Patent Application
20090264306
Particular aspects are related generally to DNA methylation and cancer, and more particularly to novel compositions and methods based on novel methylation and/or expression markers having substantial utility for cancer detection, monitoring, diagnosis, prognosis, staging, treatment response prediction/monitoring, etc., where the cancers include hematopoietic malignancies, leukemia, lymphomas, etc., (e.g., non-Hodgkin's lymphomas (NHL), small B-cell lymphomas (SBCL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma (B-CLL/SLL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), acute myelogenous leukemia (AML), acute lymphoblastic leukemia (ALL), etc.).
A Sequence Listing in paper form (----pages) and comprising SEQ ID NOS:1----- is attached to this application, is part of this application, and is incorporated herein by reference in its entirety.
CpG methylation. Methylation of cytosine residues at CpG dinucleotides is a major epigenetic modification in mammalian genomes and is known to frequently have profound effects on gene expression. This epigenetic event occurs globally in the normal genome, and 70-80% of all CpG dinucleotides are heavily methylated in human cells. However, ˜0.2 to 1-kb long DNA sequence stretches of GC-rich (G+C content: >50-60%) DNA, called CpG islands (CGI), appear to be protected from the modification in somatic cells. CpG islands are frequently located in the promoters and first exon regions of 40 to 50% of all genes. The rest may be located in the intronic or other exonic regions of the genes, or in regions containing no genes. Some of these normally unmethylated promoter CGIs become methylated in cancer cells, and this may result in loss of expression of adjacent genes. As a result, critical genes may be silenced, leading to clonal proliferation of tumor cells.
In cancer cells, patterns of DNA methylation are altered, and promoter (including the first exon) CpG island hypermethylation is a frequent epigenetic event in many types of cancer. This epigenetic process can result in gene silencing via alteration of local chromatin structure in the 5′ end of regulatory regions, preventing normal interaction of the promoters with the transcriptional machinery. If this occurs in genes critical to growth inhibition, the silencing event could promote tumor progression.
Although the list of methylation-repressed genes in Non-Hodgkin's Lymphomas (NHLs) is expanding rapidly, there is a substantial need in the art for identification of novel epigenetic biomarkers to provide for earlier and more accurate diagnoses, and for guiding therapy-related issues.
Non-Hodgkin's Lymphoma. Non-Hodgkin's Lymphoma (NHL) is the 5th most common malignancy in the United States, accounting for approximately 56,390 new cases in year 2005. Unfortunately, the incidence has increased yearly over past decades for unknown reasons, and is one of only two cancers increasing in incidence. Mature B-cell NHL including mantle cell lymphoma (MCL), B-cell chronic lymphocytic lymphoma/small lymphocytic lymphoma (B-CLL/SLL), follicular lymphoma (FL), and diffuse large B-cell lymphoma (DLBCL) comprise >80% of all NHL cases. Together, B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma (B-CLL/SLL), mantle cell lymphoma (MCL), and grades I and II follicular lymphoma (FLI/FLII) comprise one-third of all NHL cases [1]. The NHLs B-CLL/SLL and FLI/FLII are generally thought to be of low aggressiveness, but still exhibit a spectrum of clinical behavior. B-CLL/SLL is a lymphoma of at least 2 subtypes comprising both pre-germinal center and post-germinal center derivation, while MCL is also of pre-germinal center derivation, and FLI/FLII derives from germinal centers of lymphoid tissues. B-CLL/SLL is diverse across different groups of patients. Many B-CLL/SLL and FLI/FLII patients have a relatively good prognosis, with median survival of ˜7-10 years, but usually are not curable in advanced clinical stages. MCL is a pre-germinal center derived malignancy, and FLs are germinal center derived NHLs. MCL is typically more rapidly progressive than these other SBCLs.
Although advances in cancer treatment over the past several decades have improved outcomes for many patients with NHLs, the diseases are still not generally curable. The time from diagnosis to death is variable, ranging from months to many years. Current classification systems are based on clinical staging, chromosomal abnormalities and cell surface antigens, and offer important diagnostic information. Diagnostically, it is usually possible to discern each type of SBCL from the other on the basis of histologic pattern, but, there is still considerable overlap in biology, clinical behavior/disease and genetic and epigenetic alterations among the SBCL subtypes. Indolent SBCL subtypes are B cell malignancies that correlate with different stages of normal B cell differentiation. Biologically, a naive B-cell that has not been stimulated with antigen expresses a different set of genes from antigen-stimulated B-cells.
There is, therefore, a substantial need in the art for novel compositions and methods for distinguishing subtypes, and to provide improvements in therapy, as well as better ways to detect NHL and to monitor responses to therapy.
Multiple Myeloma. A number of individual genes have been reported silenced in multiple myeloma MMs. For example, alteration of p16 and p15 solely by hypermethylation has been detected in high frequencies in MMs, and hypermethylation of p16 has been shown to be associated with plasmablastic disease in primary MM. Moreover, transcriptional silencing of p16 and p 15 has been found to correlate with hypermethylation of these genes in MM-derived cell lines. These results indicate that hypermethylation of p16 and p15 plays an important role in MM tumorigenesis. Hypermethylation of the DAP-kinase (DAPK) CpG island is also a very common alteration in MM. Another example of epigenetic alteration in myeloma is dysregulation of the IL-6/JAK/STAT3 pathway, a signal pathway that is subjected to negative regulation by three families of proteins: the protein inhibitors of activated STATs (PIAS); the suppressor of cytokine signaling (SOCS); and the SH2-cotaining phosphatases (SHP). Frequent hypermethylation of both SHP-1 (79.4%) and SOCS-1 (62.9%) has been reported in multiple myelomas. Therefore, CpG island methylation is likely critical in the genesis and clinical behavior of MMs and may provide useful molecular markers for detection and determining the clinical status of these diseases.
