# Patent

## Title
Methods and compositions for diagnosing lung cancer with specific DNA methylation patterns

## Abstract

The present invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences within genomic target sequences that are associated with cancer in an individual by obtaining a biological sample comprising genomic DNA from the individual measuring the level or pattern of methylated genomic CpG dinucleotide sequences for two or more of the genomic targets in the sample, and comparing the level of methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level or pattern of methylation of the genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer. As disclosed herein, the methods of the invention have numerous diagnostic and prognostic applications. The methods of the invention can be combined with a miniaturized array platform that allows for a high level of assay multiplexing and scalable automation for sample handling and data processing. Also provided by the invention are genomic targets and corresponding nucleic acid probes that are useful in the methods of the invention as they enable detection of differentially methylated genomic CpG dinucleotide sequences associated with adenocarcinomas of the lung.

## Background
This application is a continuation-in-part of U.S. Ser. No. 10/973,783, filed Oct. 25, 2004, which is a continuation-in-part of U.S. Ser. No. 10/845,667, filed May 14, 2004 now abandoned, which is based on, and claims the benefit of, U.S. Provisional Application No. 60/471,488, filed May 15, 2003, entitled METHODS AND COMPOSITIONS FOR DIAGNOSING CANCER, each of which is incorporated herein by reference.


This invention was made with government support under grant numbers 1 R43 CA097851-01, 2 R44 CA097851-02 and R37-NL-34788 awarded by the National Institutes of Health. The United States Government has certain rights in this invention.


FIELD OF THE INVENTION
The present invention relates to conditions characterized by differentially methylated genomic CpG dinucleotide sequences and, in particular, to diagnostic and prognostic methods that exploit the presence of such genomic DNA sequences that exhibit altered CpG methylation patterns.
BACKGROUND OF THE INVENTION
Methylation of DNA is widespread and plays a critical role in the regulation of gene expression in development, differentiation and diseases such as multiple sclerosis, diabetes, schizophrenia, aging, and cancers. Methylation in particular gene regions, for example in their promoters, can inhibit the expression of these genes. Recent work has shown that the gene silencing effect of methylated regions is accomplished through the interaction of methylcytosine binding proteins with other structural components of chromatin which, in turn, makes the DNA inaccessible to transcription factors through histone deacetylation and chromatin structure changes. Differentially methylated CpG islands have long been-thought to function as genomic imprinting control regions (ICRs).
Deregulation of imprinting has been implicated in several developmental disorders. Identification of the ICRs in a large number of human genes and their regulation patterns during development can shed light on genomic imprinting as well as other fundamental epigenetic control mechanisms. Moreover, rapid advances in genomics, both in terms of technology, for example, high-throughput low-cost capillary sequencers and microarray technologies, as well as in terms of availability of information, for example, information gained by virtue of whole genome sequencing, bioinformatics tools and databases, have paved the way for new opportunities in epigenetic studies. For example, it is known that random autosomal inactivation is one of the mechanisms that mammals use to achieve gene dosage control, in addition to random X-chromosome inactivation in females and genomic imprinting. However, genes belonging to this category are just now emerging, with only a few identified so far. Technologies are needed that can provide a systematic survey for identification of genes regulated by this kind of random monoallelic expression control, and determination of when and how such genes are regulated through a wide screening of samples from different tissues or different disease stages.
Changes in DNA methylation have been recognized as one of the most common molecular alternations in human neoplasia. Hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes are now firmly established as the most frequent mechanisms for gene inactivation in cancers. In contrast, a global hypomethylation of genomic DNA and loss of IGF imprinting are observed in tumor cells; and a correlation between hypomethylation and increased gene expression has been reported for many oncogenes. In addition, monitoring global changes in methylation pattern has been applied to molecular classification of cancers. Most recently, gene hypermethylation has been associated with clinical risk groups in neuroblastoma and hormone receptor status and response to tamoxifen in breast cancer.
Lung cancer is the second most common cancer among both men and women and is the leading cause of cancer death in both sexes. There is no established early detection test for the disease, and only 15% of lung cancer cases are diagnosed when the disease is localized. The ability to accurately detect malignant cells in a wide range of clinical specimens including sputum, blood, or tissue would provide significant implications for screening high-risk individuals for this cancer.
A Human Epigenome Consortium was formed in 1999 with a mission to systematically map and catalogue the genomic positions of distinct methylation variants. It is likely that large-scale discovery of methylation patterns through de novo DNA sequencing of bisulfite-treated DNA is be carried out in the near future. This would provide a resource for methylation studies analogous to SNP databases for genetic studies, and would be expected to greatly increase the demand for high-throughput, cost-effective methods of carrying out site-specific methylation assays. A publicly accessible database, which carries information about methylation patterns in various biologically significant samples would be the first outcome of these efforts. There is a need for methods for analysis of large sample sets useful for discovering such associations.
Presently, the analysis of DNA methylation patterns in genomic DNA has been significantly hampered by the fact that methylation information is not retained during standard DNA amplification steps such as PCR or biological amplification by cloning in bacteria. Therefore, DNA methylation analysis methods generally rely on a methylation-dependent modification of the original genomic DNA before any amplification step. A battery of DNA methylation detection methods has been developed, including methylation-specific enzyme digestion (Singer-Sam, et al., Nucleic Acids Res. 18(3): 687 (1990), Taylor, et al., Leukemia 15(4): 583-9 (2001)), bisulfite DNA sequencing (Frommer, et al., Proc Natl Acad Sci U S A. 89(5): 1827-31 (1992), Feil, et al., Nucleic Acids Res. 22(4): 695-6 (1994)), methylation-specific PCR (MSP) (Herman, et al., Proc Natl Acad Sci U S A. 93(18): 9821-6 (1996)), methylation-sensitive single nucleotide primer extension (MS-SnuPE) (Gonzalgo, et al., Nucleic Acids Res. 25(12): 2529-31 (1997)), restriction landmark genomic scanning (RLGS) (Kawai, Mol Cell Biol. 14(11): 7421-7 (1994), Akama, et al., Cancer Res. 57(15): 3294-9 (1997)), and differential methylation hybridization (DMH) (Huang, et al., Hum Mol Genet. 8(3): 459-70 (1999)). However, none of these methods combines random access to specific sequences in the genome with high throughput and low cost, which facilitates analysis of methylation profiles at high resolution in large sample sets. In addition, many prior art methods are insensitive to low levels of methylation changes in diseased tissues, e.g. 10% or 20% hypermethylation.
Thus, a need exists for a highly reproducible and multiplexed method for high-throughput quantitative measurement of DNA methylation that provides not just a discrete measure of positive versus negative DNA methylation, but a continuous measure of levels of DNA methylation to classify and predict different types and stages of cancer, cancer therapeutic outcomes and patient survival. This invention satisfies this need and provides related advantages as well.
SUMMARY OF THE INVENTION
The present invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences associated with cancer in an individual by obtaining a biological sample comprising genomic DNA from the individual measuring the level or pattern of one or more methylated genomic CpG dinucleotide sequences in two or more of the genomic targets in the sample, and comparing the level of said one or more methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level or pattern of methylation of the genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer.
The methods of the invention enable detection of differentially methylated genomic CpG dinucleotide sequences associated with lung cancer. In a particular embodiment, the present invention provides genomic targets that are differentially methylated in adenocarcinoma of the lung. Notably, the invention also discloses a novel cancer marker. As disclosed herein, the methods of the invention have numerous diagnostic and prognostic applications. The methods of the invention can be combined with a miniaturized array platform that allows for a high level of assay multiplexing and scalable automation for sample handling and data processing.



BRIEF DESCRIPTION OF THE DRAWINGS
The patent or application file contains at least one drawing executed in color. Copies of this patent or application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
FIG. 1 shows an assembly of a randomly ordered fiber optic array. Panel A shows a collection of bead types, each with a distinct oligonucleotide capture probe, is pooled. An etched fiber optic bundle is dipped into the bead pool, allowing individual beads to assemble into the microwells at the bundle's end. Panel B shows a scanning electron micrograph of an assembled array containing 3 micron diameter silica beads with 5 micron core-to-core spacing between features. The beads are stably associated with the wells under standard hybridization conditions.
FIG. 2 shows a photograph of a 96-array matrix. Each array is located on the end of an optical fiber bundle containing  ̃50,000 individual fibers. The spacing of the arrays matches that of a 96-well plate, allowing 96 separate samples to be processed simultaneously.
FIG. 3 shows Illumina's SNP genotyping format.
FIG. 4 shows an oligonucleotide design scheme (SEQ ID NOS: 1509-1513 and 1554).
FIG. 5 shows plasmid controls used in the assay development. Unmethylated (green), semi-methylated (yellow) and fully-methylated (red) plasmid loci can be correctly scored in the human genomic DNA background.
FIG. 6 shows bisulfite conversion of DNA monitored with internal controls. Top panel: unconverted DNA; bottom panel: DNA after bisulfite conversion. Query oligonucleotides for converted plasmid loci (yellow) and unconverted genomic loci (green) are present in both assays. After bisulfite conversion, signal corresponding to unconverted loci disappears, and signal from converted loci becomes detectable.
FIG. 7 shows methylation assay development and data processing. Left panel: unmethylated, semi-methylated, and fully-methylated loci on the plasmids can be distinguished in the human genomic DNA background. These plasmid DNAs were spiked into human genomic DNA at a 1:1 molar ratio. Right panel: each data point is represented in a red/green/yellow plot, where red indicates a methylated state, green—unmethylated, and yellow—semi-methylated. The whole left panel (bar graph) is represented by one column on a red/green/yellow plot.
FIG. 8 shows reproducible methylation detection in two human reference DNAs: unmethylated (left panel) and methylated (right panel). The red color indicates a methylated state, green—unmethylated, and yellow—semi-methylated. White squares represent the loci with low intensity values, for which the methylation status call could not be made. In the case of amplified gDNA (left panel), some genomic loci may become underrepresented after amplification procedure.
FIG. 9 shows methylation measurement in 15 Coriell genomic DNAs and the reference DNAs.
FIG. 10 shows methylation status of any particular locus determined using a clustering algorithm. Panel A shows raw intensity data (of each bead) for one locus across all 96 replicates. Panel B shows analyzed clusters. Unmethylated, methylated and semi-methylated loci can be distinguished and called correctly by this algorithm.
FIG. 11 shows a schematic overview of a methylation assay that incorporates bisulfite conversion and a bead array format.
FIG. 12 shows reference samples for a methylation assay encompassing amplified genomic DNA in Panel A and corresponding in vitro methylated genomic DNA in Panel B.
FIG. 13 shows correlation in methylation status between replicates of lung cancer clinical samples containing 389 loci across four independent arrays.
FIG. 14 shows reproducibility of the methylation assay between technical replicates as observed in 46 lung cancer clinical samples containing 389 loci across four independent arrays.
FIG. 15 shows methylation status of two housekeeping genes located on the X chromosome. In females X inactivation correlates with promoter methylation, and methylation pattern determined for both genes in 46 samples (in duplicates) allows to match the methylation status of the promoter with the gender of the sample source.
FIG. 16 shows correlation between methylation levels and gender based on methylation status of 6 genes located on the X-chromosome as monitored in 46 samples.
FIG. 17 shows methylation profiling in 46 lung cancer and matched normal tissues based on interrogation of 162 CpG sites. Unmethylated (green), semi-methylated (yellow), methylated (red).
FIG. 18 shows distinct methylation patterns observed for 14 markers in squamous cell carcinoma versus normal matching tissue.
FIG. 19 shows cluster analysis of methylation profiles in 46 lung cancer samples which demonstrates good separation of cancer samples from normal matching pairs.
FIG. 20 shows internal methylation assay controls. Methylated, unmethylated or semi-methylated (generated by mixing the methylated and unmethylated at 1:1 ratio) plasmid DNAs were spiked into human genomic DNA at 1:1 molar ratio. Methylation states were determined in 46 such samples at 29 CpG sites from 3 plasmids: Unmethylated (Green), semi-methylated (Yellow) and fully-methylated (Red).
FIG. 21 shows a comparison of methylation profiles among lung cancer and matching normal tissues. The β-value (i.e. the methylation ratio measured for all the functional CpG sites) obtained from one replicate experiment is plotted against that obtained from another replicate experiment. The upper panels show the reproducibility of replicate assays on DNA derived from squamous cell carcinoma G12005 (left) and its matching normal tissue (center), and a comparison between the normal and the squamous cell carcinoma (right). The lower panels show the reproducibility of replicate assays on DNA derived from lung adenocarcinoma G12012 (left) and its matching normal tissue (center), and a comparison between the normal and the adenocarcinoma (right).
FIG. 22 shows methylation profiles of 6 housekeeping genes located on the X-chromosome. Data are shown for 14 CpG loci in 16 males and 6 females, which include adenocarcinomas and their matching normal tissue samples, and the two reference samples. Replicate measurements are shown in adjacent for each sample.
FIG. 23 shows methylation profiles of the lung adenocarcinoma markers and the squamous cell carcinoma markers in the training set of samples. Panel 23A shows 11 adenocarcinomas and 11 matching normal tissues. Panel 23B shows 14 squamous cell carcinomas and 10 matching normal tissues. Replicate measurements are shown in adjacent for each sample.
FIG. 24 shows a cluster analysis of lung cancer samples based on methylation profiles. Panel 24A shows adenocarcinoma (training set). Panel 24B shows squamous cell carcinoma (training set). Panel 24C shows adenocarcinoma (test set).
FIG. 25 shows box plots of the array data for selected cancer-specific methylation markers. β-values (y-axis) were calculated for the six adenocarcinoma cancer markers. For the training set (yellow plots), 11 adenocarcinoma and 21 normal tissues were used. For the validation set (grey plots), 13 adenocarcinoma and 12 normal tissues were used. The black bar represents the mean β-value. The box defines quartiles (25% and 75%, respectively). The error bars are upper and lower adjacent limits (median +/−1.5*IQR). Dots represent the outliers.
FIG. 26 shows methylation assay reproducibility and differential methylation detection. Comparison of methylation profiles between lung cancer and matching normal tissue. The β-value (i.e. the methylation ratio measured for all 1536 CpG sites) obtained from one replicate experiment is plotted against that obtained from another technical replicate experiment. The left and middle panels show the reproducibility of replicated assays on DNAs derived from lung adenocarcinoma G12022 (left) and its matching normal tissue (center). The right panel shows the comparison between the normal and the matching adenocarcinoma samples.
FIG. 27 shows methylation detection in gDNA mixtures. (A) Female genomic DNA was diluted with male genomic DNA at ratios of 5:95, 10:90, 20:80 and 50:50. Two sets of mixtures were made and measured: M1 (male NA10923)/F1 (female NA10924) and M2 (male NA07033)/F2 (female NA06999). Methylation levels of six X-chromosome linked genes were calculated as the average of several (1-5) CpG sites for each gene. (B) Methylation profiles of individual CpG sites (only four are shown). Error bars represent the standard deviation of β-values calculated from four replicate experiments, done with the first set of mixtures (NA10923/NA10924).
FIG. 28 shows methylation profiling in cancer cell lines. Seven DNA samples derived from different normal tissues and 17 colon, breast, lung and prostate cancer cell lines were profiled. All cancer samples were correctly separated from normal samples using agglomerative clustering based on 64 cancer-specific methylation makers, and highly specific methylation signatures were obtained for each cancer type. Green, yellow and red colors represent low, medium and high methylation levels, respectively.
FIG. 29 shows a comparison of methylation-specific PCR and array-based methylation data. MSP was used to confirm the methylation status of CpG sites within the promoter regions of five genes that showed distinct methylation profiles in one normal lung tissue and six lung cancer cell lines from array-based methylation analysis (1: normal lung tissue, 2: NCI-H69, 3: NCI-H526, 4: NCI-H358, 5: NCI-H1299, 6: NCI-H1395, and 7: NCI-H2126). Methylation level is represented as bars, blue for microarray data and red for MSP data.
FIG. 30 shows a cluster analysis of lung adenocarcinoma samples. Panel (A) shows eleven cancer and 11 normal tissue samples were used as a training set to identify a list of 55 CpG sites, that are differentially methylated in cancer vs. normal tissues with high confidence level (adjusted p-value<0.001) and significant change in absolute methylation level (|Δβ|>0.15). Cancer sample G12029 was mistakenly co-clustered with normal samples. Panel (B) shows use of the selected 55 CpG sites for classification of an independent test sample set of 12 lung adenocarcinoma and 12 normal tissue samples, collected from a different institute. All the normal and cancer samples were separated into two distinguished groups with a single error—cancer sample D12162 was co-clustered with normal samples. Normal samples are underlined in green, cancer, in red. The asterisks indicate mis-classified samples.
FIG. 31 shows methylation measurement with internal controls. Methylated, unmethylated and semi-methylated (generated by pre-mixing the methylated and unmethylated at 1:1 ratio) plasmid DNAs were spiked into human genomic DNA at 1:1 molar ratio, and 96 assays were run in parallel. Green, yellow and red colors represent low, medium and high methylation levels, respectively
FIG. 32 provides information about 55 CpG sites disclosed herein as differentially methylated in cancer. For each CpG site, the following information is provided: (A) Target ID (B) Contig (C) PromoterStart-PromoterEnd; (D) CpG position relative to TSS; (E) Orientation; (F) Strand; (G) Start; (H) End; (I) Length; (J) CpG-Position; (K) Target sequence (SEQ ID NOS: 1555-1609, respectively, in order of appearance); (L) ASO_C (Probe Sequence) (SEQ ID NOS: 1610-1664, respectively, in order of appearance); (M) ASO_T (Probe Sequence) (SEQ ID NOS: 1665-1719, respectively, in order of appearance); (N) LSO_C (Probe Sequence) (SEQ ID NOS: 1720-1774, respectively, in order of appearance); (O) LSO_T (Probe Sequence) (SEQ ID NOS: 1775-1829 respectively, in order of appearance).



