WO2009021141A1 - Réseaux complets à rendement élevé pour méthylation relative - Google Patents

Réseaux complets à rendement élevé pour méthylation relative Download PDF

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WO2009021141A1
WO2009021141A1 PCT/US2008/072529 US2008072529W WO2009021141A1 WO 2009021141 A1 WO2009021141 A1 WO 2009021141A1 US 2008072529 W US2008072529 W US 2008072529W WO 2009021141 A1 WO2009021141 A1 WO 2009021141A1
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microarray
probes
probe
discrete
discrete genomic
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PCT/US2008/072529
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Rafael Irizarry
Andrew Feinberg
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The Johns Hopkins University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6834—Enzymatic or biochemical coupling of nucleic acids to a solid phase
    • C12Q1/6837—Enzymatic or biochemical coupling of nucleic acids to a solid phase using probe arrays or probe chips

Definitions

  • the invention relates generally to the field of DNA methylation and more specifically to methods and devices for comprehensive methylome analysis utilizing high- throughput arrays to determine relative methylation.
  • the methylome is defined as the comprehensive picture of DNA methylation across the genome, and it is an important shift in focus from the individual gene level.
  • the rationale for this view is that our focus on methylation in the promoters of known genes is too constrained, that much of methylation is not where we are looking.
  • DNA methylation has made the least progress of any functional element in its understanding from a genomic perspective.
  • DNA methylation is relatively well understand from a gene perspective (i.e., its method of propagation is well known, while chromatin modification is not), and DNA methylation has a strong link to the DNA sequence itself (i.e., its encoding specifically at CpG dinucleotides, all of these much more so than other types of epigenetic information, such as chromatin modification).
  • CpG island was introduced in 1987, as regions of dense CG content normally protected from DNA methylation in vertebrates, and then found frequently to be methylated in cancer.
  • the formal definition of CpG island is GC content [(C+G)/N] > 0.5; CpG observed / CpG expected [(CpG x N) / ( C x G)] > 0.6, and a length greater than 200 bp.
  • CpG island methylation is the most critical target for understanding genomic DNA methylation, although that island-centric view is undergoing rethinking. For example, binding sites for the insulator protein CTCF within differentially methylated regions of imprinted genes appear in short stretches of about 50 nucleotides, with a relatively conserved ⁇ 20-bp core. Thus, it is likely that other minimal units of DNA methylation will be smaller and of different GC content than densely GC-rich regions.
  • the second reason for the slow pace of understanding the methylome is substantial limitations in current technology affecting sensitivity, specificity, throughput, quantitation, and cost among the currently used detection methods.
  • the most commonly used methods can themselves be divided into three categories as shown in Table 1.
  • the first category is bisulfite DNA sequencing. This involves chemical conversion of cytosine to uracil by sodium bisulfite or metabisulf ⁇ te, followed by PCR (which incorporates T for U), and then DNA sequencing.
  • the second category is a variety of methods that interrogate specific single-CpG dinucleotides or amplicons. These include MethyLightTM, COBRA, bisulfite pyrosequencing, and the Illumina GoldenGateTM methylation assay. While sensitive, specific, and relatively inexpensive, none of these methods is suitable for analysis of the whole genome, which includes ⁇ 28 million CpG dinucleotides.
  • the third category includes microarray-based methods. These can interrogate much larger numbers of CpG than the other approaches, at extremely low unit cost, since the pricing is similar to other non-methylation-based array methods. Table 1. Current Methods for DNA Methylation Analysis
  • the second type of microarray-based methylation analysis is methylated DNA immunoprecipitation (MeDIP), in which methylated DNA is fractionated using an antibody and then hybridized, with a differentially labeled total DNA control, to an oligonucleotide array.
  • MeDIP methylated DNA immunoprecipitation
  • the third type of microarray-based methylation analysis is restriction enzyme digestion using methylcytosine-sensitive enzymes, followed by ligation-mediated PCR amplification of the targets.
  • the paradigm of this method is the HELP (Hpall tiny fragment enrichment by ligation-mediated PCR) assay.
  • DNA is digested in parallel with Mspl (resistant to DNA methylation), and then the Hpall and Mspl products are amplified by ligation-mediated PCR and hybridized using separate fluorochromes to a customized array.
  • Hpall sites comprise 8% of CpG, that represents a fixed limit of sensitivity of the method.
  • the restriction enzyme-digested DNA can be directly sequenced rather than hybridized to microarrays, although one is still limited by the relatively small number of methylcytosine-sensitive restriction sites in the genome.
  • the fourth type of microarray-based methylation analysis is restriction enzyme digestion of methylated DNA using McrBC, without PCR, and differential hybridization to an array.
  • DNA is digested with McrBC, an enzyme with the unusual and desirable property of cutting methylated DNA promiscuously (recognition sequence R m C(N) 55 -io 3 R m C), cleaving half of the methylated DNA in the genome and all methylated CpG islands.
  • the enzyme is used on size-selected (1.5-4.0 kb) DNA to fractionate unmethylated (i.e., gel- purified high molecular weight) DNA after digestion, which is comparatively (two-color) hybridized with DNA similarly processed but not cut with the enzyme, on high density arrays.
  • the original method was developed for Arabidopsis, where it has value in eliminating the large fraction of methylated repetitive DNA in the plant genome.
  • a selection algorithm has been applied to obtain specific array probes thought to represent the state of a given methylation target.
