Dna methylation biomarkers in lymphoid and hematopoietic malignancies

A high-throughput method for identifying, or for distinguishing between and among subtypes of small B-cell lymphomas (SBCL), comprising:

• obtaining a test sample comprising genomic DNA;
• contacting the DNA with a reagent or reagents that distinguish between cytosine and 5-methylcytosine to provide for a treated DNA;
• and
• determining, using the treated DNA and at least one suitable methylation assay, a methylation state or level of at least one CpG dinucleotide sequence of at least one promoter CpG-island region selected from the promoter group consisting of LHX2, POU3F3, HOX10, NRP2, PRKCE, RAMP, MLLT2, NKX6-1, LPR1B, and ARF4, wherein distinguishing, based on the determined methylation state or level relative to a respective control or normalized control methylation state or level, germinal center-derived tumors from pre- and/or post-germinal center lymphomas is, at least in part, afforded.

A high-throughput method for identifying, or for distinguishing between and among subtypes of non-Hodgkin's Lymphoma (NHL), comprising:

• obtaining a test sample comprising genomic DNA;
• contacting the DNA with a reagent or reagents that distinguish between cytosine and 5-methylcytosine to provide for a treated DNA;
• and
• determining, using the treated DNA and at least one suitable methylation assay, a methylation state or level of at least one CpG dinucleotide sequence of at least one promoter CpG-island region selected from the promoter group consisting of DLC-1, PCDHGB7, CYP27B1, EFNA5, CCND1 and RARβ2, wherein identifying or distinguishing between or among, based on the determined methylation state or level relative to a respective control or normalized control methylation state or level, subtypes of non-Hodgkin's Lymphoma (NHL) is, at least in part, afforded.

A high-throughput method for diagnosis, prognosis or monitoring multiple myeloma (MM), comprising:

• obtaining a test sample comprising genomic DNA;
• contacting the DNA with a reagent or reagents that distinguish between cytosine and 5-methylcytosine to provide for a treated DNA;
• and
• determining, using the treated DNA and at least one suitable methylation assay, a methylation state or level of at least one CpG dinucleotide sequence of at lease one promoter CpG-island region selected from the promoter group consisting of DL C-1, PCDHGB7, CYP27B1 and NOPE, wherein diagnosing, prognosing or monitoring multiple myeloma (MM), based on the determined methylation state or level relative to a respective control or normalized control methylation state or level is, at least in part, afforded.

A high-throughput method for identifying acute lymphoblastic leukemia (ALL), or for distinguishing ALL from normal bone marrow, comprising:

• obtaining a test sample comprising genomic DNA;
• contacting the DNA with a reagent or reagents that distinguish between cytosine and 5-methylcytosine to provide for a treated DNA;
• and
• determining, using the treated DNA and at least one suitable methylation assay, a methylation state or level of at least one CpG dinucleotide sequence of at least one promoter CpG-island region selected from the promoter group consisting of DCC, DLC-1, DDX51, KCNK2, LRP1B, NKX6-1, NOPE, PCDHGA12, RPIB9/ABCB1(MDR1) and SLC2A14, wherein identifying acute lymphoblastic leukemia (ALL) or distinguishing acute lymphoblastic leukemia (ALL) from normal bone marrow, based on the determined methylation state or level relative to a respective control or normalized control methylation state or level, is, at least in part, afforded.

A high-throughput method for distinguishing B-ALL from T-ALL, comprising:

• obtaining a test sample comprising genomic DNA;
• contacting the DNA with a reagent or reagents that distinguish between cytosine and 5-methylcytosine to provide for a treated DNA;
• and
• determining, using the treated DNA and at least one suitable methylation assay, a methylation state or level of at least one CpG dinucleotide sequence of a DDX51 promoter CpG-island region, wherein distinguishing B-ALL from T-ALL, based on the determined methylation state or level relative to a respective control or normalized control methylation state or level, is, at least in part, afforded.

A high-throughput method for identifying subtypes of acute myelogenous leukemia (AML), or for distinguishing between acute myelogenous leukemia (AML) and acute lymphoblastic leukemia (ALL), comprising:

• obtaining a test sample comprising genomic DNA;
• contacting the DNA with a reagent or reagents that distinguish between cytosine and 5-methylcytosine to provide for a treated DNA;
• and
• determining, using the treated DNA and at least one suitable methylation assay, a methylation state or level of at least one CpG dinucleotide sequence of at least one promoter CpG-island region selected from the promoter group consisting of DDX51, EXOSC8, NOPE, FBX036, SMAD9, and RP1B9, wherein distinguishing subtypes of acute myelogenous leukemia (AML), or distinguishing between acute myelogenous leukemia (AML) and acute lymphoblastic leukemia (ALL), based on the determined methylation state or level relative to a respective control or normalized control methylation state or level, is, at least in part, afforded.

A method for identification of methylation markers for cancer, comprising:

• obtaining a plurality of pathologically classified cancer tissue samples corresponding to at least one particular form, type or subtype of cancer, the samples comprising genomic DNA and corresponding to a plurality of different individuals or sources;
• extracting and normalizing intensity data values corresponding to test nucleic acid samples hybridized to at least one nucleic acid-based probe array, wherein the intensity data values correspond to the methylation level of particular candidate marker DNA sequences, to provide for extracted features;
• conducting a gene-finding step, comprising conducting a plurality of feature selection methods;
• clustering, with respect to each of the feature selection methods, the pathologically classified cancer tissue samples or sources using a cross-correlation matrix;
• assessing the clustering by using multidimensional scaling to provide for a selected gene marker set corresponding to each of the feature selection methods;
• fusing the results of the plurality of feature selection methods to provide for at least one list of candidate differentially methylated gene markers, wherein said fusion comprises voting such that only candidate gene markers selected by all, or majority of the plurality of feature selection methods as being uniquely methylated in a given class are selected for further validation;
• and
• validating of the listed candidate gene markers using at least one suitable methylation assay with cancer tissue or cells.