However, because of the limited number of informative genes analyzed so far analyzed, there is a substantial need in the art for additional methylation markers for MM.
Acute myelogenous leukemia (AML). Aberrant DNA methylation is believed to be important in the tumorigenesis of numerous cancers by both silencing transcription of tumor suppressor genes and destabilizing chromatin. Previous studies have demonstrated that several tumor suppressor genes are hypermethylated in AML, suggesting a roll for this epigenetic process during tumorigenesis. However, it is unknown how the genomic methylation profiles differ among AML variants, or even whether AML can be distinguished on this basis from normal bone marrow or other hematologic malignancies.
There is, therefore, a pronounced need in the art for novel compositions and methods for detecting and distinguishing AML.
Acute Lymphoblastic Leukemia (ALL). Acute lymphoblastic leukemia (ALL) arises when B or T cell progenitors are unable to differentiate into mature B or T cells resulting in the rapid proliferation of immature cells. A multitude of factors are known to be responsible for blocking this process including translocations and epigenetic modifications which can nullify the function of a gene or cause a change in the regulation of a gene product. Many non-random translocations are known to occur in ALL resulting in aberrant proliferation, differentiation, apoptosis and gene transcription. Assays to detect these molecular anomalies have been developed and some are currently being used as prognostic markers. However, a major shortcoming of these assays has been the reliance of their detection in specific morphological subtypes of ALL (Faderl et al. 1998) demonstrating the need for alternative prognostic and classification tools in ALL.
There is a pronounced need in the art for novel compositions and methods for detecting and distinguishing ALL and/or its subtypes.
Differential Methylation Hybridization (DMH) was used to identify novel methylation markers and methylation profiles for hematopoieetic malignancies, leukemia, lymphomas, etc. (e.g., non-Hodgkin's lymphomas (NHL), small B-cell lymphomas (SBCL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma (B-CLL/SLL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), acute myelogenous leukemia (AML), acute lymphoblastic leukemia (ALL), etc.).
According to particular aspects, the use of a quantitative assay for DLC-1 promoter methylation has substantial utility to improve the detection rate of NHL in tissue biopsies, and from blood and/or plasma samples. Moreover, gene promoter methylation of DLC-1 occurred in a differentiation-related manner and has substantial utility as a biomarker in non-Hodgkin's Lymphoma (NHL) (e.g., for distinguishing between and among MCL (mantle cell lymphoma), B-CLL/SLL (B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma), FL (follicular lymphoma), and DLBCL (diffuse large B-cell lymphoma) samples (see Example 1).
Particular aspects therefore provide novel non-invasive blood tests for lymphomas and leukemias (Id).
In further aspects, down-regulation of DLC-1 expression was correlated with NHL compared to normal lymph nodes (Id).
In additional aspects, differential methylation of LHX2, POU3F3, HOX10, NRP2, PRKCE, RAMP, MLLT2, NKX6-1, LPR1B, and ARF4 markers was validated, and demonstrated a preferential methylation pattern in germinal center-derived tumors compared to pre- and post-germinal center tumors. Therefore, in particular embodiments, these markers define distinct sub-types of SBCL that are not recognized by current classification systems, and have substantial utility for detecting and characterizing the biology of these tumors (see Example 2).
Further aspects provide promoter region markers for Non-Hodgkin's Lymphoma (NHL) and NHL subtypes, including markers based on PCDHGB7, EFNA5, CYP27B1, CCND1, DLC-1, NOPE, RPIB9, FLJ39155, PON3 and RARβ2 gene sequences that provide novel methylated gene markers relevant to molecular pathways in NHLs, and that have substantial utility as biomarkers of disease (e.g., cancer, and specific subtypes thereof). Preferably, the NHL and NHL subtype methylation markers include markers based on DLC-1, PCDHGB7, CYP27B1, EFNA5, CCND1 and RARβ2 promoter region sequences (see Example 3).
Additional aspects provide methylation markers for Multiple Myeloma (MM) and subtypes thereof, including markers based on PCDHGB7, CYP27B1, DLC-1, NOPE, FLJ39155, PON3, PITX2, DCC, FTHFD and RARβ2 promoter region sequences. Preferably, the markers for Multiple Myeloma (MM) and subtypes thereof, include markers based on PCDGHB7, CYP27B1, and NOPE promoter region sequences (see Example 4).
Yet additional aspects provide methylation markers for Acute Myelogenous Leukemia (AML) having substantial utility for distinguishing NHL FAB M0-M3 subtypes, based on their methylation profiles. For example, markers are provided that are based on genes not previously associated with abnormal methylation in AML, including the dual-specificity tyrosine phosphorylation regulated kinase 4, structural maintenance of chromosome 2-like-1, and the exportin 5 genes (see Example 5).
Additional aspects provide promoter region markers for Acute Lymphoblastic Leukemia (ALL), including markers based on ABCB1/MDR1, DLC-1, DCC, LRP1B, PCDHGAI2, RPIB9, KCNK2, NOPE, DDX51, SLC2A14, LRP1B and NKX6-1 promoter region sequences (see Example 6).
Further aspects provide for a novel goal oriented approach and algorithm for finding differentially methylated gene markers (e.g., in small B-cell lymphoma) was developed. The inventive gene selection algorithm comprises 3 main steps: array normalization; gene selection (based on idealized methylation patterns, and comprising fused gene rankings); and gene clustering (see Example 7). Variants of this approach, comprising fusion of differential methylation ranking and differential expression ranking are also disclosed.
