic locus; detect mutations within a target genetic locus; detect genetic fusions within a target genetic locus; or measure copy number fluctuations within a target genetic locus.
  • E. Companion Diagnostics
  • In various embodiments, a companion diagnostic for a genetic disease is provided, comprising: isolating or obtaining cfDNA from a biological sample of a subject; treating the cfDNA with one or more end-repair enzymes to generate end-repaired cfDNA; ligating one or more adaptors to each end of the end-repaired cfDNA to generate a cfDNA library; amplifying the cfDNA library to generate a cfDNA clone library; determining the number of genome equivalents in the cfDNA clone library; and performing a quantitative genetic analysis of one or more biomarkers associated with the genetic disease in the cfDNA clone library, wherein detection of, or failure to detect, at least one of the one or more biomarkers indicates whether the subject should be treated for the genetic disease.
  • As used herein, the term “companion diagnostic” refers to a diagnostic test that is linked to a particular anti-cancer therapy. In a particular embodiment, the diagnostic methods comprise detection of genetic lesion in a biomarker associated with in a biological sample, thereby allowing for prompt identification of patients should or should not be treated with the anti-cancer therapy.
  • Anti-cancer therapy includes, but is not limited to surgery, radiation, chemotherapeutics, anti-cancer drugs, and immunomodulators.
  • Illustrative examples of anti-cancer drugs include, but are not limited to: alkylating agents such as thiotepa and cyclophosphamide (CYTOXAN™); alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethylenethiophosphaoramide and trimethylolomelamine resume; nitrogen mustards such as chlorambucil, chlornaphazine, cholophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembichin, phenesterine, prednimustine, trofosfamide, uracil mustard; nitrosureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, ranimustine; antibiotics such as aclacinomysins, actinomycin, authramycin, azaserine, bleomycins, cactinomycin, calicheamicin, carabicin, carminomycin, carzinophilin, chromomycins, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, doxorubicin and its pegylated formulations, epirubicin, esorubicin, idarubicin, marcellomycin, mitomycins, mycophenolic acid, nogalamycin, olivomycins, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; anti-metabolites such as methotrexate and 5-fluorouracil (5-FU); folic acid analogues such as denopterin, methotrexate, pteropterin, trimetrexate; purine analogs such as fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs such as ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, floxuridine, 5-FU; androgens such as calusterone, dromostanolone propionate, epitiostanol, mepitiostane, testolactone; anti-adrenals such as aminoglutethimide, mitotane, trilostane; folic acid replenisher such as frolinic acid; aceglatone; aldophosphamide glycoside; aminolevulinic acid; amsacrine; bestrabucil; bisantrene; edatraxate; defofamine; demecolcine; diaziquone; elformithine; elliptinium acetate; etoglucid; gallium nitrate; hydroxyurea; lentinan; lonidamine; mitoguazone; mitoxantrone; mopidamol; nitracrine; pentostatin; phenamet; pirarubicin; podophyllinic acid; 2-ethylhydrazide; procarbazine; PSK®; razoxane; sizofiran; spirogermanium; tenuazonic acid; triaziquone; 2,2′,2″-trichlorotriethylamine; urethan; vindesine; dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside (“Ara-C”); cyclophosphamide; thiotepa; taxoids, e.g., paclitaxel (TAXOL®, Bristol-Myers Squibb Oncology, Princeton, N.J.) and doxetaxel (TAXOTERE®., Rhne-Poulenc Rorer, Antony, France); chlorambucil; gemcitabine; 6-thioguanine; mercaptopurine; methotrexate; platinum analogs such as cisplatin and carboplatin; vinblastine; platinum; etoposide (VP-16); ifosfamide; mitomycin C; mitoxantrone; vincristine; vinorelbine; navelbine; novantrone; teniposide; aminopterin; xeloda; ibandronate; CPT-11; topoisomerase inhibitor RFS 2000; difluoromethylomithine (DMFO); retinoic acid derivatives such as Targretin™ (bexarotene), Panretin™ (alitretinoin); ONTAK™ (denileukin diftitox); esperamicins; capecitabine; and pharmaceutically acceptable salts, acids or derivatives of any of the above. Also included in this definition are anti-hormonal agents that act to regulate or inhibit hormone action on cancers such as anti-estrogens including for example tamoxifen, raloxifene, aromatase inhibiting 4(5)-imidazoles, 4-hydroxytamoxifen, trioxifene, keoxifene, LY117018, onapristone, and toremifene (Fareston); and anti-androgens such as flutamide, nilutamide, bicalutamide, leuprolide, and goserelin; and pharmaceutically acceptable salts, acids or derivatives of any of the above.