DETAILED DESCRIPTION OF THE INVENTION
This application file contains drawings executed in color. Copies of this patent or application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
The invention disclosed herein provides diagnostic and prognostic methods for a condition that is characterized by differential methylation of genomic CpG dinucleotide sequences. Also provided are populations of genomic targets and corresponding nucleic acid probes that useful for the detection of differentially methylated genomic CpG dinucleotide sequences that can be correlated to the presence of or susceptibility to cancer in an individual.
In particular, the present invention provides genomic targets that are differentially methylated in adenocarcinoma of the lung. The following 14 genomic targets are disclosed herein as differentially methylated in adenocarcinoma: ADCYAP1—2, CDH13—3 (CDH13-1358), GDF10—2, GDF10—3, HOXA5—2 (HOXA5-1247), MAGEA1—3, RUNX3—1, SCGB3A1—3, SERPINB5—1, SFN—2, SFTPA1—2, TERT—1, TERT—2, and TNF—2 (TNF-1343). The 14 genomic targets correspond to twelve gene targets.
The following 52 genomic target sites also are disclosed herein as differentially methylated in adenocarcinoma: ADCYAP1-1103, APBA2-1397, ASCL2-856, ASCL2-1038, ASCL2-1048, ASCL2-1339, CCND2-604, CD2-1433, CFTR-1051, CFTR-1191, DBC1-1053, DIO3-1355, DLC1-1012, DLK1-1119, DLK1-1185, DLK1-1226, DLK1-1287, GABRB3-1408, GALR1-1270, GDF10-1224, HOXA5-75, HOXA11-802, HOXA11-558, HOXA5-1094, HS3ST2-311, HTR2A-1387, HTR1B-315, HTR1B-1278, IGF2AS-1145, IGF2-112, NEFL-524, NEFL-600, NEFL-1341, NEFL-1367, NPY-793, NPY-931, NPY-1009, OPCML-967, PAX6-1337, PENK-1480, PENK-1293, POMC-1303, PTPN6-1214, PTPRO-1206, RET-1149, RUNX3-368, SEZ6L-1046, TERT-900, TNF-1371, TP73-358, TWIST1-1348, TWIST1-524. The 52 target sites correspond to 35 gene targets.
In a further embodiment, the present invention provides genomic targets that are differentially methylated in squamous cell carcinoma of the lung. The following 21 genomic targets are disclosed herein as differentially methylated in squamous cell carcinoma: ADCYAP1—1, ADCYAP1—2, ADCYAP1—3, BCR—2, CALCA—1, GDF10—3, HOXA5—1, HOXA5—2, HOXA5—3, MAGEA1—1, MAGEA1—2, PGR—2, PRKCDBP—3, SCGB3A1—3, SERPINB5—1, SFN—2, SFTPC—3, TERT—1, TERT—2, TERT—3, TNF—2 (FIG. 23B). The 21 genomic targets correspond to 14 gene targets.
Notably, the invention also discloses a novel cancer marker. As described herein, the gene target adenylate cyclase activating polypeptide 1 (ADCYAP1) provided by the present invention and shown to be methylated in adenocarcinoma and squamous cell carcinoma represents a novel cancer marker based on methylation status. Genomic targets designated SEQ ID NOS: 264, 265 and 266 correspond to gene target adenylate cyclase activating polypeptide 1 (ADCYAP1) and are shown herein to be methylated in squamous cell carcinoma, while SEQ ID NOS: 265 and 1555 (corresponding to ADCYAP1-1103) are shown to be methylated in adenocarcinoma (FIGS. 23A and B).
The methods of the invention are directed to methods for diagnosing an individual with a condition that is characterized by a level and/or pattern of methylated genomic CpG dinucleotide sequences distinct from the level and/or pattern of methylated genomic CpG dinucleotide sequences exhibited in the absence of the particular condition. This invention also is directed to methods for predicting the susceptibility of an individual- to a condition that is characterized by a level and/or pattern of methylated genomic CpG dinucleotide sequences that is distinct from the level and/or pattern of methylated genomic CpG dinucleotide sequences exhibited in the absence of the condition.
Because methylation detection interrogates genomic DNA, rather than RNA or protein, it offers several technological advantages in a clinical diagnostic setting: (1) readily available source materials, particularly important for prognostic research, because typically DNA can be more reliably extracted than RNA from archived biological samples for study; (2) capability for multiplexing, allowing simultaneous measurement of multiple targets to improve assay specificity; (3) easy amplification of assay products to achieve high sensitivity; and (4) the ability to detect a positive signal in tumors that arises from methylation inactivation of one allele of tumor suppressor genes. Detecting the appearance of a positive signal is a more robust and reliable measurement than detecting a two-fold gene expression change at the mRNA level in these tumors.
In various distinct embodiments, the present invention is based, in part, on the identification of reliable CpG methylation markers for the improved prediction of susceptibility, diagnosis and staging of lung cancer. The invention provides a population of reliable genomic target sequences or genomic targets for use in the diagnostic and prognostic methods provided by the present invention, which have been designated herein as SEQ ID NOS: 57, 58, 59, 72, 76, 139, 140, 141, 174, 264, 265, 266, 293, 294, 305, 309, 313, 314, 315, 345, 350, 353, 360, 364, 371 and 373. The genomic targets provided by the invention are associated with the following gene targets: ADCYAP1, APBA2, ASCL2, BCR, CALCA, CCND2, CD2, CDH13, CTFR, GALR1, GDF10, HOXA5, HOXA11, HS3ST2, HTR2A, HTR1B, DBC1, DIO3, DLC1, DLK1, GABRB3, IGF2AS, IGF2, NEFL, NPY, MAGEA1, OPCML, PAX6, PENK, PGR, POMC, PRKCDBP, PTPN6, PTPRO, RET, RUNX3, SCGB3A1, SERPINB5, SEZ6L, SFNT, SFTPA1, SFTPC, TERT, TNF, TP73, and TWIST1 (Table 1 and FIG. 32). The genomic targets provided by the invention thus represent gene targets for methylation of genomic CpG dinucleotide sequences associated with lung cancer. Also provided are nucleic acid probes that correspond to the genomic target sites of the invention and that can be used to detect differential methylation of selected genomic CpG dinucleotide sequences that serve as markers associated with lung cancer.
It is understood that the genomic target sequences provides the context for the one or more selected genomic CpG dinucleotide sequences being measured within a particular genomic target sequence. Furthermore, according to the invention, any fraction of the total genomic CpG dinucleotide sequences within a genomic target sequence can be measured, including one or more, two or more three or more, four or more, five or more or all of the genomic CpG dinucleotide sequences within a genomic target sequence. In addition, since it is understood that the genomic target sequence provides the context for the one or more selected genomic CpG dinucleotide sequences being, measured, the invention encompasses measurement of the particular genomic CpG dinucleotide sequences encompassed within any of the genomic target sequences designated as SEQ ID NOS: 1-366, regardless of the particular nucleic probes that may be used for detecting the methylation of the particular genomic CpG dinucleotide sequence. Although Table 1 sets forth particular nucleic acid probes provided by the invention that correspond to the genomic targets of SEQ ID NOS: 1-366, the skilled person can practice the invention using any desired nucleic acid probe capable of detecting the methylation status of one or more genomic CpG dinucleotide sequences within a particular genomic target sequence.
The genomic targets and nucleic acid probes provided by the present invention are set forth in Table 1, below, and provide diagnostic and prognostic tools based on their ability to detect differential methylation of selected genomic CpG dinucleotide sequences associated with cancer. In the methods provided by the invention, the genomic targets and nucleic acid probes capable of detecting markers located within the genomic targets can be employed to detect altered levels of methylation of genomic CpG dinucleotide sequences in a biological sample compared to a reference level. Furthermore, the methods of the invention allow for use of the genomic markers and nucleic acid probes for the determination of methylation patterns, which are represented by differential methylation of selected genomic CpG dinucleotide sequences that serve as markers in particular sets or subsets of genomic targets. In embodiments directed to the detection of methylation patterns, it is possible to diagnose or predict the susceptibility of an individual to a specific tumor-type based on the correlation between the pattern and the tumor type.
DNA methylation is a mechanism for changing the base sequence of DNA without altering its coding function. DNA methylation is a heritable, reversible and epigenetic change. Yet, DNA methylation has the potential to alter gene expression, which has profound developmental and genetic consequences. The methylation reaction involves flipping a target cytosine out of an intact double helix to allow the transfer of a methyl group from S adenosylmethionine in a cleft of the enzyme DNA (cystosine-5)-methyltransferase (Klimasauskas et al., Cell 76:357-369, 1994) to form 5-methylcytosine (5-mCyt). This enzymatic conversion is the most common epigenetic modification of DNA known to exist in vertebrates and is essential for normal embryonic development (Bird, Cell 70:5-8, 1992; Laird and Jaenisch, Human Mol. Genet. 3:1487-1495, 1994; and Bestor and Jaenisch, Cell 69:915-926, 1992). The presence of 5-mCyt at CpG dinucleotides has resulted in a 5-fold depletion of this sequence in the genome during vertebrate evolution, presumably due to spontaneous deamination of 5-mCyt to T (Schoreret et al., Proc. Natl. Acad. Sci. USA 89:957-961, 1992). Those areas of the genome that do not show such suppression are referred to as “CpG islands” (Bird, Nature 321:209-213, 1986; and Gardiner-Garden et al., J Mol. Biol. 196:261-282, 1987). These CpG island regions comprise about 1% of vertebrate genomes and also account for about 15% of the total number of CpG dinucleotides. CpG islands are typically between 0.2 to about 1 kb in length and are located upstream of many housekeeping and tissue-specific genes, but may also extend into gene coding regions. Therefore, the methylation of cytosine residues within CpG islands in somatic tissues can modulate gene expression throughout the genome (Cedar, Cell 53:3-4, 1988; Nature 421:686-688, 2003).
Methylation of cytosine residues contained within CpG islands of certain genes has been inversely correlated with gene activity. Thus, methylation of cytosine residues within CpG islands in somatic tissue is generally associated with decreased gene expression and can be the effect a variety of mechanisms including, for example, disruption of local chromatin structure, inhibition of transcription factor-DNA binding, or by recruitment of proteins which interact specifically with methylated sequences indirectly preventing transcription factor binding. Despite a generally inverse correlation between methylation of CpG islands and gene expression, however, most CpG islands on autosomal genes remain unmethylated in the germline and methylation of these islands is usually independent of gene expression. Tissue-specific genes are usually unmethylated at the receptive target organs but are methylated in the germline and in non-expressing adult tissues. CpG islands of constitutively-expressed housekeeping genes are normally unmethylated in the germline and in somatic tissues.
Abnormal methylation of CpG islands associated with tumor suppressor genes can cause decreased gene expression. Increased methylation of such regions can lead to progressive reduction of normal gene expression resulting in the selection of a population of cells having a selective growth advantage. Conversely, decreased methylation (hypomethylation) of oncogenes can lead to modulation of normal gene expression resulting in the selection of a population of cells having a selective growth advantage.
The present invention harnesses the potential of genomic methylation of CpG islands as indicators of the presence of a condition in an individual and provides a reliable diagnostic and/or prognostic method applicable to any condition associated with altered levels or patterns of genomic methylation of CpG islands. CpG islands are contiguous regions of genomic DNA that have an elevated frequency of CpG dinucleotides compared to the rest of the genome. CpG islands are typically, but not always, between about 0.2 to about 1 kb in length, and may be as large as about 3 Kb in length. Generally, for the methods provided by the invention at least two or more, at least three or more, at least four or more CpG dinucleotide sequences are selected that are located within a genomic marker so as to allow for determination of co-methylation status in the genomic DNA of a given tissue sample. Preferably the primary and secondary CpG dinucleotide sequences are co-methylated as part of a larger co-methylated pattern of differentially methylated CpG dinucleotide sequences in the genomic marker. The size of such context regions varies, but generally reflects the size of CpG islands as described above, or the size of a gene promoter region, including the first one or two exons.
With particular regard to cancer, changes in DNA methylation have been recognized as one of the most common molecular alternations in human neoplasia. Hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes is a well-established and common mechanism for gene inactivation in cancer (Esteller, Oncogene 21(35): 5427-40 (2002)). In contrast, a global hypomethylation of genomic DNA is observed in tumor cells; and a correlation between hypomethylation and increased gene expression has been reported for many oncogenes (Feinberg, Nature 301(5895): 89-92 (1983), Hanada, et al., Blood 82(6): 1820-8 (1993)). Thus, a detailed study of methylation pattern in selected, staged tumor samples compared to matched normal tissues from the same patient offers a novel approach to identify unique molecular markers for cancer classification.
Monitoring global changes in methylation pattern has been applied to molecular classification in breast cancer (Huang, et al., Hum Mol Genet. 8(3): 459-70 (1999)). In addition, many studies have identified a few specific methylation patterns in tumor suppressor genes, for example, p16, a cyclin-dependent kinase inhibitor, in certain human cancer types (Otterson, et al., Oncogene 11(6): 1211-6 (1995), Herman, et al., Cancer Res. 55(20): 4525-30 (1995)). Some of the most recent examples include the discoveries of causal relationship between the loss of RUNX3 expression, due to hypermethylation, and gastric cancer (Li, et al., Cell 109(1): 113-24 (2002)); loss of IGF2 imprinting in colorectal cancer (Cui, et al., Science 299(5613): 1753-5 (2003); and reduced Hic gene expression in several types of human cancer (Chen, et al., Nat Genet. 33(2): 197-202 2003), Fujii, et al., Oncogene 16(16): 2159-64 (1998), Kanai, et al., Hepatology 29(3): 703-9 (1999)).
In one embodiment, the invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences associated with cancer in an individual by obtaining a biological sample comprising genomic DNA from the individual; measuring the level of one or more methylated genomic CpG dinucleotide sequences in two or more of the genomic target sequences set forth herein and designated as SEQ ID NOS: 1-366 in the sample, and comparing the level of the one or more methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of said one or more genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer. In additional embodiments, the level of methylated genomic CpG dinucleotide sequences is measured for one or more, three or more, four or more, five or more, six ore more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, twenty or more, twenty-five or more, thirty or more, fifty or more, of the genomic target sequences set forth herein and designated as SEQ ID NOS: 1-377 in the sample. A subset of the genomic target sequences or nucleic acid probes of the invention can be one ore more nucleic acid sequences.
In a further embodiment, the invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences associated with lung cancer in an individual by obtaining a biological sample comprising genomic DNA from the individual; measuring the level of one or more methylated genomic CpG dinucleotide sequences in two or more of the genomic target sequences set forth herein and designated as SEQ ID NOS: 57, 58, 59, 72, 76, 139, 140, 141, 174, 264, 265, 266, 293, 294, 305, 309, 313, 314, 315, 345, 350, 353, 360, 364, 371, 373, and any of the sequences set forth in FIG. 32 designated SEQ ID NOS: 1555-1609, in the sample, and comparing the level of the one or more methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of said genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer. In additional embodiments, the level of one or more methylated genomic CpG dinucleotide sequences is measured for one or more, three or more, four or more, five or more, six ore more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, twenty or more, twenty-two or more, twenty-four or more, of the markers set forth herein and designated as SEQ ID NOS: 57, 58, 59, 72, 76, 139, 140, 141, 174, 264, 265, 266, 293, 294, 305, 309, 313, 314, 315, 345, 350, 353, 360, 364, 371, 373, and any of the sequences set forth in FIG. 32 designated SEQ ID NOS: 1555-1609, in the sample. A subset of the genomic markers or nucleic acid probes of the invention can be one or more nucleic acid sequences.
In one embodiment, the invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences associated with adenocarcinoma of the lung in an individual by obtaining a biological sample comprising genomic DNA from the individual; measuring the level of one or more methylated genomic CpG dinucleotide sequences for two or more of the markers set forth herein and designated as SEQ ID NOS: 59, 140, 266, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364, 373, and any of the sequences set forth in FIG. 32 designated SEQ ID NOS: 1555-1609, in the sample, and comparing the level of one or more methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of said genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer. In additional embodiments, the level of one or more methylated genomic CpG dinucleotide sequences is measured for one or more, three or more, four or more, five or more, six ore more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, of the markers set forth herein and present in SEQ ID NOS: 59, 140, 266, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364, 373, and any of the sequences set forth in FIG. 32 designated SEQ ID NOS: 1555-1609, in the sample. A subset of the genomic markers or nucleic acid probes of the invention can be one or more nucleic acid sequences.
In one embodiment, the invention provides a method for identification of differentially methylated genomic CpG dinucleotide sequences associated with squamous cell carcinoma of the lung in an individual by obtaining a biological sample comprising genomic DNA from the individual; measuring the level of one or more methylated genomic CpG dinucleotide sequences for two or more of the markers set forth herein and designated as SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371, in the sample, and comparing the level of one or more methylated genomic CpG dinucleotide sequences in the sample to a reference level of methylated genomic CpG dinucleotide sequences, wherein a difference in the level of methylation of said genomic CpG dinucleotide sequences in the sample compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with cancer. In additional embodiments, the level of one or more methylated genomic CpG dinucleotide sequences is measured for one or more, three or more, four or more, five or more, six ore more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, twenty or more, of the markers set forth herein and designated as SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371, in the sample. A subset of the genomic markers or nucleic acid probes of the invention can be one ore more nucleic acid sequences.
The level of methylation of the differentially methylated genomic CpG dinucleotide sequences can provide a variety of information about the cancer and can be used, for example, to diagnose cancer in the individual; to predict the course of the cancer in the individual; to predict the susceptibility to cancer in the individual, to stage the progression of the cancer in the individual; to predict the likelihood of overall survival for the individual; to predict the likelihood of recurrence of cancer for the individual; to determine the effectiveness of a treatment course undergone by the individual.
As described herein, the level of methylation that is detected in a biological sample can be decreased or increased in comparison to the reference level and alterations that increase or decrease methylation can be detected and provide useful prognostic or diagnostic information. For example, hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes have been established as common mechanisms for gene inactivation in cancers (Esteller, Oncogene 21(35): 5427-40 (2002)). Thus, a detailed study of methylation pattern in selected, staged tumor samples compared to matched normal tissues from the same patient can identify unique molecular markers for cancer classification.
In addition to detecting levels of methylation, the present invention also allows for the detection of patterns of methylation. It has been confirmed previously that neoplastic cells can exhibit unusual patterns of gene methylation (Feinberg and Vogelstein, Nature 301:89-92 (1983)). Previous genetic studies of various conditions, for example, schizophrenia and bipolar disorder, seemed to implicate regions of particular chromosomes 22, but studies failed to identify a susceptibility gene. Analysis of methylation patterns across these chromosome in biological samples from afflicted individuals can reveal epigenetic changes in the form of altered levels of methylation of subsets of genomic CpG dinucleotide sequences that make up a pattern of affected genomic targets that can be correlated with a condition.
In one embodiment of the invention, an altered level of methylation of genomic CpG dinucleotide sequences is observed only in a subset of the genomic targets set forth in Table 1 and designated SEQ ID NOS: 1-377. In this embodiment, the subset can represent a methylation pattern characteristic of a particular type of cancer. A particular subset provided by the invention encompasses genomic targets that are differentially methylated in lung cancer and set forth herein and designated as SEQ ID NOS: 57, 58, 59, 72, 76, 139, 140, 141, 174, 264, 265, 266, 293, 294, 305, 309, 313, 314, 315, 345, 350, 353, 360, 364, 371 and 373. A further subset provided by the invention encompasses genomic targets that are differentially methylated in adenocarcinoma of the lung and set forth herein and designated as SEQ ID NOS: 59, 140, 266, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364, 373, and any of the sequences set forth in FIG. 32 designated SEQ ID NOS: 1555-1609. An additional subset provided by the invention encompasses genomic targets that are differentially methylated in squamous cell carcinoma of the lung and set forth herein and designated as SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371. Therefore, as described herein with reference to cancer, methylation patterns can be correlated with a particular type, class or origin of a condition and detection and comparison of methylation patterns across samples that share a phenotypic characteristic can be useful to identify further methylation patterns.
In a further embodiment the present invention provides a population of genomic targets comprising nucleic acid sequences designated SEQ ID NOS: 1-377, and set forth in Table 1. Also provided in a distinct, but related embodiment is a population of genomic targets selected from the group consisting of nucleic acid sequences designated SEQ ID NOS: 1-377. In a further embodiment the invention provides a population of genomic targets comprising a subset of the nucleic acid sequences designated SEQ ID NOS: 1-366, and set forth in Table 1. Differential methylation of genomic CpG dinucleotide sequences in a subset of SEQ ID NOS: 1-366 can be characteristic of a particular type, class or origin of cancer. Detection of differential methylation in a subset of genomic targets can be useful to diagnose or predict susceptibility for a particular type, class or origin of cancer.
In a further embodiment the invention provides a population of genomic targets comprising a subset of the nucleic acid sequences designated SEQ ID NOS: 1-377, and set forth in FIG. 32 designated SEQ ID NOS: 1555-1609. Differential methylation of genomic CpG dinucleotide sequences in a subset of SEQ ID NOS: 1-377 and 1555-1609 can be characteristic of a particular type, class or origin of cancer. Detection of differential methylation in a subset of genomic targets can be useful to diagnose or predict susceptibility for a particular type, class or origin of cancer.
In one embodiment, a population of genomic targets selected from the group consisting of nucleic acid sequences designated SEQ ID NOS: 1-366 can encompass SEQ ID NOS: 57, 58, 59, 72, 76, 139, 140, 141, 174, 264, 265, 266, 293, 294, 305, 309, 313, 314, 315, 345, 350, 353, 360, 364, 371 and 373. In yet another embodiment, a population of genomic targets selected from the group consisting of nucleic acid sequences designated SEQ ID NOS: 1-377 can encompass SEQ ID NOS: 59, 140, 266, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364, 373, and any of the sequences set forth in FIG. 32 designated SEQ ID NOS: 1555-1609. In a further embodiment, a population of genomic targets selected from the group consisting of nucleic acid sequences designated SEQ ID NOS: 1-377 can encompass SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371. In a further embodiment, a population of genomic targets selected from the group consisting of nucleic acid sequences designated SEQ ID NOS: 1-377 can encompass SEQ ID NOS: 57, 58, 59, 72, 76, 139, 141, 264, 265, 293, 309, 315, 345, 350, 353, 364 and 373. In a further embodiment, a population of genomic targets selected from the group consisting of nucleic acid sequences designated SEQ ID NOS: 1-377 can encompass SEQ ID NOS: 59, 293, 353, 364 and 373. The genomic targets are capable of exhibiting altered levels of methylation of genomic CpG dinucleotide sequences that are predictive of the presence or susceptibility of an individual for cancer.
Also provided by the present invention is a population of nucleic acid probes capable of detecting methylation of genomic CpG dinucleotide sequences of two or more genomic targets selected from the group consisting of the nucleic acid sequences designated SEQ ID NOS: 1-377, and set forth in Table 1. The population of nucleic acid probes provided by the invention consists of two or more nucleic acid sequences selected from the group consisting of SEQ ID NOS: 378-1508, and set forth in Table 1, which sets forth four probes for each genomic target of SEQ ID NOS: 1-377. Also provided by the present invention is a population of nucleic acid probes capable of detecting methylation of genomic CpG dinucleotide sequences of two or more genomic targets selected from the group consisting of the nucleic acid sequences designated SEQ ID NOS: 378-1508, and set forth in FIG. 32 designated SEQ ID NOS: 1610-1829. The population of nucleic acid probes provided by the invention consists of two or more nucleic acid sequences selected from the group consisting of SEQ ID NOS: 378-1508, and set forth in FIG. 32 designated SEQ ID NOS: 1610-1829, which sets forth four probes for each genomic target of SEQ ID NOS: 378-1508 and 1610-1829. The nucleic acid probes of the invention are capable of detecting altered levels of methylation of genomic CpG dinucleotide sequences of two or more genomic targets, wherein altered levels are predictive of the presence or susceptibility of an individual for cancer. Based on the observation that adjacent CpG sites tend to be co-methylated or co-de-methylated, a design scheme can be applied in which a “CG” sequence was used for all the CpG sites within the vicinity of the design, in particular for the landing sites for both ASO and LSO, to target any methylated CpG site; while a “TG” sequence was used for all the CpG sites within the vicinity of the design, to target any un-methylated CpG site. This approach requires two separate LSO oligos, but adds better discrimination between the methylated and unmethylated alleles.
In a further embodiment aimed at determination of patterns of DNA methylation, a population of nucleic acid probes is utilized that is capable of detecting altered levels of methylation of genomic CpG dinucleotide sequences of a subset of a population of two or more genomic targets. Thus, the detection of differential methylation of genomic CpG dinucleotide sequences in only a subset of genomic targets can be used to identify a pattern that correlates with a particular type, class or origin of cancer.
As disclosed herein, the present invention provides a subset of genomic targets consisting of nucleic acid sequences set forth in Table 1 and designated SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371 which exhibits differential methylation of genomic CpG dinucleotide sequences associated with lung squamous cell carcinoma. FIG. 23B shows the differential methylation pattern observed for genomic targets designated SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371, which correspond to gene targets MAGEA1 (SEQ ID NOS: 57 and 58), BCR (SEQ ID NO: 72), CALCA (SEQ ID NO: 76), HOXA5 (SEQ ID NOS: 139, 140, and 141), TNF (SEQ ID NO: 174), ADCYAP1 (SEQ ID NOS: 264, 265 and 266), GDF10 (SEQ ID NO: 294), SFN (SEQ ID NO: 305), SFTPC (SEQ ID NO: 309), TERT (SEQ ID NOS: 313, 314 and 315), PRKCDBP (SEQ ID NO: 345), PGR (SEQ ID NO: 350), SERPINB (SEQ ID NO: 360) and SCGB3A1 (SEQ ID NO: 371).
As disclosed herein, the present invention provides a subset of genomic targets consisting of nucleic acid sequences set forth in FIG. 32 and designated SEQ ID NOS: 1555-1609, which exhibit differential methylation of genomic CpG dinucleotide sequences associated with lung adenocarcinoma. FIG. 32 lists the differential methylation pattern observed for genomic targets designated SEQ ID NOS: 1555-1609, which correspond to gene targets ADCYAP1 (SEQ ID NO: 1555), APBA2 (SEQ ID NO: 1556), ASCL2 (SEQ ID NO: 1557-1560), CCND2 (SEQ ID NO: 1561), CD2 (SEQ ID NO: 1562), CDH13 (SEQ ID NO: 1563), CTFR (SEQ ID NO: 1564-1565), DBC1 (SEQ ID NO: 1566), DIO3 (SEQ ID NO: 1567), DLC1 (SEQ ID NO: 1568), DLK1 (SEQ ID NO: 1569-1572), GABRB3 (SEQ ID NO: 1573), GALR1 (SEQ ID NO: 1574), GDF 10 (SEQ ID NO: 1575), HOXA5 (SEQ ID NO: 1578-1580), HOXA11 (SEQ ID NO: 1576-1577), HS3ST2 (SEQ ID NO: 1581), HTR2A (SEQ ID NO: 1584), HTR1B (SEQ ID NO: 1582-1583), IGF2AS (SEQ ID NO: 1586), IGF2 (SEQ ID NO: 1585), NEFL (SEQ ID NO: 1587-1590), NPY (SEQ ID NO: 1591-1593), OPCML (SEQ ID NO: 1594), PAX6 (SEQ ID NO: 1595), PENK (SEQ ID NO: 1596-1597), POMC (SEQ ID NO: 1598), PTPN6 (SEQ ID NO: 1599), PTPRO (SEQ ID NO: 1600), RET (SEQ ID NO: 1601), RUNX3 (SEQ ID NO: 1602), SEZ6L (SEQ ID NO: 1603), TERT (SEQ ID NO: 1604), TNF (SEQ ID NO: 1605-1606), TP73 (SEQ ID NO: 1607), and TWIST1 (SEQ ID NO: 1608-1609).
In a further embodiment, the present invention provides a subset of genomic targets consisting of nucleic acid sequences set forth in Table 1 and designated SEQ ID NOS: 59, 140, 265, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364 and 373 which exhibits differential methylation of genomic CpG dinucleotide sequences associated with lung adenocarcinoma. FIG. 23A shows the differential methylation pattern observed for genomic targets designated SEQ ID NOS: 59, 140, 174, 265, 293, 294, 305, 313, 314, 353, 360, 364, 371, and 373, which correspond to gene targets MAGEA1 (SEQ ID NO: 59), HOXA5 (SEQ ID NO: 140), ADCYAP1 (SEQ ID NO: 265), GDF10 (SEQ ID NOS: 293 and 294), TNF (SEQ ID NO: 174), ADCYAP1 (SEQ ID NO: 265), GDF10 (SEQ ID NO: 293, 294), SFN (SEQ ID NO: 305), TERT (SEQ ID NO: 313, and 314), CDH13 (SEQ ID NO: 353), SERPINB (SEQ ID NO: 360) RUNX3 (SEQ ID NO: 364) and SFTPA1 (SEQ ID NO: 373).
As demonstrated in Example IV, the differentially methylated markers identified from squamous cell carcinomas largely overlap with those identified from adenocarcinomas. Methylation of the gene target adenylate cyclase activating polypeptide 1 (ADCYAP1) as shown in the present disclosure for adenocarcinoma and squamous cell carcinoma has not previously been reported. Genomic targets designated SEQ ID NOS: 264, 265 and 266 correspond to gene target adenylate cyclase activating polypeptide 1 (ADCYAP1) and are shown herein to be methylated in squamous cell carcinoma, while SEQ ID NO: 265 is shown to be hypermethylated in adenocarcinoma and squamous cell carcinoma (FIGS. 23A and B). Gene targets ADCYAP1, CDH13, GDF10, HOXA5, SCGB3A1 and TERT had increased methylation levels in adenocarcinoma samples compared to normal (FIGS. 23A & 26), while MAGEA1, RUNX3, SERPINB5, SFN, SFTPA1 and TNF were less methylated in cancer (FIG. 23A). Methylation of CDH13, HoxA5, SCGB3A1 and GDF10 is associated with tumor progression in various types of cancer as described by Hibi et al., Br J Cancer 91:1139-1142 (2004); Hibi et al., Br J Cancer 90, 1030-3 (2004); Ogama et al. Int J Oncol 25, 685-9-1 (2004); Chen et al., Am J Pathol 163, 37-45 (2003); Fackler et al., Int J Cancer 107, 970-5 (2003); Aldred et al., Cancer Res, 63, 2864-71 (2003). The human telomerase reverse transcriptase gene (TERT) is shown to be inactivated in most differentiated cells, but is reactivated in the majority of cancer cells (Liu et al., Genes Chromosomes Cancer 41, 26-37 (2004)).
As shown in Examples IV and V, the method disclosed herein provides not just a discrete measure of positive versus negative DNA methylation, but a continuous measure of levels of DNA methylation. For a 17% difference in absolute methylation level (e.g. 10% vs. 27%), signals are expected to have largely non-overlapping distributions. The assay can detect as little as 2.5% of methylation for some CpG sites. Unlike restriction enzyme-based methods, assay probes can be specifically designed for most of the CpG sites in the genome, and assay oligos can be designed to interrogate either the Watson or Crick strand or both at each CpG site (FIG. 4). Assay results can be read out on a universal array. As a result, gene (or CpG) sets can be refined iteratively, if desired, because no custom arrays need to be developed. The invention method can detect changes in methylation status at up to 1536 different CpG sites simultaneously using as little as 200 nanograms of genomic DNA.
Lung cancer is the second most common cancer among both men and women and is the leading cause of cancer death in both genders. There is no established early detection test for the disease, and only 15% of lung cancer cases are diagnosed when the disease is localized. The ability to accurately detect malignant cells in a wide range of clinical specimens including sputum, blood, or tissue has significant implications for screening high-risk individuals for this cancer.
Also provided are target nucleic acid probes for detection of a subset of genomic targets consisting of nucleic acid sequences designated SEQ ID NOS: 1610-1829 which exhibits differential methylation of genomic CpG dinucleotide sequences associated with squamous cell carcinoma (FIG. 23B). In the presence of differentially methylated genomic CpG dinucleotide sequences, the subsets of genomic targets signify a pattern distinctive of a particular type of cancer, for example, adenocarcinoma or squamous cell carcinoma of the lung tissue. The p value can be calculated for each individual marker. More than one CpG dinucleotide sequence that serves as genomic marker can be selected from the same gene if desired. Thus a gene can provide the context for more than one genomic CpG dinucleotide sequence such that methylation is determined for more than one CpG dinucleotide sequence within a single gene.
The p-value can be calculated based on a t-test of the level of methylation [i.e. methylation allele intensity/(methylation allele intensity+un-methylation allele intensity)] as is shown among 9 lung squamous cell carcinoma and 5 matching normal samples or 14 other normal samples in Table 2 and among 16 lung adenocarcinoma and 11 normal samples in Table 3.
As demonstrated in Example IV, a data matrix of β values can be used to identify CpG loci which show differential methylation in cancer. A p-value cutoff can be chosen by the skilled person to minimize the number of false positives and additional filters can be incorporated to select markers which have the largest difference between cancer and normal tissues. As shown in FIG. 23A for adenocarcinoma, a list of 14 differentially methylated markers was obtained: ADCYAP1—2, CDH13—3, GDF10—2, GDF10—3, HOXA5—2, MAGEA1—3, RUNX3—1, SCGB3A1—3, SERPINB5—1, SFN—2, SFTPA1—2, TERT—1, TERT—2, and TNF—2. Utilizing the same methylation analysis with the normal and squamous cell carcinoma samples, a list 21 differentially methylated markers was obtained: ADCYAP1—1, ADCYAP1—2, ADCYAP1—3, BCR—2, CALCA—1, GDF10—3, HOXA5—1, HOXA5—2, HOXA5—3, MAGEA1—1, MAGEA1—2, PGR—2, PRKCDBP—3, SCGB3A1—3, SERPINB5—1, SFN—2, SFTPC—3, TERT—1, TERT—2, TERT—3, TNF—2 (FIG. 23B).
The invention also provides subsets of target nucleic acid probes capable of detecting a pattern of methylation of genomic CpG dinucleotide sequences that is associated with a particular type of cancer. Thus, the invention provides a subset of genomic targets consisting of nucleic acid sequences designated SEQ ID NOS: 59, 140, 266, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364, 373, and the nucleic acid sequences set forth in FIG. 32 and designated SEQ ID NOS: 1555-1609, which exhibits differential methylation of genomic CpG dinucleotide sequences associated with adenocarcinoma (FIG. 23A and FIG. 32). The population of nucleic acid probes capable of detecting altered levels of methylation of genomic CpG dinucleotide sequences of a subset of said two or more genomic targets associated with adenocarcinoma are set forth in Table 1 and designated as SEQ ID NOS: 378-1508.
In a further embodiment, the invention provides a subset of genomic targets consisting of nucleic acid sequences set forth in FIG. 23B and designated SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371, which exhibits differential methylation of genomic CpG dinucleotide sequences associated with squamous cell carcinoma. The population of nucleic acid probes capable of detecting altered levels of methylation of genomic CpG dinucleotide sequences of a subset of said two or more genomic targets associated with squamous cell carcinoma are set forth in Table 1 and designated as SEQ ID NOS: 378-1508.
In one embodiment, this invention provides diagnostic markers for cancer. The markers of the invention are genomic sequences having methylation states that are diagnostic or prognostic of the presence or severity of cancer. A list of exemplary genes for which methylation state can be used to determine the presence or severity of cancer is provided in Table 1. Cancer diagnosis or prognosis in a method of the invention can be made in a method of the invention based on the methylation state of particular sequence regions of the gene including, but not limited to, the coding sequence, the 5′-regulatory regions, or other regulatory regions that influence transcription efficiency.
The prognostic methods of the invention are useful for determining if a patient is at risk for recurrence. Cancer recurrence is a concern relating to a variety of types of cancer. For example, of patients undergoing complete surgical removal of colon cancer, 25-40% of patients with stage II colon carcinoma and about 50% of patients with stage III colon carcinoma experience cancer recurrence. One explanation for cancer recurrence is that patients with relatively early stage disease, for example, stage II or stage III, already have small amounts of cancer spread outside the affected organ that were not removed by surgery. These cancer cells, referred to as micrometastases, cannot typically be detected with currently available tests.
The prognostic methods of the invention can be used to identify surgically treated patients likely to experience cancer recurrence so that they can be offered additional therapeutic options, including preoperative or postoperative adjuncts such as chemotherapy, radiation, biological modifiers and other suitable therapies. The methods are especially effective for determining the risk of metastasis in patients who demonstrate no measurable metastasis at the time of examination or surgery.
The prognostic methods of the invention also are useful for determining a proper course of treatment for a patient having cancer. A course of treatment refers to the therapeutic measures taken for a patient after diagnosis or after treatment for cancer. For example, a determination of the likelihood for cancer recurrence, spread, or patient survival, can assist in determining whether a more conservative or more radical approach to therapy should be taken, or whether treatment modalities should be combined. For example, when cancer recurrence is likely, it can be advantageous to precede or follow surgical treatment with chemotherapy, radiation, immunotherapy, biological modifier therapy, gene therapy, vaccines, and the like, or adjust the span of time during which the patient is treated. As described herein, the diagnosis or prognosis of cancer state is typically correlated with the degree to which one or more of the genes in Table I is methylated. Thus, the invention can include a determination made based on the methylation state for the entire set of genes in Table I or a subset of the genes.
Furthermore, as the list in Table I is exemplary, the methylation state of other genes or genomic sequences can also be used in a method of the invention to determine the presence or severity of cancer. Exemplary cancers that can be evaluated using a method of the invention include, but are not limited to hematoporetic neoplasms, Adult T-cell leukemia/lymphoma, Lymphoid Neoplasms, Anaplastic large cell lymphoma, Myeloid Neoplasms, Histiocytoses, Hodgkin Diseases (HD), Precursor B lymphoblastic leukemia/lymphoma (ALL), Acute myclogenous leukemia (AML), Precursor T lymphoblastic leukemia/lymphoma (ALL), Myclodysplastic syndromes, Chronic Mycloproliferative disorders, Chronic lymphocytic leukemia/small lymphocytic lymphoma (SLL), Chronic Myclogenous Leukemia (CML), Lymphoplasmacytic lymphoma, Polycythemia Vera, Mantle cell lymphoma, Essential Thrombocytosis, Follicular lymphoma, Myelofibrosis with Myeloid Metaplasia, Marginal zone lymphoma, Hairy cell leukemia, Hemangioma, Plasmacytoma/plasma cell myeloma, Lymphangioma, Glomangioma, Diffuse large B-cell lymphoma, Kaposi Sarcoma, Hemanioendothelioma, Burkitt lymphoma, Angiosarcoma, T-cell chronic lymphocytic leukemia, Hemangiopericytoma, Large granular lymphocytic leukemia, head & neck cancers, Basal Cell Carcinoma, Mycosis fungoids and sezary syndrome, Squamous Cell Carcinoma, Ceruminoma, Peripheral T-cell lymphoma, Osteoma, Nonchromaffin Paraganglioma, Angioimmunoblastic T-cell lymphoma, Acoustic Neurinoma, Adenoid Cystic Carcinoma, Angiocentric lymphoma, Mucoepidermoid Carcinoma, NK/T-cell lymphoma, Malignant Mixed Tumors, Intestinal T-cell lymphoma, Adenocarcinoma, Malignant Mesothelioma, Fibrosarcoma, Sarcomotoid Type lung cancer, Osteosarcoma, Epithelial Type lung cancer, Chondrosarcoma, Melanoma, cancer of the gastrointestinal tract, olfactory Neuroblastoma, Squamous Cell Carcinoma, Isolated Plasmocytoma, Adenocarcinoma, Inverted Papillomas, Carcinoid, Undifferentiated Carcinoma, Malignant Melanoma, Mucoepidermoid Carcinoma, Adenocarcinoma, Acinic Cell Carcinoma, Gastric Carcinoma, Malignant Mixed Tumor, Gastric Lymphoma, Gastric Stromal Cell Tumors, Amenoblastoma, Lymphoma, Odontoma, Intestinal Stromal Cell tumors, thymus cancers, Malignant Thymoma, Carcinids, Type I (Invasive thymoma), Malignant Mesothelioma, Type II (Thymic carcinoma), Non-mucin producing adenocarcinoma, Squamous cell carcinoma, Lymph epithelioma, cancers of the liver and biliary tract, Squamous Cell Carcinoma, Hepatocellular Carcinoma, Adenocarcinoma, Cholangiocarcinoma, Hepatoblastoma, papillary cancer, Angiosarcoma, solid Bronchioalveolar cancer, Fibrolameller Carcinoma, Small Cell Carcinoma, Carcinoma of the Gallbladder, Intermediate Cell carcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, Undifferentiated cancer, cancer of the pancreas, cancer of the female genital tract, Squamous Cell Carcinoma, Cystadenocarcinoma, Basal Cell Carcinoma, Insulinoma, Melanoma, Gastrinoma, Fibrosarcoma, Glucagonamoa, Intaepithelial Carcinoma, Adenocarcinoma Embryonal, cancer of the kidney, Rhabdomysarcoma, Renal Cell Carcinoma, Large Cell Carcinoma, Nephroblastoma (Wilm's tumor), Neuroendocrine or Oat Cell carcinoma, cancer of the lower urinary tract, Adenosquamous Carcinoma, Urothelial Tumors, Undifferentiated Carcinoma, Squamous Cell Carcinoma, Carcinoma of the female genital tract, Mixed Carcinoma, Adenoacanthoma, Sarcoma, Small Cell Carcinoma, Carcinosarcoma, Leiomyosarcoma, Endometrial Stromal Sarcoma, cancer of the male genital tract, Serous Cystadenocarcinoma, Mucinous Cystadenocarcinoma, Sarcinoma, Endometrioid Tumors, Speretocytic Sarcinoma, Embyonal Carcinoma, Celioblastoma, Choriocarcinoma, Teratoma, Clear Cell Carcinoma, Leydig Cell Tumor, Unclassified Carcinoma, Sertoli Cell Tumor, Granulosa-Theca Cell Tumor, Sertoli-Leydig Cell Tumor, Disgerminoma, Undifferentiated Prostatic Carcinoma, Teratoma, Ductal Transitional carcinoma, breast cancer, Phyllodes Tumor, cancer of the bones joints and soft tissue, Paget's Disease, Multiple Myeloma, Insitu Carcinoma, Malignant Lymphoma, Invasive Carcinoma, Chondrosacrcoma, Mesenchymal Chondrosarcoma, cancer of the endocrine system, Osteosarcoma, Adenoma, Ewing Tumor, endocrine Carcinoma, Malignant Giant Cell Tumor, Meningnoma, Adamantinoma, Cramiopharlingioma, Malignant Fibrous Histiocytoma, Papillary Carcinoma, Histiocytoma, Follicular Carcinoma, Desmoplastic Fibroma, Medullary Carcinoma, Fibrosarcoma, Anoplastic Carcinoma, Chordoma, Adenoma, Hemangioendothelioma, Memangispericytoma, Pheochromocytoma, Liposarcoma, Neuroblastoma, Paraganglioma, Histiocytoma, Pineal cancer, Rhabdomysarcoms, Pineoblastoma, Leiomyosarcoma, Pineocytoma, Angiosarcoma, skin cancer, cancer of the nervous system, Melanoma, Schwannoma, Squamous cell carcinoma, Neurofibroma, Basal cell carcinoma, Malignant Periferal Nerve Sheath Tumor, Merkel cell carcinoma, Sheath Tumor, Extramamary Paget's Disease, Astrocytoma, Paget's Disease of the nipple, Fibrillary Astrocytoma, Glioblastoma Multiforme, Brain Stem Glioma, Cutaneous T-cell lymphoma, Pilocytic Astrocytoma, Xanthorstrocytoma, Histiocytosis, Oligodendroglioma, Ependymoma, Gangliocytoma, Cerebral Neuroblastoma, Central Neurocytoma, Dysembryoplastic Neuroepithelial Tumor Medulloblastoma, Malignant Meningioma, Primary Brain Lymphoma, Primary Brain Germ Cell Tumor, cancers of the eye, Squamous Cell Carcinoma, Mucoepidermoid Carcinoma, Melanoma, Retinoblastoma, Glioma, Meningioma, cancer of the heart, Myxoma, Fibroma, Lipoma, Papillary Fibroelastoma, Rhasdoyoma, or Angiosarcoma among others.
This invention provides methods for determining a prognosis for survival for a cancer patient. One method involves (a) measuring a level of methylation for one or more of the genes listed in Table 1 in a neoplastic cell-containing sample from the cancer patient, and (b) comparing the level of methylation in the sample to a reference level of methylation for the gene, wherein a low level of methylation for the gene in the sample correlates with increased survival of the patient.
In a particular embodiment, the invention provides methods for determining a prognosis for survival for a lung cancer patient. In this embodiment, the method involves (a) measuring a level of methylation for one or more of the genes listed in Table 1 and described herein to be associated with genomic targets differentially methylated in lung cancer in a neoplastic cell-containing sample from the lung cancer patient, and (b) comparing the level of methylation in the sample to a reference level of methylation for the one or more genes, wherein a low level of methylation for the one or more genes in the sample correlates with increased survival of the patient. In this embodiment, the level of methylation can be measured for one or more gene targets, including, for example, ADCYAP1, BCR, CALCA, CDH13, GDF10, HOXA5, MAGEA1, PGR, PRKCDBP, RUNX3, SCGB3A1, SERPINB5, SFN, SFTPA1, SFTPC, TERT, and TNF. Genomic markers associated with each of these genes are described throughout this application and are set forth in Table 1 along with their corresponding proble sequences.
Another method involves (a) measuring a level of methylation for one or more of the genes listed in Table 1 in a neoplastic cell-containing sample from the cancer patient, and (b) classifying the patient as belonging to either a first or second group of patients, wherein the first group of patients having low levels of methylation for a gene is classified as having an increased likelihood of survival compared to the second group of patients having high level of methylation for a gene.
In a particular embodiment, the invention provides methods for determining a prognosis for survival for a lung cancer patient. In this embodiment, the method involves (a) measuring a level of methylation for one or more of the genes listed in Table 1 and described herein to be associated with genomic targets differentially methylated in lung cancer in a neoplastic cell-containing sample from the cancer patient, and (b) classifying the patient as belonging to either a first or second group of patients, wherein the first group of patients having low levels of methylation for a gene is classified as having an increased likelihood of survival compared to the second group of patients having high level of methylation for a gene. In this embodiment, the level of methylation can be measured for one or more gene targets, including, for example, ADCYAP1, BCR, CDH13, CALCA, GDF10, HOXA5, PRKCDBP, SCGB3A1 SFTPC, TERT. Genomic markers associated with each of these genes are described throughout this application and are set forth in Table 1 along with corresponding probe sequences.
Another method involves (a) measuring a level of methylation for one or more of the genes listed in Table 1 in a neoplastic cell-containing sample from the cancer patient, and (b) classifying the patient as belonging to either a first or second group of patients, wherein the first group of patients having high levels of methylation for a gene is classified as having an increased likelihood of survival compared to the second group of patients having low level of methylation for a gene.
In a particular embodiment, the invention provides methods for determining a prognosis for survival for a lung cancer patient. In this embodiment, the method involves (a) measuring a level of methylation for one or more of the genes listed in Table 1 and described herein to be associated with genomic targets differentially methylated in lung cancer in a neoplastic cell-containing sample from the cancer patient, and (b) classifying the patient as belonging to either a first or second group of patients, wherein the first group of patients having high levels of methylation for a gene is classified as having an increased likelihood of survival compared to the second group of patients having low level of methylation for a gene. In this embodiment, the level of methylation can be measured for one or more gene targets, including, for example, MAGEA1, PGR, RUNX3, SERPINB5, SFN, SFTPA1, and TNF. Genomic markers associated with each of these genes are described throughout this application and are set forth in Table 1 along with their corresponding proble sequences.
The invention also provides a method for monitoring the effectiveness of a course of treatment for a patient with cancer. The method involves (a) determining a level of one or more of the genes listed in Table 1 in a neoplastic cell containing sample from the cancer patient prior to treatment, and (b) determining the level of methylation for the gene in a neoplastic cell-containing sample from the patient after treatment, whereby comparison of the level of methylation for the gene prior to treatment with the level of methylation for the gene after treatment indicates the effectiveness of the treatment.
In a particular embodiment, the invention also provides a method for monitoring the effectiveness of a course of treatment for a patient with lung cancer. The method involves (a) determining a level of one or more of the genes listed in Table 1 and described herein to be associated with genomic targets differentially methylated in lung cancer in a neoplastic cell containing sample from the lung cancer patient prior to treatment, and (b) determining the level of methylation for the gene in a neoplastic cell-containing sample from the patient after treatment, whereby comparison of the level of methylation for the gene prior to treatment with the level of methylation for the gene after treatment indicates the effectiveness of the treatment. In this embodiment, the level of methylation can be measured for one or more gene targets, including, for example, ADCYAP1, BCR, CALCA, CDH13, GDF10, HOXA5, MAGEA1, PGR, PRKCDBP, RUNX3, SCGB3A1, SERPINB5, SFN, SFTPA1, SFTPC, TERT, and TNF. Genomic markers associated with each of these genes are described throughout this application and are set forth in Table 1 along with their corresponding proble sequences.
For adenocarcinoma, the level of methylation is elevated compared to normal tissue samples for genomic targets corresponding to gene targets, including, for example, ADCYAP1, CDH13, GDF10, HOXA5, SCGB3A1, SERPINB5, SFN, SFTPA1, TERT; and decreased compared to normal tissue samples for genomic targets corresponding to gene targets, including, for example, MAGEA1, RUNX3, SERPINB5, SFN, SFTPA1, and TNF (Table 23 A). For squamous cell carcinoma, the level of methylation is elevated compared to normal tissue samples for genomic targets corresponding to gene targets, including, for example, ADCYAP1, BCR, CALCA, GDF10, HOXA5, PRKCDBP, SCGB3A1, SFTPC, and TERT; and decreased compared to normal tissue samples for genomic targets corresponding to gene targets, including, for example, TNF, MAGEA1, PGR, SERPINB5 and SFN (Table 23 B). Based on discovery of these differential methylation states associated with lung cancer, the skilled person can design various application of the present invention by selecting whether to measure for elevated or decreased methylation within one or more gene targets.
As used herein, the term “reference level” refers to a control level of expression of a marker used to evaluate a test level of expression of a biomarker in a neoplastic cell-containing sample of a patient. For example, when the level of methylation of one or more genes, referred to herein as “genomic targets,” in the neoplastic cells of a patient are higher than the reference level of methylation for the genes, the cells are considered to have a low level of expression of the gene. Conversely, when the level of methylation of one or more genes in the neoplastic cells of a patient are lower than the reference level, the cells are considered to have a low level of expression, of the gene. It is also possible that the level of methylation of one or more genes in the neoplastic cells of a patient can be higher than the reference level and the cells are considered to have a high level of expression, of the gene. Furthermore, the level of methylation of one or more genes in the neoplastic cells of a patient can be lower than the reference level and the cells are considered to have a low level of expression, of the gene.
A reference level can be determined based on reference samples collected from age-matched normal classes of adjacent tissues, and with normal peripheral blood lymphocytes. The reference level can be determined by any of a variety of methods, provided that the resulting reference level accurately provides a level of a marker above which exists a first group of patients having a different probability of survival than that of a second group of patients having levels of the biomarker below the reference level. The reference level can be determined by, for example, measuring the level of expression of a biomarker in non-tumorous cells from the same tissue as the tissue of the neoplastic cells to be tested. The reference level can also be a level of a biomarker of in vitro cultured cells which can be manipulated to simulate tumor cells, or can be manipulated in any other manner which yields expression levels which accurately determine the reference level. The reference level can also be determined by comparison of the level of a biomarker, such as methylation of one or more genes, in populations of patients having the same cancer. This can be accomplished, for example, by histogram analysis, in which an entire cohort of patients are graphically presented, wherein a first axis represents the level of the biomarker, and a second axis represents the number of patients in the cohort whose neoplastic cells express the biomarker at a given level.
Two or more separate groups of patients can be determined by identification of subset populations of the cohort which have the same or similar levels of the biomarker. Determination of the reference level can then be made based on a level which best distinguishes these separate groups. A reference level also can represent the levels of two or more markers. Two or more markers can be represented, for example, by a ratio of values for levels of each biomarker. The reference level can be a single number, equally applicable to every patient, or the reference level can vary, according to specific subpopulations of patients. For example, older individuals might have a different reference level than younger individuals for the same cancer. In another example, the reference level might be a certain ratio of a biomarker in the neoplastic cells of a patient relative to the biomarker levels in non-tumor cells within the same patient. Thus the reference level for each patient can be proscribed by a reference ratio of one or more genomic markers, such as methylation of one or more genes, wherein the reference ratio can be determined by any of the methods for determining the reference levels described herein.
It is understood that the reference level has to correspond to the level of one or more methylated genomic CpG dinucleotide sequences present in a corresponding sample that allows comparison to the desired phenotype. For example, in a diagnostic application a reference level can be based on a sample that is derived from a cancer-free origin so as to allow comparison to the biological test sample for purposes of diagnosis. In a method of staging a cancer it can be useful to apply in parallel a series of reference levels, each based on a sample that is derived from a cancer that has been classified based on parameters established in the art, for example, phenotypic or cytological characteristics, as representing a particular cancer stage so as to allow comparison to the biological test sample for purposes of staging. In addition, progression of the course of a condition can be determined by determining the rate of change in the level or pattern of methylation of genomic CpG dinucleotide sequences by comparison to reference levels derived from reference samples that represent time points within an established progression rate. It is understood, that the user will be able to select the reference sample and establish the reference level based on the particular purpose of the comparison.
As used herein, the term “neoplastic cell” refers to any cell that is transformed such that it proliferates without normal homeostatic growth control. Such cells can result in a benign or malignant lesion of proliferating cells. Such a lesion can be located in a variety of tissues and organs of the body. Exemplary types of cancers from which a neoplastic cell can be derived are set forth above.
As used herein, the term “cancer” is intended to mean a class of diseases characterized by the uncontrolled growth of aberrant cells, including all known cancers, and neoplastic conditions, whether characterized as malignant, benign, soft tissue or solid tumor. Specific cancers include digestive and gastrointestinal cancers, such as anal cancer, bile duct cancer, gastrointestinal carcinoid tumor, colon cancer, esophageal cancer, gallbladder cancer, liver cancer, pancreatic cancer, rectal cancer, appendix cancer, small intestine cancer and stomach (gastric) cancer; breast cancer; ovarian cancer; lung cancer; renal cancer; CNS 30 cancer; leukemia and melanoma. By exemplification, a list of known cancers is set forth above.
As used herein, the term “sample” is intended to mean any biological fluid, cell, tissue, organ or portion thereof, that contains genomic DNA suitable, for methylation detection via the invention methods. A test sample can include or be suspected to include a neoplastic cell, such as a cell from the colon, rectum, breast, ovary, prostate, kidney, lung, blood, brain or other organ or tissue that contains or is suspected to contain a neoplastic cell. The term includes samples present in an individual as well as samples obtained or derived from the individual. For example, a sample can be a histologic section of a specimen obtained by biopsy, or cells that are placed in or adapted to tissue culture. A sample further can be a subcellular fraction or extract, or a crude or substantially pure nucleic acid molecule or protein preparation. A reference sample can be used to establish a reference level and, accordingly, can be derived from the source tissue that meets having the particular phenotypic characteristics to which the test sample is to be compared.
A sample may be obtained in a variety of ways known in the art. Samples may be obtained according to standard techniques from all types of biological sources that are usual sources of genomic DNA including, but not limited to cells or cellular components which contain DNA, cell lines, biopsies, bodily fluids such as blood, sputum, stool, urine, cerebrospinal fluid, ejaculate, tissue embedded in paraffin such as tissue from eyes, intestine, kidney, brain, heart, prostate, lung, breast or liver, histological object slides, and all possible combinations thereof. In particular embodiments, the methods described herein can be performed on one or more samples from lung cancer patients at the time of bronchoscopy: bronchoaveolar lavage (BAL) fluid, sputum, or whole blood. The multiplexed methylation analysis system can be applied to early and non-invasive diagnosis of lung cancer, or to monitor cancer progression or response to treatment. In general, this technology can be useful for comprehensive DNA methylation analyses in large populations, with potential application to the classification or diagnosis of a broad range of cancers and other diseases.
A suitable biological sample can be sourced and acquired subsequent to the formulation of the diagnostic aim of the marker. A sample can be derived from a population of cells or from a tissue that is predicted to be afflicted with or phenotypic of the condition. The genomic DNA can be derived from a high-quality source such that the sample contains only the tissue type of interest, minimum contamination and minimum DNA fragmentation. In particular, samples should be representative of the tissue or cell type of interest that is to be handled by the diagnostic assay. It is understood that samples can be analyzed individually or pooled depending on the purpose of the user. In addition, a population or set of samples from an individual source can be analyzed to maximize confidence in the results and can be a sample set size of 10, 15, 20, 25, 50, 75, 100, 150 or sample set sizes in the hundreds.
In subsequent steps of the method, the methylation levels of CpG positions are compared to a reference sample, to identify differentially methylated CpG positions. Each class may be further segregated into sets according to predefined parameters to minimize the variables between the at least two classes. In the following stages of the method, all comparisons of the methylation status of the classes of tissue, are carried out between the phenotypically matched sets of each class. Examples of such variables include, age, ethnic origin, sex, life style, patient history, drug response etc.
As used herein, the term “disease-free survival” refers to the lack of tumor recurrence and/or spread and the fate of a patient after diagnosis, for example, a patient who is alive without tumor recurrence.
The phrase “overall survival” refers to the fate of the patient after diagnosis, regardless of whether the patient has a recurrence of the tumor. As used herein, the term “risk of recurrence” refers to the probability of tumor recurrence or spread in a patient subsequent to diagnosis of cancer, wherein the probability is determined according to the process of the invention. Tumor recurrence refers to further growth of neoplastic or cancerous cells after diagnosis of cancer. Particularly, recurrence can occur when further cancerous cell growth occurs in the cancerous tissue. Tumor spread refers to dissemination of cancer cells into local or distant tissues and organs, for example during tumor metastasis. Tumor recurrence, in particular, metastasis, is a significant cause of mortality among patients who have undergone surgical treatment for cancer. Therefore, tumor recurrence or spread is correlated with disease free and overall patient survival.
The methods of the invention can be applied to the characterization, classification, differentiation, grading, staging, diagnosis, or prognosis of a condition characterized by a pattern of one or more methylated genomic CpG dinucleotide sequences that is distinct from the pattern of one or more methylated genomic CpG dinucleotide sequences exhibited in the absence of the condition. A condition that is suitable for practicing the methods of the invention can be, for example, cell proliferative disorder or predisposition to cell proliferative disorder; metabolic malfunction or disorder; immune malfunction, damage or disorder; CNS malfunction, damage or disease; symptoms of aggression or behavioural disturbance; clinical, psychological and social consequences of brain damage; psychotic disturbance and personality disorder; dementia or associated syndrome; cardiovascular disease, malfunction and damage; malfunction, damage or disease of the gastrointestinal tract; malfunction, damage or disease of the respiratory system; lesion, inflammation, infection, immunity and/or convalescence; malfunction, damage or disease of the body as an abnormality in the development process; malfunction, damage or disease of the skin, the muscles, the connective tissue or the bones; endocrine and metabolic malfunction, damage or disease; headache or sexual malfunction, and combinations thereof.
Methylation of CpG dinucleotide sequences can be measured using any of a variety of techniques used in the art for the analysis of specific CpG dinucleotide methylation status. For example, methylation can be measured by employing a restriction enzyme based technology, which utilizes methylation sensitive restriction endonucleases for the differentiation between methylated and unmethylated cytosines. Restriction enzyme based technologies include, for example, restriction digest with methylation-sensitive restriction enzymes followed by Southern blot analysis, use of methylation-specific enzymes and PCR, restriction landmark genomic scanning (RLGS) and differential methylation hybridization (DMH).
Restriction enzymes characteristically hydrolyze DNA at and/or upon recognition of specific sequences or recognition motifs that are typically between 4- to 8-bases in length. Among such enzymes, methylation sensitive restriction enzymes are distinguished by the fact that they either cleave, or fail to cleave DNA according to the cytosine methylation state present in the recognition motif, in particular, of the CpG sequences. In methods employing such methylation sensitive restriction enzymes, the digested DNA fragments can be separated, for example, by gel electrophoresis, on the basis of size, and the methylation status of the sequence is thereby deduced, based on the presence or absence of particular fragments. Preferably, a post-digest PCR amplification step is added wherein a set of two oligonucleotide primers, one on each side of the methylation sensitive restriction site, is used to amplify the digested genomic DNA. PCR products are not detectable where digestion of the subtended methylation sensitive restriction enzyme site occurs. Techniques for restriction enzyme based analysis of genomic methylation are well known in the art and include the following: differential methylation hybridization (DMH) (Huang et al., Human Mol. Genet. 8, 459-70, 1999); Not I-based-differential methylation hybridization (see e.g., WO 02/086163 A1); restriction landmark genomic scanning (RLGS) (Plass et al., Genomics 58:254-62, 1999); methylation sensitive arbitrarily primed PCR (AP-PCR) (Gonzalgo et al., Cancer Res. 57: 594-599, 1997); methylated CpG island amplification (MCA) (Toyota et. al., Cancer Res. 59: 2307-2312, 1999). Other useful methods for detecting genomic methylation are described, for example, in US Pat. App. pub. No. 2003/0170684 or WO 04/05122.
Methylation of CpG dinucleotide sequences also can be measured by employing cytosine conversion based technologies, which rely on methylation status-dependent chemical modification of CpG sequences within isolated genomic DNA, or fragments thereof, followed by DNA sequence analysis. Chemical reagents that are able to distinguish between methylated and non methylated CpG dinucleotide sequences include hydrazine, which cleaves the nucleic acid, and bisulfite treatment. Bisulfite treatment followed by alkaline hydrolysis specifically converts non-methylated cytosine to uracil, leaving 5-methylcytosine unmodified as described by Olek A., Nucleic Acids Res. 24:5064-6, 1996 or Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992). The bisulfite-treated DNA can subsequently be analyzed by conventional molecular techniques, such as PCR amplification, sequencing, and detection comprising oligonucleotide hybridization.
Techniques for the analysis of bisulfite treated DNA can employ methylation-sensitive primers for the analysis of CpG methylation status with isolated genomic DNA as described by Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996, and in U.S. Pat. Nos. 5,786,146 and 6,265,171. Methylation sensitive PCR (MSP) allows for the detection of a specific methylated CpG position within, for example, the regulatory region of a gene. The DNA of interest is treated such that methylated and non-methylated cytosines are differentially modified, for example, by bisulfite treatment, in a manner discernable by their hybridization behavior. PCR primers specific to each of the methylated and non-methylated states of the DNA are used in a PCR amplification. Products of the amplification reaction are then detected, allowing for the deduction of the methylation status of the CpG position within the genomic DNA. Other methods for the analysis of bisulfite treated DNA include methylation-sensitive single nucleotide primer extension (Ms-SNuPE) (Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531, 1997; and see U.S. Pat. No. 6,251,594), and the use of real time PCR based methods, such as the art-recognized fluorescence-based real-time PCR technique MethyLightTM (Eads et al., Cancer Res. 59:2302-2306, 1999; U.S. Pat. No. 6,331,393 to Laird et al.; and see Heid et al., Genome Res. 6:986-994, 1996). It is understood that a variety of methylation assay methods can be used for the determination of the methylation status of particular genomic CpG positions. Methods which require bisulfite conversion include, for example, bisulfite sequencing, methylation-specific PCR, methylation-sensitive single nucleotide primer extension (Ms-SnuPE), MALDI mass spectrometry and methylation-specific oligonucleotide arrays and are described, for example, in U.S. patent application Ser. No. 10/309,803 and international application International Patent Application No.: PCT/US03/38582.
In one embodiment, methylation of genomic CpG positions in a sample can be detected using an array of probes. In particular embodiments, a plurality of different probe molecules can be attached to a substrate or otherwise spatially distinguished in an array. Exemplary arrays that can be used in the invention include, without limitation, slide arrays, silicon wafer arrays, liquid arrays, bead-based arrays and others known in the art or set forth in further detail below. In preferred embodiments, the methods of the invention can be practiced with array technology that combines a miniaturized array platform, a high level of assay multiplexing, and scalable automation for sample handling and data processing.
An array of arrays, also referred to as a composite array, having a plurality of individual arrays that is configured to allow processing of multiple samples can be used. Exemplary composite arrays that can be used in the invention are described in U.S. Pat. No. 6,429,027 and US 2002/0102578 and include, for example, one component systems in which each array is located in a well of a multi-well plate or two component systems in which a first component has several separate arrays configured to be dipped simultaneously into the wells of a second component. A substrate of a composite array can include a plurality of individual array locations, each having a plurality of probes and each physically separated from other assay locations on the same substrate such that a fluid contacting one array location is prevented from contacting another array location. Each array location can have a plurality of different probe molecules that are directly attached to the substrate or that are attached to the substrate via rigid particles in wells (also referred to herein as beads in wells).
In a particular embodiment, an array substrate can be fiber optical bundle or array of bundles, such as those generally described in U.S. Pat. Nos. 6,023,540, 6,200,737 and 6,327,410; and PCT publications WO9840726, WO9918434 and WO9850782. An optical fiber bundle or array of bundles can have probes attached directly to the fibers or via beads. Other substrates having probes attached to a substrate via beads are described, for example, in US 2002/0102578. A substrate, such as a fiber or silicon chip, can be modified to form discrete sites or wells such that only a single bead is associated with the site or well. For example, when the substrate is a fiber optic bundle, wells can be made in a terminal or distal end of individual fibers by etching, with respect to the cladding, such that small wells or depressions are formed at one end of the fibers. Beads can be non-covalently associated in wells of a substrate or, if desired, wells can be chemically functionalized for covalent binding of beads. Other discrete sites can also be used for attachment of particles including, for example, patterns of adhesive or covalent linkers. Thus, an array substrate can have an array of particles each attached to a patterned surface.
In a particular embodiment, a surface of a substrate can include physical alterations to attach probes or produce array locations. For example, the surface of a substrate can be modified to contain chemically modified sites that are useful for attaching, either-covalently or non-covalently, probe molecules or particles having attached probe molecules. Chemically modified sites can include, but are not limited to the linkers and reactive groups set forth above. Alternatively, polymeric probes can be attached by sequential addition of monomeric units to synthesize the polymeric probes in situ. Probes can be attached using any of a variety of methods known in the art including, but not limited to, an ink-jet printing method as described, for example, in U.S. Pat. Nos. 5,981,733; 6,001,309; 6,221,653; 6,232,072 or 6,458,583; a spotting technique such as one described in U.S. Pat. No. 6,110,426; a photolithographic synthesis method such as one described in U.S. Pat. No. 6,379,895 or 5,856,101; or printing method utilizing a mask as described in U.S. Pat. No. 6,667,394. Accordingly, arrays described in the aforementioned references can be used in a method of the invention.
The size of an array used in the invention can vary depending on the probe composition and desired use of the array. Arrays containing from about 2 different probes to many millions can be made. Generally, an array can have from two to as many as a billion or more probes per square centimeter. Very high density arrays are useful in the invention including, for example, those having from about 10,000,000 probes/cm2 to about 2,000,000,000 probes/cm2 or from about 100,000,000 probes/cm2 to about 1,000,000,000 probes/cm2. High density arrays can also be used including, for example, those in the range from about 100,000 probes/cm2 to about 10,000,000 probes/cm2 or about 1,000,000 probes/cm2 to about 5,000,000 probes/cm2. Moderate density arrays useful in the invention can range from about 10,000 probes/cm2 to about 100,000 probes/cm2, or from about 20,000 probes/cm2 to about 50,000 probes/cm2. Low density arrays are generally less than 10,000 probes/cm2 with from about 1,000 probes/cm2 to about 5,000 probes/cm2 being useful in particular embodiments. Very low density arrays having less than 1,000 probes/cm2, from about 10 probes/cm2 to about 1000 probes/cm2, or from about 100 probes/cm2 to about 500 probes/cm2 are also useful in some applications.
Thus, the invention provides a robust and ultra high-throughput technology for simultaneously measuring methylation at many specific sites in a genome. The invention further provides cost-effective methylation profiling of thousands of samples in a reproducible, well-controlled system. In particular the invention allows implementation of a process, including sample preparation, bisulfite treatment, genotyping-based assay and PCR amplification that can be carried out on a robotic platform.
The methods of the invention can be carried out at a level of multiplexing that is 96-plex or even higher including, for example, as high as 1,500-plex. An advantage of the invention is that the amount of genomic DNA used for detection of methylated sequences is low including, for example, less that 1 ng of genomic DNA per locus. In one embodiment, the throughput of the methods can be 96 samples per run, with 1,000 to 1,500 methylation assays per sample (144,000 data points or more per run). In the embodiment exemplified herein, the system is capable of carrying out as many as 10 runs per day or more. A further object of the invention is to provide assays to survey methylation status the 5′-regulatory regions of at least 1,000 human genes per sample. Particular genes of interest are tumor suppressor genes or other cancer-related genes, as well as genes identified through RNA profiling.
Therefore, the invention makes available diagnostic and/or prognostic assays for the analysis of the methylation status of CpG dinucleotide sequence positions as markers for disease or disease-related conditions. As exemplified herein for lung cancer, the invention provides a systematic method for the identification, assessment and validation of genomic targets as well as a systematic means for the identification and verification of multiple condition relevant CpG positions to be used alone, or in combination with other CpG positions, for example, as a panel or array of markers, that form the basis of a clinically relevant diagnostic or prognostic assay. The inventive method enables differentiation between two or more phenotypically distinct classes of biological matter and allows for the comparative analysis of the methylation patterns of CpG dinucleotides within each of the classes.
A further object of the invention is to provide assays for specific identifying methylation patterns in different cancer types and cancer stages. The invention provides assays for specific methylation patterns characteristic of different cancer types and cancer stages. For example, the invention provides genomic targets that give rise to differential methylation patterns characteristic of lung cancer in general, adenocarcinoma of the lung, squamous cell carcinoma of the lung as well as methylation patterns that can distinguish between adenocarcinoma and squamous cell carcinoma of the lung. As described herein, the genomic targets designated SEQ ID NOS: 57, 58, 59, 72, 76, 139, 140, 141, 174, 264, 265, 266, 293, 294, 305, 309, 313, 314, 315, 345, 350, 353, 360, 364, 371 and 373 show differential methylation patterns characteristic of lung cancer. The subset of genomic targets designated SEQ ID NOS: 59, 140, 266, 293, 294, 140, 174, 371, 313, 314, 353, 360, 364 and 373 shows differential methylation patterns characteristic of adenocarcinoma of the lung, while the genomic targets designated SEQ ID NOS: 57, 58, 72, 76, 139, 140, 141, 174, 264, 265, 266, 294, 305, 309, 313, 314, 315, 345, 350, 360 and 371 show differential methylation patterns characteristic of squamous cell carcinoma of the lung. The genomic targets designated SEQ ID NOS: 59, 293, 353, 364 and 373 show differential methylation patterns in adenocarcinoma of the lung, but not in squamous cell carcinoma of the lung and can thus be used to distinguish between the two types of lung cancer. The genomic targets designated SEQ ID NOS: 57, 58, 59, 72, 76, 139, 141, 264, 265, 293, 309, 315, 345, 350, 353, 3-64 and 373 show differential methylation patterns in squamous cell carcinoma of the lung, but not in adenocarcinoma of the lung and can also be used to distinguish between the two types of lung cancer.
A further object of the invention is to provide software to retrieve and annotate CpG island sequence information, design and analyze primers, track sample information, and analyze and report results obtained from methylation profiling methods of the invention. An advantage of the invention is that it provides a high throughput methylation analysis system that can be commercialized, both through a service business—in which customers can provide samples and a gene list (CpG site list) for analysis in the methods—and through products that can used in standard laboratory conditions.
RLGS profiling of the methylation pattern of 1184 CpG islands in 98 primary human tumors revealed that the total number of methylated sites is variable between and in some cases within different tumor types, suggesting there may be methylation subtypes within tumors having similar histology (Costello, et al., Nat Genet. 24(2): 132-8 (2000)). Aberrant methylation of some of these genes correlates with loss of gene expression. Based on these observations, it should be feasible to use the methylation pattern of a sizable group of tumor suppressor genes or other cancer-related genes to classify and predict different kinds of cancer, or the same type of cancer in different stages. It promises to provide a useful tool for cancer diagnosis, or preferably, for detection of premalignant changes. When combined with the development of sensitive, non-invasive methods (e.g. a blood test; indeed, circulating tumor nucleic acids in blood have been demonstrated to reflect the biologic characteristics of tumors (Cui, et al., Science 299 (5613): 1753-5 (2003)), to detect such methylation signatures) this may provide a viable method to screen subjects at risk for cancer as well as to monitor cancer progression and response to treatment.
Because methylation detection interrogates genomic DNA, but not RNA or protein, it offers several technological advantages in a clinical diagnostic setting: (1) readily available source materials. This is particularly important for prognostic research, when only DNA can be reliably extracted from archived paraffin-embedded samples for study; (2) capability for multiplexing, allowing simultaneous measurement of multiple targets to improve assay specificity; (3) easy amplification of assay products to achieve high sensitivity; (4) robust measurement in tumors that arise from methylation inactivation of one allele of tumor suppressor genes—a process called “functional haploinsufficiency” (Balmain, et al., Nat Genet. 33 Suppl: 238-44 (2003)). It is much easier to detect a methylation change (from negative to positive) than to detect a two-fold gene expression change in these tumors. In summary, when combined with RNA-based gene expression profiling and/or protein-based immunoassays, DNA methylation profiling should provide a sensitive, accurate and robust tool for cancer diagnosis and prognosis (Wong, et al., Curr Oncol Rep. 4(6): 471-7 (2002)).
The present invention is directed to a method for the identification of differentially methylated CpG dinucleotides within genomic DNA that are particularly informative with respect to disease states. These may be used either alone or as components of a gene panel in diagnostic and/or prognostic assays.
In particular embodiments, the invention is directed to methods of prediction and diagnosis of conditions characterized by a pattern of one or more methylated genomic CpG dinucleotide sequences that is distinct from the pattern of methylated genomic CpG dinucleotide sequences exhibited in the absence of the particular condition, for example, cell proliferative disorders, such as cancer; dysfunctions, damages or diseases of the central nervous system (CNS), including aggressive symptoms or behavioral disorders; clinical, psychological and social consequences of brain injuries; psychotic disorders and disorders of the personality, dementia and/or associates syndromes; cardiovascular diseases, malfunctions or damages; diseases, malfunctions or damages of the gastrointestine diseases; malfunctions or damages of the respiratory system; injury, inflammation, infection, immunity and/or reconvalescence, diseases; malfunctions or damages as consequences of modifications in the developmental process; diseases, malfunctions or damages of the skin, muscles, connective tissue or bones; endocrine or metabolic diseases malfunctions or damages; headache; and sexual malfunctions; or combinations thereof.
It is understood that modifications which do not substantially affect the activity of the various embodiments of this invention are also included within the definition of the invention provided herein. Accordingly, the following examples are intended to illustrate but not limit the present invention.
EXAMPLE I
Design of Target Nucleic Acid Probes
This Example shows design of target nucleic acid probes for detection of genomic loci.
First, a human gene promoter database was prepared that includes all CpG regions of potential interest for methylation profiling. A fully automated SNP genotyping assay design program was adapted for methylation application and CpG islands of interest are selected and “converted by bisulfite” computationally. For each CpG locus, three probes are designed: two allele-specific oligonucleotides, one corresponding to the methylated, and the other to the unmethylated state of the CpG site and one locus-specific oligo (FIG. 4). If other CpG loci are present in the close vicinity of the chosen CpG site, a wobble base [A or G] is used for the corresponding probe position. Assays for more than 60 CpG sites from 20 different genes were designed, mostly selected from the methylation database on the world-wide-web at methdb.de. Approximately half of the sites were used in the assay development.
EXAMPLE II
Development of Internal Controls and Confirmation of Completeness of Bisulfite Conversion
This example shows the development of internal controls that allow optimization of protocols, determination of assay specificity, troubleshooting, and evaluation of overall assay performance.
Plasmids pUC19, pACYC184 and phage phiX174 were selected to serve as control DNAs. These DNAs can be spiked into the genomic DNA assays to provide internal controls, and would not interfere with human genomic DNA reactions. It is easy to prepare completely unmethylated plasmid DNAs and then methylate them in vitro using Sss I (CpG) methylase to produce substrates with known methylation status. Plasmids can be methylated virtually to completion. The quality of in vitro methylation was tested by restriction enzyme digestion of unmethylated and methylated DNAs using the methylation sensitive enzyme Hpa II and its isoschisomer Msp I, which is not sensitive to methylation. It was not possible to detect any bands resulting from restriction digest on the agarose gel after incubation of methylated pUC19, pACYC184 and phiX174 with Hpa II for two hours at 37° C., while the unmethylated DNAs were completely digested. Both methylated and unmethylated DNAs were completely digested by Msp I.
Plasmid controls (unmethylated, methylated or mixed at a 1:1 ratio) were spiked into human genomic DNA at a 1:1 molar ratio (at approximately 2-4 pg plasmid DNA/1 μg gDNA, depending on the plasmid size), and were used in every methylation experiment to monitor both bisulfite conversion efficiency and accuracy of methylation detection. As shown in FIG. 5, unmethylated, semi-methylated and fully-methylated loci can be easily distinguished by the assay.
The utility of bisulfite conversion of DNA for methylation detection is based on the different sensitivity of cytosine and 5-methylcytosine to deamination by bisulfite. Under acidic conditions, cytosine undergoes conversion to uracil, while methylated cytosine remains unreactive. An efficient bisulfite conversion protocol facilitates a high-throughput methylation profiling assay. Incomplete conversion of cytosine to uracil by bisulfite can result in appearance of false-positive signals for 5-methylcytosine, and reduce the overall quality of the assay data. In order to monitor the effectiveness of bisulfite treatment, a set of oligonucleotides (a standard set of SNP genotyping probes) designed for unconverted genomic DNA sequences was included with the plasmid control oligos in the assay. As shown in FIG. 6, if bisulfite conversion is successful, the signal from oligonucleotides targeted to the unconverted DNA (the SNP set) will disappear, and signals from oligonucleotides targeted to the converted DNA will be present. Incomplete conversion will result in low and inconsistent signals across all targeted loci.
Data Processing
Development of a robust and high-throughput method for simultaneous measurement of methylation at many specific sites in many samples requires a highly efficient analysis and data export process. Each data point in the methylation assay can be represented as a ratio of the fluorescent signals from M (methylated) and U (unmethylated) specific PCR products after array hybridization. This value indicates the methylation status of the CpG locus and may range from 0 in the case of completely unmethylated sites to 1 in completely methylated sites. The value also can be visually presented as a red/green/yellow plot (FIG. 7). In addition, each locus is characterized by a locus intensity value, which allows filtering of failed loci. This combination of numerical and color outputs allows for quick comparison of genes and samples of interest, and processing of thousands of loci across hundreds of samples.
Reference Samples for Genome-Wide Methylation Profiling
To calibrate quantitative measurements of methylation, “fully methylated” and “unmethylated” genomic templates were developed (FIG. 8). The fully unmethylated templates were generated by genome-wide amplification of human genomic DNA. With more than 1000-fold amplification, any endogenous methylation is effectively “diluted/erased”, and the sample can be used as an “unmethylated” reference DNA. Methylated templates were generated by in vitro methylation of amplified gDNA using Sss I (CpG-methylase) enzyme. The reproducibility of the methylation detection method was confirmed by typing 27 human gene-specific CpG sites in these reference DNAs over 30 times. A high degree of reproducibility was obtained (FIG. 8).
Methylation Profiles of Randomly Selected Human Genomic DNAs
We have also monitored a set of CpG sites in randomly selected human genomic DNAs. The DNA samples were obtained from the Human Genetic Cell Repository, Coriell Institute for Medical Research, NJ. In this experiment, we measured 7 females, 5 males and 3 of unknown gender specificity; each was done in duplicate and the results are shown in columns next to each other in FIG. 9. Distinguishable methylation patterns were obtained with DNAs isolated from male and female cell lines, especially in genes that are located on the X-chromosome (FIG. 9). The overall methylation profiles are quite reproducible among the duplicates.
Assay Reproducibility
One of the most important assay characteristics is its reproducibility. This was addressed by monitoring several plasmid loci with known methylation status across multiple replicates in several independent experiments. The experimental setup also allowed for estimation of the assay accuracy. A mixture of unmethylated, methylated and 1:1 mixed plasmid DNAs was prepared, and spiked into 1 μg of human genomic DNA in a 1:1 molar ratio. Each experiment included three plasmid mixtures:


