Therefore, particular aspects of the present invention provide for novel biomarkers for NHL, SBCL and subtypes thereof (e.g., for distinguishing MCL, B-CLL/SLL, FL, DLBCL, etc.), and for AML, ALL and MM. In particular embodiments, these markers have substantial utility in providing for non-invasive tests (e.g. blood tests) for lymphomas and leukemias.
In additional aspects these markers have substantial utility for detection, diagnosis, prognosis, monitoring responses to therapies, detection of relapse patients, and the respective genes provide targets for therapeutic demethylating methods and treatments.
Further aspects provide markers for classification or staging of cancer (e.g., lymphomas and leukemias), based on characteristic methylation profiles.
Yet further aspects provide expression markers and respective methods for detection, diagnosis, prognosis, monitoring responses to therapies, detection of relapse patients.
a-c show, according to particular aspects, the modified method “idealized methylation pattern” (IMP) method (one of two methods used in gene selection; Example 7). To determine if a gene is exclusively hypermethylated in CLL, the ideal hypermethylation profile for the CLL class (
Particular aspects of the present invention provide novel methylation and/or expression markers that serve as biomarkers in novel methods for detection, monitoring, diagnosis, prognosis, staging, treatment response prediction/monitoring/guidance, etc., of cancer including hematopoietic malignancies, leukemia, lymphomas, etc., (e.g., non-Hodgkin's lymphomas (NHL), small B-cell lymphomas (SBCL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma (B-CLL/SLL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), acute myelogenous leukemia (AML), acute lymphoblastic leukemia (ALL), etc.).
A high-throughput array-based technique called differential methylation hybridization (DMH) was used in particular aspects of the Examples (below) to study and characterize hematopoietic malignancies, leukemia, lymphomas, etc. (and in particular instances, subtypes/stages thereof), based on establishing a set of novel methylation and/or expression biomarkers.
From the initial microarray experiments, several statistical methods were used to generate limited sets of genes for further validation by methylation specific PCR (MSP) and/or COBRA using cancer tissue and/or relevant cell lines. Hierarchical clustering of the DNA methylation data was then used to characterize a particular cancer type, or subtype, on the basis of their DNA methylation patterns/profiles, revealing, as disclosed herein, that there is diversity of characteristic DNA methylation patterns between and among the different cancers and cancer subtypes.
In EXAMPLE 1 herein, DLC-1 promoter methylation was demonstrated by quantitative analysis, to have substantial utility as a differentiation-related biomarker of non-Hodgkin's Lymphoma (NHL).
Applicants previously used an Expressed CpG Island Sequence Tags (ECIST) microarray technique (11) and identified DLC-1 as a gene whose promoter is methylated in NHLs and results in gene silencing. Example 1 discloses quantitative real-time methylation-specific PCR analysis to examine promoter methylation of DLC-1 (deleted in liver cancer 1, a putative tumor suppressor) and its relationship to gene silencing in non-Hodgkin's lymphomas (NHL). Gene promoter methylation of DLC-1 occurred in a differentiation-related manner and has substantial utility as a biomarker in non-Hodgkin's Lymphoma (NHL).
Specifically, a high frequency of DLC-1 promoter hypermethylation was found to occur across different subtypes of NHLs, but not in cases of benign follicular hyperplasia (BFH). More specifically, methylation of DLC-1 was observed in 77% (79 of 103) of NHL cases; including 62% (8 of 13) in MCL, 71% (22 of 31) in B-CLL/SLL (B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma), 83% (25 of 30) in FL, and 83% (24 of 29) in DLBCL samples. When thresholded values of methylation of DLC-1 were examined, 100% specificity was obtained, with 77% sensitivity.
Expression studies demonstrated down-regulation of DLC-1 in NHL compared to normal lymph nodes, and this may be re-activated using therapies/agents that modulate methylation and acetylation.
According to additional aspects, GSTP1, CDKN1A, RASSF1A and DAPK methylation markers have substantial utility as biomarkers of cancer (e.g., non-Hodgkin's Lymphoma).
The DLC-1 gene has been mapped to chromosome 8p21.3-22, a region suspected to harbor tumor suppressor genes and deleted in several solid tumors (21-23). The DLC-1 sequence shares high homology with rat p122RhoGAP, a GTPase-activating protein for Rho family proteins, and DLC-1 protein was shown to be a RhoGAP specific for RhoA and Cdc42 (24). RhoGAPs serve as tumor suppressors by balancing the oncogenic potential of Rho proteins. Recent evidence suggests that RhoA GTPase regulates B-cell receptor (BCR) signaling and may be an important regulator of many aspects of B-cell function downstream of BCR activation (25). Consistent with this notion, the reintroduction of DLC-1 inhibits the proliferation of DLC-1-defective cancer cells (26). Applicants have herein demonstrated that DLC-1 is frequently methylated across all 4 major sub-classes of NHLs. Further, this promoter methylation is reciprocal to DLC-1 mRNA in most of the NHLs examined. Therefore, according to particular aspects of the present invention, the use of this quantitative assay has substantial utility to improve the detection rate of NHL in tissue biopsies, and from blood and/or plasma samples.
In EXAMPLE 2 herein, a CpG island microarray study of DNA methylation was performed with samples of Non-Hodgkin's Lymphomas (NHL) with different clinical behaviors. Non-Hodgkin's Lymphoma (NHL) is a group of malignancies of the immune system that encompasses subtypes with variable clinical behaviors and diverse molecular features. Small B-cell lymphomas (SBCL) are low grade NHLs including mantle cell lymphoma, B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma, and grades I and II follicular lymphoma.