  • the present invention is based in part on the seminal discovery of microarray array designs and statistical methods of analyzing such arrays for comprehensive methylation analysis. Accordingly, the present invention provides methods and devices for comprehensive methylation analysis utilizing high-throughout arrays and methods for statistical analysis to determine relative methylation, capable of detecting DNA genome- wide methylation with up to 100% sensitivity and greater than 90% specificity.
  • the invention provides a method of designing a microarray for DNA methylation analysis.
  • the method includes identifying CpG islands in a polynucleotide sequence and generating a plurality of probe sequences corresponding to the identified CpG islands. Thereafter, a plurality of discrete genomic regions may be generated such that each genomic region includes more than one of the generated plurality of probe sequences separated by less than about 300 base pairs.
  • each probe for use with the microarray may correspond to a probe sequence within each of the generated discrete genomic regions to design the array.
  • generating the plurality of probe sequences includes removing each probe sequence with more than one match with the genome sequence.
  • generating the plurality of discrete genomic regions may include omitting each region including less than about 15 of the probe sequences that is generated.
  • each probe for use with the microarray includes a sequence determined by tiling each discrete genomic region. Tiling of the region may be performed using oligonucleotide sequences of about 50 base pairs and about 35 base pairs apart.
  • the invention provides a microarray for performing DNA methylation analysis.
  • the microarray includes a plurality of probe sets, with each probe set corresponding to a discrete region of a human genome that includes one or more CpG islands. Further, in one aspect, each of the probe sets include at least one probe corresponding to a CpG island within the corresponding discrete region. In another aspect, the mean distance between each of the discrete regions is between about 500 to 50,000 base pairs and the mean distance between each probe within each discrete region is less than about 40. base pairs.
  • the invention provides a method of manufacturing a microarray for DNA methylation analysis.
  • the method includes identifying CpG islands in a polynucleotide sequence and generating a plurality of probe sequences corresponding to the identified CpG islands. Thereafter a plurality of discrete genomic regions may be generated such that each genomic region includes more than one of the plurality of probe sequences that was generated separated by less than about 300 base pairs. A plurality of probes may then be generated and affixed to a suitable microarray substrate corresponding to probe sequences within each of the generated discrete genomic regions. Accordingly, in one aspect, each probe includes one of the plurality of probe sequences within each of the plurality of discrete genomic regions to manufacture a microarray.
  • the invention provides a method for performing methylome analysis.
  • the method includes identifying CpG islands in a polynucleotide sequence and generating a plurality of probe sequences corresponding to the identified CpG islands. Thereafter a plurality of discrete genomic regions may be generated such that each genomic region includes more than one of the plurality of probe sequences that was generated separated by less than about 300 base pairs.
  • a plurality of probes may then be generated and affixed to a suitable microarray substrate corresponding to probe sequences within each of the generated discrete genomic regions.
  • each probe includes one of the plurality of probe sequences within each of the plurality of discrete genomic regions to manufacture a microarray.
  • a microarray assay may then be performed on the manufactured array and the results analyzed.
  • the microarrays of the present inventions may include probes corresponding to a widely varying number of distinct genomic regions from as few as one to over 50,000. Additionally, the microarrays of the present inventions may include from as few as 1 to over 500 probes for each distinct genomic region. As such, a microarray of the present invention may include greater than 2 million individual probes.
  • Figure 1 shows a graphical representation of box-plots for the differences between replicate M-values.
  • the first four shown in the first tile compare the McRBC method when dye-swaps are used.
  • the first two box-plots in the first tile are from DKO cells and the last two are from HCTl 16 cells.
  • the next four box-plots shown in the second tile compare the McRBC method when dye-swaps are not used.
  • the first two box-plots shown in the second tile are from DKO cells and the last two are from HCTl 16 cells.
  • the next two box-plots shown in the third tile compare the McRBC method using the promoter tiling array.
  • the first box plot of the third tile is from DKO cells and the second box-plot is from HCTl 16 cells.
  • the next two box-plots shown in the fourth tile compare the HELP method on its recommended array design.
  • the first box-plot in the fourth tile is from DKO cells and the second box-plot is from HCTl 16 cells.
  • the next two box-plots shown in the fifth tile compare the HELP method using the promoter tiling array.
  • the first box-plot of the fifth tile is from DKO cells and the second box plot is from HCTl 16 cells.
  • the next two box-plots shown in the sixth tile compare the MeDIP method.
  • the first box plot of the sixth tile is from DKO cells and the second is from HCTl 16 cells.
  • Figure 2 shows a graphical representation of density estimates (smoothed histograms) of the M- values comparing DKO and HCTl 16 samples with DKO having a higher density for three different methods.
  • Figure 3 shows a series of graphical representations for performing a comparison of method-specific methylation measurements to reference data.
  • M-values from high-throughput methods are plotted against M- values from the Reference-Array platform.
  • a 500-bp window was formed around each probe; this ratio (multiplied by 10) is displayed inside each point.
  • a regression line was calculated and is displayed for probes with ratios ⁇ 0.6 (blue line) and >0.6 (red line).
  • Figure 4 shows graphical representations of DNA fragment-length-related biases using different methods.
  • Figure 4 A shows that M-values for the HCTl 16 sample are stratified by the DNA fragment size predicted by the McrBC (left panel) and HELP (right panel) enzyme digestions.