1
pUC19 U
pACYC 184 M
phiX174 U:M



2
pUC19 U:M
pACYC 184 U
phiX174 M



3
pUC19 M
pACYC 184 U:M
phiX174 U










A typical experiment included 32 replicates of each set of the three plasmid mixtures assayed on a 96 fiber bundle array matrix. Results of the reproducibility study are summarized in Table 2, which involve (79+96+95+95) replicates×14 CpG sites=5,110 measurements. It is noticeable that some loci (e.g. phi—4972) tend to perform better than others (e.g. pACYC—360). There are also some performance variations from experiment to experiment. The overall call accuracy is averaged at  ̃97% with a high call rate of 99.6%. The accuracy was calculated using our existing SNP genotyping software, which uses a clustering algorithm to determine if a locus is methylated, unmethylated or semi-methylated (FIG. 10).







TABLE 5





Methylation measurement with 14 plasmid CpG loci.



















Experiment 1,
Experiment 2,
Experiment 3,



79 replicates
96 replicates
95 replicates

















No call,
Correct,
Wrong
No call,
Correct,
Wrong
No call,
Correct,
Wrong


Locus
%
%
call, %
%
%
call, %
%
%
call, %





pUC_229
0
100
0
0
93.75
6.25
0
93.68
6.32


pUC_964
0
100
0
0
89.58
10.42
0
94.74
5.26


pUC_1488
1.27
100
0
0
96.88
3.13
0
91.58
8.42


pUC_2077
0
100
0
8.33
88.64
11.36
6.32
91.01
8.99


pUC_2575
0
100
0
0
88.54
11.46
0
90.53
9.47


pACYC_167
0
100
0
0
88.54
11.46
0
97.89
2.11


pACYC_360
0
98.73
1.27
1.04
74.74
25.26
0
91.58
8.42


pACYC_1289
0
100
0
1.04
89.47
10.53
0
95.79
4.21


pACYC_1481
0
100
0
1.04
92.63
7.37
0
95.79
4.21


phi_2191
0
100
0
0
98.96
1.04
0
95.79
4.21


phi_3050
0
100
0
0
100
0
0
98.95
1.05


phi_3687
0
97.47
2.53
2.08
94.68
5.32
3.16
92.39
7.61


phi_4128
0
100
0
0
100
0
0
98.95
1.05


phi_4972
0
100
0
0
100
0
0
100
0


Total:
0.09
99.73
0.27
0.97
92.60
7.40
0.68
94.90
5.10













Experiment 4,




95 replicates
Summary

















No call,
Correct,
Wrong
No call,
Wrong
Correct,
Correct,



Locus
%
%
call, %
%
call, %
%
CV (%)






pUC_229
0
100
0
0
3.14
96.86
3.75



pUC_964
0
100
0
0
3.92
96.08
5.20



pUC_1488
0
100
0
0.32
2.89
97.11
4.09



pUC_2077
0
96.84
3.16
3.66
5.88
94.12
5.55



pUC_2575
0
100
0
0
5.23
94.77
6.43



pACYC_167
0
100
0
0
3.39
96.61
5.66



pACYC_360
0
97.89
2.11
0.26
9.26
90.73
12.27



pACYC_1289
0
97.89
2.11
0.26
4.21
95.79
4.75



pACYC_1481
0
100
0
0.26
2.89
97.11
3.69



phi_2191
0
100
0
0
1.31
98.69
2.02



phi_3050
0
98.95
1.05
0
0.53
99.47
0.61



phi_3687
0
97.89
2.11
1.31
4.39
95.61
2.69



phi_4128
0
98.95
1.05
0
0.53
99.47
0.61



phi_4972
0
100
0
0
0
100
0



Total:
0
99.17
0.83
0.43
3.40
96.60
4.09





Note:


Experiment 1 included 80 replicates of bisulfite converted DNA and 16 replicates of unconverted samples for background control. The other 3 experiments included only bisulfite converted DNA.






As demonstrated above, assay sensitivity and specificity were shown to be sufficient to detect changes in methylation status at more than 50 loci simultaneously in 1 microgram of human genomic DNA. A minimum of three levels of methylation was clearly distinguished: fully methylated, hemi-methylated, and unmethylated. The ability to distinguish three levels of methylation was confirmed by using plasmid control DNAs with known methylation status, spiked into human genomic DNA in a 1:1 molar ratio. Furthermore, reproducibility of methylation determination was shown to be 96.6% (which is a more stringent measurement than reproducibility), at a call rate exceeding 90% (Table 2). A set of three reference samples for 14 CpG sites was analyzed in four independent experiments. The number of measurements in each experiment was 1106, 1344, 1330 and 1330 respectively.
Overall, this example demonstrates the development of a microtiter plate based, high throughput bisulfite conversion, which as described in the following Example, can be fully integrated into the SNP genotyping system for high-throughput methylation profiling. The methylation assays can be enlarged in both the scope and capacity of methylation detection with as many as 1500 methylation sites in each assay, while using reduced amounts of genomic DNA. Since the data collection and processing are largely automated, it is possible to do at least one, two, five or ten array matrix runs per day per system, with each run providing data from 96 samples at a time, creating a highly scalable system where multiple instruments can be run in parallel if needed.
EXAMPLE III
Integration of Microtiter Plate Based, High-Throughput Bisulfite Conversion Into Genotyping System for High Throughput Methylation Profiling
This example demonstrates the integration of a microtiter plate based, high throughput bisulfite conversion as described in Example II, into the SNP genotyping system for high-throughput methylation profiling.
The assay optimization process includes measuring the array-to-array experimental variability, both within a matrix and between matrices, and dissect out contributions to variability from samples, sample processing (bisulfite conversion, allele-specific extension, ligation, and PCR amplification), and array hybridization, using carefully designed controls. The resulting data also is useful in determining thresholds of significance for analyzing and interpreting results.
Improve Assay Performance with Fully “Methylated” and “Un-Methylated” Genomic Templates
Currently, all the methods used to validate methylation status of any methylation site in any given sample, such as bisulfite sequencing and methylation-specific PCR, are either laborious and time consuming, or inaccurate and expensive. Indeed, a high performance methylation quantitation (or calibration) system is needed for large-scale genome-wide methylation assay development and validation. As described in Example II, a system has been developed that uses fully “methylated” and “un-methylated” genomic templates.
The un-methylated templates can be generated by genome-wide amplification of any genomic DNA, using random primed DNA amplification with enzymes such as Phi-29, Taq DNA polymerase or Klenow Fragment (3′->5′-exo-). After this amplification, the endogenous DNA methylation is diluted at least 100 to 1000-fold, effectively rendering the amplified genome DNA “un-methylated”.
The methylated templates can be generated by in vitro methylation using the SssI CpG-methylase. However, not all the CpG sites can be fully methylated in vitro. Some of these can result from base substitution at the CpG sites in the DNA tested, in particular, these sites become “methylation-resistant”. It is well known that CpG sites are mutation hot spots. In order to achieve higher levels of genomic DNA methylation, different experimental conditions are tested, for example, varying the concentration of magnesium in the methylation reaction and using multiple methylases.
The above-described templates are used for assay development and calibration. The fully methylated and unmethylated genomic DNA templates can be mixed at different ratio, for example, 0%, 25%, 50%, 75%, and 100% of methylated template. Methylation assays on these mixed templates generate a calibration curve for quantitative methylation measurement in unknown samples for any CpG site in the genome. The mixed templates can also be used to determine the sensitivity of methylation detection, for example, what percentage of the methylated template can be detected in the presence of un-methylated template.
Since this approach can be used to evaluate the methylation assay designed for any specific CpG site across the entire genome, it greatly aids methylation assay development such as assay specificity. For example, if an assay gives the same methylation “report” for both the unmethylated and the methylated DNA templates, it confirms that the assay is not working properly.
Finally, protocols for measuring DNA methylation in formalin-fixed, paraffin-embedded samples are created. Robust large-scale DNA methylation detection on these samples opens up a huge sample resource, for which clinical history is already available.
Improvement of Assay Sensitivity by Genomic DNA Amplification After Bisulfite Conversion
In order to improve assay sensitivity, DNA is amplified after bisulfite conversion, using a random priming approach. But, instead of using all possible random primers, advantage is taken of the unique sequence feature of genomic DNAs after bisulfite treatment, i.e. that un-methylated cytosines are converted to uracil. Therefore, these DNA templates contain mostly three bases, A, G and T (and U). The genomic amplification is carried out using (i) through (iii) as set forth in the following paragraphs.
(i) A mixture of two sets of primers that contain all possible combinations of three nucleotides: (i.e. A, T and C for one set, and A, T and G for the other set). Primers from the first set have higher affinity to the original bisulfite converted DNA strand, while primers from the second set preferentially anneal to the newly synthesized complementary strand. Using this scheme, having G and C in the same primer is avoided, thus preventing the primers from crossing over any CpG sites to be interrogated. Bias that may be introduced by the un-balanced annealing efficiency of primers corresponding to the two alleles (C or T) also is avoided. Lastly, since each primer set contains all possible combinations of three, but not four nucleotides, effective primer concentration is increased.
(ii) Simple primer sequences that contain only Adenines (A). The homopoly-A primers (for example, 6-mer, 9-mer, or longer) is used for the first strand synthesis. After that, a homopoly-T tail is added to the 3′-ends of the first strand products, using terminal deoxyribonucleotide transferase (TdT). A standard PCR is then be carried out to amplify the DNAs using a poly-A primer. Human chromosome 1 sequence was used to calculate the poly-T frequencies in the genome after bisulfite conversion: on average, the physical distance between any two poly-(T)n sequences (n>=9) is 330 bp, a perfect amplicon size range for robust amplifications.
(iii) Similar to approach (i), except that oligo-A primer is used for the first strand synthesis and primers containing combinations of A, T and G for the complementary strand synthesis.
Probe design is one of the critical components for a successful methylation assay. The fully automated SNP genotyping assay design program described herein can be used for methylation assay development. As shown above, for each CpG locus, three probes are designed: two allele-specific oligonucleotides, one corresponding to the methylated and the other to the unmethylated state of the CpG site, and one locus-specific oligo. If other CpG loci are present close to the chosen CpG site, a wobble base [A or G] is used in the corresponding position of the probes. Based on the observation that adjacent CpG sites tend to be co-methylated or co-de-methylated, a simpler design scheme is applied in which a “CG” sequence is used for all the CpG sites within the vicinity of the design, in particular for the landing sites for both ASO and LSO, to target any methylated CpG site; while a “TG” sequence is used for all the CpG sites within the vicinity of the design, to target any un-methylated CpG site. This approach requires two LSO oligos for some loci, but adds better discrimination between the methylated and unmethylated alleles.
A human gene promoter database, which includes all CpG regions of potential interest for methylation profiling was constructed by combining NCBI's RefSeq annotation, existing knowledge of some well-studied promoters, gene and promoter prediction algorithms, as well as observations of certain cancer-related genes. This database is continuously expanded by integrating more public information from literature and databases, and experimental observations. For the methylation study, a new database searching strategy is integrated into the primer design software. A modified genome database is generated in which all “C”s (except those located within a CpG dinucleotide sequence) are converted to “T”s in silico. The probe design program searches against this converted database to find unique sequences and compute melting temperature (Tm), self-complementarity and length for an optimal probe. An optimization program is applied to match address sequences with locus-specific oligos to minimize self-complementarities of combined address and probe sequences. A locus filtering program is used to filter out sequences predicted to be unsuitable on the basis of data from SNP genotyping experiments already carried out. Some sequence features have been shown to be troublesome, e.g. runs of six or more consecutive bases of a single type, extreme GC or AT content, inverted repeats (mostly due to, secondary structure), and high numbers of hits in the human genome sequence based on similarity searches by BLAST. All of these parameters can be computed in advance. These parameters are stored in a relational database for further data analysis. In addition, the program computes the sequence complementarity between the probes designed for a given set of methylation sites, especially the sequence complementary at their 3′ ends. This calculation allows assessment of the compatibility of the assays, which in turn provides guidance regarding grouping of the assays properly for multiplexing.
The main problem for the methylation assay primer design lies in the reduced complexity of the genome after bisulfite conversion of the genomic DNA. Analysis of 5′-regulatory sequences from 1200 human genes was performed and preliminary computer simulation analysis indicates that the length of the primers designed for the bisulfite-converted DNAs will have to be increased by several bases as compared to the un-converted ones to achieve the same primer specificity and melting temperature. If necessary, longer primers along with increased assay stringency is used.
In another in-silico experiment, three BLAST searchable human genomic sequence databases were created. The first database mimics methylated condition (after bisulfite treatment), where all the C residues in CpG dinucleotide sequence remains as C; the second mimics un-methylated conditions, where all the C residues in CpG dinucleotide sequence are converted into T; both of these databases have C residues from non-CpG sites converted into T. The third database has normal genomic sequences. BLAST searching against these databases using designed probes as queries was performed and, as predicted, the probes had much larger number of hits to the database that have “C” converted into “T” (the first two databases), and less number of hits to the normal database (the third database). Subsequent empirical experiments with probes that have either large or small number of BLAST hits suggested that the probes with small number of hits usually generate good assay results, while the probes with large number of hits do not. In the future, this BLAST search process is automated and integrated into the probe design software. Furthermore, probes is designed for all the CpG sites in the promoter regions. After a subsequent BLAST filtering process, only three probes are synthesized for each gene. For a small number of genes that can't have three qualified probes designed due to limited number of CpG sites in the promoter region or CpG sites too close to each other, or/and severe sequence similarity to other genomic regions.
In order to search for specific methylation patterns in different cancer types or cancer stages, we first develop methylation assays for at least 1000 human genes. These genes are selected based on the following criteria:
(i) Biological relevance. Methylation patterns in previously characterized tumor suppressor genes and oncogenes are an initial focus (Esteller, Oncogene 21(35): 5427-40 (2002), Adorjan, et al., Nucleic Acids Res. 30(5): p. e21 (2002)). Then, the target group is enlarged to include genes that are indirectly involved in cancer development, for example, DNA repair genes; metastasis-inhibitor genes, genes regulated by various signaling pathways, and/or responsible for altered cell growth and differentiation; or genes considered to be targets for oncogenic transformation.
(ii) Previous knowledge, e.g. genes located within published recurrent loss of hetrozygosity (LOH) regions or amplified genomic regions (Pollack, et al., Proc Natl Acad Sci U S A. 99(20): 12963-8 (2002)).
(iii) Gene expression profiling information, for example, genes differentially expressed in cancer and normal tissues. In the past few years, due to the rapid development of microarray technology, many specific gene-expression signatures have been identified for different cancer types (Golub, et al., Science 286(5439): 531-7 (1999), Ramaswamy, et al., Proc Natl Acad Sci U S A. 98(26): 15149-54 (2001), Perou, et al., Nature 406(6797): 747-52 (200) Bhattacharjee, et al., Proc Natl Acad Sci U S A 98(24): 13790-5 (2001), Chen, et al., Mol Biol Cell 13(6): 1929-39 (2002), Welsh, et al., Proc Natl Acad Sci U S A. 98(3): 1176-81 (2001)), cancer stages (Dhanasekaran, et al., Nature 412(6849): 822-6 (2001), Ramaswamy, et al., Nat Genet. 33(1): 49-54 (2003)), and cancer therapeutic outcomes (Shipp, et al., Nat Med. 8(1): 68-74 (2002)). Some of these differential expressions are regulated by imprinted or somatic methylation.
The methylation targets are grouped-into functionally relevant sets that are useful for focused research (e.g. based on association with a particular pathway or disease; or expressed in particular tissues of interest; or representing a particular genomic region), as well as for more global studies. For example, genes can be grouped according to their biochemical properties, such as, oncogenes/tumor suppressor genes, kinases, phosphatases, and cell surface receptors. Genes can be also grouped based on their involvement in different biological pathways/functions, for example, tumor antigen, signal transduction, apoptosis, angiogenesis, cell cycle control, cell differentiation, DNA repair, cancer metastasis/invasion, drug resistance and detoxification, and transcriptional regulation, etc. The assays are then optimized to achieve a high degree of reliability and specificity within each set.
Selection of CpG Sites to be Interrogated
Several CpG sites within each 5′-regulatory region, including CpGs over the transcriptional start site is targeted, since redundant information from multiple CpG sites can provide a better measurement of the overall methylation status in the interrogated gene. While there are many CpG sites within each CpG island, only those for which robust assays can be designed is used. Each potential CpG site is BLAST searched against human dbSNP databases to avoid any potential “polymorphic” CpG site (i.e. the “methylation-resistant” site), to ensure clean data interpretation. If desired, the consequence of the polymorphic CpG sites, for example, their effect on methylation of adjacent CpG sites, and subsequently on gene expression level can be determined.
It has been estimated that the human genome has 26,000 to 45,000 CpG islands (Antequera, et al., Proc Natl Acad Sci USA. 90(24): 11995-9 (1993), Ewing, et al., Nat Genet. 25(2): 232-4 (2000)). Among them, those found in gene 5′-regulatory regions are the most biologically significant ones. The work does not only utilize CpG rich promoters, but also investigates less CpG rich promoters since they might also be subjected to aberrant methylation and silencing.
In order to correctly identify genes and their upstream regulatory regions, public databases such as NCBI RefSeq, UCSC Human Genome Project (HGP) Working Draft, ENSEMBL and Unigene are utilized. These databases annotate known or verified genes according to sequence similarity to mRNA, EST and protein sequences, and novel genes predicted using gene prediction programs. They also contain reference links to genetic and physical mapping data, as well as other features such as CpG islands. The accuracy of these annotated features is confirmed by re-running gene prediction programs and searching for sequence similarity to mRNA, EST, and known protein sequences. Existing promoter databases such as Eukaryotic Promoter Database (EPD) (Praz, V., et al., Nucleic Acids Res. 30(1): 322-4 (2002)), are used to identify the 5′-regulatory sequences. Meantime, promoter and first exon prediction algorithms such as FirstEF (Davuluri, et al., Nat Genet. 29(4): 412-7 (2001)) are used to identify potential regulatory sequences. This approach provides a well-annotated collection of regulatory sequences of human genes and allow for identification of the CpG sites within these regulatory regions and design assay probes as described above.
A CpG site within a CpG island doesn't automatically qualify it as a biologically significant methylation target and art knowledge is applied in order to design most valuable methylation assays. In the past few years, tremendous progress has been made in the epigenetics field, which uncovered many epigenetic regulation mechanisms in various biological pathways (Strichman-Almashanu, et al., Genome Res. 12(4): 543-54 (2002)), and cancers (Widschwendter, et al., Oncogene 21(35): 5462-82 (2002), Tsou, et al., 21(35): p. 5450-61 (2002)). Moreover, large methylation projects such as bisulfite sequencing of CpG island-enriched libraries (Cross, et al., Nat Genet. 6(3): 236-44 (1994)), or entire human chromosomes or even the entire genome are likely to be developed. These efforts should produce tremendous amounts of methylation data and reveal hundreds of thousands of new methylation sites in many different tissue types.
Assay Development, and Array Data Extraction and Analysis
Once the assays are designed, they are tested with publicly available genomic DNAs isolated from various cancerous or normal human tissues or cell lines of different tissue origins, and obtain tissue-specific methylation profiles for individual genes (CpG sites). These methylation profiles serve as references for analyzing unknown samples. Building on SNP genotyping technology, a quantitative metric to guide the methylation assay development is formulated and provide a quality assurance to data-generated in a production setting. The metric takes into consideration all aspects of assay performance and data quality (e.g. assay specificity and quantitation), including efficiency of bisulfite conversion, overall signal intensity of all targeted CpG sites, concordance among the measurements of the three CpG sites within each gene, specificity of detection in control samples (e.g. plasmids, reference samples as well mixtures of the reference samples), and measurement variations in replicated samples, etc.
In addition the quantitative performance of methylation detection is tested at various multiplexing levels, for example, high (>1000-plex), medium ( ̃300-plex), and low (<100-plex), and validate the specificity and sensitivity of the assays at high multiplexing levels. Meanwhile, as a measurement of the assay specificity, concordance of methylation profiles generated from a given sample at different multiplexing levels are compared. Finally, methylation-specific PCR is used to validate some of the array results (Herman, et al., Proc Natl Acad Sci U S A. 93(18): 9821-6 (1996)). All qualified assays are re-pooled and used for large-scale DNA methylation profiling.
Since the sequence complexity is significantly reduced following bisulfite conversion of the genomic DNA, the assay oligo/genomic DNA annealing protocol is optimized to minimize cross-hybridization. Tetramethylammonium (TMACl) (De Murcia, et al., Biophys Chem. 8(4): 377-83 (1978), Sorg, et al., Nucleic Acids Res. 19(17): 4782 (1991)), and/or Betaine (Rees, et al., Biochemistry, 32(1): 137-44 (1993)) is used to normalize the base composition dependence of DNA/oligo hybridization. Nevertheless, high locus specificity should be achieved by the requirement that both ASO and LSO oligos need to hybridize to the same genomic target site and then get extended and ligated (FIG. 3).
The existing SNP genotyping software is modified and adapted for the methylation data analysis. The current software takes the raw intensity data and transforms them into a genotype call using a clustering algorithm (FIG. 10). However, for most of the methylation data analysis cluster analysis cannot be utilized due to the need not only to distinguish three methylation states of each locus (unmethylated, methylated and semi-methylated), but do it in a more quantitative manner, for example, estimate percentage of methylation of certain loci in a given sample. The methylated and unmethylated reference samples and assay controls are used in every experiment for software calibration. Assay intensity data of unknown sample is compared to those obtained with the reference samples, and used to calculate the methylation level of the locus of interest. Software is developed for comparison of various samples and detection of differential methylation profiles to allow for identification of differences between normal tissues and tumors, and/or create tissue-specific methylation profiles for genes and loci of interest.
Search for Specific Methylation Patterns in Different Cancer Types or Cancer Stages
Once the gene-specific methylation assays are developed as described above, a large-scale DNA methylation survey is carried out in a large number of samples. The experiment is designed to compare methylation patterns in (1) normal and cancerous tissues; (2) different cancer types or cancer stages; (3) or responsive to (or associated with) treatment with certain growth factors or drugs, activation of oncogenes or inactivation of tumor suppressor genes, changes in a developmental program, etc. The main objective is to find unique methylation patterns for specific cancer types/stages and develop molecular markers for classification and diagnosis of cancers, which can be used to complement existing morphological and clinical parameters. This can be particularly useful for cancer types which appear similar by histological assessments, but follow different clinical courses (e.g. different therapeutic responses). The results also provide important clues to the mechanisms of specific cellular responses; and this information can prove critical for devising strategies for cancer prevention and treatment.
Malignant tissues obtained by laser capture microdissection (LCM) are used to identify specific cell and tissue types for methylation profiling. In these cases, a more sensitive strategy is employed, which involves DNA amplification after bisulfite conversion. If an assay can be established to detect tumor-specific methylation patterns in a very small amount of diseased tissue in the presence of a large amount of normal tissue, it may find wide application in clinical cancer diagnosis.
As a pilot study, DNA samples isolated from 50 or more lung tissues, for example, 98 lung tissues and 50 or more breast tissues, for example, 101 breast tissues, including both normal and cancerous tissues, are used. Among these tissues, a portion, for example, 169 (98 lung and 71 breast) are frozen and the remainder, for example, 30 (breast) are paraffin-fixed. These tumor tissues are classified upon resection, and basic (anonymous) data about each tumor is kept in the tumor bank database. The tissues were resected, sent to the Pathology Department for pathological examination, and then sent to the tumor bank with the initial pathology report. The tissue was quickly frozen and stored at −80° C. The tissue procurement, storage, and documentation of clinical specimens are very well documented, in accordance with guidelines for human subject research. Thirty of the 101 breast tumor tissues are formalin-fixed, paraffin-embedded tissues. Slides were made from the fixed tissue block and stored at room temperature. Each tumor sample was examined by a pathologist to confirm the clinical diagnosis. Basic (anonymous) data about each tumor, such as the information on the gender, age, and ethnic background of the patient as well as diagnosis are available to us for data analysis.
Initially, DNA methylation profiles are generated for these samples for a list of 141 genes, selected fully based on their biological functions (see Appendix I). If both DNA and RNA samples are available for a subject in the study, both DNA methylation and gene expression is measured, including allele-specific expression, using a sensitive RNA profiling method (Fan, et al., Genome Res. 14:878-885 (2003), Yeakley, et al., Nat Biotechnol. 20(4): 353-8 (2002)). Gene-specific as well as allele-specific probes are designed to measure expression levels of specific transcripts and their isoforms. Cross-referencing gene expression results to DNA methylation data confirms not only the gene silencing caused by DNA methylation, but also helps interpret the association study results. Once specific methylation patterns are derived from this preliminary study, they are validated in (larger) independent sample sets.
Finally, comparing results obtained from human and animal studies sheds light on the underlying molecular mechanisms of tumorigenesis. For example, there is a well-characterized rat mammary tumor model involving mammary glands from virgin and parlous animals exposed to MNU (Sivaraman, et al., Carcinogenesis 19(9): 1573-81 (1998)). It provides an excellent biological model for studying molecular events in human breast cancer, especially those occurred in early cancer developments leading to mammary tumorigenesis (Russo, et al., Br J Cancer 64(3): 481-4 (1991)).
Bioinformatics, Array Information Management, and Statistical Data Analysis
Software development focuses on algorithms and software tools to process and analyze the large amount of methylation assay data as efficiently as possible. A database developer/administrator organizes, track and maintain all the methylation site information, primer design, sample information, the day-to-day experimental data, as well as design and implement web browser interfaces to provide search, query and report functions.
To develop a robust and high-throughput technology for simultaneous measurement of methylation at many specific sites in many samples, a highly efficient analysis and data export process is needed. Algorithms for analyzing data to determine methylation status automatically are developed. Again, this is based on experience with analysis of SNP genotyping data. There are some important differences in how the data is analyzed, such as the requirement for more quantitative analysis and output as described above. Experiments is designed and analysis procedures developed to determine the quantitative limits of our system, such as the limit of detection of methylated DNA in a mixed sample, and linearity of signal as a function of amount of methylated DNA.
Once tools are developed to extract information about methylation status from raw data, and to determine the significance of the measurements and assign confidence indices, the focus is on analyses of patterns of methylation and their correlations with different phenotypes. For example, analyses are carried out to detect and verify any correlations between specific methylation patterns and particular cancer types. Techniques (methods) to perform this type of analysis are the subject of intensive research in the microarray field. Many powerful algorithms/tools have been developed, such as supervised or unsupervised hierarchical clustering analysis (Dhanasekaran, et al., Nature 412(6849): 822-6 (2001), Eisen, et al., Proc Natl Acad Sci U S A. 1998. 95(25): 14863-8 (1991), Khan, et al., Nat Med. 7(6): 673-9 (2001)), K-means clustering, bootstrapping or jackknife (Kerr, M. K. et al. Proc Natl Acad Sci U S A. 98(16): 8961-5 (2001)), principal component analysis, etc. However, each of these methods has its own advantages and limitations; no clear advantage exists for any given algorithm in application to our study. Therefore, multiple algorithms are tested and the most suitable ones are selected to carry out the analyses.
The technology is upscaled to meet commercial requirements by implementing the entire process, including sample preparation, bisulfite treatment, genotyping-based assay and PCR amplification on a robotic platform; increasing the level of multiplexing to at least 96-plex, and as high as 1,500-plex; and reducing the amount of genomic DNA required such that, on average, <1 ng of genomic DNA is used per methylation site analyzed.
The assay described herein allows measurement of the methylation status in at least 10, 100, or 1,000 human genes' 5′-regulatory regions, and validate the sensitivity and specificity of the assays at low or high multiplexing levels. The assay further allows for a systematic search for specific methylation patterns in different cancer types and cancer stages.
Overall, this Example describes a system for methylation detection by leveraging various technologies for high-throughput array-based assays and SNP genotyping, and to validate the technology in real-world applications. The technology is highly scalable, both in terms of the number of assays carried out on a single sample, and the number of samples that can be processed in parallel. Furthermore, it can be used has the potential for broad application in many areas of cancer and fundamental biomedical research. The assays and assay protocols, and the specific methylation patterns (in various cancers) to be developed in this study can generally be useful to the research community.
EXAMPLE IV
DNA Methylation Profiling in Lung Cancer
This Example demonstrates the identification of adenocarcinoma markers by virtue of differential methylation patterns and use of the identified markers to distinguish adenocarcinoma from normal tissue with high specificity and sensitivity.
As described in detail above, the SNP genotyping system was adapted for DNA methylation detection based on “SNP” genotyping of bisulfite-converted genomic DNA. In this assay, non-methylated cytosines (C) are converted to uracil (U) when treated with bisulfite, while methylated cytosines remain unchanged. Hybridization behavior of uracil is similar to that of thymine (T). The detection of the methylation status of a particular cytosine can thus be carried out using a genotyping assay for a C/T polymorphism.
Assay Probe Design
A 1.5 kb sequence from the 5′-regulatory region (in some cases, including the first exon) is extracted for each target gene based on human genome public databases NCBI RefSeq build 33 released on Jun. 6, 2003. CpG islands of interest from this 1.5 kb region were selected and “converted by bisulfite” computationally. As described in Example 1, an automated SNP genotyping assay design program was adapted for methylation application. For each CpG locus, four probes were designed: two allele-specific oligos and two locus-specific oligos, each corresponding to the methylated or unmethylated state of the CpG site respectively (FIG. 20). If other CpG loci were present in the close vicinity of the chosen CpG site, an assumption is made that it has the same methylation status as the site of interest. While there are many CpG sites within each CpG island, only those for which robust assays could be designed were used. Assays were designed for 389 CpG sites from 141 genes: 114 genes with three CpG assays, 20 genes with two and 7 genes with only one assay. The designed site sequence information is included in Table 1.
DNA Samples for Methylation Analysis
Lung tissue specimens were collected from subjects with lung cancer. Under an Institutional Review Board approved protocol, the samples of lung tissue classified as cancerous and samples adjacent to the cancerous tissue but classified as normal were utilized in this study. After pathological classification upon resection, the tissues were frozen and stored at −80° C. Forty-six samples were obtained from Philipps-University of Marburg, Germany, and 25 samples were from The Pennsylvania State University College of Medicine Tumor Bank. Specifically, a total of 38 lung cancer and 33 normal tissues were used, including 22 matched pairs (Table 4). The cancer samples included 14 squamous cell carcinoma and 24 adenocarcinoma cancer tissues. The samples were pulverized under liquid nitrogen. DNA was extracted from the tissue powder by QIAamp DNA Mini Kit (Qiagen Inc. Valencia, Calif.) according to manufacturer's instruction. The DNA was eluted from the column with dH2O, and stored at −80° C. until use.
Plasmid DNA Controls.
Plasmids pUC19, pACYC184 and phage phiX174 served as control DNAs as described above in Example II.
Bisulfite Conversion of DNA and Methylation Assay
The EZ DNA methylation kit (Zymo Research, Orange Calif.) was used for bisulfite conversion of DNA samples, according to manufacturer's recommendations. One and a half microgram of genomic DNA was used for each conversion. Bisulfite-converted genomic DNA from one conversion was then used for up to 8 array experiments. After bisulfite conversion of genomic DNA, the remaining assay steps were identical to the GoldenGateTM assay (Fan et al., Cold Spring Harbor Symposia on Quantitative Biology 68: 69-78 (2003)), using manufacturer's reagents and conditions (BeadLab User Manual, Illumina).
Briefly, bisulfite-converted, biotinylated genomic DNA was immobilized on paramagnetic beads and washed. Pooled query oligos were annealed to the gDNA under a controlled hybridization program, and then washed to remove excess or mishybridized oligos. Hybridized oligos were then extended and ligated to generate amplifiable templates. A PCR reaction was performed with fluorescently labeled universal PCR primers. Single stranded PCR products were prepared by denaturation, then hybridized to a Sentrix® Array Matrix (Fan et al., Cold Spring Harbor Symposia on Quantitative Biology 68: 69-78 (2003)). The array hybridization was conducted under a temperature gradient program, and arrays were imaged using a BeadArray Reader 1000 scanner (Barker et al. Proc. SPIE 4966:1-11 (2003)). Image processing and intensity data extraction software were as describe previously (Galinsky, Bioinformatics 19, 1832-6 (2003)).
Microarrays were assembled by loading pools of glass beads (3 μm in diameter) derivatized with oligos onto the etched ends of fiber-optic bundles (Barker et al., supra, 2003). About 50,000 optical fibers are hexagonally packed to form an approximately 1.4 mm diameter bundle. The fiber optic bundles are assembled into a 96-array matrix, Sentrix® Array Matrix, which matches the dimensions of standard microtiter plates. This arrangement allows simultaneous processing of 96 samples using standard robotics (Fan et al. supra, 2003). Because the beads are positioned randomly, a decoding process is carried out to determine the location and identity of each bead in every array location (Gunderson, Genome Res 14, 870-7 (2004)). Decoding is an automated part of array manufacture.
Methylation Data Analysis
Each methylation data point is represented by fluorescent signals from the M (methylated) and U (unmethylated) alleles. M and U signals were initially computed and normalized by requiring each sample to have the same average signal in each of the two fluorescent channels. Background intensity computed from a set of negative controls was subtracted from each of the analytical data point prior to normalization. The ratio of fluorescent signals from the two alleles