Differential methylation hybridization (DMH) was used to study SBCL subtypes based on a large number of potential methylation biomarkers. From these microarrays, several statistical methods were used to generate a limited set of genes for further validation by methylation specific PCR (MSP). Hierarchical clustering of the DNA methylation data was used to group each subtype on the basis of similarities in their DNA methylation patterns, revealing that there is a characteristic diversity in DNA methylation among the different subtypes. In particular, differential methylation of LHX2, POU3F3, HOX10, NRP2, PRKCE, RAMP, MLLT2, NKX6-1, LPR1B, and ARF4 markers was validated in NHL cell lines and SBCL patient samples, and demonstrated a preferential methylation pattern in germinal center-derived tumors compared to pre- and post-germinal center tumors.
According to particular aspects of the present invention, these markers define molecular portraits of distinct sub-types of SBCL that are not recognized by current classification systems and have substantial utility for detecting, distinguishing between and among, and characterizing the biology of these tumors.
Specifically, characterization of the human lymphoma epigenome was undertaken in the context of studying 3 classes of NHL. The SBCLs, a subset of NHL, exhibit a spectrum of clinical behaviors and the cell of origin of each subtype is thought to be related to a putative stage of normal B-cell differentiation. Mutational status of the variable region of immunoglobulin heavy chain (VH) genes is a useful marker for identifying different developmental stages of NHLs, and relates to processes that occur in the germinal center reaction. MCL (mantle cell lymphoma) is considered to arise in cells at the pre-germinal center stage where VH genes have not yet become mutated (34). In FL (follicular lymphoma), somatic hypermutation of VH genes characteristic of the germinal center reaction suggests that this class of NHL derives from a germinal center stage of differentiation. Approximately half of B-CLL/SLL (B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma) cases are CD38+ with unmutated VH genes (poor prognosis) and the remaining half are CD38− with mutated VH genes (better prognosis). Thus, B-CLL/SLL may represent two separate stages of differentiation; pre-germinal center and post-germinal center, respectively. The SBCL subtypes studied in the present Example represent a spectrum of pre-germinal center, germinal-center and post germinal-center stages of B-cell differentiation and provide a good model to study epigenetic alterations as they might relate to the various compartments of secondary lymphoid tissue cell differentiation.
High-throughput technologies have clearly advanced understanding of the gene expression repertoire of human tumors. Utilization of cDNA microarray analysis allows classification of different malignancies based on dysregulation of gene expression. In one report, hierarchical clustering analysis separated FL from MCL based on gene expression profiles (35). However, such studies do not address the underlying reason(s) for changes in gene expression. In the present Example, the CGI microarray was utilized to investigate part of the NHL epigenome of SBCL subtypes based on interrogation of promoter DNA methylation, a process that plays an role in human cancers by frequently silencing not only tumor suppressor genes, but also genes that are critical to the normal functions of cells, such as apoptosis, cell cycle regulation, cellular signaling, and gene transcription (reviewed in (29, 31)). The disruption of such cellular activities may play a role in lymphomagenesis and/or secondary events such as tumor progression or transformation.
Hierarchical clustering analysis of data from the CGI microarray identified approximately 256 named, variably methylated genes, within SBCL subtypes and recognized genes that are important to many intracellular processes. Additional CGI loci were also differentially methylated, but at this time, some are hypothetical genes and some have not yet been investigated for identity.
LHX2. The LHY2 gene belongs to a superfamily of homeobox-containing genes conserved during evolution and function as transcriptional regulatory proteins in control of lymphoid and neural cell differentiation (36).
POU. The POU family proteins also act as transcriptional factors and regulate tissue-specific gene expression at different stages of development in the nervous system (37).
NRP2. Non-kinase neuropilin 2 (NRP2) was predominantly methylated in FL (p=0.001). This gene encodes a member of the neuropilin family of receptors that binds to SEMA3C (sema domain, Ig domain, short basic domain, secreted, semaphoring 3C) protein and also interacts with vascular endothelial growth factor (VEGF) (38), an important mediator of angiogenesis, a process important in NHL as well as other tumors.
ARF4. Additionally, ADP ribosylation factor 4 (ARF4), which plays a role in vesicular trafficking and as an activator of phospholipase D, was methylated in 7/12 (58.3%) of MCL and 13/15 (87%) FL cases (p=0.001).
Phospholipase D. Phospholipase D is an enzyme involved in the CD38 signaling pathway and regulates lymphocyte activation and differentiation (39).
LRP1B. The LRP1B gene is frequently deleted in various tumor types, but in this Example shows a higher frequency of gene promoter methylation in germinal center SBCLs compared to the other subtypes (p=0.001). CGI promoter hypermethylation of this gene has also been detected in esophageal squamous cell carcinomas (40).
This Example further demonstrates the value of the high-throughput CGI microarray to rapidly interrogate 8,544 (9K) clones from a CGI library isolated by the Huang laboratory (41). In a recent study (22) comparing this 9K library to another containing 12,192 (12K) clones, only 753 were found to be common between the 2 libraries, thus suggesting that the present Example examined ˜50% of potential CGIs in the human genome. Nevertheless, this does not diminish the value of finding many new, epigenetically altered, genes that segregate with subclasses of NHL.
According to particular aspects of the present invention, the herein-disclosed validated markers have substantial utility as diagnostic tools, and for monitor treatment of NHL. The Example also illustrates a very interesting biological finding; preferential methylation of multiple gene promoters in germinal-center tumors such as FL compared to pre-germinal center tumors (MCL and some B-CLL/SLL) and post-germinal center tumors (subset of B-CLL/SLL). Without being bound by mechanism, the reasons for this may be related to the ongoing somatic hypermutations and the process of DNA strand breaks and repair (both effective and ineffective) that accompanies germinal-center biology, and may be possibly carried over into germinal-center NHLs. The findings of this Example thus provide a basis for investigations of gene promoter DNA methylation in NHLs, and provide useful insights into the functional epigenomic signatures of human lymphomas.