  • Figure 4B shows that for all three methods, a 500-bp window was formed around each probe, the observed-to-expected ratio of CpG was calculated, and box- plots of the M-values are displayed by these ratios. Only probes related to fragments of sizes between 50 and 600 bp for McrBC, and between 600 and 1200 bp for HELP, are included.
  • Figure 5 shows graphical representations of M values plotted against contiguous locations on the genome for all three methods (McrBC, HELP, and MeDIP). The points of each of the graphs are the observed M-values. The M-values for probes in the same predicted segments for McrBC and HELP were averaged and are represented in the figure with straight lines. The data were smoothed using running medians with a window size of 7 and showed the results with black curves. CpG locations are shown as black tick marks at the top of the plots.
  • Figure 5 A shows a segment showing lack of methylation determined by the Reference- Array platform.
  • Figure 5B shows a segment with high methylation as determined by the Reference- Array platform. The Reference- Array probes and measured methylation percentages are shown on the bottom of the plot.
  • Figure 6 shows a graphical representation of a ROC curve demonstrating the advantage of genome- weighted smoothing.
  • all gene regions represented on the Reference-Array platform were considered.
  • highly methylated and unmethylated regions were compared. If all probes in the region showed on the Reference- Array platform a methylation percentage >90%, the region was considered a true positive. If all probes in the region reported a percentage ⁇ 10% they were considered a true negative.
  • a cut-off for the M- values was chosen. If any probe intensity within the region was above that cut-off, it was defined as positive.
  • a running median with a window size of 51 was then analyzed and defined a positive in the same way, except that the smoothed results instead of the individual probe intensities were used. Results are shown for both McrBC and MeDIP.
  • the true-positive rate is defined as the percentage of true-positive regions for which the microarray data surpasses that threshold.
  • the false-positive rate is defined in the same way but for the true-negative regions.
  • Figure 7 shows graphical representations of M values determined by various methods versus percent methylation as determined by pyro-sequencing or regions showing disagreement between Array- 1, McRBC and Reference- Array. Various CpGs in those regions were tested with pyro-sequencing. The plots show results from Reference- Array, McRBC, and MeDIP respectively.
  • Figure 8 shows graphical representations of ROC curves demonstrating the advantage of genome-weighted smoothing similar to that shown in Figure 6 but for various window sizes.
  • Figure 9 shows graphical representations of ROC curves demonstrating the advantage of genome-weighted smoothing similar to that shown in Figure 6 but the Reference- Array regions were divided into those with observed to expected CpG above 6 (inside CpG island) and below 6 (outside CpG island).
  • the present invention relates generally to the field of DNA methylation analysis and provides methods and devices for comprehensive methylome analysis utilizing high- throughput arrays to determine the relative methylation of DNA.
  • the present invention is based on the discovery of the limitations of specificity of the three major approaches to high-throughput array-based DNA methylation analysis. These current approaches include: 1) MeDIP, (methylated DNA immunoprecipitation), an example of antibody-mediated methyl-specific fractionation; 2) HELP (Hpa-II tiny fragment enrichment by ligation-mediated PCR), an example of differential amplification of methylated DNA; and 3) fractionation by McrBC, an enzyme that cuts most methylated DNA. As described herein, significant limitations to each method have been identified. Specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC.
  • the present invention provides improved methods and microarray devices for performing DNA methylation analysis including comprehensive high-throughput arrays for relative methylation (CHARM).
  • the invention allows for PCR amplification prior to hybridization, allowing for small amounts of samples to be analyzed and is capable of detecting DNA genome-wide methylation with up to 100% sensitivity and greater than 90% specificity. Additionally, the methods and devices of the present invention allow for relatively inexpensive genome-wide analysis, in which individual samples can be assayed reliably at very high density, allowing epigenetic discrimination of individuals, not just groups.
  • sample refers to any sample suitable for the methods provided by the present invention.
  • the sample can be any sample that includes nucleic acids suitable for methylation analysis.
  • the sample is a biological sample, including, for example, a cultured cell, a bodily fluid, an extract from a cell, which can be a crude extract or a fractionated extract, a chromosome, an organelle, or a cell membrane; a cell; genomic DNA, RNA, or cDNA; a tissue; or a sample of an organ.
  • a biological sample for example, from a human subject, can be obtained using well known and routine clinical methods (e.g., a biopsy procedure).
  • an exemplary sample may be cells containing genomic DNA suspected to include significant methylation, such as in a cancerous or precancerous cell.
  • cancer includes a variety of cancer types which are well known in the art, including but not limited to, dysplasias, hyperplasias, solid tumors and hematopoietic cancers. Many types of cancers have been associated with epigenetic silencing of regions (i.e. gene coding or regulatory sequences) throughout the genome due to hypermethylation of, for example, CpG islands. Cancers include, but are not limited to, the following organs or systems: cardiac, lung, gastrointestinal, genitourinary tract, liver, bone, nervous system, gynecological, hematologic, skin, and adrenal glands.
  • the methods and devices herein can be used for analyzing samples that may include various types of cancer cells such as gliomas (Schwannoma, glioblastoma, astrocytoma), neuroblastoma, pheochromocytoma, paraganlioma, meningioma, adrenalcortical carcinoma, medulloblastoma, rhabdomyoscarcoma, kidney cancer, vascular cancer of various types, osteoblastic osteocarcinoma, prostate cancer, ovarian cancer, uterine leiomyomas, salivary gland cancer, choroid plexus carcinoma, mammary cancer, pancreatic cancer, colon cancer, and megakaryoblastic leukemia; and skin cancers including malignant melanoma, basal cell carcinoma, squamous cell carcinoma, Karposi's sarcoma, moles dysplastic nevi, lipoma, angioma, dermatofibroma, keloids, and psoriasis.