  • Illustrative examples of immunomodulators include, but are not limited to: cyclosporine, tacrolimus, tresperimus, pimecrolimus, sirolimus, verolimus, laflunimus, laquinimod and imiquimod, as well as analogs, derivatives, salts, ions and complexes thereof.
  • All publications, patent applications, and issued patents cited in this specification are herein incorporated by reference as if each individual publication, patent application, or issued patent were specifically and individually indicated to be incorporated by reference.
  • Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to one of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims. The following examples are provided by way of illustration only and not by way of limitation. Those of skill in the art will readily recognize a variety of noncritical parameters that could be changed or modified to yield essentially similar results.
  • EXAMPLES Example 1 Accurate Detection of Rare Mutations using Targeted Sequence Capture Technology Purpose
  • The purpose of this experiment was to provide a direct proof-of-principle demonstration of rare variant detection using targeted sequence capture technology.
  • Background
  • Target sequence capture technology provides quantitative, sequence-based genetic analysis of nucleic acids and can be exploited to perform a combined mutational and copy number analysis of drug metabolism genes. The present inventors used targeted sequence capture technology and subsequence genetic analysis to detect rare sequence variants.
  • Genomic DNA inputs play a central role in rare variant detection, but quantitative analysis and control of genomic inputs places bounds on the estimated sensitivity of rare variant analysis. A genomic qPCR assay was used by the present inventors to estimate genomic inputs.
  • One experimental goal for rare variant analysis is to introduce 10-fold more genomic input as the target sensitivity of the assay. In other words, to measure variants with a sensitivity of 1% (1 in 100), then the experimental target is to input 1000 genomes. Downstream of sequencing, bioinformatics analysis reveals the number of unique reads, and this has the desirable quality of being both an orthogonal and a more direct measure of genomic inputs.
  • Summary
  • A cell line (ZR75-30) with known SNVs was admixed with a germ line DNA sample (NA12878) in a dilution series ranging from 1-to-1 through 1-to-1000. Target regions corresponding to known sequence differences were retrieved using targeted sequence capture technology and sequenced. Sequence variants that occur at a frequency of less than 1 per 1000 sequences were detected.
  • Methods
  • Capture Probes
  • The following table shows a collection of 62 capture probes that were used in this experiment.