The epigenome becomes even more important because there has been a great deal of recent development of pharmaceutical interventions that can potentially reverse epigenetic alterations with the intent of reactivating silenced genes in cancers as a form of chemotherapy (31-33).
In EXAMPLE 3 herein, novel epigenetic Markers for non-Hodgkin's lymphoma (NHL) were discovered using a CpG island microarray analysis. Specficially, using the CpG island microarray approach, a substantial number of additional genes were identified that are, according to particular aspects of the present invention, aberrantly methylated in NHL cell lines and in primary NHLs. According to such aspects, these markers, alone or in combination, have utility detection or diagnosis. A combination of each gene can be used as a molecular marker panel for detection or diagnosis using highly sensitive quantitative methylation specific PCR technology. An advantage of such markers is that they are derived from patients' tumor DNA, which is a more stable specimen than RNA. Hypermethylation of gene loci detected in the assay could be indirect evidence for genes down-regulated in the primary tumors. Although a growing number of genes have been identified as aberrantly methylated in lymphoma (5, 6, 19), to date few studies (7-9) have studied promoter hypermethylation in the specific NHL subtypes in detail.
Applicants have not only identified genes like DLC-1 and PCDHGB7 which are methylated in the vast majority of NHLs, but also have identified some subtype-specific markers such as CCND1, CYP27B1, RARβ2 and EFNA5 which are preferentially methylated in one or two subtypes of NHLs. Using DLC-1 as an example, the ability to detect aberrant methylated DNA in 77% of tumor and 67% of plasma samples from primary NHL patients using quantitative real time MSP was demonstrated herein. Therefore, according to particular aspects, these markers have utility as biomarkers in diagnosis and classification of NHLs, especially for early detection and monitoring therapy.
As shown herein, a candidate tumor suppressor gene DLC-1 is a frequent target of aberrant methylation in NHLs. While methylation of the gene has been previously reported in several types of non-lymphohematopoietic tumors (20-23), this is the first report of its involvement in NHL. The DLC-1 gene was mapped to 8p21.3-22, a region suspected to harbor tumor suppressor genes and recurrently deleted in several solid tumors (23-25). The DLC-1 sequence shares high homology with rat p122RhoGAP, a GTPase-activating protein for Rho family proteins and DLC-1 protein was shown to be a RhoGAP specific for RhoA and Cdc42 (26). Recent evidence suggests that RhoA GTPase regulates B-cell receptor (BCR) signaling and may be an important regulator of many aspects of B-cell function downstream of BCR activation (27). Therefore, epigenetic silencing of DLC-1 might have a profound influence on lymphomagenesis. Interestingly, DLC-1 is not expressed in peripheral blood lymphocytes but is expressed in the normal lymph node when examined by real time RT-PCR for DLC-1 mRNA and suggests tissue specific or developmental stage dependent expression. However, no methylation was found in the normal B-cells regardless of their expression status. Interestingly, reactivation of methylated DLC-1 genes in NHL cells required both DAC and TSA (
The chromosome translocation t(11;14)(q13;32), is seen in most MCLs (2, 28), and as a result, CCND1 is over-expressed in over 90% of MCL (2). A recent finding of complete hypomethylation at the CCND1 promoter in normal B cells suggests that although the CCND1 gene is inactive transcriptionally, the CCND1 promoter is still unmethylated in lymphoid cells that do not contain the translocation (18). It is possible that the mechanism of de novo methylation is dysregulated in NHLs, resulting in aberrant methylation of CCND1 despite its transcriptional status. This finding indicates that such DNA regions in the genome are prone to be methylated in cancer cells, which is consistent with an earlier report (29), although the factors that determine such susceptibility to methylation remain unresolved.
CYP27B1 encodes 1α-hydroxtylase (1α-OHase), an important enzyme in the vitamin D metabolic pathway. The loss of 1α-OHase and/or VDR activity could contribute to the ability of cancer cells to escape growth control mechanisms of vitamin D (30). Several studies have shown that reduced 1α-OHase activities in cancer cells decreased the susceptibility to 25(OH)D3 induced growth inhibition (31).
Ephrin-A5, a member of the ephrin gene family is encoded by EFNA5. The EPH and EPH-related receptors comprise the largest subfamily of receptor protein-tyrosine kinases and have been implicated in mediating developmental events, particularly in the nervous system. Himanen et al. found that ephrin-A5 binds to the EphB2 receptor(32), a tumor suppressor gene (33), leading to receptor clustering, autophosphorylation, and initiation of downstream signaling.
PCDHGB7 is a member of the protocadherin gamma gene cluster, one of three related clusters tandemly linked on chromosome five. These gene clusters have an immunoglobulin-like organization (34), suggesting that a novel mechanism may be involved in their regulation and expression (35). The two cell surface molecules are known to play a role in the nervous system, but any role they may have in NHL is unclear.