  • the methods and microarray devices described herein are suitable for methylation analysis of an entire genome, or portion of a genome of an organism, also known as methylome analysis.
  • the term “genome” is intended to mean all of the genetic information of an organism including the entire genetic complement and all of the hereditary material possessed by an organism.
  • the term “genome” is intended to mean the totality of genetic information including both the chromosomal and mitochondrial genome.
  • the term “genome” as used herein is also intended to mean a portion of an organisms entire genetic complement.
  • the methods of the present invention may be used to perform methylation (methylome) analysis and/or generate microarrays to perform such analysis on a variety of organism, whether or not the organism's genome has currently been sequenced in its entirety.
  • genomic sequencing has been completed on over 180 organisms, including such organisms as Anopheles gambiae, Apis mellifera, Caenorhabditis briggsae, Caenorhabditis elegans, Candida glabrata, Canis familiaris, Debaryomyces hansenii, Drosophila melanogaster, Gallus gallus, Homo sapiens, Kluyveromyces waltii, Mus musculus, Oryza sativa, Pan troglodytes, Populus trichocarpa, Rattus norvegicus, Schizosaccharomyces pombe, Takifugu rubripes, and Tetraodon nigroviridis.
  • Microarrays or arrays of the present invention may include any one, two or three dimensional arrangement of addressable regions bearing a particular chemical moiety or moieties (for example, polynucleotide sequences) associated with that region.
  • the chemical moieties include oligonucleotides (i.e., probes)
  • An array is addressable in that it has multiple regions of different moieties (i.e., different oligonucleotide sequences) such that a region (i.e., a feature or spot of the array) is at a particular predetermined location (i.e., an address) on the array.
  • An array layout refers to one or more characteristics of the array, such as feature positioning, feature size, and some indication of a moiety at a given location.
  • An array includes a support substrate that may be of any suitable type known in the art, such as glass, to which one or more chemical moiety or moieties are linked or bound using methods well known in the art.
  • a tiling array is utilized.
  • a tiling array is a subtype of a microarray and function on a similar principle to traditional microarrays in that chemical moieties, such as oligonucleotides are hybridized to unlabeled probes (such a oligonucleotides) fixed on the solid array substrate.
  • the probes utilized in the present invention are preferably short fragments designed to cover the entire genome of an organism or identified regions of the genome. In general, depending on the probe lengths and spacing, different degrees of resolution can be achieved.
  • the number of features on a single array can range from less than 10,000 to greater than 6,000,000, with each feature containing millions of copies of one probe.
  • polynucleotide and “oligonucleotide” refer to nucleic acid molecules.
  • a polynucleotide or oligonucleotide includes single or multiple stranded configurations, where one or more of the strands may or may not be completely aligned with another.
  • the terms “polynucleotide” and “oligonucleotide” are intended to be generic to polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D- ribose), or any other type of polynucleotide which is an N-glycoside of a purine or pyrimidine base.
  • the terms are intended to include polymers in which the conventional backbone has been replaced with a non-naturally occurring or synthetic backbone or in which one or more of the conventional bases has been replaced with a non-naturally occurring or synthetic base.
  • a polynucleotide or oligonucleotide may include naturally occurring nucleotides and phosphodiester bonds that are chemically synthesized.
  • an "oligonucleotide” may generally refer to a nucleotide multimer of about 2 to 100 nucleotides in length, such as a probe, while a “polynucleotide” includes a nucleotide multimer having any number of nucleotides, such as the entire genome of an organism or a portion thereof.
  • the probes of the present invention may be oligodeoxyribonucleotides or oligoribonucleotides, or any modified forms of these polymers that are capable of hybridizing with a target nucleic sequence by complementary base-pairing.
  • Complementary base pairing means sequence-specific base pairing which includes, for example, Watson-Crick base pairing as well as other forms of base pairing such as Hoogsteen base pairing.
  • Modified forms include 2'-O-methyl oligoribonucleotides and so-called PNAs, in which oligodeoxyribonucleotides are linked via peptide bonds rather than phosphodiester bonds.
  • the invention provides a method of designing a microarray for DNA methylation analysis.
  • the method includes identifying CpG islands in a polynucleotide sequence and generating a plurality of probe sequences corresponding to the identified CpG islands. Thereafter, a plurality of discrete genomic regions may be generated such that each genomic region includes more than one of the generated plurality of probe sequences separated by less than about 300 base pairs.
  • each probe for use with the microarray may correspond to a probe sequence within each of the generated discrete genomic regions to design the array.
  • CpG islands may be identified by analyzing the polynucleotide sequence and applying the standard definitions.
  • a CpG island is GC content [(C+G)/N] > 0.5; CpG observed / CpG expected [(CpG x N) / ( C x G)] > 0.6, and a length greater than 200 bp.
  • a CpG island is GC content > 0.55, CG obs/exp > 0.65, and length greater than 500 bp. Identification of CpG islands may be performed using other known definitions accepted in the art.