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The β value reflects the methylation status of each CpG locus, ranging from 0 in the cases of completely unmethylated sites to 1 in completely methylated sites. A constant offset of 100 was added to the denominator of the formula, as a compensation for any “negative signals” which may arise from global background subtraction (i.e. over-subtraction). Hierarchical clustering was performed using average linkage method and Pierson's correlation coefficient (r) as a similarity measure. K-nearest neighbor class prediction was done using 1-r for distance calculation and k=1 (nearest neighbor).

To choose optimal number of neighbor k, classification of the training set was performed first with different values of k. Leave-one-out cross validation gave misclassification rate of 0.03 (one out of 32 samples) using k=1. This result did not improve with a larger k.
According to the above-described protocols, for each CpG locus, four probes were designed: two allele-specific oligos (ASOs) and two locus-specific oligos (LSOs), each corresponding to the methylated or unmethylated state of the CpG site respectively (FIG. 1). Each ASO consists of a 3′ portion that hybridizes to the bisulfite-converted genomic DNA, with the 3′ base complementary to either the “C” or “T” allele of the targeted CpG locus, and a 5′ portion that incorporates a universal PCR primer sequence (P1 or P2, each associated with an allele respectively). The LSOs consists of three parts: At the 5′ end is a CpG locus-specific sequence; in the middle is an address sequence, complementary to a corresponding capture sequence on the array; and at the 3′ end is a universal PCR priming site (P3). The flexibility in primer design (the gap size between the ASO and LSO oligos varies from 1 base to 20 bases) is particularly important for methylation studies, since it can be implemented in a way that will avoid difficult sequences or ambiguous bases in the CpG islands of interest. In this study, assay oligos were designed for 389 CpG sites from the 5′-regulatory region of 141 genes.
Typically, 144-160 CpG sites were assayed together. Pooled assay oligos were first annealed to bisulfite-converted genomic DNA. An allele-specific primer extension step was then carried out; ASOs got extended if their 3′ base is complementary to their cognate CpG site in the gDNA template. Allele-specific extension was followed by ligation of the extended ASOs to their corresponding LSOs, to create PCR templates. Requiring the joining of two fragments to create a PCR template provides an additional level of genomic specificity. Any incorrectly hybridized ASOs and LSOs are unlikely to be adjacent, and therefore should not be able to ligate.
The ligated products were then amplified by PCR using common primers P1, P2, and P3, and hybridized to a microarray bearing the complementary address sequences. P1 and P2 are fluorescently labeled, each with a different dye, and associated with the “T” (unmethylated) allele or the “C” (methylated) allele respectively. As described in this example, methylation status of an interrogated locus is determined by quantifying β, which is defined as the ratio of the fluorescent signal from the methylated allele to the sum of the fluorescent signals of both methylated and unmethylated alleles.
Development of reliable internal controls is a prerequisite for development of a robust methylation detection method. Plasmids pUC19, pACYC184 and phage phiX174 as internal control DNAs. These DNAs (unmethylated, methylated or mixed at a 1:1 ratio) were spiked into 200 ng human genomic DNA at a 1:1 molar ratio (at approximately 2-4 pg plasmid DNA/1 μg gDNA, depending on the plasmid size), and were used in every methylation experiment to monitor both bisulfite conversion efficiency and accuracy of methylation detection. As shown in FIG. 2, unmethylated, semi-methylated and fully-methylated plasmid loci can be easily distinguished by the assay.
“Methylated” and “fully unmethylated” genomic templates were developed to calibrate quantitative measurements of methylation. The fully unmethylated templates were generated by genome-wide amplification of human genomic DNA using Repli-g DNA amplification kit (Molecular Staging). After this amplification, the endogenous DNA methylation is diluted at least 100 to 1000-fold, effectively rendering the amplified genomic DNA “unmethylated”. Methylated templates were generated by in vitro methylation using Sss I (CpG-methylase) enzyme (New England BioLabs). Among the 389 CpG sites targeted, 70 of them can only be methylated to less than 50% completion. Some of these sites may be less accessible to methylase because of the DNA secondary structure; some may result from base substitution at the CpG sites in the DNA tested, i.e. these sites become “methylation-resistant” (it is well known that CpG sites are mutation hot spots).
The above two reference templates were used for assay development and 20 CpG assays were disqualified due to similar β values for both unmethylated and methylated templates (a threshold of Δβ>0.1 was applied). Therefore, the data analyses is based on the remaining 369 functional CpG assays.
As described above, the use of bisulfite conversion of DNA for methylation detection is based on the different sensitivity of cytosine and 5-methylcytosine to deamination by bisulfite. Under acidic conditions, cytosine undergoes conversion to uracil, while methylated cytosine remains unreactive. Thus, an effective bisulfite conversion protocol facilitates a robust methylation profiling assay. Incomplete conversion of cytosine to uracil by bisulfite can result in appearance of false-positive signals for 5-methylcytosine, and reduce the overall quality of the assay data. In order to monitor the effectiveness of bisulfite treatment, a set of oligos (a standard set of SNP genotyping probes) designed for unconverted genomic DNA sequences was used in the assay. If bisulfite conversion is successful, the signal from assays designed against the unconverted DNA (i.e. the SNP set) will disappear, and only signals from assays designed against the converted DNA are present. Incomplete conversion results in low and inconsistent signals across all loci targeted to both unconverted and converted DNA.
Each gDNA sample was assayed in duplicate (Table 4). According to the methods described herein, highly reproducible DNA methylation profiles within replicates were obtained with 200 ng genomic DNA (average r2 was 0.97) (FIG. 21, left and center panels). As a result, differential methylation between different samples was readily detected (FIG. 3, right panel).
As described above, DNA methylation is involved in transcriptional inactivation of genes on one of the two X-chromosomes in female somatic cells (i.e. X-chromosome silencing), compensating the dosage of functional X-linked genes between male and female. The methylation status of six X-linked housekeeping genes—BCAP31, GPC3, FMR, ELK1, G6PD and GLA, was analyzed to test if the assay could detect the methylation difference between males and females. As shown in FIGS. 16 and 22, methylation levels of these genes correlated well with the gender of the sample source.
There are four major histologic types of lung carcinomas: squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and adenocarcinoma. The methylation status of 369 CpG sites located in the 5′-regulatory regions of 141 genes in 14 squamous cell carcinoma, 24 adenocarcinoma and 33 normal tissue samples was measured. The genes were selected based on their biological relevance. These include tumor suppressor genes and oncogenes, genes that are indirectly involved in cancer development, for example, DNA repair genes; metastasis-inhibitor genes, genes regulated by various signaling pathways, and/or responsible for altered cell growth, differentiation and apoptosis; genes considered to be targets for oncogenic transformation; imprinting genes; previously reported differentially methylated genes (Esteller, Oncogene 21, 5427-40 (2002), Tsou et al., Oncogene 21, 5450-61 (2002); Adodjan et al., Nucleic Acids Res 30, e21 (2002)).
A data matrix of β values was used to identify CpG loci that show differential methylation in cancer. Using a Mann-Whitney test, 21 normal samples were first compared to 11 adenocarcinoma samples referred thereafter the “training set”. With a p-value cutoff of 1/369, 79 differentially methylated loci were identified. By choice of this p-value threshold, only one false positive marker was expected and this prediction was verified by randomly permuting columns in the data matrix and repeating Mann-Whitney test. Since the permutation process is independent of disease status, it provides an accurate estimate of actual false positive rate. On average, 0.63 differentially methylated markers were observed, indicating a lower than expected actual false positive rate, which is not surprising since not all CpG loci can be considered as independent variables. To select markers which have the largest difference between cancer and normal tissues (assuming this would cause the largest biological changes), an additional filter requiring |Δβ|>0.2 was required. Ultimately, a list of 14 differentially methylated markers was obtained: ADCYAP1—2, CDH13—3, GDF10—2, GDF10—3, HOXA5—2, MAGEA1—3, RUNX3—1, SCGB3A1—3, SERPINB5—1, SFN—2, SFTPA1—2, TERT—1, TERT—2, and TNF—2 (FIG. 23A).
The same differential methylation analysis was subsequently performed for the same 21 normal samples and 14 squamous cell carcinoma samples. When the same p-value threshold and |Δβ|>0.2 filtering was used, the following list of 21 differentially methylated markers was obtained: ADCYAP1—1, ADCYAP1—2, ADCYAP1—3, BCR—2, CALCA—1, GDF10—3, HOXA5—1, HOXA5—2, HOXA5—3, MAGEA1—1, MAGEA1—2, PGR—2, PRKCDBP—3, SCGB3A1—3, SERPINB5—1, SFN—2, SFTPC—3, TERT—1, TERT—2, TERT—3, TNF—2 (FIG. 23B).
In addition, methylation profiles of the cancer and normal samples were clustered using the selected markers. Normal samples formed a tight cluster (i.e. small cluster distances) in both adenocarcinoma and squamous cell carcinoma groups (FIGS. 24A & 24B). Two early stage cancer samples, G12029 (FIG. 24A) and G12003 (FIG. 24B), were co-clustered with normal samples. Quite large cluster distances existed among cancer samples which may reflect different disease stages.
An independent set of test samples was used to assess the specificity and sensitivity of the 14 adenocarcinoma markers. This test set contained 11 normal and 14 adenocarcinoma samples. We classified these samples using a k-nearest neighbor algorithm. The misclassification rate in the test set was 0.04, with one cancer sample (D12190) misclassified as normal. The 14 markers also were evaluated by measuring their p-values from Mann-Whitney test with the test sample set. The p-values ranged between 0.00017 and 0.029. Finally, the classification accuracy derived from these 14 specific markers and randomly selected 1,000 sets of 14 CpG loci was measured. Only 2.2% of the 1,000 random sets produced same or lower misclassification rates as compared to the 14 markers, with an average misclassification rate being 28%. The above-described analyses indicate that the differential methylation pattern for the identified markers is preserved in the two completely unrelated sample sets and the identified markers can be used to distinguish adenocarcinoma from normal tissue with high specificity and sensitivity.
These results are further demonstrated by cluster analysis of the methylation patterns of the 14 identified markers in the test set samples. As shown in FIG. 24C, the cancer and normal samples are clearly separated. The array data for some of the markers which show increased methylation in cancer samples was plotted to illustrate their performance in both training set and test set of samples (FIG. 25).
EXAMPLE V
DNA Methylation Profiling in Lung Cancer
This example provides a further demonstration of the identification of adenocarcinoma markers by virtue of differential methylation patterns and use of the identified markers to distinguish adenocarcinoma from normal tissue with high specificity and sensitivity.
The methods in this Example were performed generally as described in the preceeding Examples.
Briefly, as described in Example IV, 1.5 kb sequence from the 5′-regulatory region was extracted for each target gene based on human genome RefSeq build 34, version 3 (released on Mar. 10, 2004). CpG islands of interest from this 1.5 kb region were selected and “bisulfite-converted” computationally. As described in Example 1, an automated SNP genotyping assay design program was adapted for methylation application. For each CpG site, four probes were designed: two allele-specific oligos (ASO) and two locus-specific oligos (LSO). Each ASO-LSO oligo pair corresponded to either the methylated or unmethylated state of the CpG site (FIG. 4). The gap size between the ASO and LSO oligos varied from 1 base to 20 bases, which allowed difficult sequences or ambiguous bases in CpG islands of interest to be avoided. This flexibility is particularly important for methylation studies because of a decrease in sequence complexity as a result of bisulfite treatment.
As described above, if other CpG sites were present in close vicinity of the target CpG site, we made the assumption that they had the same methylation status as the site of interest. This design hypothesis was based on previously reported bisulfite sequencing results, in which a majority (>90%) of the adjacent CpG sites was shown to be co-methylated or co-de-methylated (Bird A., Genes Dev. 16:6-21 (2002); Grunau et al., J. Nutr. 132:2435S-2439S (2002); Rakyan et al., PLoS Biol. 2:e405 (2004); Tost et al., Nucleic Acids Res. 31: e50 (2003)). This assumption was also confirmed by our own bisulfite sequencing results. This design strategy is used in methylation-specific PCR primer design (Herman, et al., Proc Natl Acad Sci USA. 93(18): 9821-6 (1996)) and other microarray-based DNA methylation analysis (Adorjan et al. 2002). While there were many CpG sites within each CpG island, only those for which robust assays could be designed were selected.
DNA from breast cancer cell lines MCF-7, MDA-MB-435, MDA-MB-468 and T-47D, colon cancer cell lines Fet, HT29, HCT116, LS174 and SW480, and prostate cancer cell lines PC3 and LNCaP was extracted using a modified Trizol method according to the manufacturer's recommendations (Invitrogen, Carlsbad, Calif.). DNA from lung cancer cell lines NCI-H69 (HTB-119D), NCI-H526 (CRL-5807D), NCI-H358 (CRL-5811D), NCI-H1299 (CRL-5803D), NCI-H1395 (CRL-5868D) and NCI-H2126 (CCL-256D) was purchased from ATCC (Manassas, Va.). DNA from normal lung, ovary, breast, colon and prostate tissues was purchased from Clinomics Biosciences (Frederick, Md.). DNA samples NA06999, NA07033, NA10923 and NA10924 were purchased from the Coriell Institute for Medical Research (Camden, N.J.).
Samples of lung tissue classified as cancerous and samples adjacent to the cancerous tissue but classified as normal were utilized in this study, under Human Subjects Institutional Review Board approved protocols. After pathological classification upon resection, the tissues were frozen and stored at −80° C. Twenty-two samples (the training set) were obtained from Philipps-University of Marburg, Germany, and 24 samples (the test set) were from The Pennsylvania State University College of Medicine Tumor Bank. Specifically, 23 lung adenocarcinoma and 23 normal tissues were used, of which 11 were matched pairs (Table 6). The samples were pulverized under liquid nitrogen. DNA was extracted from the tissue powder by QIAamp DNA Mini Kit (Qiagen Inc. Valencia, Calif.) according to the manufacturer's instructions. The DNA was eluted from the column with dH2O, and stored at −80° C. until use.
Methylation Profiling Assay with the SNP Genotyping Platform
Applying the protocols described above and in the previous examples, assays were designed and performed for 1536 CpG sites from the 5′-regulatory region of 371 genes (1-9 CpG sites per gene). These genes (shown in Table 5) were selected based on their biological relevance. They include tumor suppressor genes and oncogenes; genes that are indirectly involved in cancer development, for example, DNA repair genes; metastasis-inhibitor genes; genes regulated by various signaling pathways, and/or responsible for altered cell growth, differentiation and apoptosis; genes considered to be targets for oncogenic transformation; genes of innate host defense; genes involved in surfactant function of the lung; imprinted genes; and previously reported differentially methylated genes (Esteller, Oncogene 21, 5427-40 (2002), Tsou et al., Oncogene 21, 5450-61 (2002); Adordan et al., Nucleic Acids Res 30, e21 (2002)).
The assay procedure is similar to that described previously for standard SNP genotyping ((Fan et al., Cold Spring Harbor Symposia on Quantitative Biology 68: 69-78 (2003)), except that four oligos, two allele-specific oligos (ASOs) and two locus-specific oligos (LSOs) are used for each assay site rather than three. For each CpG site, two pairs of probes were designed to interrogate either the top or bottom strand: an allele-specific oligo (ASO) and locus-specific oligo (LSO) probe pair for the methylated state of the CpG site and a corresponding ASO-LSO pair for the unmethylated state. Each ASO consists of a 3′ portion that hybridizes to the bisulfite-converted genomic DNA, with the 3′ base complementary to either the “C” or “T” allele of the targeted CpG site, and a 5′ portion that incorporates a universal PCR primer sequence P1 or P2. The LSOs consist of three parts: at the 5′ end is a CpG locus-specific sequence; in the middle is an address sequence, complementary to a corresponding capture sequence on the array; and at the 3′ end is a universal PCR priming site (P3). Pooled assay oligos were first annealed to bisulfite-converted genomic DNA. An allele-specific primer extension step was then carried out; ASOs were extended only if their 3′ base was complementary to their cognate CpG site in the gDNA template. Allele-specific extension was followed by ligation of the extended ASOs to their corresponding LSOs, to create PCR templates. The ligated products were then amplified by PCR using common primers P1, P2, and P3′, and hybridized to a microarray bearing the complementary address sequences. P1 and P2 were fluorescently labeled, each with a different dye, and associated with the “T” (unmethylated) allele or the “C” (methylated) allele respectively.
Bisulfite-treated, biotinylated genomic DNA (gDNA) was immobilized on paramagnetic beads. Pooled query oligos were annealed to the gDNA under a controlled hybridization program, and then washed to remove excess or mishybridized oligos. Hybridized oligos were then extended and ligated to generate amplifiable templates. Requiring the joining of two fragments to create a PCR template in this scheme provided an additional level of locus specificity. It is unlikely that any incorrectly hybridized ASOs and LSOs will be adjacent, and therefore should not be able to ligate after ASO extension. A PCR reaction was performed with fluorescently labeled universal PCR primers. Methylation status of an interrogated CpG site was determined by calculating β, which is defined as the ratio of the fluorescent signal from the methylated allele to the sum of the fluorescent signals of both methylated and unmethylated alleles.
As described in Example IV above, each methylation data point is represented by fluorescent signals from the M (methylated) and U (unmethylated) alleles. Background intensity computed from a set of negative controls was subtracted from each analytical data point. The ratio of fluorescent signals was then computed from the two alleles




  β
  =
  
    
      
        max
        ⁡
        
          (
          
            M
            ,
            0
          
          )
        
      
      
        
          
          U
          
        
        +
        
          
          M
          
        
        +
        100
      
    
    .
  




The β-value reflects the methylation level of each CpG site. An absolute value was used in the denominator of the formula, as a compensation for any “negative signals” which may arise from global background subtraction (i.e. over-subtraction; a constant bias of 100 was added to regularize β when both U and M values were small). For cluster analysis, a matrix of correlation coefficients between calculated methylation signals was computed. Agglomerative nesting was applied using the Agnes function in the R package with Ward's method. Thus, the β-value provides a continuous measure of levels of DNA methylation in samples, ranging from 0 in the case of completely unmethylated sites to 1 in completely methylated sites.

Internal Controls for the Methylation Assay

Plasmids pUC19, pACYC184 and phage φX174 served as internal control DNAs in the methylation assay as described above (see Example II). Very low methylation values were obtained for most of the 1536 CpG sites in the unmethylated reference DNA sample as expected, with approximately 95% of the CpG sites showing methylation level lower than 50%. In contrast, about 93% of the CpG sites were methylated to over 50% of completion in the in vitro methylated reference DNA. In general, these results confirm that the assay is specific, and faithfully reports the methylation status of most of the targeted CpG sites in tested samples. Some degree of cross-hybridization can explain those outlier CpG sites where elevated methylation was observed in the unmethylated reference DNA and the sites that were “under-methylated” in the in vitro methylated reference DNA, as can be expected by the reduced complexity of bisulfite-converted DNA. All such un-validated sites were still included and monitored in the assay reactions, but excluded from consideration for methylation marker development (see below).
Bisulfite Conversion Efficiency
As described above, an effective bisulfite conversion protocol facilitates a robust methylation profiling assay. To estimate the conversion efficiency, an analysis was performed of 7097 cytosines in 173 independent DNA fragments derived from eight genomic regions by bisulfite sequencing as described by Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992). To avoid ambiguity, only the cytosines from non-CpG sites were counted. Primers were designed flanking the CpG sites of interest (Table 9). The primer landing sites did not contain CpG dinucleotides and therefore the nucleotide sequences remained unchanged after bisulfite treatment. As a result, the methylated and unmethylated alleles would be equally amplified in the same reaction with the designed primer pair. The PCR amplified fragments were cloned into the pCR4-TOPO vector (Invitrogen) followed by transformation into Escherichia coli TOP10 competent cells (Invitrogen). Transformants containing recombinant plasmids were selected by blue/white colony screening. PCR inserts were directly amplified from the white colonies in the reaction mixture (35 μl) containing 3.5 μl GeneAmp 10×PCR buffer (Applied Biosystems), 1.5 units of AmpliTaq Gold (Applied Biosystems), 1.5 mM MgCl2, 200 nM of dNTP, and 200 nM each of M13 forward (5′-GTAAAACGACGGCCAGT-3′ (SEQ ID NO: 1514)) and reverse primer (5′-CAGGAAACAGCTATGAC-3′ (SEQ ID NO: 1515)). The reaction was subjected to the following cycling conditions: 94° C. for 10′, followed by 35 cycles of 94° C. 30′′, 50° C. 30′′, 72° C. 30′′, and a final cycle of 72° C. for 5′. The PCR products were sequenced by Agencourt Bioscience Corporation (Beverly, Mass.). The sequence data indicated that the DNA conversion rate was 99.7%.
Methylation Assay Reproducibility
For each sample, 1 μg of genomic DNA was used for each bisulfite conversion. 20% of the converted DNA (corresponding to 200 ng starting gDNA) was then used to assay the 1536 CpG sites simultaneously on an array. Technical replicates were done for each sample using the same converted DNA. Highly reproducible DNA methylation profiles were obtained between these technical replicates (FIG. 26, left and center panels), with an average R2 of 0.98±0.02 when the β-values were compared (Table 6). When β-values for matching normal and carcinoma clinical samples were compared, differential methylation was readily detected (FIG. 26, right panel).
DNA methylation is involved in transcriptional inactivation of genes on one of the two X-chromosomes in female somatic cells (i.e. X-chromosome silencing), which compensates for the dosage of functional X-linked genes between male and female (Carrel and Willard, Nature 434:400-404 (2005)) The methylation status of six X-linked housekeeping genes—EFNB1, ELK1, FMR1, G6PD, GPC3 and GLA was measured together with the rest of the 371 genes in male and female genomic DNAs. In general, methylation levels of these genes correlated well with the gender of the sample source, i.e. no or very low methylation was detected in male DNA samples and hemi-methylation was detected in female DNA samples.
The best 18 “gender-specific” CpG sites were subsequently selected from these X-chromosomal genes (with β<0.1 in male genomic DNA) to estimate the assay's ability for detecting difference of methylation levels between samples. Female genomic DNA was diluted into male genomic DNA at ratios of 5:95, 10:90, 20:80 and 50:50, prior to bisulfite conversion. Two independent sets of mixtures were made and measured in parallel (FIG. 27 Panel A); and four replicates were done for each mixture. The standard deviation of the β-value obtained for all the 1536 CpG sites across the four replicates was less than 0.06 in 99% of cases, and the average slope of β vs. expected methylation level for the selected X-chromosomal sites was equal to one. Therefore, the method can discriminate levels of methylation (β-values) that differ by as little as 0.17 (1.96×√{square root over (2)}×0.06). Since the tails of their respective signal distributions did not have large overlapping areas (less than 16% assuming Gaussian error), there is a high probability that the response produced at one methylation level will be significantly different from the expected signal produced at another.
The standard deviation of these measurements was not uniform across the range of β-values. It had a parabolic shape with the maximum peak around β=0.5. Therefore, the absolute performance of the assay depends on the methylation level itself. For example, for 16 out of the 18 CpG sites, methylation levels from 5:95 and 0:100 mixtures could be unambiguously distinguished from each other (i.e. the maximum β-value in the 0:100 mixtures was less than the minimum β-value in the 5:95 mixtures) (FIG. 27 Panel B). This indicates that the method can detect as little as 2.5% methylation for well-performing CpG sites in the optimal range of the response curve.
Methylation Profiling in Cancer Cell Lines
To demonstrate the applicability of the disclosed method for studying DNA methylation in cancer, the assay developed for the 1536 CpG sites was applied to a panel of 17 colon, breast, lung and prostate cancer cell lines, as well as seven DNA samples derived from different normal tissues. Sixteen CpG sites distinguishing cancer from normal samples were selected based on a Mann-Whitney test (p<0.00001) and an additional filter of mean change of methylation level greater than 0.34 (0.17×2). Forty-eight CpG sites distinguishing individual cancer types were selected based on a Kruskal-Wallis test (p<0.035) and a standard deviation across cancer samples greater than 0.34. This gave us a balanced list of 16 cancer-specific markers and 48 cancer type-specific markers. Using these markers, all cancer samples were correctly separated from normal samples by hierarchical clustering, with Ward's linkage method and correlation-based distance metric. This separation of cancer samples was not sensitive to the choice of distance metric or linkage method (data not shown). FIG. 28 shows the differential methylation profiles in normal vs. cancer samples as well as specific methylation signatures that were obtained for individual cancer types. In general, our data correlate well with previous cell line methylation profiling results (Melnikov, et al., Nucleic Acids Res 33: e93 (2005); Paz et al., Cancer Res 63: 1114-1121 (2003)). For example, GSTP1 was completely methylated in the LNCaP prostate cancer cell line and semi-methylated in PC3 cell line, as previously reported by Singal et al., Cancer Res 61: 4820-4826 (2001). The overall methylation level in colon cancer cell lines appears to be higher as compared to the other cell lines, also consistent with previous results Paz et al. supra, 2003.
Validation of Microarray Data by Methylation-Specific PCR (MSP)
Methylation-specific PCR (MSP) has been widely used to monitor methylation status of individual genes (Eads et al., Nucleic Acids Res 28: E32 (2000); Herman, et al., Proc Natl Acad Sci USA. 93(18): 9821-6 (1996)). MSP was used to confirm the methylation status of CpG sites from five genes that were identified by our microarray analysis as showing distinct methylation profiles in normal lung tissue and lung cancer cell lines.
MSP primers that are specific to either methylated or unmethylated DNA were designed to target corresponding CpG sites within the promoter regions of CFTR, DBC1, DLK1, EYA4 and NPY genes (Table 5). In addition, a pair of primers recognizing a non-CpG-containing region of the β-actin gene (ACTB) was used to measure DNA input. The specificity of each set of MSP primers was first tested using in vitro methylated and unmethylated reference DNAs described above. MSP conditions were optimized to maximize the discrimination between the two methylation states. Bisulfite-treated genomic DNAs derived from one normal lung tissue and six lung cancer cell lines were analyzed using real-time MSP. The methylated and unmethylated alleles in each genomic DNA sample were amplified in separate reactions.
Briefly, the bisulfite-converted genomic DNA was amplified by real-time quantitative PCR using two sets of locus-specific MSP primers, which recognize methylated or unmethylated DNA, respectively. The MSP primers (Table 8) were designed using CpGWare, software provided by Chemicon (Temecula, Calif.). Real-time PCR analysis was performed on an ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, Calif.).
The PCR reaction was performed using a 384-well optical tray in a final volume of 10 μl. The reaction mixture consists of 5 μl of 2×SYBR Green PCR master mix (Applied Biosystems) and 250 nM each of primers and bisulfite-converted DNA template ( ̃50 ng, measured prior to bisulfite treatment). The real-time PCR cycling conditions were as follows: 50° C. for 2 min, 95° C. for 12′, followed by 40 cycles at 95° C. for 20′′, 56° C. for 30′′, and 72° C. for 1′.
After PCR, a thermal melt profile was performed to examine the homogeneity of PCR amplicons. Each DNA sample was analyzed in duplicate and the mean was used for further analysis. The difference of the threshold cycle number (the Ct-values) between the methylated and unmethylated alleles, ΔCt=Ct (unmethylated)−Ct (methylated), was first determined. The percentage of methylated DNA, designated as the methylation level “c”, can be correlated to the ΔCt value through the equation: ΔCt=Log 2[c/(1−c)] ((Martens et al., Cancer Res 65: 4101-4117 (20055); Zeschnigk et al. Nucleic Acids Res 32: e125 (2004)). The resulting methylation level thereby equals 2^ΔCt/(1+2^ΔCt).
Of the 35 MSP data points, 34 were highly concordant with the methylation status determined by the genotyping-based microarray analysis, with a Spearman correlation coefficient, r=0.89 (FIG. 29). These results confirm the overall validity of the method.
Methylation Marker Identification in Lung Adenocarcinomas
The methylation status of the 1536 CpG sites in 23 lung adenocarcinoma and 23 normal lung tissue samples was measured using a data matrix of β-values to identify CpG sites that showed differential methylation in cancers. Using a Mann-Whitney test, 11 normal samples were initially compared to 11 adenocarcinoma samples (a training set of samples obtained from Philipps-University of Marburg, Germany). A false discovery rate (FDR) approach was used as described by Benjamini, Y. and Y. Hochberg, J. R. Statist. Soc. B 57: 289-300 (1995), to select a list of differentially methylated CpG sites. At an FDR=0.001 cutoff, 207 differentially methylated sites were identified. To select markers which had the largest difference between cancer and normal tissues, an additional filter was applied that required a minimum difference of 0.15 in β between the two groups.
A list of 55 differentially methylated markers for lung adenocarcinoma was thus obtained (Table 7). Among these markers, more were hypermethylated in adenocarcinoma, including the genes ASCL2, CDH13, HOXA11, HOXA5, NPY, RUNX3, TERT and TP73 that were selected for validation by bisulfite sequencing (see below). Of these genes, methylation of CDH13 (Ogama et al., Int J Oncol 25: 685-691 (2004); Toyooka et al., Cancer Res 61: 4556-4560 (2001)); HOXA5 (Chen et al., Am J Pathol 163: 37-45 (2003)); RUNX3 (Li et al., Biochem Biophys Res Commun 314: 223-228 (2004)); and TP73 (Lomas et al., Cancer Genet Cytogenet 148: 148-151 (2004)) is known to be associated with tumor progression in various types of cancer. The human telomerase reverse transcriptase gene (TERT) was shown to be inactivated in most differentiated cells, but reactivated in the majority of cancer cells ((Liu et al., Genes Chromosomes Cancer 41, 26-37 (2004)) However, a recent study reported methylation of TERT in cervical cancer and its correlation with poor prognosis (Widschwendter et al., Gynecol Oncol 93: 407-416 (2004)). One of the markers on the list, neuropeptide Y (NPY), which was shown to be hypermethylated in 19 out of the 23 analyzed adenocarcinoma samples and had no or very low methylation in the normal samples, was not previously reported as a cancer marker. NPY may influence lipid metabolism and is potentially associated with hypertension (Tomaszewski et al., Hypertension 44: 689-694 (2004)). Accordingly, it is contemplated that NPY plays a role in lung surfactant-related function.
Clustering of Independent Sample Sets Based on the Identified Methylation Markers
Agglomerative nesting with the Ward method and correlation-based distance were used to cluster the training set samples, i.e. the German samples mentioned above, based on the methylation profiles of the 55 selected markers. Cancer samples were clearly distinguishable from normal samples with one error—cancer sample G12029 co-clustered with the normal samples (FIG. 30 Panel A).
To assess the power of the selected methylation markers for reliable classification of prospective cancer and normal samples, clustered an independent test set of samples was clustered based on the methylation profiles of these markers. This test set contained 12 normal and 12 adenocarcinoma samples, collected from The Pennsylvania State University College of Medicine Tumor Bank. A 100% specificity (12/12) and 92% sensitivity (11/12) was obtained. The specificity was calculated as True Negative (TN)/(TN+False Positive (FP)), and the sensitivity was calculated as True Positive (TP)/(TP+False Negative (FN)). One cancer sample D12162 was co-clustered with the normal samples in this test set (FIG. 30 Panel B). This analysis indicates that the differential methylation pattern for the identified markers was preserved in the two completely unrelated training and test sample sets and that methylation profiling of these markers allows the identification of cancer samples with high specificity and sensitivity.
Methylation Marker Validation by Bisulfite Sequencing
Eight CpG sites which showed elevated methylation in the adenocarcinoma samples were validated using bisulfite sequencing (Frommer, et al., Proc Natl Acad Sci USA. 89(5): 1827-31 (1992)). This method was selected over other methylation detection methods for several reasons: (1) to provide another validation of the disclosed method, in addition to the MSP method; (2) it requires less input DNA as compared to other methods; and (3) the bisulfite sequencing data was used to estimate the bisulfite conversion rate as described above).
PCR primers were designed flanking the CpG sites of interest (Table 9). DNAs from two normal and four adenocarcinoma samples were treated with bisulfite and regions of interest were amplified by PCR. PCR fragments were cloned and individual colonies were picked for sequencing as described above. Twelve cloned fragments were sequenced for each CpG site in selected samples. In all cases an increase in methylation in cancer samples was observed compared to normal samples. Even though the absolute levels of methylation detected by the two different methods were somewhat different (Table 10), a strong correlation was obtained between these two data sets, with a Spearman correlation coefficient, r=0.70. Overall, these results confirm that the disclosed methods can reliably detect methylation differences in clinical samples for more than 1,000 CpG sites and that the assay can be used for both marker discovery and validation.
This Example demonstrates measurement of the methylation status of 1536 CpG sites (derived from 371 genes) in 11 lung adenocarcinomas and 11 matching normal tissue samples. A panel of 55 adenocarcinoma-specific methylation markers was identified by combining p-value and magnitude of change thresholds (FIG. 30 Panel A). Furthermore, adenocarcinoma markers in an independent sample set (N=24) were validated with high sensitivity and specificity (FIG. 30 Panel B). The results described above demonstrate the utility of the invention method for marker identification and validate the robustness of the markers identified.
Throughout this application various publications have been referenced. The disclosures of these publications in their entireties are hereby incorporated by reference in this application in order to more fully describe the state of the art to which this invention pertains.
The term “comprising” is intended herein to be open-ended, including not only the cited elements, but further encompassing any additional elements.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains.
Although the invention has been described with reference to the disclosed embodiments, those skilled in the art will readily appreciate that the specific examples and studies detailed above are only illustrative of the invention. It should be understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is limited only by the following claims.