Remarkably, applicants found that there were statistically significant differences in DNA methylation between pre-germinal and germinal center derived NHLs. The mean methylation index of non-germinal center NHLs was lower than germinal center related NHLs. The mechanism and biological significance behind this experimental observation is not clear at this point. Although the effect of age on the increase in methylation cannot be excluded when comparing MCL with FL and DLBCL, age related methylation cannot explain the difference in methylation between CLL, FL and DLBCL. The increased methylation observed in germinal center derived NHL might be associated with over-expression of BCL6 (See
Applicants, therefore, have performed analysis of methylation alterations at the genome level in 6 cell lines derived from a spectrum of NHL subtypes, and have identified a group of aberrantly methylated genes which have utility as epigenetic biomarkers for detection of NHL. Applicants have also demonstrated that NHL exhibits nonrandom methylation patterns in which germinal center tumors seem to be prone to de novo methylation. The mechanism behind such experimental observations is unclear, but it is unlikely that all of these methylation events were induced by global deregulation of methyltransferase activity. Instead, dysregulation of a given transcriptional regulator or signaling pathway most likely selectively leads to the aberrant methylation of a portion of downstream genes and confers a growth advantage to the tumor cells
In EXAMPLE 4 herein, multiple novel methylated genes were identified by ECISTs microarray screening, were confirmed in mulitple myeloma (MM) cell lines and primary MM samples, and were shown have substantial utility for diagnosis, prognosis and monitoring of aspects of multiple myeloma.
Expressed CpG Island Sequence Tags (ECISTs) microarray (14), is an integrated microarray system that allows assessing DNA methylation and gene expression simultaneously, and provides a powerful tool to further dissect molecular mechanisms in MMs, and to assess related pharmacologic interventions by differentiating the primary and secondary causes of pharmacological demethylation. This innovative microarray profiling of DNA methylation was used in this Example to define Epigenomic Signatures of Myelomas. Novel epigenetic biomarkers were identified that have substantial utility for diagnosis, prognosis and monitoring.
Methylation microarray profiling was conducted in the context of 4 multiple myeloma (MM) cell lines, 18 MM primary tumors and 2 normal controls. Multiple novel methylated genes were identified, and a subset of these were confirmed in MM cell lines and in primary MM samples (20 primary MM samples from our cell bank, from which DNA was isolated). Additionally, a real time methylation-specific PCR assay was developed for the tumor suppressor gene DLC-1, and was optimized in terms of sensitivity and variability. Furthermore, four MM cell lines were treated with a demethylating agent and histone deacetylase inhibitor, and RNA was isolated from the drug-treated cell lines.
To applicants' knowledge, this Example is the first genome wide methylation analysis of primary MM. The significance of the findings to the scientific field and their potential impact on health is significant in view of the insights into the underlying biology of the epigenetic process of DNA methylation in both normal and neoplastic plasma cell differentiation, and further in view of the substantial diagnostic, prognostic and monitoring utilities and for therapeutic intervention methods involving respective demethylation and/or histone acetylation agents.
In EXAMPLE 5 herein, differential methylation hybridization (DMH) was used to determine and compare the genomic DNA methylation profiles of the granulocyte subtypes of acute myelogenous leukemia (AML).
This Example determines for the first time that genomic methylation profiling can be used to distinguish between clinically recognized subtypes of acute myelogenous leukemia (AML). Aberrant DNA methylation is believed to be important in the tumorigenesis of numerous cancers by both silencing transcription of tumor suppressor genes and destabilizing chromatin. Previous studies have demonstrated that several tumor suppressor genes are hypermethylated in AML, suggesting a roll for this epigenetic process during tumorigenesis. However, it is unknown how the genomic methylation profiles differ among AML variants, or even whether AML can be distinguished on this basis from normal bone marrow or other hematologic malignancies. In this Example, the epigenomic microarray screening technique called Differential Methylation Hybridization (DMH) was applied to the analysis of 23 bone marrow samples from patients having the AML granulocytic subtypes M0 to M3 as well as normal controls.
With this method, a unique genomic methylation profile was created for each patient by screening sample DNA amplicons with an array of over 8600 CpG-rich DNA tag sequences. Cluster analysis of methylation features was then performed that demonstrated these disease subtypes could be sorted according to methylation profile similarities. From this screening, over 70 genomic loci were identified as being hypermethylated in all four examined AML subtypes relative to normal bone marrow. Three hypermethylated loci in M0 samples were found to distinguish this class from all others. Sequence analysis of these loci was performed to identify their encoded genes. Confirmation of their methylation status in AML was conducted using MS-PCR and COBRA analyses.
Results of this Example indicate that genomic methylation profiling has substantial utility not only for diagnosing AML and subtypes thereof, but also in distinguishing this disease from other hematopoietic malignancies. Moreover, analysis of the impact of methylation on the expression of the identified genes will facilitate understanding the underlying molecular pathogenesis of AML.
In EXAMPLE 6 herein, differential methylation hybridization was used to determine the Genomic DNA methylation profiles of Acute Lymphoblastic Leukemia (ALL).
To attain a global view of the methylation present within the promoters of genes in ALL patients and to identify a novel set of methylated genes associated with ALL, methylation profiles were generated for 16 patients using a CGI array consisting of clones representing more than 4 thousand unique CGI sequences spanning all human chromosomes. This is the first time, to applicants' knowledge, that a whole genome methylation scan of this magnitude has been performed in ALL. From the generated profiles, 49 candidate genes were identified that were differentially methylated in at least 25% of the patient samples. Many of these genes are novel discoveries not previously associated with aberrant methylation in ALL or in other types of cancers. Methylation in ten genes found by the CGI array to be differentially methylated in at least 50% of the patients was verified by COBRA, MSP or qMSP. The observations were concordant with the methylation arrays, and the independent verifications indicated that between 10 and 90% of these genes were methylated in every patient. The genes identified in TABLE 7 are involved in a variety of cellular processes including transcription, cell cycle, cell growth, nucleotide binding, transport and cell signaling. In conjunction with the detection of promoter methylation in the ALL samples but not in the normal controls, this indicates that these genes act as tumor suppressors in ALL.