  • generating the plurality of probe sequences includes removing each probe sequence with more than one match with the genome sequence indicating that the probe sequence will not uniquely bind to only one specific sequence in the genome. This is performed to avoid binding cross-reactivity with other locations of the genome and ensure unique binding at a specific genome location.
  • a match may include instances where the probe sequence may be described as a perfect match in which there are no mismatches in base pairing between the probe sequence and the genome sequence (i.e., perfect complementarity).
  • a match may also include instances where there exists one or more mismatches in base pairing between the probe sequence and the genomic sequence (i.e., imperfect complementarity).
  • a match may include about 1-5, 5-10, 10-15, 15-20 or more than 20 mismatches (including insertions or deletions) in base pairing between the probe sequence and the genome sequence depending on the size of the probe sequence.
  • a probe sequence may be considered to have more than one match where the probe sequence includes one perfect match and one or more imperfect matches or where the probe sequence includes two or more perfect matches with the genomic sequence.
  • a discrete genomic region as used herein, is intended to mean a contiguous region or portion of a genome.
  • a genome, or portion thereof, may be fractionated into any number of different discrete genomic regions to be analyzed.
  • a discrete genomic region may be defined as a region of the genome including one or more probe sequences.
  • a discrete genomic region may be defined as a region of the genome that includes two or more probe sequences separated by less than about 10,000, 5,000, 4,000, 3,000, 2,000 or 1,000 base pairs.
  • a discrete genomic region may be defined as a region of the genome that includes two or more probe sequences separated by less than about 900, 800, 700, 600, 500, 400, 300, 200, 100 or 50 base pairs.
  • a discrete genomic region may be defined as a region of the genome that includes two or more probe sequences separated by less than about 300 base pairs.
  • the number of discrete genomic regions may vary. In part, this is dependent on the parameters used for defining the regions as described herein. Additionally, the number may also depend on the size and type of the genome utilized. As such, in various aspects, there may be more than about 10, 50, 100, 1,000, 10,000, 20,000, 30,000, 40,000, 50,000 or 100,000 discrete genomic regions identified, with all or only a portion being represented by probes on an array.
  • generating the plurality of discrete genomic regions may include omitting each genomic region including less than a certain number of probe sequences generated.
  • genomic regions including less than about 1-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, or 90-100 different probe sequences may be omitted.
  • genomic regions including less than about 15 probe sequences are omitted.
  • each probe for use with the microarray includes a sequence determined by tiling each discrete genomic region. Tiling refers to a process involving analyzing a particular discrete genomic region by moving along the genomic sequence in a frame-wise fashion to determine appropriate probe sequences used to generate probes that are used to manufacture the array.
  • a genomic region may be tiled with different sizes of oligonucleotide sequences.
  • oligonucleotide sequences may be about 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-55, 55-60, 60-65, 65-70, 70-75, 75-80, 80-85, 85-90, 90-95 or 95-100 base pairs in length.
  • each frame may be determined by the length of the oligonucleotide used to tile the region and the frame of the frame-wise shift may overlap or skip regions of the genomic region by a specific number of base pairs. As such, in various aspects, about 1-25, 25-50, 50-75, 75-100 or more than 100 base pairs may be skipped in the tiling process to determine probe sequences within a region. In an exemplary aspect, tiling of the genomic region is performed using oligonucleotide sequences of about 50 base pairs and about 35 base pairs apart.
  • a probe or probe sequence corresponds with a discrete genomic region.
  • corresponding is intended to mean that a probe or probe sequence is associated with or derived from a given identified genomic region.
  • the probe or probe sequence corresponds with a genomic region in that the region is identified as having one or more CpG islands. Therefore, a probe or probe sequence may correspond with one or more CpG islands within the genomic region and may span more than one island due to the process of tiling described herein, used to generate probe sequences.
  • an array designed or manufactured using the methods described herein may include probe sequences representative of a widely varying number of genomic regions. This is dependent, in part, on the size of the genome (i.e., the entire genome or a portion thereof), the type of genome (i.e., the type of organism) and the parameters of selecting genomic regions or interest (i.e., number of CpG islands with unique sequences). Accordingly, in various aspects, the array may include probes corresponding to more than about 10, 50, 100, 1,000, 10,000, 20,000, 30,000, 40,000, 50,000 or 100,000 discrete genomic regions.
  • the invention provides a microarray designed using the methods described herein, for performing methylation analysis of a human genome.
  • the microarray includes a plurality of probe sets, with each probe set corresponding to a discrete region of a human genome that includes one or more CpG islands. Further, in one aspect, each of the probe sets include at least one probe corresponding to a CpG island within the corresponding discrete region.
  • the human genome includes approximately 3.2 billion base pairs.
  • the microarray may be designed to interrogate all or a portion of the genome.
  • the microarray may include probes corresponding to discrete genomic regions identified throughout the entire genome or a portion thereof as described herein.
  • the microarray includes probes sets corresponding to more than about 44,000 discrete genomic regions of the human genome. Additionally, each probe set may include between about 1-310 probes, to produce a microarray with approximately 2.1 million different probes in total.
  • the distance between genomic regions identified by the methods described herein may vary depending on how many regions are identified within a given genome.
  • an exemplary mean distance between each of the discrete regions is between about 500 to 50,000 base pairs.
  • the mean distance between each probe within each discrete region may vary.
  • the mean distance between probes is less than about 200, 100, 90, 80, 70, 60, 50, 40, 30, 20 or 10 base pairs.