TABLE 1







Cancer-associated genomic markers and corresponding target nucleic acid probes.













GENBANK

PROBE SEQUENCE
PROBE SEQUENCE
PROBE SEQUENCE



ACCESSION
TARGET SEQUENCE
(ASO1) SEQ ID
(LSO1) SEQ ID
(ASO2) SEQ ID 


TARGET CPG SITE
#
SEQ ID NOS: 1-377
NOS: 378-754
NOS: 755-1131
NOS: 1132-1508





GI29736559_ESR2_1R
NT_026437
GGATGCTCCTCAGCTCTGGGG
GCCCTCACACTTCTAC
GTCCCCAAAACTAAAA
AACACCCTCACACTTC


(SEQ ID 1)

ACGCGGTGCAGAAGTGTGAGG
ACCG
AACATCC
TACACCA




GCGCC





 


GI29736559_ESR2_2R
NT_026437
CTTCCGACTTTGTCACACACC
CCGACTTTATCACACA
CGCCAAACTAAAATCG
CTTCCAACTTTATCAC


(SEQ ID 2)

TGCGCCGCCAGACTGGGGTCG
CCTACG
AACC
ACACCTACA




GGCCCC





 


GI29735559_ESR2_3R
NT_026437
CCCGGCTTCCAGGCAGTAATG
CGACTTCCAAACAATA
ATCCCTACGCGAAAAC
CCCAACTTCCAAACAA


(SEQ ID 3)

GGCGGGTCCCTGCGCGGGAGC
ATAAACG
GTAAC
TAATAAACA




GTGGCGGGCG





 


GI29791372_TGFBR2_1R
NT_022517
AATCTGAAGAAAGCTGAGGGG
CCCTAATAAATCAAAA
CTCCCCTCAACTTTCT
TCCCTAATAAATCAAA


(SEQ ID 4)

AGGCGGCAGATGTTCTGATCT
CATCTACCG
TCAAATT
ACATCTACCA




ACTAGGGA





 


GI29791372_TGFBR2_2R
NT_022517
AACTCCTGAGTGGTGTGGGAG
ACTTTCAACTACCCCT
CCTCCCACACCACTCA
AACTTTCAACTACCCC


(SEQ ID 5)

GGCGGTGAGGGGCAGCTGAAA
CACCG
AAAATT
TCACCA




GTC





GI29791372_TGFBR2_3R
NT_22517
TGGTCTAGGAAACATGATTGG
GTCCAACCCCTAACTC
AACTACCAATCATATT
ACATCCAACCCCTAAC


(SEQ ID 6)

CAGCTACGAGAGAGCTAGGGG
TCTCG
TCCTAAACCA
TCTCTCA




CTGGACGT





GI29791384_TP73_1R
NT_004321
CCGAGGAGCCCAGCGCTAGTG
ACCCGAATCTCTCCTA
CGCCACTAACGCTAAA
TAACACCCAAATCTCT


(SEQ ID 7)

GCGGCGGCCAGGAGAGACCCG
ACCG
CTCCT
CCTAACCA




GGTGTCA





GI29791384_TP73_2R
NT_004321
GACAGCAGGGAGTCCGGGGGA
ACTCTATACCCGACGC
TTCCCCCGAACTCCCT
CCAACTCTATACCCAA


(SEQ ID 8)

AACGCAGGCGTCGGGCACAGA
CTACG
ACTAT
CACCTACA




GTCGG





GI29791384_TP73_3R
NT_004321
GGCCCTGGCGCCGGACCTGCT
CTAACGCCGAACCTAC
CCCTACGTAAACGACC
AACCCTAACACCAAAC


(SEQ ID 9)

TCGGCCCTGCGTGGGCGGCCT
TTCG
TCGC
CTACTTCA




CGCCGG





GI29791621_PTGS2_1R
NT_004487
TCGGAAGCGCTCGGGCAAAGA
CGCCAAATATCTTTTC
AATCTTTACCCGAACG
CCACCAAATATCTTTT


(SEQ ID 10)

CTGCGAAGAAGAAAAGACATC
TTCTTCG
CTTCC
CTTCTTCA




TGGCGG





GI29791621_PTGS2_3R
NT_004487
AGTCTGTCCCGACGTGACTTC
TATCCCGACGTAACTT
CCCTCTAAAAACGTAC
AATCTATCCCAACATA


(SEQ ID 11)

CTCGACCCTCTAAAGACGTAC
CCTCG
AAACCAA
ACTTCCTCA




AGACCAGA





GI29793179_DAPK1_1R
NT_023935
GCGCCGGCCTGGCAGGGCAGC
CCGACCTAACAAAACA
AAATAAATAAACCGCG
ACACCAACCTAACAAA


(SEQ ID 12)

TCGGAGGTGGGTGGGCCGCGC
ACTCG
CCG
ACAACTCA




CGCCAGC





GI29793179_DAPK1_3R
NT_023935
AAACCTTCTTGCCTTCAAGCC
AACCTTCTTACCTTCA
CTCCAACACCAATCCG
AAACCTTCTTACCTTC


(SEQ ID 13)

TCGGCTCCAACACCAGTCCGG
AACCTCG
ACA
AAACCTCA




CAGA





GI29794150_CASP8_1R
NT_005403
GGAAGTGAGAAACAAGTGTGT
AAATACTCCCATTCTC
TTATCACACACTTATT
AAAAATACTCCCATTC


(SEQ ID 14)

GATAAACGGTGGAGAATGGGA
CACCG
TCTCACTTCC
TCCACCA




GCACTCTC





GI29794150_CASP8_2R
NT_005403
GCGGGAGGAGACGAGGAGGGC
ACTACCACTCCCCAAA
CCTCCTCGTCTCCTCC
CACTACCACTCCCCAA


(SEQ ID 15)

GTTCCCTGGGGAGTGGCAGTG
AAACG
C
AAAACA


GI29794150_STAT1_1R
NT_005403
AAGCCGGCGGAAATACCCCAG
CGACGAAAATACCCCA
GTAAACGAAACAACGA
AAACCAACAAAAATAC


(SEQ ID 16)

CGCGTGGGCGGAGCAGCGGCC
ACG
CCCG
CCCAACA




CGCAGA





GI29794150_STAT1_2R
NT_005403
GGCGGTGGCGCCCACGGAACA
GCGCTCAACCAATTAA
GACTATTCCGTAAACG
ACTCCACACTCAACCA


(SEQ ID 17)

GCCGCGTCTAATTGGCTGAGC
ACG
CCACC
ATTAAACA




GCGGAGC





GI29794159_STAT1_3R
NT_005403
CCAGTTCCCAGCGTGGCAACA
AATTCCCAACGTAACA
AACTAAACTACAACTC
CCAATTCCCAACATAA


(SEQ ID 18)

CGGGACTGGGCTGCACCTCAC
ACACG
ACCCAACC
CAACACA




CCAGCCG





GI29794559_CDKN2A_1R
NT_008413
GACTCACCCCTCCTTTCTGCC
ACTCACCCCTCCTTTC
TCCTTCCTTTCCTTAC
AACTCACCCCTCCTTT


(SEQ ID 19)

GCTCCTTCCTTTCCTTGCCCT
TACCG
CCTACTTT
CTACCA




GCTTT





GI29794559_CDKN2A_2R
NT_008413
GCCCGCGTCCGAGTTCCTGGA
GCGTCCGAATTCCTAA
AAACCGAACCTCGCTT
ACCCACATCCAAATTC


(SEQ ID 20)

CGAGAGCCGAGCCTCGCTTAG
ACG
AAACC
CTAAACA




ACCGC





GI29794559_CDKN2A_3R
NT_008413
TCCAGCCCGCGAGGTTTAGGA
AACCCGCGAAATTTAA
ATCCAAACAAACCGCA
TCCAACCCACAAAATT


(SEQ ID 21)

CGGATCCAGGCAGACCGCAGG
AACG
AACT
TAAAACA




CTCC





GI29794559_CDKN2B_1R
NT_008413
AGCAGTGCAGCCAGCATTCCT
CAATACAACCAACATT
CTCCCTAACCCAATCT
AACAATACAACCAACA


(SEQ ID 22)

GGCGGCTCCCTGGCCCAGTCT
CCTAACG
CTAACG
TTCCTAACA




CTGGCGCA





GI29794559_CDKN2B_2R
NT_008413
GGCTGAAGGAACAGAAATCCT
CAACTCCTAATCCCCA
AACAAAAAATTTCTAT
CAACTCCTAATCCCCA


(SEQ ID 23)

CTGCTCCGCCTACTGGGGATT
ATAAACG
TCCTTCAACC
ATAAACA




AGGAGCTG





GI29794559_CDKN2B_3R
NT_008413
AGCATCTTTGGGCAGGCTTCC
CATCTTTAAACAAACT
CCTCGTAACGCGTCGA
AACATCTTTAAACAAA


(SEQ ID 24)

CCGCCCTCGTGACGCGTCGGC
TCCCCG
C
CTTCCCCA




CCGGGC





GI29796774_MYC_1R
NT_008046
AGAGCAGGCAACCTCCCTCTC
AAACAAACAACCTCCC
CCTAACCCAACTCTAA
AAAACAAACAACCTCC


(SEQ ID 25)

GCCCTAGCCCAGCTCTGGAAC
TCTCG
AACAAACAA
CTCTCA




AGGCAG





GI29796774_MYC_2R
NT_008046
ACCTTTGATTTCTCCCAAACC
CTTTAATTTCTCCCAA
CAACCCGAAACTATTA
ACCTTTAATTTCTCCC


(SEQ ID 26)

CGGCAGCCCGAGACTGTTGCA
ACCCG
CAAACCG
AAACCCA




AACCGG





GI29796774_MYC_3R
NT_008046
CAAGGGTCTCTGCTGACTCCC
AAAAATCTCTACTAAC
CTCGATCCACAAACTC
CAAAAATCTCTACTAA


(SEQ ID 27)

CCGGCTCGGTCCACAAGCTCT
TCCCCCG
TCCACT
CTCCCCCA




CCACTT





GI29797939_APC_1R
NT_034772
CAGCAACACCTCTCACGCATG
AACAACACCTCTCACG
ATTATAATCTTCCCAC
CAACAACACCTCTCAC


(SEQ ID 28)

CGCATTGTAGTCTTCCCACCT
CATACG
CTCCCAC
ACATACA




CCCAC





GI29197939_APC_2R
NT_034772
GACAGAACAGCGAAGCAGTGC
CAAAACAACGAAACAA
CAAACGAAACGCAACA
AACAAAACAACAAAAC


(SEQ ID 29)

CCGGCAAGCGGAGCGCAGCAC
TACCCG
CC
AATACCCA




CCATTG





GI29797939_APC_3R
NT_034772
CGCCGGAAGCCTAGCCGCTGC
CGAAAACCTAACCGCT
AAAAAACCTACGAACT
CACCAAAAACCTAACC


(SEQ ID 30)

TCGGGGGGGACCTGCGGGCTC
ACTCG
CAAACCC
ACTACTCA




AGGCCCG





GI29798364_HIC1_1R
NT_010718
TGCCCAGGCCGCAGGGCTGAT
CCCCTTCCCTCAACTA
AAACATCAACCCTACG
CTTCCCCTTCCCTCAA


(SEQ ID 31)

GCCCCCGCTCAGCTGAGGGAA
AACG
ACCTAAA
CTAAACA




GGGGAAG





GI29798364_HIC1_2R
NT_010718
CTCCTGCTCCTTCTCCTGGTC
CTCCTACTCCTTCTCC
ACGAACCGACCTAAAC
CTCCTACTCCTTCTCC


(SEQ ID 32)

CGGGCGGGCCGGCCTGGGCTC
TAATCCG
TCCC
TAATCCA




CCACT





GI29798364_HIC1_3R
NT_010718
AACTGGGGCAACTTCTCCCGA
TAAAACAACTTCTCCC
AAAACGCTAATTCCTC
AACTAAAACAACTTCT


(SEQ ID 33)

GGCGGGAGGCGCTGGTTCCTC
GAAACG
GACTCC
CCCAAAACA




GGCTCCC





GI29798364_TP53_1R
NT_010718
GTTAGTATCTACGGCACCAGG
TATCTACGACACCAAA
AAATCCTAACTCTACA
ATTAATATCTACAACA


(SEQ ID 34)

TCGGCGAGAATCCTGACTCTG
TCGACG
CCCTCCTC
CCAAATCAACA




CACCCTCCTC





GI29798364_TP53_2R
NT_010718
CTCCATTTCCTTTGCTTCCTC
CCATTTCCTTTACTTC
CAAACGAATTACTTAC
CTCCATTTCCTTTACT


(SEQ ID 35)

CGGCAGGCGGATTACTTGCCC
CTCCG
CCTTACTTATCA
TCCTCCA




TTACTTGTCA





GI29798364_TP53_3R
NT_010718
CCCCGCCGCCTGCAGAGGGCG
CGCCGCCTACAAAAAA
AACAAATCTTACACCT
CCCCACCACCTACAAA


(SEQ ID 36)

CAGCAGGTCTTGCACCTCTTC
CG
CTTCTACATCTC
AAACA




TGCATCTC





GI29798595_BRCA1_1R
NT_010755
TTTCGTATTCTGAGAGGCTGC
CACGAAAACCAAAAAA
TAAACAACAACCTCTC
CCACAAAAACCAAAAA


(SEQ ID 37)

TGCTTAGCGGTAGCCCCTTGG
CTACCG
AAAATACG
ACTACCA




TTTCCGTGG





GI29798595_BRCA1_2R
NT_010755
TGGGTGGCCAATCCAGAGCCC
AATAACCAATCCAAAA
AAAACGCTTAACTCTT
TAAATAACCAATCCAA


(SEQ ID 38)

CGAGAGACGCTTGGCTCTTTC
CCCCG
TCTATCCC
AACCCCA




TGTCCCTCC





GI29798595_BRCA1_3R
NT_010755
CTCAGGTAGAATTCTTCCTCT
CAAATAAAATTCTTCC
CTCTTTCCTTTTACGT
CTCAAATAAAATTCTT


(SEQ ID 39)

TCCGTCTCTTTCCTTTTACGT
TCTTCCG
CATCCG
CCTCTTCCA




CATCCGGGG





GI29800185_PTEN_1R
NT_030059
AGACTCGAGTCAGTGACACTG
CTCGAATCAATAACAC
ACCCATCTCAACTTTC
AAACTCAAATCAATAA


(SEQ ID 40)

CTCAACGCACCCATCTCAGCT
TACTCAACG
ATCATCA
CACTACTCAACA




TTCATCATCA





GI29800185_PTEN_2R
NT_030059
AGGGTATTCCCCTTGCAGGGA
AAATATTCCCCTTACA
CCCTACATTTCCCTCT
AAAATATTCCCCTTAC


(SEQ ID 41)

CCGTCCCTGCATTTCCCTCTA
AAAACCG
ACACTAAA
AAAAACCA




CACTGAG





GI29800185_PTEN_3R
NT_030059
AAGGGAGCCGGATGAGGTGAT
CAACCTACTATTATAT
ATATCACCTCATCCGA
AAAACAACCTACTATT


(SEQ ID 42)

ACACGCTGGCGACACAATAGC
CGCCAACG
CTCCCTT
ATATCACCAACA




AGGTTGCTCT





GI29800407_RET_1R
NT_033985
CGGCTCTGCGTAGGTGCGCGG
TACGTAAATACGCGAA
ACTCCTAAATTCCATC
CAACTCTACATAATAC


(SEQ ID 43)

ACCCGGGCTCCTGGGTTCCAT
CCCG
CCCG
ACAAACCCA




CCCCGC





GI29800407_RET_2R
NT_033985
GTCGGCCAGACCTGCATCCCG
CAAACCTACATCCCGC
AACATCCCTACCCTCT
ATCAACCAAACCTACA


(SEQ ID 44)

CGTAGCATCCCTGCCCTCTCT
G
CTATACAA
TCCCACA




GTGCAGCG





GI29800407_RET_3R
NT_033985
CCGCGGAGAGCCTGGAGGCGG
CGAAAAACCTAAAAAC
CCTTCTCTAAATCCGC
CCACAAAAAACCTAAA


(SEQ ID 45)

GGCGCCCTTCTCTGAGTCCGC
GAAACG
GAAAT
AACAAAACA




GGGGTCGC





GI29800594_TGFB1_1R
NT_011109
CCCGTGCGCTTCCTGGGTGGG
CGTACGCTTCCTAAAT
AAACGACTTCAAAACC
CCCATACACTTCCTAA


(SEQ ID 46)

GCCGGGGGCGGCTTCAAAACC
AAAACCG
CCCTAC
ATAAAACCA




CCCTGCC





GI29800594_TGFB1_2R
NT_011109
CTCTTTCTCTGGTGACCCACA
TCTTTCTCTAATAACC
CCGCAAAACCACAACG
CTCTTTCTCTAATAAC


(SEQ ID 47)

CCGCCCGCAAAGCACAGCGCA
CACACCG
C
CCACACCA




TCTGG





GI29800594_TGFB1_3R
NT_011109
CCCAGCCTGACTCTCCTTCCG
CCAACCTAACTCTCCT
TCTAAATCCCCCTCCT
CCCAACCTAACTCTCC


(SED ID 48)

TTCTGGGTCCCCCTCCTCTGG
TCCG
CTAATCG
TTCCA




TCG





GI29802188_SNRPN_1R
NT_026446
TCTCTTGAATCTTGACAATCC
TCTTAAATCTTAACAA
ACACTACTCCCCGCTT
TCTCTTAAATCTTAAC


(SEQ ID 49)

CCGAACACTGCTCCCCGCTTT
TCCCCG
TATTTATT
AATCCCCA




ATTTGTTTAG





GI29802188_SNRPN_2R
NT_026446
TCCCTGGGTGCCTCCTGCGCA
CCTAAATACCTCCTAC
AATTATCCTCCTACGC
TCCCTAAATACCTCCT


(SEQ ID 50)

AGCGCAGTTGTCCTCCTGCGC
GCAAACG
CGACCT
ACACAAACA




CGACCTCG





GI29892395_GLA_1R
NT_011651
GAAAGGAAGAGGGTGATTGGT
ATCAATCACGTAAAAC
TAACCAATCACCCTCT
CAATAATCAATCACAT


(SEQ ID 51)

TAGCGGAACGTCTTACGTGAC
GTTCCG
TCCTTTC
AAAACATTCCA




TGATTATTG





GI29802395_GLA_2R
NT_011651
ACACACCAACCTCTAACGATA
ACACCAACCTCTAACG
ATAATTTTCCTCCTTC
ACACACCAACCTCTAA


(SEQ ID 52)

CCGGGTAATTTTCCTCCTTCT
ATACCG
TTCCCTC
CAATACCA




TCCCTC





GI29802395_GLA_3R
NT_011651
AACTCAGGGCCGTGGTTTTCA
CTCAAAACCGTAATTT
TTTTCGCCTTACGATC
AACTCAAAACCATAAT


(SEQ ID 53)

AACGTTTTTCGCCTTACGGTC
TCAAACG
ACC
TTTCAAACA




ACCCTTAG





GI29802670_AR_1R
NT_011669
TAGAGCTAGCCTCTCCTGCCC
AAAACTAACCTCTCCT
CCACGCTACGCCAACA
TAAAACTAACCTCTCC


(SEQ ID 54)

TCGCCCACGCTGCGCCAGCAC
ACCCTCG
CTTAT
TACCCTCA




TTGTTTCT





GI29802670_AR_2R
NT_011669
TCCTAGAGCAAATGGCACAAT
CCTAAAACAAATAACA
AACCCGATCTATCCCT
TCCTAAAACAAATAAC


(SEQ ID 55)

GCCACGAGGCCCGATCTATCC
CAATACCACG
ATAACGA
ACAATACCACA




CTATGACGGA





GI29802670_AR_3R
NT_011669
CCGGTGGGGGCGGGACCCGAC
ATAAAAACGAAACCCG
AAACTATTACATTTAC
CCAATAAAAACAAAAC


(SEQ ID 56)

TCGCAAACTGTTGCATTTGCT
ACTCG
TCTCCACCTCC
CCAACTCA




CTCCACCTCC





GI29802882_MAGEA1_1R
NT_011726
TCCCGCCAGGAAACATCCGGG
CAAATCAATAACGTCA
ACACCCGAATATTTCC
CACAAATCAATAACAT


(SEQ ID 57)

TGCCCGGATGTGACGCCACTG
CATCCG
TAACGAA
CACATCCA




ACTTGCG





GI29802882_MAGEA1_2R
NT_011726
CTCAACCCTGATGCCCATCCG
CTCAACCCTAATACCC
CCAACCATTCCACCCT
CTCAACCCTAATACCC


(SEQ ID 58)

CCCAGCCATTCCACCCTCA
ATCCG
CA
ATCCA


GI29802882_MAGEA1_1R
NT_011726
CCACTCCAATGCTCACTCCCG
CCACTCCAATACTCAC
AACCCAACCCCCTCTT
CCACTCCAATACTCAC


(SEQ ID 59)

TGACCCAACCCCCTCTTCATT
TCCCG
CATTAT
TCCCA




GT





GI29804485_CTAG2_1R
NT_025965
GACGGACAGGGCAGGCAGGGT
CGAACAAAACAAACAA
AACGATAACCGCACAA
AACACAAAACAAACAA


(SEQ ID 60)

CCGGGACGATGGCCGCACAGT
AATCCG
TCC
AATCCA




CCCGGC





GI29804485_CTAG2_2R
NT_025965
GAGACCTTGGCTGGCGCGAGG
CTTAACTAACGCGAAA
CCACCAAACATACAAT
AAAACCTTAACTAACA


(SEQ ID 61)

CCACGCCCACCAGACATGCAG
CCACG
TCCAACT
CAAAACCACA




TTCCAGCT





GI29804485_CTAG2_3R
NT_025965
GGGCGACTAGGACAGGGACAG
GAATCTCACTCCTAAA
ATTCTATCCCTATCCT
CCAAATCTCACTCCTA


(SEQ ID 62)

AACCCGTTGAACCCAGGAGTG
TTCAACG
AATCGCC
AATTCAACA




AGATCCGG





GI29804485_G6PD_1R
NT_025965
TCCCGCTCCGGAGAGAAGTCT
GCGAACCTACAAAACC
ACTCAAACTTCTCTCC
TCCACAAACCTACAAA


(SEQ ID 63)

GAGTCCGCCAGGCTCTGCAGG
TAACG
GAAACG
ACCTAACA




CCCGCGGA





GI29804485_G6PD_2R
NT_025965
CGGAAGCTCGGTAATGATAAG
ACTCGATAATAATAAA
CCACTTTACAAAACGT
CAAAAACTCAATAATA


(SEQ ID 64)

CACGCCGGCCACTTTGCAGGG
CACGCCG
CACCG
ATAAACACACCA




CGTCACCGCC





GI29804485_G6PD_3R
NT_025965
CCCAATAGGGCCGGCTTGACC
CCAATAAAACCGACTT
GAACAAACGAAAATTC
CCCAATAAAACCAACT


(SEQ ID 65)

CGCGAACAGGCGAGGGTTCCC
AACCCG
CCGA
TAACCCA




GGGGG





GI29804485_SLC6A8_1R
NT_025965
GGGAAGCTGTAGCCCCCAGTG
CTTCCTTAAACTCTCT
TACCCACTAAAAACTA
CCTTCCTTAAACTCTC


(SEQ ID 66)

GGCAGCGGTGGAGAGAGCTCA
CCACCG
CAACTTCCC
TCCACCA




AGGAAGG





GI29804485_SLC6A8_2R
NT_025965
GGTAGCAACCATCCTGCCTCC
ATAACAACCATCCTAC
TAAAACGACGTCTCCT
AATAACAACCATCCTA


(SEQ ID 67)

CGCTGGAGCGGCGTCTCCTCC
CTCCCG
CCCC
CCTCCCA




CCGG





GI29804485_SLC6A8_3R
NT_025965
CCGGCCGGCCGTGGGGGTGGG
GACCGACCGTAAAAAT
TAATAACATCACCCCG
CCAACCAACCATAAAA


(SEQ ID 68)

GCGATAGTGACATCACCCCGG
AAAACG
AAATCG
ATAAAACA




AGTCGG





GI29805823_GSTP1_2R
NT_033903
CCTCTCCCCTGCCCTGTGAAG
CCTCTCCCCTACCCTA
ATATACAAACTCCGAA
CCTCTCCCCTACCCTA


(SEQ ID 69)

CGGGTGTGCAAGCTCCGGGAT
TAAAACG
ATCGCAA
TAAAACA




CGCAGCGG





GI29805823_GSTP1_3R
NT_033903
CGGCCAGCTGCGCGGCGACTC
AACTACGCGACGACTC
AAACTCCAAAACGCCC
CAACCAACTACACAAC


(SEQ ID 70)

CGGGGACTCCAGGGCGCCCCT
CG
CTCTAC
AACTCCA




CTGCG





GI2980729_BCR_1R
NT_011520
GCTGTGAGGTGTGAGGAACTT
GAAAAACACCTTCCAT
AAATAAATTCCTCACA
CAAAAAACACCTTCCA


(SEQ ID 71)

ACCTGCGTCTCCATGGAAGGT
AAAAACG
CCTCACAAC
TAAAAACA




GCCCTCCG





GI2980729_BCR_2R
NT_011520
CTCTGACACGACGACTGGGCA
GCAATACCATAAACGT
CACTACCCAATCGTCG
ACCACAATACCATAAA


(SEQ ID 72)

GTGCCGGTGACGCTTATGGCA
CACCG
TATCAAA
CATCACCA




CTGCGGC





GI2980729_BCR_3R
NT_011520
GGGCTGCGAGTTGCACAGTCC
ACGAATTACACAATCC
TATTATTAAAACCTCA
AAACTACAAATTACAC


(SEQ ID 73)

AATTCGCTGTTGTTAGGGCCT
AATTCG
ATTTCCCAAA
AATCCAATTCA




CAGTTTCCCAAA





GI29807292_TMP3_1R
NT_011520
AAGGAGCAGTGGAAAGGGGTG
AATTAATAACCAAAAA
CACCCCTTTCCACTAC
AATAATTAATAACCAA


(SEQ ID 74)

ACGAGTTCCTGGCTGGCCACC
CTCG
TCCTT
CCAAAAACTCA




AATCATC





GI29807292_TMP3_2R
NT_011520
AACTTGTGGCATTTCTAACAG
TCTCTTTCCCTTTCTC
TTCATCCTATTAAAAA
TCTCTTTCCCTTTCTC


(SEQ ID 75)

GATGAAGCGGAAGAGAAAGGG
TTCCG
TACCACAAATT
TTCCA




AAAGAGA





GI29807454_CALCA_1R
NT_009237
GGGGCGAGGGTTCAAAACCAG
AATAAATTTCACCTCT
CCTAATTTTAAACCCT
CATAATAAATTTCACC


(SEQ ID 76)

GCCGGACTGAGAGGTGAAATT
CAATCCG
CGCCC
TCTCAATCCA




CACCATG





GI29807454_CALCA_2R
NT_009237
CAGGTTCTGGAAGCATGAGGG
AAAATCCTTTACCCCT
CACCCTCATACTTCCA
AAAAATCCTTTACCCC


(SEQ ID 77)

TGACGCAACCCAGGGGCAAAG
AAATTACG
AAACCTA
TAAATTACA




GACCCCT





GI29807454_KAI1_1R
NT_009237
CAGGGCAGGGCAGGATTAGGA
GCACCAACCTAAACTC
CTTCCTAATCCTACCC
CCACACCAACCTAAAC


(SEQ ID 78)

AGGCGCTGAGCCCAGGCTGGT
AACG
TACCCTA
TCAACA




GCGG





GI29807454_KAI1_2R
NT_009237
GATAGAGGAGAGACTCCGTAG
CCCAACTAAAACTAAA
CCCGCTACGAAATCTC
ACCCAACTAAAACTAA


(SEQ ID 79)

CGGGGCGGGGCCTCAGCTCCA
ACCCCG
TCCT
AACCCCA




GCTGGGC





GI29807454_KAI1_3R
NT_009237
GCCGCCTCCTGATAGAGGCCC
GCCTCCTAATAAAAAC
CTTAAAACACAAACCG
ACCACCTCCTAATAAA


(SEQ ID 80)

CGACTTAGGACACAAACCGCT
CCCG
CTCCC
AACCCCA




CCCAC





GI29807454_MYOD1_1R
NT_009237
TTGCCTCTCTCCAAATCTCTC
TACCTCTCTCCAAATC
CCTAATTTCTACAACC
TTACCTCTCTCCAATC


(SEQ ID 81)

ACGACCTGATTTCTACAGCCG
TCTCACG
GCTCTACC
TCTCACA




CTCTACCC





GI29807454_MYOD1_2R
NT_009237
CTTTCTGCGTGTCTCTCAGCC
TACGTATCTCTCAACC
TCCCTCTTTCACGATC
CTTTCTACATATCTCT


(SEQ ID 82)

TCTTTCGGTCCCTCTTTCACG
TCTTTCG
TCACTC
CAACCTCTTTCA




GTCTCACTCC





GI29807454_MYOD1_3R
NT_009237
GGAATATCAGAGCCTCTACGA
AATATCAAAACCTCTA
TTATCTCGAACTCGCC
AAAATATCAAAACCTC


(SEQ ID 83)

CCCGTTTGTCTCGGGCTCGCC
CGACCCG
CACTT
TACAACCCA




CACTTCAA





GI29808625_CDH1_2R
NT_010498
CGAACCCAGTGGAATCAGAAC
GAACCCAATAAAATCA
ACAAATCCCATAACCC
CAACCCAATAAAATCA


(SEQ ID 84)

CGTGCAGGTCCCATAACCCAC
AAACCG
ACCTAAA
AAACCA




CTAGA





GI29880625_CDH1_3R
NT_010498
ACCCTCAGCCAATCAGCGGTA
CCTCAACCAATCAACG
AAAACGATACCTCCGA
ACCCTCAACCAATCAA


(SEQ ID 85)

CGGGGGGCGGTGCCTCCGGGG
ATACG
AACTCAC
CAATACA




CTCACC





GI29823164_TYMS_1R
NT_010859
CTGTGCTGCTGGCTTAGAGAA
TTAAAAACCGTCTAAT
GCCTTCTCTAAACCAA
CCTTTAAAAACCATCT


(SEQ ID 86)

GGCGCGGTCGACCAGACGGTT
CGACCG
CAACACA
AATCAACCA




CCCAAAGG





GI29823164_TYMS_2R
NT_010859
GGCTCCGTTCTGTGCCACACC
TCCGTTCTATACCACA
AACTCCTACGTTTCCC
AACTCCATTCTATACC


(SEQ ID 87)

CGTGGCTCCTGCGTTTCCCCC
CCCG
CCTAAC
ACACCCA




TGGCG





GI29823171_BCL2_1R
NT_025028
CGTGTGACGTTACGCACAGGA
TACTTCATTCTCTACA
CCGATTTCCTATACGT
CTTACTTCATTCTCTA


(SEQ ID 88)

AACCGGTCGGGCTGTGCAGAG
CAACCCG
AACGTCA
CACAACCCA




AATGAAGTAAG





GI29823171_BCL2_2R
NT_025828
CGGGAGGCGGCCGTAGCCAGC
CCTCCTAATCCTACGC
CGCTAACTACGACCGC
CCTCCTCCTAATCCTA


(SEQ ID 89)

GCCGCCGCGCAGGACCAGGAG
GACG
CTC
CACAACA




GAGG





GI29823171_BCL2_3R
NT_025028
CCGTCAGCGCTCGGAGCGGGC
AACGCTCGAAACGAAC
GACGAAAACTCCGAAA
CCATCAACACTCAAAA


(SEQ ID 90)

TGCGCGGCGGGAGCTCCGGGA
TACG
AACGAC
CAAACTACA




GGCGGCC





GI29734309_SYK_1R
NT_008476
GCCCAGCTCCGGGCTCATGGG
CCAACTCCGAACTCAT
GATCAACAAAACGAAC
ACCCAACTCCAAACTC


(SEQ ID 91)

CGCGGTCAGCAGGGCGGGCCA
AAACG
CAAAACG
ATAACCA




GGGCGG





GI29734309_SYK_2R
NT_008476
GGGCTCCTCTACACCTGCCGC
ACTCCTCTACACCTAC
CTAAACCGATTCCGCG
AAACTCCTCTACACCT


(SEQ ID 92)

CGCCTGGGCCGATTCCGCGGG
CGCCG
AAC
ACCACCA




CCTCG





GI29734309_SYK_3R
NT_008476
GGACCGATGCGGTCCATGTCC
ACCGATACGATCCATA
ACAACCCCACCTTCTC
AAACCAATACAATCCA


(SEQ ID 93)

CGGGCAGCCCCACCTTCTCTG
TCCCG
TACCTAC
TATCCCA




CCTGC





GI29740881_HTR1B_1R
NT_007299
GGGAATGCAAGATCTCGGGAC
AAACCAAAACTTACAA
AAAATCCCGAAATCTT
AAAACCAAAACTTACA


(SEQ ID 94)

CTCTCGCTGGCCTGCAAGCTT
ACCAACG
ACATTCC
AACCAACA




TGGTCTC





GI29740881_HTR1B_2R
NT_007299
GGCGGAGGAATAATTGAGGAA
TTATCCCCAATTAATA
AAATTCCTCAATTATT
AATTTATCCCCAATTA


(SEQ ID 95)

CTCACGGAACTATCAACTGGG
ATTCCG
CCTCCGC
ATAATTCCA




GACAAACC





GI29740881_HTR1B_3R
NT_007299
CCCTTTTATGGCTCCGTCTCC
TTTATAACTCCGTCTC
AACAACTCGTCCGAAT
CCCTTTTATAACTCCA


(SEQ ID 96)

GCGGGGCAGCTCGTCCGAGTG
CGCG
AACCAA
TCTCCACA




GCCAGA





GI29741420_MGMT_1R
NT_008818
GGCTTGTACCGGCCGAAGGGC
CTACCCTATACGCCTA
ATAACCCTTCGACCGA
CCACTACCCTATACAC


(SEQ ID 97)

CATCCGGGTCAGGCGCACAGG
ACCCG
TACAAAC
CTAACCCA




GCAGCGG





GI29741420_MGMT_2R
NT_008818
GGCCGGCGTGCCGGCGTCCAG
ACGTACCGACGTCCAA
AAATACGCAAACTACC
AACCAACATACCAACA


(SEQ ID 98)

CGAGGATGCGCAGACTGCCTC
CG
TCAAACC
TCCAACA




AGGCCCG





GI29741420_MGMT_3R
NT_008818
GACCACTCGGGCACGTGGCAG
CACTCGAACACGTAAC
TTACACGCCCGCGAAC
AACCACTCAAACACAT


(SEQ ID 99)

GTCGCTTGCACGCCCGCGGAC
AAATCG
TAT
AACAAATCA




TATCCCT





GI29791697_CDC25A_1R
NT_005825
GAAGTTGCTTACTGATTGGTG
CTTTCCTAATTAACGC
AATCCACCAATCAATA
CCCTTTCCTAATTAAC


(SEQ ID 100)

GATTCCGTTTGGCGCCAACTA
CAAACG
ACAACTTC
ACCAAACA




GGAAAGGG





GI29791697_CDC25A_2R
NT_005825
GAGGCCGAGCCAAGGCTGGAT
AACCGAACCAAAACTA
CCAAACCTCCACAAAT
AAAACCAAACCAAAAC


(SEQ ID 101)

CCGGCCAGACCTCCACAGGTC
AATCCG
CTTCCTT
TAAATCCA




TTCCTT





GI29791697_CDC25A_3R
NT_005825
CTCCAGAGTTGGGCCCTGGTG
AATAACCCCCAAAACT
CCACCAAAACCCAACT
CAATAACCCCCAAAAC


(SEQ ID 102)

GTCGAGTCCAGTCCTGGGGGT
AAACTCG
CTAAAA
TAAACTCA




CATTG





GI29791697_PTHR1_1R
NT_005825
GGGCCCAGCCCTGGGCATCTG
CAAAAACAAATCCAAA
TATTCAAATACCCTAA
AACAAAAACAAATCCA


(SEQ ID 103)

AACACCGGCACACTTGGATCT
TATACCG
AACTAAACCC
AATATACCA




GCCTCTGTT





GI29791697_PTHR1_2R
NT_005825
CAGCCCTGGGCATCTGAACAC
CAACCCTAAACATCTA
CACACTTAAATCTACC
CAACCCTAAACATCTA


(SEQ ID 104)