It was determined herein that the 10 validated genes were silenced or down-regulated in NALM-6 and Jurkat ALL cell lines and that their expression could be up-regulated after treatment with a demethylating agent alone or in combination with TSA. Of the validated genes, the greatest post-treatment increase in mRNA expression was for ABCB1, RPIB9 and PCDHGA12 and these appear to be functional genes involved in the development or progression of ALL, and, according to particular aspects, have substantial utility for distinguishing development or progression of ALL. RPIB9 and ABCB1 are genes transcribed in opposite directions with overlapping CGI containing promoters. It has recently been shown that hypomethylation of the ABCB1 promoter leads to multi drug resistance (Baker et al. 2005) and that methylation of the ABCB1 promoter is linked to the down-regulation of gene expression in ALL (Garcia-Manero et al. 2002). This suggests that individuals with methylation in the ABCB1 promoter may better respond to chemotherapeutic treatment than individuals lacking methylation. Although the function of RPIB9 has yet to be confirmed, it likely functions as an activator of Rap which allows B-cells to participate in cell-cell interactions and contributes to the ability of B-lineage cells to bind to bone marrow stromal cells, a requisite process for the maturation of B-cells (McLeod 2004). Therefore, if methylation of the RPIB9 promoter suppresses its transcription, the ability of B-lineage cells to bind to bone marrow stromal cells will likely be inhibited causing the progression of B-lineage cells to halt and resulting in the proliferation of immature cells, a hallmark of ALL. Finally, PCDHGA12 is disclosed herein as an interesting functional gene for ALL in light of a recent report connecting promoter methylation and silencing of PCDHGA11 in astrocytomas and the suggestion that the inactivation of PCDHGA11 is involved in the invasive growth of astrocytoma cells into the normal brain parenchyma (Waha et al. 2005).
In summary, the methylation status of novel genes associated with ALL including NKX6-1, KCNK2, RPIB9, NOPE, PCDHGA12, SLC2A14 and DDX51 was validated Additionally, after treatment with a demethylating agent, mRNA expression was increased in vitro for all 10 genes validated, with the greatest increases occurring for ABCB1, RPIB9, and PCDHGA12. Although the precise role of these genes in ALL progression is unknown, the epigenetic profiles generated in this study, according to particular aspects of the present invention, provide insights to improve our understanding of ALL, provide both novel and noninvasive diagnostic (and/or prognostic, staging, etc.) tools, and novel therapeutic methods and targets for the treatment of ALL. The markers also have substantial utility for distinguishing B-ALL and T-ALL patients.
In Example 7 herein, a novel goal oriented approach for finding differentially methylated genes in, for example, small B-cell lymphoma was developed. DNA microarray data was analyzed from three types of small B-cell lymphomas that reveal the extent of CpG island methylation within the promoter and first exon regions of 8,640 loci. A gene can be represented by several loci on the array. The goal of the method is to identify loci (genes) that are uniquely hypermethylated in a specific lymphoma type and hyperplasia (HP). Hyperplasic patients are, for present purposes, considered normal. The inventive gene selection algorithm has 3 main steps (see
Table 10 shows, according to particular preferred aspects, independently validated novel epigenetic markers for NHL and ALL.
TABLE 11 shows, according to particular preferred aspects, markers for FL and MCL as identified by methylation hybridization as described in the EXAMPLES herein.
TABLE 12 shows, according to particular preferred aspects, markers for ALL as identified by methylation hybridization as described in the EXAMPLES herein.
TABLES 13 shows, according to particular preferred aspects, markers for AML as identified by methylation hybridization as described in the EXAMPLES herein.
TABLE 14 shows, according to particular preferred aspects, markers for CLL CD38as identified by methylation hybridization as described in the EXAMPLES herein.
TABLE 15 shows, according to particular preferred aspects, markers for CLL, FL and MCL as identified by methylation hybridization as described in the EXAMPLES herein.
TABLE 16 shows, according to particular preferred aspects, markers for CLL, FL and MCL as identified by methylation hybridization as described in the EXAMPLES herein.
TABLE 17 shows, according to particular preferred aspects, markers for FL and CLL as identified by methylation hybridization as described in the EXAMPLES herein.
DNA methylation is an epigenetic modification that may lead to gene silencing of genes. This Example discloses real-time methylation-specific PCR analysis to examine promoter methylation of DLC-1 (deleted in liver cancer 1, a putative tumor suppressor) and its relationship to gene silencing in non-Hodgkin's lymphomas (NHL). Applicants previously used an Expressed CpG Island Sequence Tags (ECIST) microarray technique (11) and identified DLC-1 as a gene whose promoter is methylated in NHLs and results in gene silencing. As demonstrated herein, gene promoter methylation of DLC-1 occurred in a differentiation-related manner and has substantial utility as a biomarker in non-Hodgkin's Lymphoma (NHL).
Experimental Design. A quantitative real-time methylation specific PCR ASP) assay was developed for examining DLC-1 promoter methylation. DNA was examined from 13 non-neoplastic samples including 6 cases of benign follicular hyperplasia, 29 diffuse large. B cell lymphoma, 30 follicular lymphoma, 31 B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma, and 13 mantle cell lymphoma patient samples. RNA was extracted from 5 normal controls, 9 DLBCL (diffuse large B-cell lymphoma), 10 FL (, follicular lymphoma), 11 CLL (chronic lymphocytic leukemia), and 9 MCL (mantle cell lymphoma) patient samples to determine expression of DLC-1.
Results. A high frequency of DLC-1 promoter hypermethylation was found to occur across different subtypes of NHLs, but not in cases of benign follicular hyperplasia (BFH). The expression of the DLC-1 mRNA was also shown to be down-regulated in NHLs compared to normal lymphoid cells, and this may be re-activated using therapies that modulate methylation and acetylation. More specifically, methylation of DLC-1 was observed in 77% (79 of 103) of NHL cases; including 62% (8 of 13) in MCL, 71% (22 of 31) in B-CLL/SLL (B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma), 83% (25 of 30) in FL, and 83% (24 of 29) in DLBCL samples. Expression studies demonstrate down-regulation of DLC-1 in NHL compared to normal lymph nodes. When thresholded values of methylation of DLC-1 were examined, 100% specificity was obtained, with 77% sensitivity.