  • the mean distance is less than about 45, 40 or 35 base pairs.
  • an exemplary mean distance between each probe within a given discrete region is about 35 base pairs.
  • the invention provides a method of manufacturing a microarray for methylation analysis using the methods described herein.
  • the method includes designing the microarray using the methods described herein. Thereafter, a plurality of probes may then be generated and affixed to a suitable microarray substrate corresponding to probe sequences within each of the generated discrete genomic regions. Accordingly, in one aspect, each probe includes one or more of the plurality of probe sequences within each of the plurality of discrete genomic regions to manufacture a microarray.
  • a variety of methods are well known in the art for manufacturing microarrays, including methods for binding or affixing probes in a variety of configurations to a solid support, such as glass, plastic or silicon wafer.
  • Such methods include fabrication using a variety of technologies, such as printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing, or electrochemistry on microelectrode arrays.
  • tiling arrays are synthesized using two general methods, the first is photolithographic manufacturing and the second is mechanically spotting or printing.
  • the arrays may have other components in addition to the probes, such as linkers attaching the probes to the support, depending on the type of manufacturing method.
  • the number of probes affixed to the array support can be quite large.
  • the array may include up to about 6 million probes. However, depending on the tiling strategy and the length of the probe, the total probe number may vary. Accordingly, the array may include less than 75%, 50%, 25%, 10%, 5% or 1% of the maximum number of probes (about 6 million). In an exemplary aspect the array includes about 2 million probes, each corresponding to one or more CpG islands in a discrete genomic region of a genome.
  • the invention provides a method for performing methylome analysis by designing and manufacturing a microarray as described herein and subsequently performing a microarray assay using the microarray.
  • the method includes designing and manufacturing an array using the methods described herein.
  • a microarray assay may then be performed on the manufactured array and the results analyzed.
  • microarray assays may be performed and analyzed using the microarrays and methods described herein.
  • microarray assays for use with the present invention include, but are not limited to: 1) MeDIP, (methylated DNA immunoprecipitation), an example of antibody-mediated methyl-specific fractionation; 2) HELP (Hpa-II tiny fragment enrichment by ligation-mediated PCR); and 3) fractionation by McrBC, an enzyme that cuts most methylated DNA.
  • the assay performed with the microarray is an assay including fractionation by McrBC.
  • results of an assay utilizing a microarray of the present invention may be analyzed using statistical analysis tools well known in the art. Methods of analysis are described in detail in the following example.
  • HCTl 16 cells American Type Culture Collection
  • DKO DNMT1/DNMT3B
  • Genomic DNA was isolated from HCTl 16 and DKO cell lines and was prepared using the MasterPure DNA Purification KitTM (EpiCentre) as specified by the manufacturer.
  • McrBC assay sample preparation Genomic DNA (10 ⁇ g) was prepared, and McrBC digestion and gel fractionation were performed exactly as published. As shown in Table 2, HCTl 16 and DKO samples prepared using McrBC were analyzed on Array- 1 (canonical), Promoter 2 ArrayTM (common), Imprinting array (common), and the CHARM array. [0068] The following method was used for HELP assay sample preparation. HCTl 16 and DKO samples were prepared as previously described using a standard protocol described in the art, using a total of 20 ⁇ g per sample. The LM-PCR products were labeled with Cy3- or Cy5-conjugated oligonucleotide and random primers using standard protocols. As shown in Table 2, HCTl 16 and DKO samples prepared using HpaII and Mspl were analyzed on the HELP promoter array (canonical), Promoter 2 ArrayTM (common), and Imprinting array (common).
  • MeDIP assay sample preparation was conducted according to published methods. As shown in Table 2, HCTl 16 and DKO samples prepared using MeDIP were analyzed on the Promoter 2 ArrayTM (canonical), Imprinting array (common), and CHARM (common) array. As a positive control, MeDIP was validated using real-time PCR of Sat2.
  • genomic DNA was bisulfite treated using the EpiTectTM Kit (Qiagen) according to the manufacturer's recommendations. Bisulfite treatment of genomic DNA results in unmethylated cytosine nucleotides being changed to thymidine while methylated cytosines remain unchanged. This difference is then detected as a C/T nucleotide polymorphism at each CpG site.
  • CpG-unbiased primers were designed to PCR amplify 38, 16, and 14 CpG sites, respectively, in three genes, HLA-F, KCNK4, and HLTF (previously known as SMARCA3), showing conflicting methylation across MeDIP, McrBC, and Reference- Array assays. Nested PCR was performed under standard conditions. Amplicons were analyzed on a PSQ HS 96 pyrosequencer as specified by the manufacturer (Biotage) and CpG sites quantified, from 0% to 100% methylation, using Pyro Q-CpGTM software.
  • Array- 1 (Orion Genomics, LLC, St. Louis, MO) is the canonical array for the McrBC method: 21,143 McrBC segments are represented by one probe each. Three to four replicate are used for each probe. The locations of these segments were chosen by the designer based on transcriptional start sites and CpG islands as previously described.
  • the HELP promoter array is the canonical array for the HELP method.
  • the Promoter 2 ArrayTM is one of NimbleGen's off- the-shelf products, with 12,892 promoter regions.
  • the Imprint_ tiling array represents 23 regions chosen by the present inventors to study imprinted genes. Region sizes ranged from 133,475 to 13,096,022 bp, and probes of size 50 bp were tiled at 47 bp from each other with an occasional large jump to avoid repeat elements. Table 2 provides a summary of these arrays.