  • TABLE 1
    60 base probe sequences used in the admix proof-
    of-concept study
    SEQ ID
    Target NO: Probe Sequence
    BRAF  1 TAAACATTGGAAAGGTTTCTAATTAACCAGG
    AGATCCAAAAGAAAGCGGTTCAAGTAGCA
     2 GATCTCAGTTTTTTTGGTTAACTATGTATTT
    TGGTATATGAAGCTTCTGGGTTTTGCACA
    MYCN  3 GACAGATAAGCATACATATTAACATGGATAT
    ATATGTGAATTTCATTCAAATGGTTCTCA
     4 AGCTCTTAGCCTTTGGGGGGATGACACTCTT
    GAGCGGACGTGGGGACGCCTCGCTCTTTA
    BRAF  5 AAGCCCCCACCGCCGCCTCTTTCCAAAATAA
    ACACCAGCCAGCCGCCGAGCCCGGAGTCG
     6 GCCTCCCTTCCCCCTCCCCGCCCGACAGCGG
    CCGCTCGGGCCCCGGCTCTCGGTTATAAG
    CDH1  7 GGTGTGGCAGCCAGGGGGGCGCACTCTGCTC
    TGGCTGGGCCCCTTCTCCCATGTTTTCTT
     8 TTACACAACCTTTGGGCTTGGACAACACTTT
    GGGGTCCAAAGAACCTAAGAGTCTTTCTG
    EPHX2  9 TGATGAAACTTGGGCTGGATGGGGCACAGGT
    AGGGTGCTTGTTGCTTTCAGTCAGATGAA
    10 AATGAAAGAAAAGGAGGCCAGATTGCTACTC
    CTGGTCCCTGCCACACACTAGGTACCCTA
    BRCA1 11 ATTGACAATACCTACATAAAACTCTTTCCAG
    AATGTTGTTAAGTCTTAGTCATTAGGGAG
    12 GGATTTCCACCAACACTGTATTCATGTACCC
    ATTTTTCTCTTAACCTAACTTTATTGGTC
    BRCA2 13 CAAAGGGGGAAAACCATCAGGACATTATTTA
    ACAACGGAAATATCTAACTGAAAGGCAAA
    14 CAGGCAGACCAACCAAAGTCTTTGTTCCACC
    TTTTAAAACTAAATCACATTTTCACAGAG
    MYCN 15 CCCCAGCCAGCGGTCCGCAACCCTTGCCGCA
    TCCACGAAACTTTGCCCATAGCAGCGGGC
    MYCr1_F1 16 CGACTCATCTCAGCATTAAAGTGATAAAAAA
    ATAAATTAAAAGGCAAGTGGACTTCGGTG
    MYCr1_R1 17 CTGTGGCGCGCACTGCGCGCTGCGCCAGGTT
    TCCGCACCAAGACCCCTTTAACTCAAGAC
    MYC_r2_F1 18 TTCTACTGCGACGAGGAGGAGAACTTCTACC
    AGCAGCAGCAGCAGAGCGAGCTGCAGCCC
    MYC_r2_F2 19 ACCGAGCTGCTGGGAGGAGACATGGTGAACC
    AGAGTTTCATCTGCGACCCGGACGACGAG
    MYC_r2_F3 20 GCCGCCGCCTCAGAGTGCATCGACCCCTCGG
    TGGTCTTCCCCTACCCTCTCAACGACAGC
    MYC_r2_F4 21 GGCGGCTAGGGGACAGGGGCGGGGTGGGCAG
    CAGCTCGAATTTCTTCCAGATATCCTCGC
    MYC_r2_R1 22 AGACGAGCTTGGCGGCGGCCGAGAAGCCGCT
    CCACATACAGTCCTGGATGATGATGTTTT
    MYC_r2_R2 23 AGGAGAGCAGAGAATCCGAGGACGGAGAGAA
    GGCGCTGGAGTCTTGCGAGGCGCAGGACT
    MYC_r2_R3 24 TAAGAGTGGCCCGTTAAATAAGCTGCCAATG
    AAAATGGGAAAGGTATCCAGCCGCCCACT
    MYC_r2_R4 25 TTGTATTTGTACAGCATTAATCTGGTAATTG
    ATTATTTTAATGTAACCTTGCTAAAGGAG
    MYC_r3_F1 26 GAGGCCACAGCAAACCTCCTCACAGCCCACT
