Patent Grant
8150627
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.
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.
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.
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.
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 (
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 (
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
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
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
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
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
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
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
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.
As disclosed herein, the present invention provides a subset of genomic targets consisting of nucleic acid sequences set forth in
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.
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 (
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 (
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 (
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
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
In a further embodiment, the invention provides a subset of genomic targets consisting of nucleic acid sequences set forth in
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 MethyLight™ (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.
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 (
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
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
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 (
Reference Samples for Genome-Wide Methylation Profiling
To calibrate quantitative measurements of methylation, “fully methylated” and “unmethylated” genomic templates were developed (
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
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:
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 (
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.
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 (
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 (
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.
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 (
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 GoldenGate™ 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
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 (
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
“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) (
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
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 (
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 (
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 (
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
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 (
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
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 (
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 (
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) (
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).
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 (
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 (
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 (
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 (
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.
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.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5786146 | Herman et al. | Jul 1998 | A |
| 5856101 | Hubbell et al. | Jan 1999 | A |
| 5981733 | Gamble et al. | Nov 1999 | A |
| 6001309 | Gamble et al. | Dec 1999 | A |
| 6017704 | Herman et al. | Jan 2000 | A |
| 6023540 | Walt et al. | Feb 2000 | A |
| 6110426 | Shalon et al. | Aug 2000 | A |
| 6200737 | Walt et al. | Mar 2001 | B1 |
| 6232072 | Fisher | May 2001 | B1 |
| 6251594 | Gonzalgo et al. | Jun 2001 | B1 |
| 6265171 | Herman et al. | Jul 2001 | B1 |
| 6280949 | Lizardi | Aug 2001 | B1 |
| 6327410 | Walt et al. | Dec 2001 | B1 |
| 6329150 | Lizardi et al. | Dec 2001 | B1 |
| 6331393 | Laird et al. | Dec 2001 | B1 |
| 6379895 | Fodor et al. | Apr 2002 | B1 |
| 6429027 | Chee et al. | Aug 2002 | B1 |
| 6667394 | Pease et al. | Dec 2003 | B2 |
| 20020102578 | Dickinson | Aug 2002 | A1 |
| 20030170684 | Fan | Sep 2003 | A1 |
| 20040137473 | Wigler et al. | Jul 2004 | A1 |
| 20040259100 | Gunderson et al. | Dec 2004 | A1 |
| 20050026183 | Fan et al. | Feb 2005 | A1 |
| 20050164246 | Fan et al. | Jul 2005 | A1 |
| 20070128592 | Burger et al. | Jun 2007 | A1 |
| Number | Date | Country |
|---|---|---|
| WO 9840726 | Sep 1998 | WO |
| WO 9850782 | Dec 1998 | WO |
| WO 9918434 | Apr 1999 | WO |
| WO 02086163 | Oct 2002 | WO |
| WO 03052135 | Dec 2002 | WO |
| WO 03048732 | Jun 2003 | WO |
| WO 2004051224 | Jun 2004 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 20070231797 A1 | Oct 2007 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60471488 | May 2003 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10973783 | Oct 2004 | US |
| Child | 11259546 | US | |
| Parent | 10845667 | May 2004 | US |
| Child | 10973783 | US |