  • the immunoprecipitate intensity was in the numerator and the total DNA intensity in the denominator. Note that each feature on the array was associated with one M-value. Each array was then normalized so that unmethylated regions, on average, produced M-values of 0. Details of the normalization technique are available in the Methods section.
  • the Reference- Array platform quantifies methylation as a percentage. However, the raw data files report the Cy3 and Cy5 intensities related to the unmethylated and methylated pseudoalleles, thus, M- values were formed in a similar way.
  • M is a continuous variable, so that methylation could be assessed in a quantitative way, which has not been performed previously for array-based methylation analysis. This is critical for biological analysis, since epigenetic information is often chromosome-specific (e.g., imprinted genes). Furthermore, DNA methylation may have a threshold effect for regulating gene expression (e.g., -25% for E-cadherin in a broad range of cell types). Note that transforming M directly into estimates of absolute methylation is not straightforward. However, later in this section it is demonstrated that by using cut-off values a strategy with high sensitivity and specificity is obtained.
  • MeDIP is comparatively imprecise.
  • the precision of each method was assessed by comparing M-values from replicate arrays, specifically studying the distribution of the differences between replicated M-values: Mu - M 2 , where i represents a feature, and 1 and 2 represent the two replicate hybridizations. In principle, these values should all be 0, since Mi,- and M 2 ,- were measures of the same quantity.
  • differences were observed due to natural variation in the sample preparation and array hybridization. These differences were studied using the canonical arrays for each method, because each method was likely optimized on their canonical arrays and for interest in each method at its optimal condition in addition to the common arrays.
  • SD standard deviation
  • McrBC and HELP can discriminate DKO from HCTl 16.
  • a global assessment of sensitivity was performed by comparing the distribution of the M- values from the HCTl 16 and DKO samples, for example, a highly methylated and a highly unmethylated reference sample, respectively.
  • the M- values for DKO sample should mostly be centered at 0, and HCTl 16 should be shifted to a substantial number of positive values.
  • Figure 2 demonstrates that the MeDIP method can barely distinguish between the two cell lines of differing methylation on a global scale, although at individual loci differences are clearly seen (discussed below).
  • the McrBC and HELP arrays perform better than the MeDIP array at globally distinguishing the DKO from the HCTl 16 sample, with HELP to a somewhat greater degree.
  • the McrBC canonical arrays used three to four replicate probes for 21,143 locations. Thus, at least four probes were used with each Reference- Array probe.
  • a similar approach was used for the HELP method except the cleavage occurs at CCGG sites.
  • the HELP canonical arrays used 14-15 tiled probes in each of the HELP segments.
  • the canonical arrays for MeDIP were the Promoter 2 ArraysTM, which represent 12,892 promoter regions.
  • the expected number of CpGs was defined as the proportion of Cs multiplied by the proportion of Gs.
  • the observed-to-expected ratio is the proportion of CpGs divided by the expected proportion of CpGs. Notice that the traditional definition of a CpG island requires this ratio to be >0.6.
  • the probes were stratified into two groups: low CpG density (ratio ⁇ 0.6) and high CpG density (ratio > 0.6). A regression line was fitted to each group.
  • the correlation between Reference- Array M-values and microarray M-values is shown in Table 4. While McrBC showed similar sensitivity for both high- and low-density groups, HELP showed better sensitivity for the lower CpG density group than for the higher CpG density group.
  • dRange of correlations between microarray and Reference- Array M- values The range is over all replicates.
  • the methylation status of neighboring CpGs tends to be highly correlated served as a partial basis for methylation analysis of genome- weighted smoothing: averaging probes within small contiguous genomic regions taking into account the biases illustrated in Figure 4.
  • a key aspect of the approach is that information derived from the genome sequence is combined with microarray data. By characterizing each of the segments induced by laboratory protocols, it is possible to quantify the utility of the associated microarray data. This information is then used to adapt the averaging used in the smoothing step by assigning weights. Details on the smoothing strategy are described in the methods section herein.
  • the canonical arrays designed for the McrBC and HELP methods use multiple array features to probe a selected subset of the McrBC and HELP segments described above. These segments in the canonical designs are not contiguous, thus smoothing is not possible with data from these arrays.
  • Figure 5 demonstrates the advantage of genome-weighted smoothing.
  • M-values are plotted against location on the genome. The points are the ⁇ f-values observed for each probe.
  • the averaged M- values for probes in the same McrBC and HELP segments are shown with orange and green lines for McrBC and HELP, respectively.
  • the results obtained using genome- weighted smoothing are shown with black curves. Note that for the McrBC and MeDIP methods, the range of the probe-level and segment M- values associated with unmethylated ( Figure 5A) and methylated ( Figure 5B) regions overlap; the results from smoothing do not.
  • the segment M- values range from -0.75 to 0.5 and from -0.75 to 3 for the unmethylated and methylated regions, respectively.
  • the values obtained from smoothing range from —0.2 to 0.25 and from 0.6 to 2.5 for the unmethylated and methylated regions, respectively.
  • the averaging performed in the smoothing procedure greatly reduces noise, and the fact that the averaging is local, for example, performed in small regions, permits preservation of the ability to discriminate.
  • the first component of the method is a tiling array specifically designed to maximize the number of assayed CpGs.