    GGTCCTCAAGAGGTGCCACGTCTCCACAC
    MYC_r3_F2 27 AGAGGAGGAACGAGCTAAAACGGAGCTTTTT
    TGCCCTGCGTGACCAGATCCCGGAGTTGG
    MYC_r3_F3 28 TCCAACTTGACCCTCTTGGCAGCAGGATAGT
    CCTTCCGAGTGGAGGGAGGCGCTGCGTAG
    MYC_r3_R1 29 GCTTGGACGGACAGGATGTATGCTGTGGCTT
    TTTTAAGGATAACTACCTTGGGGGCCTTT
    MYC_r3_R2 30 GCATTTGATCATGCATTTGAAACAAGTTCAT
    AGGTGATTGCTCAGGACATTTCTGTTAGA
    MYC_r3_R3 31 CGCCCCGCGCCCTCCCAGCCGGGTCCAGCCG
    GAGCCATGGGGCCGGAGCCGCAGTGAGCA
    ERBB2r1r 32 CTCTGGCCCCGCCGGCCGCGGGACCTCGGCG
    GGGCATCCACAGGGCAGGGTCCCGCCGCT
    ERBB2r2f 33 GGCATGACTTGGAGTGAGTTTGGATGGGGTG
    GCCAGGTCTGAGAAGGTCCCCCGCCAGTG
    ERBB2r2r 34 GCAGGGCACCTTCTTCTGCCACCCACCTGTA
    AACAGAGGGCTCAGCCCAGCTGGAGGCAG
    ERBB2r3f 35 CCCAAGATCTCCAAGTACTGGGGAACCCCAG
    GGAGGCCCTGGGGGGTGGCAGTGTTCCTA
    ERBB2r3r 36 CTAATGCACACAAAGCCTCCCCCTGGTTAGC
    AGTGGCCCTGGTCAGCTCTGAATAACCAA
    ERBB2r4f 37 CTGCTCCTCTTTTAGAAGGCAGGAGGGCCCC
    AAGGGAAGCAGAAGGTGACAGAAGGGGAA
    ERBB2r4r 38 TGGGGCAGTGGCGGGCAGGCACTGGGTTGTA
    AGTTGGGAGTTTGCGGCTGGGGTCAGGCT
    ERBB2r5f 39 TCTGCTGCTGTTTGTGCCTCTCTCTGTTACT
    AACCCGTCCTCTCGCTGTTAGACATCTCT
    ERBB2r5r 40 CCCACCCCTCCCATGTCACCTGTATGACACC
    TGCATTCCACCCGGCCCCAGCCCTCCCCT
    ERBB2r6f 41 TGGGCCAGGTAGTCTCCCTAGAAGGTGATGC
    TGATGAGGGTCTGGTGCCCAGGGCGCCAC
    ERBB2r6r 42 GGTGCCCACCCCTTGCATCCTGGGGGGTAGA
    GCACATTGGGCACAAAGCAGAGGCACATA
    ERBB2r7f 43 CACCCTGCCTGGTACTGCCCTATTGCCCCTG
    GCACACCAGGGCAAAACAGCACAGTGAAA
    ERBB2r7r 44 CCATTTACAGAAACAAACCTCCCCACCAAAA
    TGAGAAAACTGTGTTTCTCCCTGGCACTC
    ERBB2r8f 45 TTATTCTTCTTGTGCCTGGGCACGGTAATGC
    TGCTCATGGTGGTGCACGAAGGGCCAGGG
    ERBB2r8r 46 GAAGGATAGGACAGGGTGGGCTGGGCCAGGC
    TGCATGCGCAGAGGGACAGGAACTGCAGC
    ERBB2r9f 47 GGGCCCGGACCCTGATGCTCATGTGGCTGTT
    GACCTGTCCCGGTATGAAGGCTGAGACGG
    ERBB2r9r 48 TCTGTCTCCTGCCATCCCCAAGAGATGCTGC
    CACATCTGGATCCTCAGGACTCTGTCTGC
    TYMSr2f 49 TCACGTCCCAGGGCAGTTTTCTTCCCTGAAG
    AAAGTTGGATGGCATGATCTGTCTTCCCA
    TYMSr2r 50 GTGTTGAGAACAGACTACTGACTTCTAATAG
    CAGCGACTTCTTTACCTTGATAAACCACA
    TYMSr3f 51 AAAAAAAGGATGGGTTCCATATGGGTGGTGT
    CAAGTGCCCACCTCCTAGCAAGTCAGCAG
    TYMSr3r 52 CCCTCACAAGGTCAAAGCTATACATCAGCTC