CGGCACACTTGGATCTGCCTC
AACACCG
TCTATTACCTCC
AACACCA




TGTTGCCTCC





GI29791697_PTHR1_3R
NT_005825
GGGGCTGTAGGGGTCAGGACA
TCATTCCACTCCAACC
ATCCTAACCCCTACAA
TCATTCCACTCCAACC


(SEQ ID 105)

CGGGCTGGAGTGGAATGA
CG
CCCC
CA


GI29792366_SOD3_1R
NT_006316
TCTGGGAAGTCTCCCTCTTAT
CTAAAAAATCTCCCTC
AAAATACCTATCCCTA
TCTAAAAAATCTCCCT


(SEQ ID 106)

CTCGCAGAATGCCTGTCCCTG
TTATCTCG
AATAAAAATCATT
CTTATCTCA




GATAAAGATCATT





GI29792366_SOD3_2R
NT_006316
TTCTCAGCGGGCCAGAAGGAA
AACCCTTCCACCCTAC
CCTTCCTTCTAACCCG
CAACCCTTCCACCCTA


(SEQ ID 107)

GGACGGTAGGGTGGAAGGGCT
CG
CTAAA
CCA




G





GI29792366_SOD3_3R
NT_006316
CACAAGTCCTAGAATACCAGA
CACAATCCTAAAATAC
AAACGTACTTTCTTAA
CACAAATCCTAAAATA


(SEQ ID 108)

ACGGAGACGTGCTTTCTTGGA
CAAAACG
ACCTTAAAGG
CCAAAACA




CCTTAAACGAAA





GI29792503_VHL_1R
NT_005027
CAAGGCTGCAGTGAGCCAAGC
AACTACAATAAACCAA
CACTACACTCCAACCC
CAAAACTACAATAAAC


(SEQ ID 109)

TCGCGCCACTGCACTCCAGCC
ACTCGCG
GAAC
CAAACTCACA




CGGGCGAC





GI29792503_VHL_1R
NT_005927
CCCGTGTGAGATGCGCCACCC
GTATAAAATACGCCAC
ACCTTATTACGACGTC
CCCATATAAAATACAC


(SEQ ID 110)

TCGAACCTTGTTACGACGTCG
CCTCG
GACACAT
CACCCTCA




GCACATTGCG





GI2979312_CD1A_1R
NT_004668
GGGGAATTAAGACCCAATGGG
CCCATCCCCTAATAAT
ATTACCCATTAAATCT
TCCCATCCCCTAATAA


(SEQ ID 111)

CAATCCGTGACTATTAGGGGA
CACG
TAATTCCCC
TCACA




TGGGA





GI29793120_CD1A_2R
NT_004668
CATAGCAGTAGCAGACATCTC
AACAATAACAAACATC
ACCTTACACCTCAACC
CATAACAATAACAAAC


(SEQ ID 112)

TTGTACCGTGCCTTACACCTC
TCTTATACCG
CATTT
ATCTCTTATACCA




AGCCCATTT





GI29793120_S100A2_1R
NT_004668
AGACTGTGGCCCAGCCCAACT
ACTATAACCCAACCCA
CTATATATAAAACAAC
AAACTATAACCCAACC


(SEQ ID 113)

GCGGCTGTGTGTAGAGCAACC
ACTACG
CCCATTTCTCA
CAACTACA




CCATTTCTCA





GI29793120_S100A2_2R
NT_004668
GAGCACTGGACCATGAAGTCT
AACACTAAACCATAAA
ATACTCACAACCTCTC
AAACACTAAACCATAA


(SEQ ID 114)

CAGCGTGTGCTCACAGCCTCT
ATCTCAACG
ACACAAA
AATCTCAACA




CACACAGGA





GI29793234_ABCC5_1R
NT_005962
GGGCAGTTGGTGTTAAAGTGA
CCTACAATCTACGCAA
CTTTCTCACTTTAACA
AACCCTACAATCTACA


(SEQ ID 115)

GAAAGGCGGTATCTGCGCAGA
ATACCG
CCAACTACCC
CAAATACCA




CTGCAGGGTC





GI29793234_ABCC5_2R
NT_005962
CGCAGGGTGGGACTGCTCTGC
CCTATATTCGCACACA
AAACAAAACAATCCCA
CCCTATATTCACACAC


(SEQ ID 116)

CTACGAACAGCACTGTGTGCG
ATACTATTCG
CCCTAC
AATACTATTCA




AACACAGGG





GI29793234_ABCC5_3R
NT_005962
CTGGCCGGCCTGACGACTCTC
ACCGACCTAACGACTC
ATTAAACGCGAACTAC
CTAACCAACCTAACAA


(SEQ ID 117)

AACGGGTTAGACGCGGGCTAC
TCAACG
GATAACC
CTCTCAACA




GATGACCTTCACG





GI29793234_HRASLS_2R
NT_005962
TGGAGGTCTCTGCTGGCTATC
ACACCACACACACACA
CAAATAACCAACAAAA
CACACCACACACACAC


(SEQ ID 118)

TGGCGTGTGTGTGTGTGTGGT
CACG
ACCTCCA
ACACA




GTG





GI29793234_HRASLS-3R
NT_005962
CAAAACCGATTATCTTTATAA
AACCGATTATCTTTAT
CGCCTAACACAACGCC
CAAAACCAATTATCTT


(SEQ ID 119)

CCGCGGCGCCTAGCACAGCGC
AACCGCG
TAATAC
TATAACCACA




CTGGTGCCCT





GI29793705_CD2_1R
NT_004754
GGAGCACATCAGAAGGGCTGG
CACAAAAAACAAAACG
ACAAACCAACCCTTCT
CACATACACAAAACAA


(SEQ ID 120)

CTTGTGCGCGCTCTTGCTCTC
CG
AATATACTCC
CACA




TGTGTATGTG





GI29793872_RASSF_1R
NT_006014
TCCTCACCCCAAGTGAAGGCT
CCTCACCCCAAATAAA
AACTTCCTACCCCACC
TCCTCACCCCAAATAA


(SEQ ID 121)

CGAGACTTCCTGCCCCACCCA
AACTCG
CAATAAA
AAACTCA




GTGGG





GI29793872_RASSF_2R
NT_006014
CCTGCACCCAGGTTTCCATTG
CTACACCCAAAATTCC
GACTCTCCTCAACTCC
CCTACACCCAAATTTC


(SEQ ID 122)

CGCGGCTCTCCTCAGCTCCTT
ATTACG
TTCCC
CATTACA




CCCGC





GI29793872_RASSF1_3R
NT_006014
AGATCACGGTCCAGCCTCTGC
AATCACGATCCAACCT
AACCCCAATCTCCGCA
AAATCACAATCCAACC


(SEQ ID 123)

CGGAGCCCCAGTCTCCGCAGT
CTACCG
ATAAA
TCTACCA




GGAG





GI29794089_PRDM2_1R
NT_004873
CTGGAACATCGCGGGCACAGG
ATACCCAAAAACAATA
TACCCTATACCCGCGA
AATACCCAAAAACAAT


(SEQ ID 124)

GCAGCGTTGGTCACTGCTCCT
ACCAACG
TATTCC
AACCAACA




GGGTACT





GI29794089_PRDM2_2R
NT_004873
GGGGGTCCCGGAGCCCCCCAG
GAACGACGAAACAATA
AACTAAAAAACTCCGA
TCAAACAACAAAACAA


(SEQ ID 125)

CCCCGCAGGCCACTGCCTCGC
ACCTACG
AACCCC
TAACCTACA




CGCCCGA





GI29794089_PRDM2_3R
NT_004873
TCTTCATCAGAACATCTATGA
TCATCAAAACATCTAT
CTCTACAATCCATTTA
TCTTCATCAAAACATC


(SEQ ID 126)

GTCGTCGTCTCTGCAGTCCAT
AAATCGTCG
TTTACCCG
TATAAATCATCA




TTGTTTGCCCGC





GI29794150_NCL_1R
NT_005403
ACTCCAAGGCTGCCCAAGCCT
TCCAAAACTACCCAAA
ACCCAACCACATTAAC
ACTCCAAAACTACCCA


(SEQ ID 127)

ACGGACCCAGCCACATTGGCG
CCTACG
GAACC
AACCTACA




AACCG





GI29794150_NCL_2R
NT_005403
ACTCCGCGGTCCCTGAACTTC
GCGATCCCTAAACTTC
TACTAACGAACTCCTC
ACTCCACAATCCCTAA


(SEQ ID 128)

CGGTGCTGGCGGACTCCTCGC
CG
GCTCCAA
ACTTCCA




TCCAGG





GI29794150_NCL_3R
NT_005403
GGGGTGCCTCCGGCCCCATGC
CACAACTTATCCCTAC
GAACATAAAACCGAAA
AACACAACTTATCCCT


(SEQ ID 129)

TCGCGGGCAAGCAGGGATAAG
TTACCCG
ACACCC
ACTTACCCA




CTGTGCC





GI29794150_TMEFF2_1R
NT_005403
GGCCATCCGGCAAAGACCCGA
TTAACCCAAACAATAA
TCCTTACTCGAATCTT
ATTAACCCAAACAATA


(SEQ ID 130)

GTAAGGAACGCAGGGTCACTG
CCCTACG
TACCGAA
ACCCTACA




CCTGGGCCAAC





GI29794150_TMEFF2_2R
NT_005403
TTTCTCTCAGACCACTTGTCC
TTCTCTCAAACCACTT
CCAATCTAACCTTCCA
TTTCTCTCAAACCACT


(SEQ ID 131)

CGACCAATCTGACCTTCCAAA
ATCCCG
AACACAT
TATCCCA




CACAT





GI29794150_TMEFF2_3R
NT_005403
CGCAGAGCCAGAGACTCCTGC
GCAAAACCAAAAACTC
ATTAAACCTTCTCTCG
CACAAAACCAAAAACT


(SEQ ID 132)

CGAGTTAGACCTTCTCTCGTC
CTACCG
TCGCC
CCTACCA




GCCCC





GI29795229_DBCCR1_1R
NT_008470
GCGCGGTCCCTTTGGATGCTC
CGATCCCTTTAAATAC
ATAAACACAACACCCT
ACACAATCCCTTTAAA


(SEQ ID 133)

GTGCGCATAGACACAACACCC
TCGTACG
ACACGC
TACTCATACA




TACACGCC





GI29795229_DBCCR1_2R
NT_008470
CCCCAGAAACACTCAGGTACT
CCCAAAAACACTCAAA
GACACACACAATACAA
CCCCAAAAACACTCAA


(SEQ ID 134)

CGCGACACACACAGTACAGTC
TACTCG
TCACGCT
ATACTCA




ACGCTTAA





GI29795229_DBCCR1_3R
NT_008470
GCAGACACAAACGGACCCACA
ACACAAACGAACCCAC
ACAACTCCCGAAACAA
ACAAACACAAACAAAC


(SEQ ID 135)

CGGGCAACTCCCGAGACAAAA
ACG
AACC
CCACACA




CCC





GI29795229_TMEFF1_1R
NT_008470
GGACACAAAGGGAAGGCGAGG
AACAAACCCAACCTCT
CTCCTCGCCTTCCCTT
AAACAAACCCAACCTC


(SEQ ID 136)

AGGCGAGCAAGAGGCTGGGCC
TACTCG
TATATC
TTACTCA




TGCCT





GI29795229_TMEFF1_2R
NT_008470
CGGGCGGGCCGGGGTCTTTGT
GAACCGAAATCTTTAT
TAACAACGACCACGAA
CAAACAAACCAAAATC


(SEQ ID 137)

GACGCGGTGGCAACGGCCACG
AACGCG
CACAA
TTTATAACACA




GACACAAAG





GI29795229_TMEFF1_3R
NT_008470
GACAAGTCACCTTTACCTCTT
CAAATCACCTTTACCT
AACTCAATTTCTTCCA
AACAAATCACCTTTAC


(SEQ ID 138)

CCGTGACTCAGTTTCTTCCAC
CTTCCG
CCTAAAAAC
CTCTTCCA




CTAAAAAC





GI29796755_HOXA5_1R
NT_007819
TGCAGCCCCCGGTCGGAAGCT
CCGCTAAAAACAAAAC
CCAACTTCCGACCGAA
ACCCACTAAAAACAAA


(SEQ ID 139)

GGGCGATGAGCCCTGCCTCCA
TCATCG
AACTAC
ACTCATCA




GCGGGT





GI29796755_HOXA5_2R
NT_007819
GGGCGATGAGCCCTGCCTCCA
CGATAAACCCTACCTC
ATAACGCTCGAATCCG
AAACAATAAACCCTAC


(SEQ ID 140)

GCGGGTGGCGCTCGAGTCCGG
CAACG
ACTAAAC
CTCCAACA




CTGAACGGCG





GI29796755_HOXA5_3R
NT_007819
ATCCTCTGCATCCTCGCCGGG
CTACATCCTCGCCGAA
GCGATCGACAACTAAC
ATCCTCTACATCCTCA


(SEQ ID 141)

CGCGCGATCGGCAGCTGACGG
CG
GACCTAA
CCAAACA




CCTAACAA





GI29796755_IGFBP1_2R
NT_007819
GGCGCGGCCTGTGCCCTTTAT
ATACTCGCTAAACACA
ACCTTATAAAAAACAC
AACCAATACTCACTAA


(SEQ ID 142)

AAGGTGCGCGCTGTGTCCAGC
ACGCG
AAACCGC
ACACAACACA




GAGCATCGGCC





GI29796755_IGFBP1_3R
NT_007819
CGCGGCCTGTGCCCTTTATAA
GACCTATACCCTTTAT
TATATCCAACGAACAT
CACAACCTATACCCTT


(SEQ ID 143)

GGTGCGCGCTGTGTCCAGCGA
AAAATACGCG
CGACCA
TATAAAATACACA




GCATCGGCCACC





GI29796755_IL6_1R
NT_007819
TCTGCTTCTTAGCGCTAGCCT
TCTTAACGCTAACCTC
CCTAAACTACACTTTT
TCTACTTCTTAACACT


(SEQ ID 144)

CAATGACGACCTAAGCTGCAC
AATAACG
CCCCCT
AACCTCAATAACA




TTTTCCCCCT





GI29796755_IL6_2R
NT_007819
CCTGGAGACGCCTTGAAGTAA
TACCTAACCATCCTCA
ACAATTACTTCAAAAC
CTACCTAACCATCCTC


(SEQ ID 145)

CTGCACGAAATTTGAGGATGG
AATTTCG
GTCTCCAA
AAATTTCA




CCAGGCAG





GI29796755_TWIST1_1R
NT_007819
GTTTTTGAATGGTTTGGGAGG
CTTCCTCGAAATCTAA
CCTCCCAAACCATTCA
CCTTCCTCAAAATCTA


(SEQ ID 146)

ACGAATTGTTAGACCCCGAGG
CAATTCG
AAAAC
ACAATTCA




AAGG





GI29796755_TWIST1_2R
NT_007819
CCACTCCGGATGGGGCTGCCA
ACTCCGAATTAAAACT
GACCAAAACAATCTCC
CCACTCCAAATAAAAC


(SEQ ID 147)

CCGCGGCCAGGACAGTCTCCT
ACCACCG
TCCGAC
TACCACCA




CCGAC





GI29796755_TWIST1_3R
NT_007819
GGCCTTTGGAACTCCAAGGGG
CCCACCCAATAATCAA
ACCCCTTAAAATTCCA
CCCACCCAATAATCAA


(SEQ ID 148)

TTCGTCTACCTGACCATTGGG
ATAAACG
AAAACC
ATAAACA




TGGG





GI29796774_TRC8_1R
NT_008046
GAAGCTAGAGTAAGCTGAGGA
ACCCATAATTACCATA
CCACCTCCTCAACTTA
CACCCATAATTACCAT


(SEQ ID 149)

GGTGGGCGGAAACCATGGCAA
ATTTCCG
CTCTAACTTC
AATTTCCA




CCATGGGTG





GI29796774_TRC8_2R
NT_008046
ACCACACCCAGCCTAGTGCCA
CACACCCAACCTAATA
ACCGCAAACGCTCCAT
ACCACACCCAACCTAA


(SEQ ID 150)

CGCACCGCAAGCGCTCCATAA
CCACG
AA
TACCACA




ACGCA





GI29796774_TRC8_3R
NT_008046
CGCAGGCAGTGGGCGCGGACT
AAACAATAAACGCGAA
TTCGCTTAACTAACGA
CACAAACAATAAACAC


(SEQ ID 151)

CTGCGGTTCGCTTGACTGACG
CTCTACG
CGCAAC
AAACTCTACA




GCGCAGCCTCC





GI29804415_C4B_1R
NT_007592
TTGTGAGGGCCTTTAAATATC
AAACCCAACATAACCC
ATACAAAATATTTAAA
AAAACCCAACATAACC


(SEQ ID 152)

CTGTACTCGTGGGCCATGTTG
ACG
AACCCTCACAA
CACA




GGCCCT





GI29804415_C4B_2R
NT_007592
TCAGGCACTGGAATGAGAGGA
AACCCTATCCTTCCCC
TAACTCCTCTCATTCC
AAAATAACCCTATCCT


(SEQ ID 153)

GTTAACGGGGAAGGACAGGGT
G
AATACCTAA
TCCCCA




TATTTC





GI29804415_C4B_3R
NT_007592
CCCATGGACACCCAGGTGTCC
CCATAAACACCCAGAT
AATACCCCCACAACTC
CCCATAAACACCCAAA


(SEQ ID 154)

GGGGTGCCCCCACAACTCTGG
ATCCG
TAAACCT
TATCCA




GCCT





GI29804415_CDKN1A_1R
NT_007592
TGGCTCTGATTGGCTTTCTGG
TCAACATATTAAAACA
CCAAAAAACCAATCAA
ACTCAACATATTAAAA


(SEQ ID 155)

CCGTCAGGAACATGTCCCAAC
TATTCCTAACG
AACCA
CATATTCCTAACA




ATGTTGAGC





GI29804415_CSNK2B_1R
NT_007592
CCCACATTACTTGAGGGCTCG
CCACATTACTTAAAAA
ACGCAAAACTCCGAAT
CCCACATTACTTAAAA


(SEQ ID 156)

GGCGTGCGCAAAGCTCCGGGT
CTCGAACG
TCAAT
ACTCAAACA




TCAGTTTC





GI29804415_CSNK2B_2R
NT_007592
ACAATCAAATAGCCACACGGC
AATCAAATAACCACAC
AAACGCATACGTAACG
ACAATCAAATAACCAC


(SEQ ID 157)

ACGAAGACGCATGCGTGGCGA
GACACG
ACAACA
ACAACACA




CAACAACAAC





GI29804415_CSNK2B_3R
NT_007592
CAGCCTGGCCCTTTAAGTCTT
AACCTAACCCTTTAAA
GATCCCATTTCGAAAT
CAACCTAACCCTTTAA


(SEQ ID 158)

CCGCGATCCCATTTCGGAGTT
TCTTCCG
TTCCTCT
ATCTTCCA




TCCTCT





GI29804415_EDN1_1R
NT_007592
TCTTTTTCTTAGCCCTGCCCC
CTTTTTCTTAACCCTA
ATTATCAAACGACGAA
TCTTTTTCTTAACCCT


(SEQ ID 159)

CGAATTGTCAGACGGCGGGCG
CCCCCG
CGTCTACC
ACCCCCA




TCTGCCTCT





GI29804415_EDN1_2R
NT_007592
GGCTGGCAGCTTGCAAAGGGG
TACCCGTACAATACTA
TTCCCCTTTACAAACT
CCTACCCATACAATAC


(SEQ ID 160)

AAGCGGACTCCAGCACTGCAC
AAATCCG
ACCAACC
TAAAATCCA




GGGCAGG





GI29804415_EDN1_3R
NT_007592
GGCCTGGCCTTATCTCCGGCT
ATTATTAATCACCAAC
ACAACCGAAAATAAAA
TTATATTATTAATCAC


(SEQ ID 161)

GCACGTTGCCTGTTGGTGACT
AAACAACG
CCAAACC
CAACAAACAACA




AATAACACAA





GI29804415_HLA-F_1R
NT_007592
AAGTTCAATCAAGGGACTGGG
TCTCCCTTCATTATTC
AATCCCAATCCCTTAA
CATCTCCCTTCATTAT


(SEQ ID 162)

ATTTCGGAATGAATAATGAAG
ATTCCG
TTAAACTT
TCATTCCA




GGAGATG





GI29804415_HLA-F_2R
NT_007592
TCCTTCTTCCTGGATACTCAT
TCTTCCTAAATACTCA
CCCCATTTCTCACTCC
TCCTTCTTCCTAAATA


(SEQ ID 163)

AACGCGGCCCCATTTCTCACT
TAACGCG
CATTAA
CTCATAACACA




CCCATTGG





GI29804415_LY6G6E_1R
NT_007592
GGGGCAGGCTGGGGGCCCCCG
AACCTAACCCAACAAA
AAACCCCCAACCTACC
CAACCTAACCCAACAA


(SEQ ID 164)

CTGCCTGCTGGGTCAGGCTG
CAACG
CC
ACAACA


GI29804415_LY6G6E_2R
NT_007592
AACTCTACAAATCCCGGGATC
TCTACAAATCCCGAAA
AATACAAATCACCTCT
AACTCTACAAATCCCA


(SEQ ID 165)

TCGGGGTGCAGATCACCTCTC
TCTCG
CCCAAA
AAATCTCA




CCAGA





GI29804415_LY6G6E_3R
NT_007592
CTGAAGCTGGGTGGGGTCCGC
CACAAAACCCAACTAT
ACCCCACCCAACTTCA
ACACAAAACCCAACTA


(SEQ ID 166)

CCTTACACAGCTGGGCTTTGT
ATAAAAACG
AA
TATAAAACA




GT





GI29804415_NEU1_1R
NT_007592
GCTGAGGCAGGAGAATCGCTT
TCACTACAACCTCTAC
ATTCAAACGATTCTCC
ACTCACTACAACCTCT


(SEQ ID 167)

GAACCCGGGAGGCAGAGGTTG
CTCCCG
TACCTCA
ACCTCCCA




CAGTGAGC





GI29804415_NEU1_2R
NT_007592
GAGGGCCAATCGGAAGGGCAA
ACGTAATCACGCAACA
AACTTACCCTTCCGAT
CCACATAATCACACAA


(SEQ ID 168)

GCTTCGAGATGCTGCGTGATC
TCTCG
TAACCCT
CATCTCA




ACGTGG





GI29804415_NEU1_3R
NT_007592
GGGCGGATCACCTGAGTCAGG
TCACCATATTAACCAA
ACTCCTAACTCAAATA
TTCACCATATTAACCA


(SEQ ID 169)

AGTTCGAGACCAGCCTGGCCA
ACTAATCTCG
ATCCGCC
AACTAATCTCA




ACATGGTGAA





GI29804415_RDBP_1R
NT_007592
GGTGAAGGAGGTTTGGACTCA
TTTTAACCCTAACCTT
ATTAAATCCAAACCTC
CTTTTAACCCTAACCT


(SEQ ID 170)

ATGCGGGTCAAAGGTTAGGGT
TAACCCG
CTTCACC
TTAACCCA




CAAAAG





GI29804415_RDBP_2R
NT_007592
GGGATCTGGAGGGGGTAGCAC
CCTAACTTTTAACCTT
AATACTACCCCCTCCA
CCCTAACTTTTAACCT


(SEQ ID 171)

TACGGGGAAAGGTCAAAAGTC
TCCCCG
AATCCC
TTCCCCA




AGGG





GI29804415_RDBP_3R
NT_007592
TGGCAGCCCGGAAGTGCGGCA
AAACGAAACTTACTTC
CTACTTACCGCACTTC
CCAAAACAAAACTTAC


(SEQ ID 172)

AGTAGTCGCTGCGAAGTAAGC
GCAACG
CGAACTA
TTCACAACA




CCCGCCCCGG





GI29804415_TNF_1R
NT_007592
ATAGGTTTTGAGGGGATGGGG
AAACCCTAAAAACTAA
CCCCATACCCCTCAAA
AAAACCCTAAAAACTA


(SEQ ID 173)

ACGGGGTTCAGCCTCCAGGGT
ACCCCG
ACCTAT
AACCCCA




CCT





GI29804415_TNF_2R
NT_007592
CCCGCCCCCGCGATGGAGAAG
AATAAACCCTACACCT
TTTCTTCTCCATCGCG
ATAATAAACCCTACAC


(SEQ ID 174)

AAACCGAGACAGAAGGTGCAG
TCTATCTCG
AAA
CTTCTATCTCA




GGCCCACTAC





GI29804415_TNF_3R
NT_007592
CAGAAGGTGCAGGGCCCACTA
AAAAAATACAAAACCC
TTCCTCCAAATAAACT
CAAAAAATACAAAACC


(SEQ ID 175)

CCGCTTCCTCCAGATGAGCTC
ACTACCG
CATAAATTTC
CACTACCA




ATGGGTTTC





GI29804415_VEGF_1R
NT_007592
GTCTCTGGACAGAGTTTCCGG
ACAACCTAAAAATTAC
CCCCGAAAACTCTATC
CACAACCTAAAAATTA


(SEQ ID 176)

GGGCGGATGGGTAATTTTCAG
CCATCCG
CAAAA
CCCATCCA




GCTGTG





GI29804415_VEGF_2R
NT_007592
TCCCCTTCATTGCGGCGGGCT
CCCTTCATTACGACGA
ACCAAACTTCACTAAA
TCCCCTTCATTACAAC


(SEQ ID 177)

GCGGGCCAGGCTTCACTGAGC
ACTACG
CGTCCG
AAACTACA




GTCCGCA





GI29804415_VEGF_3R
NT_007592
GAACGGCTCTCAGGCCCTGTC
AACGACTCTCAAACCC
ACGTAACCTCACTTTC
AAACAACTCTCAAACC


(SEQ ID 178)

CGCACGTAACCTCACTTTCCT
TATCCG
CTACTCC
CTATCCA




GCTCCCT





GI29807454_WT1_1R
NT_009237
CATCTCTACTCCCACCGCATT
CTACTCCCACCGCATT
CCCTACCCGAACTCAC
CATCTCTACTCCCACC


(SEQ ID 179)

CGACCCTGCCCGGACTCACTG
CG
TACTTAC
ACATTCA




CTTACC





GI29824571_MOS_1R
NT_008193
TCAGTCATGTTTCCAAAGTCC
CAATCATATTTCCAAA
GATTTCCCCTAATCTC
TCAATCATATTTCCAA


(SEQ ID 180)

CGCGGTTTCCCCTAGTCTCTT
ATCCCG
TTCATTCA
AATCCCA




CATTCA





GI29789877_CSF1_2R
NT_019273
GTTTGCTGAAGGCTTGGAAGT
TCCTAATCACCCTCTA
TACACTTCCAAACCTT
TTCCTAATCACCCTCT


(SEQ ID 181)

GCAGCGCAGAAGACAGAGGGT
TCTTCTACG
CAACAAAC
ATCTTCTACA




GACTAGGAA





GI29789877_CSF1_3R
NT_019273
TGTGTGTGTGTATGTGTGTGT
AAAAAATCACCCTAAC
CAAACACACACATACA
ATAAAAAATCACCCTA


(SEQ ID 182)

CTGGCGCCTGGCCAGGGTGAT
CAAACG
CACACACA
ACCAAACA




TTCCCAT





GI29789881_MTHFR_1R
NT_021937
GAGCATCTCTGGTTGGAATCA
AAATAACCTAATCACT
AACATTATAATTCCAA
AATAAATAACCTAATC


(SEQ ID 183)

TAATGCTTCGGCCTGAAGTGA
TCAAACCG
CCAAAAATACTC
ACTTCAAACCA




CCAGGCCACTCACT





GI29791372_MLH1_1R
NT_002517
CGGGAGGCCACAAGAGCAGGG
CCCCTTACGACCTTTC
TAACCCTACTCTTATA
TCTCCCCTTACAACCT


(SEQ ID 184)

CCAACGTTAGAAAGCCGCAAG
TAACG
ACCTCCCG
TTCTAACA




GGGAGA





GI29791372_MLH1_2R
NT_002517
TGGCGTAAGCTACAGCTGAAG
CACCTCAATACCTCGT
TCTTCCTTCAACTATA
TCACCTCAATACCTCA


(SEQ ID 185)

GAAGAACGTGAGCACGAGGCA
ACTCACG
ACTTACGCC
TACTCACA




CTGAGGTGA





GI29791372_MLH1_3R
NT_002517
CCGCCACATACCGCTCGTAGT
CACATACCGCTCGTAA
ACTCAACCTCGTAATA
CCACCACATACCACTC


(SEQ ID 186)

ATTCGTGCTCAGCCTCGTAGT
TATTCG
ACGCCTA
ATAATATTCA




GGCGCCTGACG





GI29791392_EGR4_1R
NT_022184
CCACAGGAAATGCACAGGTGA
TAACACTCAATCCCCC
CAATTTCTCACCTATA
TTAACACTCAATCCCC


(SEQ ID 187)

GAAACTGACGTTAAGGGGGAC
TTAACG
CATTTCCTATAA
CTTAACA




TGAGTGTCAA





GI29791392_EGR4_2R
NT_022184
AGGGCAGATTCAAACCCAACA
AAACAAATTCAAACCC
TCCTCCCCTACTACCC
AAAACAAATTCAAACC


(SEQ ID 188)

CGGTCCTCCCCTGCTGCCCCT
AACACG
CTCG
CAACACA




CGGC





GI29791392_EGR4_3R
NT_022184
CGGTCGGCCCGGTGAGGCGCA
ACCCGATAAAACGCAA
CCCAAACTAACGCATC
CAATCAACCCAATAAA


(SEQ ID 189)

GCGCCCCAGACTGGCGCATCC
CG
CG
ACACAACA




GCGGC





GI29791392_POMC_1R
NT_022184
GGCGAGCGGCCAGGTGCGCCT
AACGACCAAATACGCC
CAAAACAATACTAATT
AACAAACAACCAAATA


(SEQ ID 190)

TCGGCAGGACAGTGCTAATTC
TTCG
CCAACCCC
CACCTTCA




CAGCCCC





GI29791392_POMC_2R
NT_022184
CACACGGGGGTGCTAAGCCTC
CACGAAAATACTAAAC
CCGTTCTAAACGAAAC
CACACAAAAATACTAA


(SEQ ID 191)

CCGCCCGTTCTAAGCGGAGAC
CTCCCG
CCAAC
ACCTCCCA




CCAACG





GI29791392_POMC_3R
NT_022184
CGCACGCAGGTAACTTCACCC
ACGCAAATAACTTCAC
CTCAACGACCTCAAAA
CACACACAATAACTTC


(SEQ ID 192)

TCGCCTCAACGACCTCAGAGG
CCTCG
ACTACCC
ACCCTCA




CTGCCC





GI29791392_SFTPB_1R
NT_022184
GAGGTCGCTGCCACTCCTACA
CCCCTTATAACTAAAC
AAAAACTCTATAAAAA
CATAACCCCTTATAAC


(SEQ ID 193)

GAGCCCCCACGCCCCGCCCAG
GAAACG
TAACAACGACCT
TAAACAAAACA




CTATAAGGGGCCATG





GI29791392_SFTPB_2R
NT_022184
ACTCCTACAGAGCCCCCACGC
TCCTACAAAACCCCCA
CCCGCCCAACTATAAA
ACTCCTACAAAACCCC


(SEQ ID 194)

CCCGCCCAGCTATAAGGGGCC
CG
AAA
CACA




A





GI29791392_TGFA_1R
NT_022184
ACGGTAGCCGCCTTCCTATTT
ATAACCGCCTTCCTAT
CCGACGAACAACGCTA
ACAATAACCACCTTCC


(SEQ ID 195)

CCGCCCGGCGGGCAGCGCTGC
TTCCG
CG
TATTTCCA




GGGGCGA





GI29791392_TGFA_2R
NT_022184
CGCCGCCTAGAGCCTGGAAGC
CCGCCTAAAACCTAAA
CACTACGACCCAAAAC
CACCACCTAAAACCTA


(SEQ ID 196)

CGCCACTGCGGCCCAGGACAA
AACCG
AATCC
AAAACCA




TCCGG





GI29791392_TGFA_3R
NT_022184
CCAAGTCTTGGCAAGCGGCCG
AAATCTTAACAAACGA
AACTCACAAATCCCTT
CCAAATCTTAACAAAC


(SEQ ID 197)

GCGAAACTCACAGGTCCCTTT
CCGACG
TCCTAAC
AACCAACA




CCTGGC





GI29794065_N33_1R
NT_015280
CTCCTCTCCTCAGCGCTGGTC
CTCTCCTCAACGCTAA
AAAAAACAAACTCCGA
CTCCTCTCCTCAACAC


(SEQ ID 198)

CGGGAAAGGCAAGCTCCGGGC
TCCG
ACGAAA
TAATCCA




GGGAGCG





GI29794065_N33_2R
NT_015288
GGGAATAGAGATTGCTGGGGA
AAAATAACAACCTAAC
TCCCCAACAATCTCTA
AAAAATAACAACCTAA


(SEQ ID 199)

CCGCAGGGGCCAGGTTGTCAT
CCCTACG
TTCCC
CCCCTACA




TCCC





GI29794065_N33_3R
NT_015288
CCCGAAGCCTGGCTCCCTCGC
CCGAAACCTAACTCCC
CACGCCCACTTCCTAC
CCCAAAACCTAACTCC


(SEQ ID 200)

CACGCCCACTTCCTGCCCC
TCG
CC
CTCA


GI29798364_UBB_1R
NT_010718
CACCAATCAGCGCCGACCTCG
AATCAACGCCGACCTC
CTTCGCAAACCTAACC
CACCAATCAACACCAA


(SEQ ID 201)

CCTTCGCAGGCCTAACCAATC
G
AATCAAT
CCTCA




AGT





GI29798364_UBB_2R
NT_010718
CGCGCTCAGTTACTTAGCAAC
CGCTCAATTACTTAAC
CGCTAAACCACCCCAA
CACACTCAATTACTTA


(SEQ ID 202)

CTCGGCGCTAAGCCACCCCAG
AACCTCG
ATAAAAC
ACAACCTCA




GTGGAGC





GI29798364_UBB_3R
NT_010718
GGAGAATCGCTTGAACCCGGG
GACTCATCGCAACCTC
CTCCCGAATTCAAACG
ATCTCAACTCATCACA


(SEQ ID 203)

AGGCGGAGGTTGCGATGAGCC
CG
ATTCT
ACCTCCA




GAGAT





GI29798595_ERBB2_1R
NT_010755
GGCAGCCTAGGGAATTTATCC
CTCTACCCCCTCCCCC
AATCCGAAATAAATTC
ATAACTCTACCCCCTC


(SEQ ID 204)

CGGACTCCGGGGGAGGGGGCA
G
CCTAAACT
CCCCA




GAGTCAC





GI29798595_ERBB2_2R
NT_010755
GATTCTCCGAGGAAAAGTGTG
AACGCCTAAATTACCT
TCTCACACTTTTCCTC
CAAAACACCTAAATTA


(SEQ ID 205)

AGAACGGCTGCAGGCAACCCA
ACAACCG
GAAAAAT
CCTACAACCA




GGCGTCCCG





GI29798595_ERBB2_3R
NT_010755
AGGAGGGACGCACCCAGGCCT
ACTTCACTTTCTCCCT
GCAAACCTAAATACGT
AACTTCACTTTCTCCC


(SEQ ID 206)

GCGCGAAGAGAGGGAGAAAGT
CTCTTCG
CCCTCCT
TCTCTTCA




GAAGCT





GI29799031_ATP5G1_1R
NT_010783
GTGGCCCAAGCGCCCGAATGA
TCAAACACATAACTTT
ACCTTCATTCGAACGC
AATCAAACACATAACT


(SEQ ID 207)

AGGCTCGGGGCCAAAGTCATG
AACCCCG
TTAAAC
TTAACCCCA




TGTCTGACT





GI29799031_ATP5G1_2R
NT_010783
CTGTGGAGGGAGAGGAAGCAG
TCCCCACTCTTATTAA
AACTACTTCCTCTCCC
TTCCCCACTCTTATTA


(SEQ ID 208)

CTGCGGAAAGCCAATAAGAGT
CTTTCCG
TCCACAA
ACTTTCCA




GGGGAA





GI29799031_ATP5G1_3R
NT_010783
TCCCGCCAACTCTATGGTCGA
CGCCAACTCTATAATC
TTCAAAAATCGACCAA
TCCCACCAACTCTATA


(SEQ ID 209)

GCGTTTCAGGGATCGGCCAAT
GAACG
TCGTAAT
ATCAAACA




CGTAATCC





GI29799031_NME1_1R
NT_010783
CTCGGGAAGCCAATTTGCTCG
GAATTCTCTAACTCAC
TCGCGAACAAATTAAC
CAAATTCTCTAACTCA


(SEQ ID 210)

CGAACGAAGGAAGTGAGTCAG
TTCCTTCG
TTCCC
CTTCCTTCA




AGAACCCG





GI29799031_NME1_2R
NT_010783
GGGTGGAGAGAAGAAAGCAAG
GCTAACTTTTTCAAAC
TTAACTACTTACTTTC
CACTAACTTTTTCAAA


(SEQ ID 211)

CAGCTAACCGGAAAGGTCTGA
CTTTCCG
TTCTCTCCACCC
CCTTTCCA




AAAAGCTAGCG





GI29799031_NME1_3R
NT_010783
CGCAACGTGTGAGCGCCACCT
AACGTATAAACGCCAC
AAAACCAATTTACTCG
CACAACATATAAACAC


(SEQ ID 212)

CTCGGGAAGCCAATTTGCTCG
CTCTCG
CGAACG
CACCTCTCA




CGAACGAAG





GI29799354_NF1_1R
NT_010799
CTCCCGGGTCAGCTCTGGCAC
CCCGAATCAACTCTAA
CAACTAAACCCAACGC
CTCCCAAATCAACTCT


(SEQ ID 213)

TCGCCAGCTGAGCCCAGCGCC
CACTCG
CAATCTA
AACACTCA




AGTCTAG





GI29799354_NF1_2R
NT_010799
GGGCGCCCTAACTTCCAACTC
GCCCTAACTTCCAACT
AAACAATCCAAACCCG
AAACACCCTAACTTCC


(SEQ ID 214)

CGGGAGCAATCCAAACCCGGA
CCG
AAAAC
AACTCCA




GGCC





GI29799354_NF1_3R
NT_010799
TTTTCATTAATGAAACCGGCC
TCATTAATAAAACCGA
GAACGCATACGCGACA
TTTTCATTAATAAAAC


(SEQ ID 215)

GGCGCGGGCGCATGCGCGGCA
CCGACG
AAC
CAACCAACA




GGCCGCC





GI29800594_APOC2_1R
NT_011109
TTCAGGAGGGTGAGGGCAGGA
AAACCTACTAACTCCA
TCCTACCCTCACCCTC
AAAAACCTACTAACTC


(SEQ ID 216)

GCGTGGGTGGAGTCAGCAGGT
CCCACG
CTAAA
CACCCACA




CCCC





GI29800594_APOC2_2R
NT_011109
CAACCCAGCCTCTGTCGGAGG
AACCCAACCTCTATCG
ATTCTCAAAATAAAAA
CAACCCAACCTCTATC


(SEQ ID 217)

CGAATTCTCAGAGTGAGGGTT
AAAACG
TTCCCTATCA
AAAAACA




CCCTGTCA





GI29800594_APOC2_3R
NT_011109
TTCCCTGTGACGTGACCTTGG
TCCCTATAACGTAACC
CATTACCCTTTCTATC
TTCCCTATAACATAAC


(SEQ ID 218)

GGGACGTCATTGCCCTTTCTG
TTAAAAAACG
CCCACC
CTTAAAAAACA




TCCCCACC





GI29800594_KLK10_3R
NT_011109
GGTTTCACTATGTTGGCCAGG
GAATCACGAAATCAAA
AACCAACCTAACCAAC
AACAAATCACAAAATC


(SEQ ID 219)

CTGGTCTCGAACTCCTGACCT
AATTCG
ATAATAAAACC
AAAAATTCA




CGTGATCCGCC





GI29800594_TSLL2_2R
NT_011109
CGGTGGCTCAGCTCACAGCCT
ATAACTCAACTCACAA
TAAACTCCAAACACCC
CAATAACTCAACTCAC


(SEQ ID 220)