  • the number of CpGs assayed, for which reliable detection of methylation status may be determined were maximized.
  • the design incorporates smoothing, isolated CpGs were not assayed.
  • An analysis of different numbers of probes included in the smoothing demonstrated that in a preferred aspect, including at least 15 probe intensities produce exemplary results.
  • the procedure for creating the CHARM array was as follows. 1) All the CpGs in the genome were identified. Any region of 300 bp with no CpGs was discarded.
  • Regions were tiled, using 50-mers 35 bp apart. It is also possible to prioritize for economy to limit to a single array by calculating the ratio of CpGs per probes in the region and by assigning higher priority to those with a higher ratio.
  • This CHARM array design improves the detection strategy for any of the methods because it facilitates the smoothing strategy and assays many more CpGs. Probes associated with problematic segments (e.g., very small segments in the HELP assay) could be removed in the analysis stage. However, we selected McrBC for the application of this approach because of its superior sensitivity and specificity described earlier. Going forward, samples were also hybridized using the CHARM design as well as the MeDIP assay as well. Use of the HELP assay was discontinued mainly because of its limited number of detectable sites (Hpall dependence).
  • a genomic region was defined as "methylated” if all probes from the Reference-Array platform in the region were >90%. Similarly, unmethylated regions were defined as those with all probes ⁇ 10%; 100 Reference- Array probe sets fulfilled these criteria. If the smoothed M-value within any of these regions was above a predetermined threshold, the region was considered methylated. Various thresholds were considered, and each defines a point in the ROC curve. The results greatly improved with CHARM. Notice that for a specificity of 90%, the McrBC sensitivity improved from 60% without CHARM to 100% with CHARM.
  • the CHARM method unlike MeDIP, HELP, or nonsmoothed McrBC, is highly quantitative, meaning that there was a linear relationship between methylation measured on the array and the reference methylation platform (Reference- Array), as shown clearly in Figure 3.
  • the correlation coefficient comparing these two values was substantially better for CHARM compared to the other methods (Table 4), as was the ROC curve ( Figure 6).
  • the genome coverage on the array is genome sequence-driven, rather than based on arbitrary assumptions about the likely location of methylated sites (e.g., promoters) that might miss substantial numbers of regulatory sequences. Even with this unbiased, non-promoter-driven selection strategy, 87% of the Reference- Array-selected methylation cancer panel 1 genes are present on the NimbleGen HD2 ArrayTM.
  • results show inherent limitations of MeDIP and HELP.
  • the results obtained with the MeDIP method barely distinguished the HCTl 16 and DKO samples.
  • IP immunoprecipitation
  • the IP sample will be enriched with CpGs regardless of the number of segments that pass the filter. This is likely to result in cross-hybridization problems, for example, probes with more CpGs might result in higher intensities only because of cross-hybridization with the high CpG content sample.
  • McrBC fractionation was originally applied to analysis of the plant genome and subsequently used as a discovery tool for methylated CpG islands, but it has not come into common use.
  • the original whole-genome array design represented a few thousand segments with one probe each.
  • Averaging replicate probes does not reduce this variability as they have the same sequence. Therefore the precision is relatively low in data provided by a single probe replicated four times, as performed in the literature.
  • McrBC fractionation also suffers to some degree within CpG islands in the ability to discriminate highly methylated from highly unmethylated sequences, although it still outperforms MeDIP which works only within CpG islands.

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Abstract

La présente invention concerne des procédés et des dispositifs à micro-réseaux améliorés destinés à effectuer une analyse de méthylation d'ADN incluant des réseaux complets à rendement élevé pour méthylation relative (CHARM). L'invention permet une amplification de PCR avant l'hybridation, permettant l'analyse de petites quantités d'échantillons, et elle est susceptible de détecter la méthylation d'ADN à l'échelle du génome avec une sensibilité allant jusqu'à 100 % et une spécificité supérieure à 90 %. En outre, les procédés et dispositifs de la présente invention permettent une analyse à l'échelle du génome relativement peu coûteuse, qui permet d'analyser de manière fiable et avec une très haute densité des échantillons individuels, permettant une discrimination épigénétique des individus et pas seulement des groupes.
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US10544467B2 (en) 2016-07-06 2020-01-28 Youhealth Oncotech, Limited Solid tumor methylation markers and uses thereof
US11396678B2 (en) 2016-07-06 2022-07-26 The Regent Of The University Of California Breast and ovarian cancer methylation markers and uses thereof
US10513739B2 (en) 2017-03-02 2019-12-24 Youhealth Oncotech, Limited Methylation markers for diagnosing hepatocellular carcinoma and lung cancer
US11433075B2 (en) 2017-06-22 2022-09-06 Triact Therapeutics, Inc. Methods of treating glioblastoma
US11628144B2 (en) 2017-09-29 2023-04-18 Triact Therapeutics, Inc. Iniparib formulations and uses thereof
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WO2020150705A1 (fr) * 2019-01-18 2020-07-23 The Regents Of The University Of California Mesure de méthylation d'adn pour des mammifères sur la base de loci conservés
US11578373B2 (en) 2019-03-26 2023-02-14 Dermtech, Inc. Gene classifiers and uses thereof in skin cancers
WO2020212588A1 (fr) 2019-04-17 2020-10-22 Igenomix, S.L. Dosage non invasif de pré-éclampsie et de pathologies associéés à la pré-éclampsie

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