CTCGCTAGACTCCAGACACCC
CCTCTCG
ACCTACC
AACCTCTCA




ACCTGCC





GI29800594_TSLL2_3R
NT_011109
ATCCTTGACCTGCCTCGACCT
CTTAACCTACCTCGAC
ACCCTCCGAACCTCCA
ATCCTTAACCTACCTC


(SEQ ID 221)

CCGGACCCTCCGGACCTCCAG
CTCCG
ACTAC
AACCTCCA




CTGC





GI29801019_STK11_1R
NT_011255
AGCCAGCCTGGGGACTGGAGG
ACCAACCTAAAAACTA
CTACATCTACTCATTT
AACCAACCTAAAAACT


(SEQ ID 222)

GTGGCGGCTGCATCTGCTCAT
AAAAATAACG
CCTCCCA
AAAAAATAACA




TTCCTCCCA





GI29801019_STK11_2R
NT_011255
GGAGTCTTGCTGTGTCTCCCA
AAATCTTACTATATCT
AATACAATAACATAAC
AAAATCTTACTATATC


(SEQ ID 223)

GGCCGGAGTGCAATGGCATGA
CCCAAACCG
CTCTACCTCCC
TCCCAAACCA




CCTCTGCCTCCC





GI29801019_STK11_3R
NT_011255
TGGAGTCTTGCTGTGTCTCCC
CAAAAATCATACCATT
CCTAAAAAACACAACA
CAAAAATCATACCATT


(SEQ ID 224)

AGGCCGGAGTGCAATGGCATG
ACACTCCG
AAACTCCA
ACACTCCA




ACCTCTG





GI29801560_CDKN2D_1R
NT_011295
GCCATTGCCGGCGGTCCACCG
ATTACCGACGATCCAC
TTACCACACTCTAACC
ACCATTACCAACAATC


(SEQ ID 225)

GTTGCCACACTCTGACCAATC
CG
AATCAAAA
CACCA




AGGA





GI29801560_CDKN2D_2R
NT_011295
AGTTAAACCAGCCTTCTTTCC
ATTAAACCAACCTTCT
CTACCGAATTCATTTA
AATTAAACCAACCTTC


(SEQ ID 226)

CGCCTGCCGGGTTCATTTGAA
TTCCCG
AAAACCG
TTTCCCA




AACCGAAA





GI29801560_CDKN2D_3R
NT_011295
CTCATCCACTCCGTCTCTCCG
CATCCACTCCGTCTCT
TTCCCTTTCTTCACGA
CTCATCCACTCCATCT


(SEQ ID 227)

TTTCCCTTTCTTCACGGTGCT
CCG
TACTTAACA
CTCCA




TGACA





GI29801560_ICAM_1R
NT_011295
AGCTTGGAAATTCCGGAGCTG
ATCCTCCCTCGCTAAC
TTCAACTCCGAAATTT
AAATCATCCTCCCTCA


(SEQ ID 228)

AAGCGGCCAGCGAGGGAGGAT
CG
CCAAACT
CTAACCA




GACCC





GI29801560_ICAM1_2R
NT_011295
TCCCCGGACGTGGTGAGACCG
CGAACGTAATATAACC
TTCGTCACTCCCACGA
TCCCCAAACATAATAA


(SEQ ID 229)

CGCTTCGTCACTCCCACGGTT
GCG
TTAAC
AACCACA




AGCGG





GI29801560_ICAM1_3R
NT_011295
CCTGGCTCTGCTCTGGCCGCT
CCTAACTAACACGAAC
AAAACGACCAAAACAA
CACCTAACTAACACAA


(SEQ ID 230)

TCTCGAGAAATGCCCGTGTCA
ATTTCTCG
AACCAA
ACATTTCTCA




GCTAGGTG





GI29801767_THBS1_1R
NT_010194
CCAGTCTCTAGTATCCACCTC
CCAATCTCTAATATCC
CATCAACCAAACATTC
CCAATCTCTAATATCC


(SEQ ID 231)

TCGCCATCAACCAGGCATTCC
ACCTCTCG
CGAAA
ACCTCTCA




GGGAG





GI29801767_THBS1_2R
NT_010194
GGGCCAGCTCAGGACAGGCGC
CAACTCAAAACAAACG
AAAACGCGTATCCTCA
AAACCAACTCAAAACA


(SEQ ID 232)

TCGGGGGACGCGTGTCCTCAC
CTCG
CCC
AACACTCA




CCCAC





GI29801767_THBS1_3R
NT_010194
GTGGAGGAGAGTCAGCGAGGG
TTCGTTAAAATACCTA
ACCCTCGCTAACTCTC
CATTCATTAAAATACC


(SEQ ID 233)

CCCGAGGGGCAGGTACTTTAA
CCCCTCG
CTCC
TACCCCTCA




CGAATG





GI29801784_ELK1_1R
NT_011568
GCCTTTTCAGTTGCTCACAGT
CCTTTTCAATTACTCA
CAATCCTTAAACCAAT
ACCTTTTCAATTACTC


(SEQ ID 234)

CCGTCAGTCCTTGAGCCAATC
CAATCCG
CGACGTA
ACAATCCA




GGCGTGGA





GI29801784_ELK1_2R
NT_011568
AGCGCTTTGGCCAATCAGCGA
GCTTTAACCAATCAAC
CGAAACATTAAACTCC
AACACTTTAACCAATC


(SEQ ID 235)

GCGGCGGGACATTGGGCTCCT
GAACG
TCCTCCT
AACAAACA




CCTCCTC





GI29801784_ELK1_3R
NT_011568
CCATTTCTATACAAGCCCTGC
CATTTCTATACAAACC
TAAACAACATAACGTA
CCATTTCTATACAAAC


(SEQ ID 236)

TTCCGCTGAGCAGCATGGCGT
CTACTTCCG
CGACACCG
CCTACTTCCA




GCGACACCGCC





GI29802832_FMR1_1R
NT_011681
CGGGGGTTCGGCCTCAGTCAG
AAAATTCGACCTCAAT
TCAACTCCGTTTCGAT
CAAAAATTCAACCTCA


(SEQ ID 237)

GCGCTCAGCTCCGTTTCGGTT
CAAACG
TTCACT
ATCAAACA




TCACTTC





GI29802832_FMR1_2R
NT_011681
CAGAAATGGGCGTTCTGGCCC
AATAAACGTTCTAACC
AACAATACGACCTATC
CAAAAATAAACATTCT


(SEQ ID 238)

TCGCGAGGCAGTGCGACCTGT
CTCGCG
ACCGC
AACCCTCACA




CACCGCCCT





GI29802832_FMR1_3R
NT_011681
ACCTCCCGCTCAGTCAGACTG
TCCCGCTCAATCAAAC
TACTTTAAACCGAACC
ACCTCCCACTCAATCA


(SEQ ID 239)

CGCTACTTTGAACCGGACCAA
TACG
AAACCAA
AACTACA




ACCAAA





GI29802923_APAF1_1R
NT_019546
TCCACTGCGATATTGCTCCAA
CACTACGATATTACTC
AAAAATTCAAACTCCC
TCCACTACAATATTAC


(SEQ ID 240)

ATCCGAGGAAATTCAAACTCC
CAAATCCG
GAACG
TCCAAATCCA




CGGGCGCG





GI29802923_APAF1_2R
NT_019546
AGCAGGGGCTCCCTTGGGCCC
ACAAAAACTCCCTTAA
CTTCTTCCGACTCTTC
AACAAAAACTCCCTTA


(SEQ ID 241)

CGACTTCTTCCGGCTCTTCAC
ACCCCG
ACCTCAA
AACCCCA




CTCAG





GI29802923_APAF1_3R
NT_019546
CCCGAGTCCGGCATTGGTGGG
CGAATCCGACATTAAT
GACGCGTCCCTAAAAC
CCCAAATCCAACATTA


(SEQ ID 242)

AACGCGGCGCGTCCCTGAGGC
AAAAACG
TTAACC
ATAAAAACA




TTAGCCACG





GI29803889_GPC3_1R
NT_011786
GCTGAAAGAAGGCCTGTGGCG
CCTAACTTAAATCCCC
TTCGCCACAAACCTTC
CCTAACTTAAATCCCC


(SEQ ID 243)

AAGCGGAGGQGGACCTAAGTC
CTCCG
TTTC
CTCCA




AGG





GI29803889_GPC3_2R
NT_011786
CCAGAGCGCCCTGTGTAGAGC
ACGCCCTATATAAAAC
ACGAACAACTAAACTC
CCAAAACACCCTATAT


(SEQ ID 244)

GGCTGCGAGCGGGCAGCTGGG
GACTACG
GACTACCG
AAAACAACTACA




CTCGGCTGCCGGG





GI29803889_GPC3_3R
NT_011786
CCAGAGCCAGTCAGAGCGGAC
CAAAACCAATCAAAAC
CTACTAAAAAACCAAT
CCAAAACCAATCAAAA


(SEQ ID 245)

GGCTGCTGGGAAGCCAATCAG
GAACG
CAACGCG
CAAACA




CGCGCTCG





GI29805200_CCND2_1R
NT_009759
AAATACAAGGGCAGGAGGATT
CCAACTTTAACTTCTT
ATCCTAATCCTCCTAC
CTCCAACTTTAACTTC


(SEQ ID 246)

AGGATCCGTTTTGAAGAAGCC
CAAAACG
CCTTATATTT
TTCAAAACA




AAAGTTGGAG





GI29805200_CCND2_3R
NT_009759
GGGAGGAGAGCTAACTGCCCA
TCTCCGCTCTAAAACG
AAACTAACTAAACAAT
CTCTTCTCCACTCTAA


(SEQ ID 247)

GCCAGCTTGCGTCACCGCTTC
ATAACG
TAACTCTCCTCCC
AACAATAACA




AGAGCGGAGAAGAG





GI29806267_SOD1_1R
NT_011512
AGAAGGTTGTTTTCTCCACAT
AAAAATTATTTTCTCC
AATTCTAAACGTTTCC
AAAAAATTATTTTCTC


(SEQ ID 248)

TTCGGGGTTCTGGACGTTTCC
ACATTTCG
CGACTAC
CACATTTCA




CGGCTGCGGG





GI29806267_SOD1_2R
NT_011512
TTTACATCATTTTGCCAATTT
ACATCATTTTACCAAT
ACTACAACCGACGAAC
TTTACATCATTTTACC


(SEQ ID 249)

CGCGTACTGCAACCGGCGGGC
TTCGCG
CACG
AATTTCACA




CACGCC





GI29806267_SOD1_3R
NT_011512
CAGTCATTCCCGGCCACTCGC
AATCATTCCCGACCAC
GACCCGAAACTACCGC
CAATCATTCCCAACCA


(SEQ ID 250)

GACCCGAGGCTGCCGCAGGGG
TCG
AAA
CTCA




GC





GI29806588_GP1BB_1R
NT_011519
CCCCAGCACCGCTGTGGTGTG
CCAACACCGCTATAAT
AATCCTAAACCTAAAC
CCCCAACACCACTATA


(SEQ ID 251)

CCGGGATCCTGAGCCTAGGCC
ATACCG
CTCCCGA
ATATACCA




TCCCGA





GI29806588_GP1BB_2R
NT_011519
GAGACCCCATTTTCTGTCGAG
AACCCCATTTTCTATC
ACCGAATCTTCCCTTA
AAAACCCCATTTTCTA


(SEQ ID 252)

GCGGGCCGAGTCTTCCCTTAT
GAAACG
TCCC
TCAAAACA




CCCC





GI29806588_GP1BB_3R
NT_011519
AAGGAGGCCAGAGGCTGCAAG
AATATAAACGATCACG
TCCTTACAACCTCTAA
AAAATATAAACAATCA


(SEQ ID 253)

GAGCGGGGTCGTGACCGCTTA
ACCCCG
CCTCCTT
CAACCCCA




CACCCC





GI29807454_WT1_2R
NT_009237
TTTCTGCGCTTTCCTGAAGTT
TACGCTTTCCTAAAAT
CCTCTTAAAACCTACC
TTTCTACACTTTCCTA


(SEQ ID 254)

CCCGCCCTCTTGGAGCCTACC
TCCCG
TACCCCTC
AAATTCCCA




TGCCCCTC





GI29807454_WT1_3R
NT_009237
CGCCCGGCTTATAACTGGTGC
AACGTCCCTCAATTAA
AAATTACACCAATTAT
AAACATCCCTCAATTA


(SEQ ID 255)

AACTCCCGGCCACCCAACTGA
ATAACCG
AAACCGAACG
AATAACCA




GGGACGTTC





GI29808062_SOCS1_1R
NT_010393
CGAACAGGCGGGCAGAGGGCC
CCTCTCTTCTAAACCC
GAAACCCTCTACCCGC
CCTCTCTTCTAAACCC


(SEQ ID 256)

CCGCGGGAGGGTCCAGAAGAG
TCCCG
CTATT
TCCCA




AGG





GI29808062_SOCS1_2R
NT_010393
GGGTCCGAGAAGTGGCCGGAA
CCGAAAAATAACCGAA
AAAATCGAAACCAAAA
AAATCCAAAAAATAAC


(SEQ ID 257)

GGCGCAGGGTCGGGGCCAGAG
AAACG
CCCC
CAAAAAACA




CCCCTC





GI29808062_SOCS1_3R
NT_010393
CGGGCGCCGAACAGAGCGAGC
GCCGAACAAAACGAAC
CCGTAACAACTACACG
CAAACACCAAACAAAA


(SEQ ID 258)

TGCGGCCGTGGCAGCTGCACG
TACG
ACTCCTAA
CAAACTACA




GCTCCTGGCC





GI29808625_CDH3_2R
NT_010498
GAGAATCGCTTCAGCCCGGGA
GATCTCGACTCACTAC
CCTCCCGAACTAAAAC
CAATCTCAACTCACTA


(SEQ ID 259)

GGTCGAGGCTGTAGTGAGCCG
AACCTCG
GATTCT
CAACCTCA




AGATCG





GI29808625_CDH3_3R
NT_010498
CGAGATCGCGCTACTGCACTC
CGCGCTACTACACTCC
CAAAACGAAACCCTAT
CAAAATCACACTACTA


(SEQ ID 260)

CTGGGCGACAGAGCGAGACCC
TAAACG
CTCCAA
CACTCCTAAACA




TGTCTCCAAA





GI29808952_TUBB4_1R
NT_010542
AAAGACAGGGAGCTGGGATGG
CCGATTTAAAAACCAA
ACCATCCCAACTCCCT
ACACCAATTTAAAACC


(SEQ ID 261)

TGCGGGTTGGTCTCTAAACCG
CCCG
ATCTTT
AACCCA




GCGT





GI29808952_TUBB4_2R
NT_010542
CGCGCGGTGCGGAGCCTGCGG
CGATACGAAACCTACG
ACGAAACTCTACGACG
CACACAATACAAAACC


(SEQ ID 262)

GCCGGGCGGGGCTCTGCGGCG
AACCG
ACGC
TACAAACCA




GCGCCTCC





GI29808952_TUBB4_3R
NT_010542
CCCGCGGTGACATCAGCCGAT
GCGATAACATCAACCG
AAAACGAAACCGCGAC
CCCACAATAACATCAA


(SEQ ID 263)

GCGAAGGGCGGGGCCGCGGCT
ATACG
TATAAA
CCAATACA




ATAAGAGCG





GI29823164_ADCYAP1_1R
NT_010859
ATGCCTCTCGGGTGGTGACTC
AACGCTTCTTCAAATT
TAAAATCACCACCCGA
CAAAACACTTCTTCAA


(SEQ ID 264)

CAGCGCAGGAACTTGAAGAAG
CCTACG
AAACAT
ATTCCTACA




CGCTTTG





GI29823164_ADCYAP1_2R
NT_010859
GGTAAGGGAGGGAAAATCTTA
CAACAATCGAATAAAC
TTTAATAAAATTTTCC
AAATCAACAATCAAAT


(SEQ ID 265)

CCAAAGCGACCGGCTCACTCG
CGATCG
CTCCCTTACC
AAACCAATCA




ACTGCTGATTC





GI29823164_ADCYAP1_3R
NT_010859
TCCTGCTGCTCCCGCTGGTTC
TACTACTCCCGCTAAT
CTTCTACTCAAACACC
TCCTACTACTCCCACT


(SEQ ID 266)

CTGCGGCTTCTGCTCAGACAC
TCCTACG
AACGCC
AATTCCTACA




CAACGCCA





GI29823164_MC2R_1R
NT_010859
CCTGAAGAATCAATCAAGTTT
AAAAAATCAATCAAAT
AAAATCAAATCCAAAT
CCTAAAAAATCAATCA


(SEQ ID 267)

TCCGTGAAGTCAAGTCCAAGT
TTTCCG
AACATCCC
AATTTTCCA




AACATCCC





GI29823167_ATP5A1_1R
NT_010966
TCTCCTCCTGCGGCTCCATAG
CCTCCTACGACTCCAT
CTCCCGCCACTTTACT
TCTCCTCCTACAACTC


(SEQ ID 268)

CTGCGGCTCCCGCCACTTTAC
AACTACG
AAAAACT
CATAACTACA




TAGGAACTCC





GI29823167_ATP5A1_2R
NT_010966
GGCTCTGGCATTGCAAGCCTC
CTAACATTACAAACCT
TACCACTTCCCAACTC
AACTCTAACATTACAA


(SEQ ID 269)

GCTTCGTTGCCACTTCCCAGC
CGCTTCG
TTCCC
ACCTCACTTCA




TCTTCCC





GI29823167_ATP5A1_3R
NT_010966
CTCCCAGTGTTGGGATGCAGG
CTTCCACATCTAAACT
GCCCTACATCCCAACA
CAACTTCCACATCTAA


(SEQ ID 270)

GCGCGGAAGCAGTTCAGATGT
ACTTCCG
CTAAAA
ACTACTTCCA




GGAAGCCG





GI17458490_PLAGL2_1R
NT_028392
CGGGTCCCAGGAGCCCGCAGG
TACTACTACTTCTACC
AAATACCTACGAACTC
TTCCTACTACTACTTC


(SEQ ID 271)

CATCCCCGGGAGCGGCAGAAG
GCTCCCG
CTAAAACCC
TACCACTCCCA




CAGCAGCAGGAA





GI17458490_PLAGL2_2R
NT_028392
ACGCTCAGCGGCCGTGCCCAC
TCAACGACCGTACCCA
GACGACGCCCATATCG
ACACTCAACAACCATA


(SEQ ID 272)

GCGGCGGCGCCCATGTCGGCC
CG
AC
CCCACA




CG





GI17458490_PLAGL2_3R
NT_028392
GGCTTAGACCTCAAGCAAGTC
ACTTAAACCTCAAACA
AACCTCTCCCAACCTC
AACTTAAACCTCAAAC


(SEQ ID 273)

ACGTAACCTCTCCCAGCCTCA
AATCACG
AATTTT
AAATCACA




GTTTT





GI29732427_ABL1_1R
NT_035014
AAAGCAGAGAAGCGAGAGCGG
TTCCAACAAATTTCCT
ACTAATAACCGCTCTC
ATCTTCCAACAAATTT


(SEQ ID 274)

CCACTAGTTCGGCAGGAAATT
ACCG
GCTTCT
CCTACCA




TGTTGGAAGAT





GI29732427_ABL1_2R
NT_035014
CAATGCATTCCTTTTCGTCAG
ACATTCCTTTTCGTCA
AAACAAACTCGCTAAA
CAATACATTCCTTTTC


(SEQ ID 275)

AGTCGAGGGCAAACTCGCTGA
AAATCG
ATCTAAATAAC
ATCAAAATCA




AATCTGGGTGACCC





GI29732427_ABL1_3R
NT_035014
GGATTGGTTCCACAGGCCTTT
CATTACCAACAACTTT
TTTTTAAAAAACCTAT
CATTACCAACAACTTT


(SEQ ID 276)

TAAAAAGCGGACTTAAAAGTT
TAAATCCG
AAAACCAATCC
TAAATCCA




GCTGGCAATG





GI29736559_CRIP1_1R
NT_026437
GGGACAGACTCCGCCTGGCAC
ACAAACTCCGCCTAAC
AACCATCCTCCGCCTC
AAAACAAACTCCACCT


(SEQ ID 277)

CGGGACCATCCTCCGCCTCAA
ACCG
A
AACACCA




CTTTG





GI29736559_CRIP1_2R
NT_026437
GTGCCAGACGCCTGTGCCTTG
CCAAACGCCTATACCT
CCTACAACCCTACTAC
ATACCAAACACCTATA


(SEQ ID 278)

GCCGTCCTACAGCCCTGCTGC
TAACCG
CCCTACC
CCTTAACCA




CCCTGCC





GI29736559_CRIP1_3R
NT_026437
CTGGAGTGTGGCCTCTGTGCC
TAAACGACCAAAACAC
CTAACACAAAACCACA
CTATAAAACAACCAAA


(SEQ ID 279)

AGACGCCTGTGCCTTGGCCGT
AAACG
CTCCAA
ACACAAACA




CCTACAG





GI29736559_DAD1_1R
NT_026437
CCTAGAGGAAGTTTAGTCCTT
TCAAAAACTAACAAAC
CCCAAAAACTAAACTT
TTTCAAAAACTAACAA


(SEQ ID 280)

GGGACGCTTGGCCTGCCAGTC
CAAACG
CCTCTAAA
ACCAAACA




TCTGAAA





GI29736559_DAD1_2R
NT_026437
GATGGTGGTGGTGGTAGTGCA
ATCCCCTCAACCCAAC
ACACTACCACCACCAC
ATCCCCTCAACCCAAC


(SEQ ID 281)

CGTTGGGTTGAGGGGAC
G
CATC
A


GI29736559_FOS_1R
NT_026437
TCTGAGACAGGAACTGCGAAA
CCTTAACGCGTATCCT
AAACATTTCGCAATTC
CCACCTTAACACATAT


(SEQ ID 282)

TGCTCACGAGATTAGGACACG
AATCTCG
CTATCTC
CCTAATCTCA




CGCCAAGGCGG





GI29736559_FOS_2R
NT_26437
CAAGGGTCCGCATTGAACCAG
AATCCGCATTAAACCA
ATATTCTCTCTCATTC
CAAAAATCCACATTAA


(SEQ ID 283)

GTGCGAATGTTCTCTCTCATT
AATACG
TACGCCG
ACCAAATACA




CTGCGCCGTTCC





GI29736559_FOS_3R
NT_026437
CGGGAGCCGAGGCTTAAGTCC
GAAACCGAAACTTAAA
AATCCTATACTCGATA
CAAAAACCAAAACTTA


(SEQ ID 284)

TCGGGGTCCTGTACTCGATGC
TCCTCG
CCGTTTCTC
AATCCTCA




CGTTTCTCC





GI29736559_HSPA2_1R
NT_026437
AGACATAGGGTTTGGTGTGGC
AAAACCCGAAAACCCC
ACAACCACACCAAACC
ACAAAACCCAAAAACC


(SEQ ID 285)

TGTGCGGGGTTCCCGGGCCTT
G
CTATATCT
CCA




GC





GI29736559_HSPA2_2R
NT_026437
GCTGGCCTGTCGGCCTCTGAT
CCTATCGACCTCTAAT
ACTTCCAACCCTTAAA
ACTAACCTATCAACCT


(SEQ ID 286)

GCACTCGAACTTCCAGCCCTT
ACACTCG
ACAAACA
CTAATACACTCA




GGAGCAGACA





GI29738185_ESR1_1R
NT_023451
TCAGGGAAGCTGCTCTTTGGG
CAAACACAACTCGATT
TCCCAAAAAACAACTT
TCCAAACACAACTCAA


(SEQ ID 287)

ATCGCTCCAAATCGAGTTGTG
TAAAACG
CCCTAA
TTTAAAACA




CCTGGA





GI29738185_ESR1_2R
NT_023451
GGCCCCTGGATCCGTCTTTCG
CCTAAATCCGTCTTTC
TTATTTTAAACCCAAT
AACCCCTAAATCCATC


(SEQ ID 288)

CGTTTATTTTAAGCCCAGTCT
GCG
CTTCCCT
TTTCACA




TCCCT





GI29738185_ESR1_3R
NT_023451
CCCCATTCTATCTGCCCTATC
CCCATTCTATCTACCC
TTACAATATAATCCTC
CCCCATTCTATCTACC


(SEQ ID 289)

TCGGTTACAGTGTAGTCCTCC
TATCTCG
CCCAAAATC
CTATCTCA




CCAGGGTC





GI29738645_RB1_1R
NT_024924
CCTGGAAGGCGCCTGGACCCA
TAAAAAACGCCTAAAC
CAAATTTCCCAATTTA
CCTAAAAAACACCTAA


(SEQ ID 290)

CGCCAGGTTTCCCAGTTTAAT
CCACG
ATTCCTCA
ACCCACA




TCCTCA





GI29738645_RB1_3R
NT_024524
GCGGGCGGAAGTGACGTTTTC
AACGAAAATAACGTTT
GATTAAACGCGACGCT
ACAAACAAAAATAACA


(SEQ ID 291)

CCGCGGTTGGACGCGGCGCTC
TCCCG
CAATTAC
TTTTCCCA




AGTTGCCGGG





GI29738863_GDF10_1R
NT_030772
TCGCAGCCAGGGCAGGCTGCG
GCAACCAAAACAAACT
CGACACACAAAAACCG
TCACAACCAAAACAAA


(SEQ ID 292)

GCGCCGACACACAGGAGCCGG
ACGACG
ACTAC
CTACAACA




CTGCGGG





GI29738863_GDF10_2R
NT_030772
TCATCTTGCAATCACATAGCC
TCATCTTACAATCACA
ATACCATTACAAAAAC
TCATCTTACAATCACA


(SEQ ID 293)

ATCGCATGCCATTGCAGGGGC
TAACCATCG
AACCCAACA
TAACCATCA




AACCCAGCA





GI29738863_GDF10_3R
NT_030772
CATACGCTGACTCCGAGGCAA
GCTAACTCCGAAACAA
ACACATATCCCCTCTC
CATACACTAACTCCAA


(SEQ ID 294)

CTGGGCGCACACATATCCCCT
CTAAACG
TAATCCTC
AACAACTAAACA




CTCTGGTCCTC





GI29739550_OAT_1R
NT_035040
CAGGGTTCAAGGGCATGGGGC
CTCTACCCCAACCACA
AAACCCCATACCCTTA
ACTCTACCCCAACCAC


(SEQ ID 295)

TCCCGCAGCTGTGGCTGGGGC
ACTACG
AAACCCTA
AACTACA




AGAGC





GI29739550_OAT_2R
NT_035040
GCCCAACAGACTTTTCCTTTT
CCCAACAAACTTTTCC
ACCTCAATCTTCTCGT
ACCCAACAAACTTTTC


(SEQ ID 296)

CGGGCCTCAGTCTTCTCGTCA
TTTTCG
CAACAAA
CTTTTCA




GCAAG





GI29739550_OAT_1R
NT_035040
CACGCGATTGGTATCCTGCCC
GCGATTAATATCCTAC
CCCAACCAATAAACGA
CACACAATTAATATCC


(SEQ ID 297)

TCCGCCCCAGCCAATGAGCGG
CCTCCG
CGAAA
TACCCTCCA




CGAGGGT





GI29789893_CTNNB1_1R
NT_037565
TCCCCGATGCAGACCACAGCG
CCCGATACAAACCACA
CCTCACGAACTACCCT
TCCCCAATACAAACCA


(SEQ ID 298)

CCCTCACGGGCTGCCCTCAGG
ACG
CAAAC
CAACA




CC





GI29791372_RARB_1R
NT_022517
AAAGGGAGAGAAGTTGGTGCT
GAAACGCTACTCCTAA
TAAACACCAACTTCTC
AACCAAAACACTACTC


(SEQ ID 299)

CAACGTGAGCCAGGAGCAGCG
CTCACG
TCCCTTT
CTAACTCACA




TCCCGGCT





GI29791372_RARB_2R
NT_022517
CGTGAGCCAGGAGCAGCGTCC
GTAAACCAAAAACAAC
CTCCTCCCCTACTCAT
CATAAACCAAAAACAA


(SEQ ID 300)

CGGCTCCTCCCCTGCTCATTT
GTCCCG
TTTAAAA
CATCCCA




TAAAA





GI29791372_RARB_3R
NT_022517
GAGCGGGCGCAGGCGGAACAC
AACGCAAACGAAACAC
TTTCCAAACTAAACCG
AAACAAACACAAACAA


(SEQ ID 301)

CGTTTTCCAAGCTAAGCCGCC
CG
CCG
AACACCA




GCAAA





GI29791375_ARH1_1R
NT_032977
GGCTGGGGCACACAAAACCAG
ACCCTATAAACCTAAT
TAACACCTAATTTTAT
CAATACCCTATAAACC


(SEQ ID 302)

GTGCTACCGTCAACGACTAGG
CGTTAACG
ATACCCCAACC
TAATCATTAACA




CCCATAGGGTACTG





GI29791375_ARH1_2R
NT_032977
AAGGGAGAAAGAAGCCAGACG
GTCTACCCTCCACATC
ACTCCGTCTAACTTCT
TACATCTACCCTCCAC


(SEQ ID 303)

GAGCTCGGAGATGTGGAGGGC
TCCG
TTCTCCC
ATCTCCA




AGACGCA





GI29791375_ARH1_3R
NT_032977
CCCAAAACGCTCGGTAGGCGG
AACGCTCGATAAACGA
AACTTTCAATACATCC
CCCAAAACACTCAATA


(SEQ ID 304)

TGGTGCGCAGCTTTCAATGCA
TAATACG
GCCG
AACAATAAATACA




TCCGCCGCCA





GI29791382_SFN_2R
NT_037485
CTCCTGAACCTTTATGGGCTC
ACCCCTAACTACTTCA
CAAAACCCATAAAAAT
AACCCCTAACTACTTC


(SEQ ID 305)

TGTCGAGGCTGAAGCAGCCAG
ACCTCG
TCAAAA
AACCTCA




GGGCT





GI29791382_SFN_3R
NT_037485
GCAGGTCTTGGGCCCTGGTCC
CAAATCTTAAACCCTA
CCCCTACTCCTCCCCA
ACAAATCTTAAACCCT


(SEQ ID 306)

CGCCCCCTGCTCCTCCCCAC
ATCCCG
C
AATCCCA


GI29794267_SFTPC_1R
NT_023666
GAGGGGCTGGAGGAGGAAACG
CCCTTCTATATAAACC
TTCCTCCTCCAACCCC
CCCTTCTATATAAACC


(SEQ ID 307)

GGGAGGCCCACACAGAAGGG
TCCCCG
TC
TCCCCA


GI29794267_SFTPC_2R
NT_023666
GGGCAACTGTCCCCACCCGTC
AAACAACTATCCCCAC
CCCTAACACGACTCTA
AAACAACTATCCCCAC


(SEQ ID 308)

CCTGGCACGGCTCTGCCCAGT
CCG
CCCAA
CCA


GI29794267_SFTPC_3R
NT_023666
TCCTTGCTGCCACACATTTAG
CCTTACTACCACACAT
CCTCCCCATACCAACT
TCCTTACTACCACACA


(SEQ ID 309)

CCGCCCTCCCCATGCCAGCTT
TTAACCG
TAAAA
TTTAACCA




GGGG





GI29794674_EGFR_1R
NT_033968
GTTGGGTGCCCTCATTTCAGA
TCAAATCTTATCAAAC
AATCATCTAAAATAAA
TTCAAATCTTATCAAA


(SEQ ID 310)

TGATTTCGAGGGTGCTTGACA
ACCCTCG
AACACCCAAC
CACCCTCA




AGATCTGAA





GI29794674_EGFR_2R
NT_033968
GGGGACCCTGGCACAGATTTG
AAACCCAACCTATATC
ACCAAATCTATACCAA
CAAACCCAACCTATAT


(SEQ ID 311)

GCTCGACCTGGACATAGGCTG
CAAATCG
AATCCCC
CCAAATCA




GGCCTG





GI29794674_EGFR_3R
NT_033968
GGCAGTGCTGGGAACGCCCCT
AATACTAAAAACGCCC
AAATTAACTCCTCAAA
AACAATACTAAAAACA


(SEQ ID 312)

CTCGGAAATTAACTCCTCAGG
CTCTCG
ACACCCG
CCCCTCTCA




GCACCCGC





GI29796148_TERT_1R
NT_023089
GACCTGGAGGCAGCCCTGGGT
CTTTAACCGCTAACCT
AAACCCWAACTACCTC
CCCTTTAACCACTAAC


(SEQ ID 313)

CTCCGGATCAGGCCAGCGGCC
AATCCG
CAAATC
CTAATCCA




AAAGGG





GI29796148_TERT_2R
NT_023089
CCGCCCGGAGCAGCTGCGCTG
CCCGAAACAACTACGC
AACCAAACCGAACTCC
CCACCCAAAACAACTA


(SEQ ID 314)

TCGGGGCCAGGCCGGGCTCCC
TATCG
CAATAA
CACTATCA




AGTGGA





GI29796148_TERT_3R
NT_023089
GGGTTACCCCACAGCCTAGGC
AATTACCCCACAACCT
TTCGACCTCTCTCCGC
AAATTACCCCACAACC


(SEQ ID 315)

CGATTCGACCTCTCTCCGCTG
AAACCG
TAAA
TAAACCA




GGGCC





GI29797939_IL13_1R
NT_034772
GGCCGCGCAGATCCCGCTTAT
CGCAAATCCCGCTTAT
ACCCATCTCCCGTTAC
AACCACACAAATCCCA


(SEQ ID 316)

CGGGCCCATCTCCCGTTACAT
CG
ATAAAAC
CTTATCA




AAGGC





GI29797939_IL13_2R
NT_034772
GAGCCGCTGGCCCGGGTGTCC
GCCAAACAAAACAAAA
CTAAACACCCGAACCA
AACACCAAACAAAACA


(SEQ ID 317)

AGCCGGCCCTTGCCCTGCCTG
ACCG
ACGACT
AAAACCA




GCGCT





GI29797939_IL13_3R
NT_034772
CACGCAGCGCATTGCAGGCAG
GCAACGCATTACAAAC
ACCACATACGCCAAAT
CACACAACACATTACA


(SEQ ID 318)

GTGCGAGCCACATGCGCCAAG
AAATACG
ACCC
AACAAATACA




TGCCCGGA





GI29797939_LOX_1R
NT_034772
ATCCTGGGGTTTATTTGCTGA
CACTCTCTCGCTTTTA
CCTCAACAAATAAACC
CACACTCTCTCACTTT


(SEQ ID 319)

GGGCGCTTCCCATAAAAGCGA
TAAAAAACG
CCAAAAT
TATAAAAAACA




GAGAGTGTG





GI29797939_LOX_2R
NT_034772
GTGAACAAATAGCTGAGGGGC
ATTACACAAACCGTTC
CCGCCCCTCAACTATT
AAATTACACAAACCAT


(SEQ ID 320)

GGCCGGGCCAGAACGGCTTGT
TAACCCG
TATTC
TCTAACCCA




GTAACTT





GI29797939_LOX_3R
NT_034772
AGGCGAGGAGCTGTCCGCCTT
GAAAAACTATCCGCCT
TTCCAATCGCATTACG
AAACAAAAAACTATCC


(SEQ ID 321)

GCACGTTTCCAATCGCATTAC
TACACG
TAAACA
ACCTTACACA




GTGAACAAATAG





GI29800185_SFTPD_1R
NT_030059
TTGTGAATATCAGTGGCAGGT
TCACTCTTATCCCCAC
TCTAAAAACCTACCAC
ACTCACTCTTATCCCC


(SEQ ID 322)

TTCCAGAACGCAGGTGGGGAT
CTACG
TAATATTCACAA
ACCTACA




AAGAGTGAGT





GI29800185_SNCG_1R
NT_030059
CGGTTTCATGGCAGCCCAGGG
CCACCAACATCCTAAA
TAAACCCTAAACTACC
CCCACCAACATCCTAA


(SEQ ID 323)

TCCAGCGGCATCCAGGATGCT
TACCG
ATAAAACCG
ATACCA




GGTGGG





GI29800185_SNCG_2R
NT_030059
AGCTGCACAGCCCAGGCCGCG
ACACAACCCAAACCGC
AAAATTAACTACTCTC
AACTACACAACCCAAA


(SEQ ID 324)

GGAGGTTGGCTGCTCTCACCT
G
ACCTAACAAACC
CCACA




AACAGGCC





GI29800185_SNCG_3R
NT_030059
ACCTCAGATCCTCCAACCGGT
ACCTCAAATCCTCCAA
TTTCATAACAACCCAA
ACCTCAAATCCTCCAA


(SEQ ID 325)

TTCATGGCAGCCCAGGGTCCA
CCG
AATCCA
CCA


GI29801752_PLAGL1_1R
NT_025741
AGTGCCTGGCTCACGGTCAGT
CCCAAATACAACTACC
AAACACTAACCGTAAA
ACCCAAATACAACTAC


(SEQ ID 326)

GCTCACGTTTGGGCAGCTGCA
CAAACG
CCAAACAC
CCAAACA




CTTGGGC





GI29801752_PLAGL1_2R
NT_025741
CCAGGCCGGCTCGGGTCTACC
AACCGACTCGAATCTA
CAACGCTATACCTAAA
CCAAACCAACTCAAAT


(SEQ ID 327)

TGCGCCAGCGCTGTACCTGGG
CCTACG
CGACCTT
CTACCTACA




CGACCTTGGCTT





GI29801752_PLAGL1_3R
NT_025741
GGGCTCTGGCGGCCCATCCTG
ACTCTAACGACCCATC
AAACTTCGACTAACAA
AAACTCTAACAACCCA


(SEQ ID 328)

GCGGAGACTTCGGCTAGCAGG
CTAACG
ACCCCG
TCCTAACA




CCCCGC





GI29803948_CD63_1R
NT_029419
CTGGCTTTGTCTTCTGGGATA
CTTACATCCTCTAAAT
CTTATCCCAAAAAACA
ACTTACATCCTCTAAA


(SEQ ID 329)

AGACGGGGAAATCCAGAGGAT
TTCCCCG
AAACCAA
TTTCCCCA




GTAAGC





GI29803948_CD63_2R
NT_029419
GGCAGCTTGATCCCCCTAGCC
ACAACTTAATCCCCCT
TTCCAACCCTCACCTA
AACAACTTAATCCCCC


(SEQ ID 330)

CGCTTCCAGCCCTCACCTGGC
AACCCG
ACTTTATC
TAACCCA




TTTGTC





GI29803948_CD63_3R
NT_029419
GGTGTGACGGCCGGTCCTACT
ATAACGACCGATCCTA
CCTATTCAAATCAACT
AATATAACAACCAATC


(SEQ ID 331)

CTCCGGCCTGTTCAGATCAGC
CTCTCCG
CCTTCTAAA
CTACTCTCCA




TCCTTCTGGG





GI29803948_CDK4_1R
NT_029419
TCCTGGGGACCCTTGACCCTC
CTAAAAACCCTTAACC
CACAAACAAACGCTCC
TCCTAAAAACCCTTAA


(SEQ ID 332)

CGACACAAACAAGCGCTCCAG
CTCCG
AAAA
CCCTCCA




GAGTC





GI29803948_CDK4_2R
NT_029419
CCCTTAGCTTACGGAGACCCT
ACTATTCTCTCCTATT
CCCAAAATCTCCGTAA
AACTACTATTCTCTCC


(SEQ ID 333)

GGGGCGAGGGAAATAGGAGAG
TCCCTCG
ACTAAAA
TATTTCCCTCA




AACAGCAGTC





GI29803948_CDK4_3R
NT_029419
TGTTAATGGGTGAGAGGGATT
TAAAACTTTCTCCCCG
AAAATCCCTCTCACCC
ATAAAACTTTCTCCCC


(SEQ ID 334)

TTGCGCTCTCCGGGGAGAAAG
AAAAACG
ATTAACA
AAAAAACA




CCCCAC





GI29804485_BCAP31_1R
NT_025965
GGCCCTCCAGGGTCGCGGCCT
AATTCCTCCACTTAAA
AAAAACCGCGACCCTA
AAATTCCTCCACTTAA


(SEQ ID 335)

CTTCGGGCAGCCCAAGTGGAG
CTACCCG
AAAA
ACTACCCA




GAACTT





GI29804485_BCAP31_2R
NT_025965
GGGCTCAGGGCCCTCCAGGGT
AACTCAAAACCCTCCA
GACCTCTTCGAACAAC
AAACTCAAAACCCTCC


(SEQ ID 336)

CGCGGCCTCTTCGGGCAGCCC
AAATCG
CCAAATA
AAAATCA




AAGTG





GI29804485_CTAG1_1R
NT_025965
CCAGCTGGGCGACCAGGACAG
CTAACTAAACTCAACA
CCCTATCCTAATCGCC
CTCTAACTAAACTCAA


(SEQ ID 337)

GGACGGAGGCTGCTGAGCCCA
ACCTCCG
CAACTAA
CAACCTCCA




GTTAGAG





GI29804485_CTAG1_2R
NT_025965
GAGACCTTGGCTGGCGCGAGG
CTTAACTAACGCGAAA
CCACCAAACATACAAT
AAAACCTTAACTAACA


(SEQ ID 338)

CCACGCCCACCAGACATGCAG
CCACG
TCCAACT
CAAAACCACA




TTCCAGCT





GI29804485_CTAG1_3R
NT_025965
GGGCGACTAGGACAGGGACAG
GAATCTCACTCCTAAA
ATTCTATCCCTATCCT
CCAAATCTCACTCCTA


(SEQ ID 339)

AACCCGTTGAACCCAGGAGTG
TTCAACG
AATCGCC
AATTCAACA




AGATCCGG





GI29804485_STK23_1R
NT_025965
GGGGGGCCTCTGCCCGGGGGC
AAAACCTCTACCCGAA
ACTCTCTAAAACCGCC
AAAAAACCTCTACCCA


(SEQ ID 340)

CGGGCTCTCTGGGGCCGCCGT
AACCG
GTCG
AAAACCA




CGCGCC





GI29804485_STK23_2R
NT_025965
GGGGTACGGGCTGCTTCACCC
AATACGAACTACTTCA
GTAAACCTCCCCCTCA
AAAATACAAACTACTT


(SEQ ID 341)

CCGCGTGGACCTCCCCCTCAG
CCCCCG
ACTAAAA
CACCCCCA




CTGGGG





GI29804485_STK23_3R
NT_025965
CTGGGAGCCAGAGCGCCAGGC
CAACCCAAAACCTACT
CGCTACCTAACGCTCT
CCAACCCAAAACCTAC


(SEQ ID 342)

AGCGTCGCAGGAGCAGGTTCT
CCTACG
AACTCC
TCCTACA




GGGCTGG





GI29804900_PRKCDBP_1R
NT_028310
TGAAACAGGCACACCAGGGAA
AAATTTAAATTACCCT
TCCAATTCCCTAATAT
CTCTAAATTTAAATTA


(SEQ ID 343)

TTGGAGCGGAGGAGGGTAACT
CCTCCG
ACCTATTTCA
CCCTCCTCCA




CAAACTCAGAG





GI29804900_PRKCDBP_2R
NT_028310
GGCCCCTCTGGTTATCTCTTT
CCCCTCTAATTATCTC
ACCAACACAATCTCTA
AACCCCTCTAATTATC


(SEQ ID 344)

GCCGTGCCAACACAGTCTCTG
TTTACCG
CGCCC
TCTTTACCA




CGCCCAC





GI29804900_PRKCDBP_3R
NT_028310
GCAGCCTGTCCAAGTCACAAA
CAACCTATCCAAATCA
AACCTCGAACCTTAAC
ACAACCTATCCAAATC


(SEQ ID 345)

GCGGGGCCTCGGGCCTTGACA
CAAAACG
AATTCG
ACAAAACA




GTTCGCG





GI29805597_APOA1_1R
NT_033899
AAGGGGATGAGTGCAGGGAAC
CAAATCTCCCGAATAA
AATTCCCTACACTCAT
CTTACAAATCTCCCAA


(SEQ ID 346)

CCCGACCCCACCCGGGAGACC
AATCG
CCCCTT
ATAAAATCA




TGCAAG





GI29805597_APOA1_2R
NT_033899
GCAGACACTCCCCTCCCGCCC
CAAACACTCCCCTCCC
CCCCACTAAACCCTTA
ACAAACACTCCCCTCC


(SEQ ID 347)

CCACTGAACCCTTGACCC
G
ACCC
CA


GI29805597_APOA1_3R
NT_033899
CGAAACCTCAGTCTGGGAGCC
AAACCTCAATCTAAAA
AAAACTCTCCCCTCTC
CAAAACCTCAATCTAA


(SEQ ID 348)

ACGGAGGGCTCTCCCCTCTCC
ACCACG
CCC
AAACCACA




CC





GI29805597_PGR_1R
NT_033889
GCCCCTCCCCAGATGATCGAG
CCCCTCCCCAAATAAT
AAAATCCAAACTCCTT
ACCCCTCCCCAAATAA


(SEQ ID 349)

GGGTCCAGGCTCCTTACCTCT
CG
ACCTCTAAT
TCA




AGT





GI29805597_PGR_2R
NT_033099
CATATTATATCCTGCCATTTG
CATATTATATCCTACC
TAAACATTCTATACCT
CATATTATATCCTACC


(SEQ ID 350)

ATGCCCGGTGAACATTCTATA
ATTTAATACCCG
ACTTCCCAAAA
ATTTAATACCCA




CCTGCTTCCCAGAA





GI29809602_CDH13_1R
NT_024797
GAAAAGTGGAATCAGCTGGCA
AAACTCAACCTCACAA
TAAACAATACCAACTA
TTAAAACTCAACCTCA


(SEQ ID 351)

TTGCCCAGCGTGATTTGTGAG
ATCACG
ATTCCACTTTTC
CAAATCACA




GCTGAGCCCCAA





GI29809602_CDH13_2R
NT_024797
ACGAGGGAGCGTTAGGAAGGA
CCAAAACCAATAACTT
ATTCCTTCCTAACGCT
CCAAAACCAATAACTT


(SEQ ID 352)

ATCCGTCTTGTAAAGCCATTG
TACAAAACG
CCCTC
TACAAAACA




GTCCTGG





GI29809602_CDH13_3R
NT_024797
AAGCAAATGGGATGCCACCTC
AAATAAAATACCACCT
AACTCGCTCCTCGCGA
AAACAAATAAAATACC


(SEQ ID 353)

CGCGGGGCTCGCTCCTCGCGA
CCGCG
AATAC
ACCTCCACA




GGTGCTCAC





GI29809804_ASC_1R
NT_024812
GGTTCGGGGAACCGCGGAGGT
AAAAACCGCGAAAATT
AATTCTAAAAATCCGA
AATTCAAAAAACCACA


(SEQ ID 354)

TTCGGGGTTCTAGAAATCCGA
TCG
AATTCTAAACC
AAAATTTCA




GGTTCTAAGCC





GI29809804_ASC_2R
NT_024812
GGAGGTTTCGGGGTTCTAGAA
AAAACACCTAAACTTA
ATTTCTAAAACCCCGA
TTAAAACACCTAAACT


(SEQ ID 355)

ATCCGAGGTTCTAAGCCTAGG
AAACCTCG
AACCTC
TAAAACCTCA




TGCTCCAA





GI29809804_ASC_3R
NT_024812
GTGAGAGCCAGCCCAGGTTTC
AAACCAACCCAAATTT
TCTATACCCGCTAATA
ATAAAAACCAACCCAA


(SEQ ID 356)

CGGTCTGTACCCGCTGGTGCA
CCG
CAAACCC
ATTTCCA




AGCCCAGA





GI29809804_PRSS8_1R
NT_024812
CACCACATACACACTACACAC
ACCACATACACACTAC
AACACACACACAAAAA
CACCACATACACACTA


(SEQ ID 357)

CGGGACACACACACAAAAGGC
ACACCG
ACCTATACA
CACACCA




CTATGCA





GI29809804_PRSS8_2R
NT_024812
CACACACACAAACCACATGCA
ACACACACAAACCACA
ACATACACGCACACTC
CACACACACAAACCAC


(SEQ ID 358)

CGTGCATGCACGCACACTCAG
TACACG
AAACA
ATACACA




ACACAC





GI29809804_PRSS8_3R
NT_024812
TGAGCTCAGGGCTCAAGGGGG
AAACAAACACCAACCC
CCCCCTTAAACCCTAA
AAAACAAACACCAACC


(SEQ ID 359)

ACGGGGCTGGTGCCTGTTTT
CG
ACTCA
CCA


GI29823171_SERPINB5_1R
NT_025028
TTCACCTTCCGGTCCTGCGTG
ACATACGTACGACAAT
CCCACGCAAAACCGAA
CAAACATACATACAAC


(SEQ ID 360)

GGCCGAGAGGATTGCCGTACG
CCTCTCG
AA
AATCCTCTCA




CATGTCTG





GI29823171_SERPINB5_1R
NT_025020
CAAACTCCTGGGCTCAAGCAA
AACTCCTAAACTCAAA
TCACGTCAACCTCCCC
CAAACTCCTAAACTCA


(SEQ ID 361)

TCCGCTCACGTCAACCTCCCC
CAATCCG
AAATA
AACAATCCA




AAATGCT





GI29789893_CTNNB1_2R
NT_037565
AGCGGTACTCGAAGGCCGGGG
GATACTCGAAAACCGA
AATACCACCTTCCGCA
AACAATACTCAAAAAC


(SEQ ID 362)

CCGAGATGCCACCTTCCGCAG
AACCG
AACC
CAAAACCA




GCCGC





GI29789893_CTNNB1_3R
NT_037565
GGGTGCTGTGAGACTGGGCTG
CACACTCCCTACTAAA
AACCCAATCTCACAAC
CACACTCCCTACTAAA


(SEQ ID 363)

CGACCCAGGTCCAGCAGGGAG
CCTAAATCG
ACCC
CCTAAATCA




TGTG





GI29794147_RUNX3_1R
NT_077383
TGAGCCAAGGCCGCGAGCAGG
CCAACCTCAACTCACA
AAAACCTACTCGCGAC
CCAACCTCAACTCACA


(SEQ ID 364)

CTTCTCGCATCCTGTGAGCTG
AAATACG
CTTAACT
AAATACA




AGGTTGG





GI29794147_RUNX3_2R
NT_077383
GTGACATCACGGCCCAGGTGA
TAACATCACGACCCAA
GACCCAACCAATAAAC
ATAACATCACAACCCA


(SEQ ID 365)

CCGCGGCCCAGCCAATGAGCC
ATAACCG
CAAAAC
AATAACCA




AAGGCC





GI29794147_RUNX3_3R
NT_077383
TGGTGGACAATGGCAGGGAGT
ACCTCGCCCCTAAACT
CTAACTCCCTACCATT
ATACCACCTCACCCCT


(SEQ ID 366)

CAGCCGCAGCCCAGGGGCGAG
ACG
ATCCACCA
AAACTACA




GTGGCAT





GI29794313_MYCL1_1R
NT_077386
CCCAGCTCGGAGTGGGCAAGG
TACCAACCGACTAAAC
ACTCCTTACCCACTCC
CAATACCAACCAACTA


(SEQ ID 367)

AGCACGGTTTAGCTCAGCCGG
TAAACCG
GAACTAA
AACTAAACCA




CTGGCACCG





GI29794313_MYCL1_2R
NT_077386
CGACGGGTGACCAGACGGACG
AATAACCAAACGAACG
AACCAACCCAAATTCA
CAACAAATAACCAAAC


(SEQ ID 368)

CCGGGACCAGCCCAAGTTCAG
CCG
AAACTAA
AAACACCA




GGCTGA





GI29799662_SCGB3A1_1R
NT_077451
GGCAGGGACCAGGGAGCCAGG
CAAAACAAAAACGAAA
AATTCCTAACTCCCTA
ACCAAAACAAAAACAA


(SEQ ID 369)

AACTGCGCCGCCCCCGCCCCT
ACGACG
ATCCCTACC
AAACAACA




GCCCTGGC





GI29799662_SCGB3A1_2R
NT_077451
GGCGAGGACCGGGTATAAGAA
ACTACCCGAACAAAAC
AACTTCTTATACCCGA
ACAACTACCCAAACAA


(SEQ ID 370)

GCCTCGTGGCCTTGCCCGGGC
CACG
TCCTCG
AACCACA




AGCCGC





GI29799662_SCGB3A1_3R
NT_077451
CGCGTGGGGTCAGACCGCAAA
GTAAAATCAAACCGCA
AAATACGAACCGAAAT
CACATAAAATCAAACC


(SEQ ID 371)

GCGAAGGTGCGGGCCGGGGTG
AAACG
AAACCTC
ACACAAAACA




GGCCTCGCG





GI29801002_SFTPA1_1R
NT_077575
TGGGGCTCATGGCTGAGCCAG
CCAAACCAACTTATCT
CCTAACTCAACCATAA
TCCAAACCAACTTATC


(SEQ ID 372)

GTCGCAGGACAGACAAGTTGG
ATCCTACG
ACCCCA
TATCCTACA




CCTGGA





GI29801002_SFTPA1_2R
NT_077575
TTACAGACCTGGAGTTCCTCT
CAAACCTAAAATTCCT
AAATTCTATACTCCCC
TTACAAACCTAAAATT


(SEQ ID 373)

TTCGCAGGTTCTGTGCTCCCC
CTTTCG
TCAAAAATC
CCTCTTTCA




TCAAGGGTC





GI29801002_SFTPA1_3R
NT_077575
AGACAGAAACTGCAGCTCTCC
AACAAAAACTACAACT
AACCTTCCAAATACTA
AAACAAAAACTACAAC


(SEQ ID 374)

CGTGACCTTCCAGGTGCTGCC
CTCCCG
CCCTAACTC
TCTCCCA




CTGACTC





GI29804083_UNG_1R
NT_078089
CCGAGAGACAGGGTCTCGCTC
CCAATACACTCCAAAC
CAAAACGAAACCCTAT
ACCAATACACTCCAAA


(SEQ ID 375)

TGTCGCCCAGGCCTGGAGTGC
CTAAACG
CTCTCG
CCTAAACA




ATTGGC





GI29804083_UNG_2R
NT_078089
CGCTAGGATTACAGGCGTGGG
AAAATTACAAACGTAA
GCCTAACCAATCTTCT
CACTAAAATTACAAAC


(SEQ ID 376)

CCACCGCGCCTGACCAGTCTT
ACCACCG
CTTCTTACA
ATAAACCACCA




CTCTTCTTGCAG





GI29804083_UNG_3R
NT_078089
GCTCAGACCCTCTGGCCTCAA
TCAAACCCTCTAACCT
TCCTCCAACCTAAACC
ACTCAAACCCTCTAAC


(SEQ ID 377)

GCGATCCTCCAGCCTGGGCCT
CAAACG
TCCC
CTCAAACA




CCC
















TABLE 2







Methylation Markers for Squamous Cell Carcinoma


METHYLATION MARKERS FOR SQUAMOUS CELL CARCINOMA










Panel_I
Panel_II
Panel_III
Panel_IV














Marker Name
p-value
Marker Name
p-value
Marker Name
p-value
Marker Name
p-value





HTR1B_2r
2.559E−06
MTHFR_1r
0.00004
HTR1B_2r
0.00043
HTR1B_2r
0.00043


GDF10_3r
1.065E−05
TSLL2_2r
0.00053
MTHFR_1r
0.00080
MTHFR_1r
0.00080


ARHI_2r
2.616E−05
HTR1B_2r
0.00056
MLH1_2r
0.00099
MLH1_2r
0.00099


SFN_1r
4.169E−05
ABCC5_2r
0.00206
GDF10_3r
0.00183
GDF10_3r
0.00183


CALCA_1r
4.187E−05
GDF10_3r
0.00244
APC_1r
0.00228
APC_1r
0.00228


ADCYAP1_2r
4.533E−05
MLH1_2r
0.00290
TP53_3r
0.00279
TP53_3r
0.00279


TERT_1r
1.094E−04
VHL_1r
0.00350
ARHI_2r
0.00339
ARHI_2r
0.00339


RASSF1_3r
1.384E−04
UBB_2r
0.00400
BRCA1_3r
0.00397
BRCA1_3r
0.00397


MOS_1r
1.497E−04
CALCA_1r
0.00474
CALCA_1r
0.00468
CALCA_1r
0.00468


SFTPC_3r
4.626E−04
SNCG_3r
0.00522
ADCYAP1_2r
0.00505
ADCYAP1_2r
0.00505


TERT_3r
4.681E−04
S100A2_1r
0.00532
SFTPC_3r
0.00555
SFTPC_3r
0.00555


MYOD1_2r
6.924E−04
SFN_2r
0.00639
TSLL2_2r
0.00676
TSLL2_2r
0.00676


C4B_2r
7.712E−04
SFTPC_3r
0.00817
TERT_1r
0.00713
TERT_1r
0.00713


CALCA_2r
7.857E−04
ADCYAP1_2r
0.00910
MYC_2r
0.00745
MYC_2r
0.00745




SFN_3r
0.00923
RASSF1_3r
0.00825
RASSF1_3r
0.00825






MOS_1r
0.00872
MOS_1r
0.00872






SNCG_3r
0.00876
SNCG_3r
0.00876






TGFBR2_3r
0.00957
TGFBR2_3r
0.00957








APC_2r
0.01187








HOXA5_3r
0.01204








TYMS_2r
0.01442








CDKN2B_3r
0.01455








CALCA_2r
0.01579








MYOD1_2r
0.01594








TERT_3r
0.01734








PTEN_1r
0.01896








C4B_2r
0.01927








GSTP1_3r
0.01964
















TABLE 3







Methylation Markers for Adenocarcinoma


Methylation Markers for Adenocarcinoma










Panel_I

Panel_II











Marker Name
p-value
Marker Name
p-value





SFN_2r
0.00000000007
SFN_2r
0.000009


TWIST1_3r
0.00000093146
CD1A_1r
0.000511


SERPINB5_1r
0.00000098742
TNF_2r
0.000526


GDF10_3r
0.00000130650
SERPINB5_1r
0.000638


PGR_1r
0.00000188610
PGR_1r
0.001117


SFTPD_1r
0.00000420738
TWIST1_3r
0.001288


ARHI_3r
0.00000598158
GDF10_3r
0.001469


CALCA_1r
0.00000933174
SFTPD_1r
0.002031


SFTPB_1r
0.00004835043
ADCYAP1_2r
0.002176


CALCA_2r
0.00008410116
ARHI_3r
0.002211


WT1_1r
0.00013356747
IL13_3r
0.002844


PRDM2_3r
0.00014989843
HOXA5_2r
0.002966


TERT_1r
0.00019412964
CALCA_1r
0.003758


S100A2_2r
0.00031181656
HOXA5_1r
0.004410


ADCYAP1_3r
0.00033644602
SFTPB_1r
0.004675


RUNX3_2r
0.00045917044
MC2R_1r
0.005590


MYOD1_2r
0.00061315386
HOXA5_3r
0.005836


TWIST1_2r
0.00077702738
WT1_1r
0.005920




SERPINB5_3r
0.008011




CDH13_3r
0.008365




CALCA_2r
0.008434




RUNX3_1r
0.008640
















TABLE 4







Samples for Methylation Analysis














Sample
Diagnosis
Age
Sex
Norm Match
Staging
Grading
R2 correlation

















D12152
Adenocarcinoma
73
F

no data

0.958


D12155
Adenocarcinoma
60
M

no data

0.964


D12157
Normal
79
M



0.811


D12158
Adenocarcinoma
87
M

no data

0.963


D12160
Adenocarcinoma
79
M

no data

0.986


D12162
Adenocarcinoma
47
M

no data

0.970


D12163
Adenocarcinoma
54
M

no data

0.990


D12164
Normal
21
M



0.980


D12165
Adenocarcinoma
76
F

no data

0.964


D12170
Adenocarcinoma
62
M

no data

0.984


D12173
Normal
77
M



0.988


D12180
Normal
69
M



0.876


D12181
Adenocarcinoma
64
M

no data

0.962


D12182
Normal
64
M



0.981


D12184
Normal
75
F



0.984


D12188
Normal
66
M



0.940


D12190
Adenocarcinoma
49
M

no data

0.961


D12195
Normal
62
F



0.982


D12197
Adenocarcinoma
50
F

no data

0.982


D12198
Normal
74
F



0.976


D12202
Normal
71
M



0.988


D12203
Adenocarcinoma
71
M
D12202
no data

0.940


D12205
Normal
68
M



0.961


D12207
Adenocarcinoma
62
M

no data

0.978


D12209
Normal
6
M



0.990


G12001
Squamous cell
74
M
B12001
pT1 N0
1
0.995



carcinoma



Mx




G12002
Squamous cell
63
M
B12002
pT4 N2
3
0.988



carcinoma



Mx




G12003
Squamous cell
67
M
B12003
pT1 N3
2
0.981



carcinoma



Mx




G12004
Squamous cell
69
M
B12004
pT2 N1
3
0.983



carcinoma



Mx




G12005
Squamous cell
59
M
B12005
pT2 N0
2
0.994



carcinoma



M0




G12006
Squamous cell
49
M
B12006
pT2 N1
2
0.989



carcinoma



Mx




G12007
Squamous cell
71
M

pT2 N1
2
0.988



carcinoma



Mx




G12008
Squamous cell
57
F
B12008
pT2 N0
3
0.964



carcinoma



Mx




G12009
Squamous cell
62
M
B12009
pT2 N1
3
0.986



carcinoma



Mx




G12010
Squamous cell
67
M
B12010
pT2 N1
2
0.921



carcinoma



Mx




G12011
Squamous cell
73
M

pT2 N0
2
0.977



carcinoma



Mx




G12012
Squamous cell
71
M
B12012
pT2 N0
2
0.994



carcinoma



Mx




G12013
Squamous cell
73
M

pT2 N0
1
0.938



carcinoma



Mx




G12014
Squamous cell
64
M

pT2 N2
3
0.933



carcinoma



Mx




G12018
Adenocarcinoma
60
F
B12018
pT1 N0
2
0.996







Mx




G12019
Adenocarcinoma
78
M
B12019
pT2 N1
3
0.985







M0




G12020
Adenocarcinoma
64
M
B12020
pT2 N0
3
0.990







Mx




G12022
Adenocarcinoma
76
M
B12022
pT2 N0
3
0.989







Mx




G12023
Adenocarcinoma
55
M
B12023
pT2 N0
2
0.832







Mx




G12024
Adenocarcinoma
54
M
B12024
pT2 N3
3
0.971







Mx




G12025
Adenocarcinoma
52
F
B12025
pT2 N2
2
0.987







Mx




G12026
Adenocarcinoma
57
F
B12026
pT2 N2
3
0.992







Mx




G12027
Adenocarcinoma
65
M
B12027
pT2 N0
1
0.990







Mx




G12028
Adenocarcinoma
75
M
B12028
pT3 N3
3
0.980







Mx




G12029
Adenocarcinoma
71
M
B12029
pT2 N0
3
0.992







M0




B12001
Normal
74
M



0.994


B12002
Normal
63
M



0.977


B12003
Normal
67
M



0.994


B12004
Normal
69
M



0.982


B12005
Normal
59
M



0.997


B12006
Normal
49
M



0.985


B12008
Normal
57
F



0.987


B12009
Normal
62
M



0.994


B12010
Normal
67
M



0.991


B12012
Normal
71
M



0.995


B12018
Normal
60
F



0.992


B12019
Normal
78
M



0.978


B12020
Normal
64
M



0.978


B12022
Normal
76
M



0.993


B12023
Normal
55
M



0.979


B12024
Normal
54
M



0.964


B12025
Normal
52
F



0.973


B12026
Normal
57
F



0.931


B12027
Normal
65
M



0.984


B12028
Normal
75
M



0.995


B12029
Normal
71
M



0.995
















TABLE 5





Gene list.






















ABCB1
CDH13
EDNRB
HDAC11
LAT
NNAT
RBP1
TES


ABCB4
CDH3
EFNB1
HFE
LIG4
NPY
RDBP
TFPI2


ABCC5
CDK2
EGFR
HIC1
LMO2
NUFIP1
RET
TGFA


ABL1
CDK4
EGR4
HIC2
LOX
OAT
RNF139
TGFB1


ABO
CDKN1A
ELK1
HLA-DPA1
LRP2
OPCML
RPS26
TGFBR1


ADAMTS12
CDKN1B
EMR3
HLA-DPB1
LTB4R
PADI4
RUNX3
TGFBR2


ADCYAP1
CDKN1C
EP300
HLA-DQA1
LU
PAR-SN
S100A12
THBS1


AGXT
CDKN2A
EPHA3
HLA-DQA2
LY6G6E
PAX6
S100A2
THBS2


APAF1
CDKN2B
EPM2A
HLA-DRA
M6PR
PDGFRB
SCA7
TIMP3


APBA1
CDKN2C
EPO
HLA-DRB1
MAD2L1
PEG10
SCGB3A1
TJP2


APBA2
CDKN2D
ERBB2
HLA-F
MAGEA1
PEG3
SDBCAG84
TM7SF3


APC
CFTR
ERN1
HOXA11
MAGEC3
PENK
SDHD
TMEFF1


APOA1
CHC1
ESR1
HOXA5
MAGEL2
PGR
SEMA3B
TMEFF2


APOC1
CHD2
ESR2
HOXB13
MAP2K4
PHLDA2
SERPINB5
TNF


APOC2
CHEK2
EYA4
HOXC6
MAS1
PI3
SEZ6L
TNFRSF10C


APP
CHFR
F2R
HRAS
MC2R
PLAGL1
SFN
TNFRSF10D


AR
CHGA
FABP3
HRASLS
MDM2
PLAGL2
SFRP1
TP53


ARHI
CHI3L2
FANCF
HS3ST2
MEG3
PLAU
SFTPA1
TP73


ASB4
COL1A2
FEN1
HSD17B12
MEST
PLS3
SFTPB
TRPM5


ASC
COPG2
FGFR2
HSPA2
MGMT
PMP22
SFTPC
TSC1


ASCL2
CPA4
FGFR3
HTR1B
MKRN3
PMS1
SFTPD
TSC2


ATM
CPNE1
FLJ12455
HTR2A
MKRN4
PMS2
SGCE
TSLL2


ATP10A
CRIP1
FLJ20712
ICAM1
MLH1
POMC
SIAT1
TSP50


ATP5A1
CSF1
FMR1
IGF2
MLH3
PPAT
SLC22A18
TSSC4


ATP5G1
CSNK2B
FN1
IGF2AS
MME
PPP2R1A
SLC22A2
TUBB4


BCAP31
CSPG2
FOS
IGF2R
MOS
PPP2R1B
SLC22A3
TUSC3


BCL2
CSTB
G6PD
IGFBP1
MRE11A
PRDM2
SLC38A4
TWIST1


BCL6
CTAG1
GABRA5
IGFBP3
MSF
PRKAR1A
SLC5A5
TYMS


BCR
CTAG2
GABRB3
IGFBP7
MSH2
PRKCDBP
SLC5A8
UBB


BLM
CTBP1
GABRG3
IGSF4
MSH3
PROK2
SLC6A8
UBE3A


BRAF
CTLA4
GAGE7B
IL13
MSH6
PRSS8
SMARCA3
UNG


BRCA1
CTNNB1
GALR1
IL16
MT1A
PTCH
SMARCB1
USP29


BRCA2
CTSH
GDF10
IL17RB
MTHFR
PTEN
SNCG
VAMP8


C4B
CYLD
GJB2
IL6
MYC
PTGS2
SNRPN
VEGF


CALCA
CYP1A1
GLA
IMPACT
MYCL1
PTHR1
SNURF
VHL


CAPG
DAB21P
GNAS
INS
MYCN
PTPN6
SOCS1
WT1


CASP8
DAD1
GNMT
IRF5
MYOD1
PTPNS1
SOD1
YWHAH


CAV1
DAPK1
GP1BB
IRF7
NBL1
PTPRO
SOD3
ZIM3


CCNA1
DBC1
GPC3
ITGB1BP1
NBS1
PWCR1
SPDEF
ZMYND10


CCND2
DCC
GPX3
ITPR2
NCL
RAB32
ST7
ZNF215


CD1A
DDX17
GRB10
ITPR3
NDN
RAD50
STAT1
ZNF264


CD2
DIO3
GSTM2
KAI1
NEFL
RAD51
STK11
ZP3


CD44
DLC1
GSTP1
KCNK4
NEU1
RARB
STK23



CD63
DLK1
H19
KCNQ1
NF1
RARRES1
SYBL1



CD81
DMP1
H2AFX
KLF5
NF2
RASGRF1
SYK



CDC25A
DNAJD1
HBII-13
KLK10
NKX3-1
RASSF1
TCEB3C



CDH1
EDN1
HBII-52
KRAS2
NME1
RB1
TERT
















TABLE 6







Sample information and array data reproducibility.











Sample
Diagnosis
Age
Sex
R2 correlation














D12152
Adenocarcinoma
73
F
0.981


D12155
Adenocarcinoma
60
M
0.982


nD12157
Normal
79
M
0.970


D12158
Adenocarcinoma
87
M
0.992


D12160
Adenocarcinoma
79
M
0.980


D12162
Adenocarcinoma
47
M
0.989


D12163
Adenocarcinoma
54
M
0.992


nD12164
Normal
21
M
0.988


D12165
Adenocarcinoma
76
F
0.979


D12170
Adenocarcinoma
62
M
0.988


nD12173
Normal
77
M
0.990


nD12180
Normal
69
M
0.972


D12181
Adenocarcinoma
64
M
0.990


nD12182
Normal
64
M
0.980


nD12184
Normal
75
F
0.979


nD12188
Normal
66
M
0.974


nD12195
Normal
62
F
0.989


D12197
Adenocarcinoma
50
F
0.988


nD12198
Normal
74
F
0.988


nD12202
Normal
71
M
0.970


D12203
Adenocarcinoma
71
M
0.983


nD12205
Normal
68
M
0.970


D12207
Adenocarcinoma
62
M
0.983


nD12209
Normal
6
M
0.985


G12018
Adenocarcinoma
60
F
0.996


G12019
Adenocarcinoma
78
M
0.997


G12020
Adenocarcinoma
64
M
0.996


G12022
Adenocarcinoma
76
M
0.997


G12023
Adenocarcinoma
55
M
0.997


G12024
Adenocarcinoma
54
M
0.998


G12025
Adenocarcinoma
52
F
0.998


G12026
Adenocarcinoma
57
F
0.997


G12027
Adenocarcinoma
65
M
0.997


G12028
Adenocarcinoma
75
M
0.995


G12029
Adenocarcinoma
71
M
0.996


nB12018
Normal
60
F
0.997


nB12019
Normal
78
M
0.995


nB12020
Normal
64
M
0.997


nB12022
Normal
76
M
0.998


nB12023
Normal
55
M
0.995


nB12024
Normal
54
M
0.997


nB12025
Normal
52
F
0.998


nB12026
Normal
57
F
0.871


nB12027
Normal
65
M
0.998


nB12028
Normal
75
M
0.998


nB12029
Normal
71
M
0.998
















TABLE 7







55 CpG sites differentially methylated in adenocarcinoma.











Target ID
adj. p-value
Delta β















ASCL2-856
0.000112
−0.1545



DBC1-1053
0.000112
−0.1724



HS3ST2-311
0.000112
−0.1909



IGF2AS-1145
0.000112
−0.1884



NEFL-1367
0.000112
−0.1943



NEFL-524
0.000112
−0.1690



RET-1149
0.000112
−0.2052



RUNX3-368
0.000112
−0.3127



CD2-1433
0.000167
0.2286



HTR2A-1387
0.000167
0.1518



TNF-1343
0.000167
0.2784



TNF-1371
0.000167
0.2310



ASCL2-1038
0.000223
−0.1921



ASCL2-1048
0.000223
−0.2236



CCND2-604
0.000223
−0.2003



DIO3-1355
0.000223
−0.3358



DLK1-1226
0.000223
−0.1780



DLK1-1287
0.000223
−0.2780



HOXA11-558
0.000223
−0.1906



HOXA5-1247
0.000223
−0.2068



ASCL2-1339
0.000293
−0.1837



CDH13-1358
0.000293
−0.1966



HTR1B-1278
0.000293
−0.1615



NEFL-1341
0.000293
−0.3134



PENK-1293
0.000293
−0.1785



SEZ6L-1046
0.000293
−0.1715



TP73-358
0.000293
−0.1973



TWIST1-1348
0.000293
−0.2190



DLK1-1119
0.000396
−0.1689



GABRB3-1408
0.000396
−0.1658



IGF2-112
0.000396
−0.1611



NPY-931
0.000396
−0.2512



OPCML-967
0.000396
−0.3449



PTPN6-1214
0.000396
0.2082



PTPRO-1206
0.000396
−0.1668



DLC1-1012
0.00053
0.1928



DLK1-1185
0.00053
−0.2347



HOXA11-802
0.00053
−0.3419



HOXA5-75
0.00053
−0.2342



NPY-793
0.00053
−0.2576



PAX6-1337
0.00053
−0.1534



PENK-1480
0.00053
−0.2212



POMC-1303
0.00053
−0.2172



TERT-900
0.00053
−0.1561



APBA2-1397
0.000747
0.1555



CFTR-1051
0.000747
−0.2994



GDF10-1224
0.000747
−0.2025



NPY-1009
0.000747
−0.2484



ADCYAP1-1103
0.000947
−0.2275



CFTR-1191
0.000947
−0.2710



GALR1-1270
0.000947
−0.1698



HOXA5-1094
0.000947
−0.2434



HTR1B-315
0.000947
−0.1932



NEFL-600
0.000947
−0.1868



TWIST1-524
0.000947
−0.1811

















TABLE 8







Methylation-specific PCR primer sequence (SEQ ID NOS 1516-1537,


respectively, in order of appearance) and amplicon size.










Primer
Forward primer,
Reverse primer,
Size,


set
5′→3′
5′→3′
bp





ACTB
TGGTGATGGAGGAGGTTTAGTAAGT
AACCAATAAAACCTACTCCTCCCTTAA
130


 


CFTR-M
GTTTTGGGTTTGGCGGATTTTGACGC
CCCGCAAATAAACGACAATCGCGAC
142


 


CFTR-U
GGTTTTGGGTTTGGTGGATTTTGATGT
CATCCCACAAATAAACAACAATCACAAC
146


 


DBC1-M
ACGCGATCCCTTTAAATACTCGTACG
GAGGAGAGACGGGAGGTCGTTTCG
131


 


DBC1-U
ATATACACAATCCCTTTAAATACTCATACA
GGAGGAGAGATGGGAGGTTGTTTTG
136


 


DLK1-M
TCCGAAAAAATCTACGACCCAAATTCG
AGACGGGTATAGGTATTTCGCGAGC
132


 


DLK1-U
TCTCCAAAAAAATCTACAACCCAAATTCA
GTTAGATGGGTATAGGTATTTTGTGAGT
137


 


EYA4-M
TACGGAGATTACGGCGGCGTTATTC
AACGCGAAAAACGACGACGCGCGA
150


 


EYA4-U
GGGTATGGAGATTATGGTGGTGTTATTT
CCAAAAACACAAAAAACAACAACACACAA
158


 


NPY-M
TTAAAACCCTCTAACCGAAAACTTCCG
ACGATTAGCGCGGTATTTTCGTCGG
131


 


NPY-U
CTTTAAAACCCTCTAACCAAAAACTTCCA
TTTTTATGATTAGTGTGGTATTTTTGTTGG
138
















TABLE 9







Bisulfite sequencing primer sequence (SEQ ID NOS 1538-1553,


respectively, in order of appearance) and amplicon size.










Primer
Forward primer,
Reverse primer,
Size,


set
5′→3′
5′→3′
bp





ASCL2
GTAGTTTATTTTTATTTTTAGTAGATTAA
AAAACCAAAATCTCAACCAATC
260


 


CDH13
ATTTTTTGGAAAAGTGGAATTAGTT
CCAAATAAATCAACAACAACATCAC
241


 


HOXA11
TAGTTTTTGTGTTTTTGTTTTTGT
ATAACTTAATTACACTCTCTCATTCATAAT
272


 


HOXA5
GTTGTAGGGAGTTTGGGTTTATT
CCTAAAAAATCTTCATCACAAAATC
102


 


NPY
GAGAAGGGGTAGAAGTTTTTGAAAT
ATCTCCTACCAACAAAACTACCAAC
267


 


RUNX3
TTTTGTAGTTATTGTTGTTTTTTTT
CAAATTTCAAAATCTTACAAACCTC
187


 


TERT
TTTGAGAATTTGTAAAGAGAAATGA
AATATAAAAACCCTAAAAACAAATAC
290


 


TP73
AGTAAATAGTGGGTGAGTTATGAAGATG
TACACCAAACCCTAACTAAAAAACC
287
















TABLE 10







Methylation results generated from microarray


analysis and bisulfite sequencing.












% methylation,
% methylation,


CpG site
Sample ID
Bisulfite sequencing
Array analysis





ASCL2_1038
nD12209
0.00
0.09



D12155
0.67
0.79


CDH13_1358
nD12195
0.00
0.11



D12170
0.40
0.71


HOXA11_802
nD12209
0.17
0.09



D12155
0.92
0.92


HOXA5_576
nD12209
0.20
0.60



D12163
0.75
0.75


NPY_1009
nD12209
0.00
0.12



D12165
1.00
0.88


RUNX3_368
nD12195
0.90
0.27



D12170
1.00
0.67


TERT_900
nD12209
0.00
0.60



D12165
0.44
0.92


TP73_377
nD12195
0.09
0.05



D12170
0.46
0.62

## Claims

1. A method for identifying differentially methylated genomic CpG dinucleotide sequences associated with adenocarcinoma in an individual, said method comprising:
(a) obtaining a biological sample comprising genomic DNA from said individual;
(b) measuring the level of methylation in said biological sample at a CpG dinucleotide sequence in two or more genomic targets designated as SEQ ID NOS: 59, 293, 354, 373, 1556-1578, 1580-1604 or 1606-1609 in said sample, and
(c) comparing the levels of methylation at said genomic CpG dinucleotide sequences in the biological sample to a reference level of methylation of said genomic CpG dinucleotide sequences, wherein said reference level comprises the level of methylation at said genomic CpG dinucleotide sequences in a normal sample,
wherein an increase in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 293, 354, 1557-1561, 1563-1567, 1569-1578, 1580-1583, 1585-1598, 1600-1604 or 1607-1609, or a decrease in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 59, 373, 1556, 1562, 1568, 1584, 1599 or 1606, compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with adenocarcinoma in said individual.

2. The method of claim 1, wherein the level of methylation of said differentially methylated genomic CpG dinucleotide sequences is used to diagnose adenocarcinoma in the individual.

3. The method of claim 1, wherein step (b) further comprises measuring the level of methylation in said sample at a CpG dinucleotide sequence in at least one of the genomic targets designated as SEQ ID NOS: 140, 174, 264, 265, 294, 305, 313, 314, 360, or 371, wherein an increase in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 140, 264, 265, 294, 313, 314 or 371, or a decrease in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 174, 305 or 360, compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with adenocarcinoma.

4. A method for identifying differentially methylated genomic CpG dinucleotide sequences associated with squamous cell carcinoma in an individual, said method comprising:
(a) obtaining a biological sample comprising genomic DNA from said individual;
(b) measuring the level of methylation in said biological sample at a CpG dinucleotide sequence in two or more genomic targets designated as SEQ ID NOS: 57, 58, 72, 76, 139, 141, 266, 309, 315, 345 or 350 in said sample, and
(c) comparing the levels of methylation at said genomic CpG dinucleotide sequences in the biological sample to a reference level of methylation of said genomic CpG dinucleotide sequences, wherein said reference level comprises the level of methylation at said genomic CpG dinucleotide sequences in a normal sample,
wherein an increase in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 72, 76, 139, 141, 266, 309, 315 or 345, or a decrease in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 57, 58 or 350, compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with squamous cell carcinoma in said individual.

5. The method of claim 4, wherein step (b) further comprises measuring the level of methylation in said sample at a CpG dinucleotide sequence in at least one of the genomic targets designated as SEQ ID NOS: 140, 174, 264, 265, 294, 305, 313, 314, 360 or 371, wherein an increase in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 140, 174, 264, 265, 294, 313, 314 or 371, or a decrease in the level of methylation of said genomic CpG dinucleotide sequences in the biological sample for the genomic targets designated as SEQ ID NOS: 305 or 360, compared to the reference level identifies differentially methylated genomic CpG dinucleotide sequences associated with squamous cell carcinoma.

6. The method of claim 4, wherein the level of methylation of said differentially methylated genomic CpG dinucleotide sequences is used to diagnose squamous cell carcinoma in the individual.