Clinical Samples. Tissue and blood samples were obtained from patients after diagnostic evaluation for suspected lymphoma at the Ellis Fischel Cancer Center (Columbia, Mo.) and the Holden Comprehensive Cancer Center (Iowa City, Iowa) in compliance with local Institutional Review Boards. DNA was isolated from a total of 126 specimens consisting of the following: 31 from patients with B-CLL/SLL, 30 from FL, 13 MCL, and 29 from DLBCL. In addition, 13 non-neoplastic samples were included. All cases of B-CLL/SLL had peripheral blood and bone marrow involvement, and thus were technically categorized as CLL. These are all referred to in this Example as B-CLL/SLL. Total RNA was extracted from 5 normal controls, 9 DLBCL, 10 FL, 11 CLL, and 9 MCL patient samples using the RNeasy kit (Qiagen, Valencia, Calif.).
Bisulfite treatment. Genomic DNA (0.2 to 1 μg) was treated with sodium bisulfite using the EZ DNA methylation kit according to the manufacturer's recommendations (Zymo Research, Orange, Calif.). This treatment converts unmethylated, but not methylated, cytosine to uracil in the genome. For the preparation of 100% methylated DNA, a blood DNA sample was treated with SssI methyltransferase that methylates all cytosine residues of CpG dinucleotides in the genome. Sodium bisulfite modification of the test and SssI-treated DNA samples were then performed as described above.
Standard and Quantitative Real Time MSP assay.
Quantitative Real-Time RT PCR assay. Total RNA (2 μg) was pre-treated with DNase I to remove potential DNA contaminants and reverse-transcribed in the presence of SuperScript III™ reverse transcriptase (Invitrogen). The CDNA generated was used for PCR amplification with appropriate reagents in QPCR mix (ABgene) as recommended by the manufacturer. The Taqman™ probe and primer sets for real-time PCR were purchased from Applied Biosystem's Assay-on-Demand™ services. The reaction was carried out in 40-45 cycles using a SmartCycler™ real-time PCR instrument (Cepheid). All cDNA samples were synthesized in parallel. Separate parallel reactions were run for GAPDH CDNA using a series of diluted cDNA samples as templates to generate standardization curves. The mRNA levels were derived from the standardization curves and expressed as relative changes after normalization to those of GAPDH.
Methylation status of DLC-1 CpG island in NHLs. A conventional MSP assay for DLC-1 was performed initially in 30 FL and B-CLL/SLL samples, primarily to confirm applicants' observations from ECISTs experiments. Representative MSP assay examples are illustrated in
To quantify the methylation level in each sample, a probe was designed to include the CGI (CpG island) in the DLC-1 promoter (
Loss of Expression of DLC-1 mRNA in NHLs. The mRNA expression level of DLC-1 was normalized against GAPDH as a housekeeping gene. As shown in
Clinical Sensitivity and Specificity of Quantitative Methylation Specific PCR. The ideal disease biomarker test should exhibit high (100%) sensitivity and high (100%) specificity. These are quantifiable features of a defined, standardized biomarker/measurement system. In probabilistic terms, the ideal test should always detect the presence of NHL when present in the patient. This means the true positive rate (TPR) should be 100%. Few if any biomarker testing systems achieve 100% TPR, although this can be approached by refinement of technology and testing interpretation. TPR is synonymous with the widely used term clinical sensitivity. Furthermore, the ideal test should never signal the presence of NHL when it is absent. Thus, the false positive rate (FPR) should be 0%. Among clinical investigators, a more widely used test statistic, specificity, is formally identical to the quantity [1-FPR], thus with 0% FPR, the test would have 100% specificity.
The candidate biomarker methylated DLC-1 was measured on a binary scale positive or negative), and the TPR (the proportion of tumors that are biomarker positive) and the FPR (the proportion of BFH (benign follicular hyperplasia) samples that are biomarker positive), were used to summarize our ability to discriminate between NHL and BFH. Sensitivity (TPR) was calculated as (TP/(TP+FN)). In some cases, it has been found beneficial to set quantitative thresholds in analysis of methylation data (14). When we set an empirical threshold for positivity at 13 in
Intra- and Inter-Assay Variability. To reliably determine a quantitative cut-off for positivity, it is important to understand the limits of the variability of the assay system. In a first example, the intra-assay variability was examined. Three NHL cell lines, Daudi, Raji, and Granta 519, were used in this experiment. Five aliquots of each cell line (15 total samples) were bisulfite-treated and examined for quantitative levels of DLC-1 methylation within the same analytical run on the same day to represent the variation that might be expected within a single analytical run. The intra-assay co-efficient of variation (CV) ranged from 0.42%-0.64% when the variable was the qMSP cycle number (Ct). For the P-actin internal control, the range of the CV was 0.34%-0.74%. When the ratio of DLC-1 methylation: P-actin was plotted on the standard curve, the CV increased to a range of 9.92%-16.6%, dependent on the cell line. To test the inter-assay variability, 5 aliquots of each cell line were independently treated and assayed on 5 separate days to represent the variation that might occur between different analytical runs. The inter-assay CV for DLC-1 ranged from 0.82%-2.31% when the variable was the Ct. For the β-actin internal control, the range of CV was 0.70%-1.92%. When the ratio of DLC-1 methylation: β-actin was plotted on the standard curve, the CV increased to a range of 5.71%-17.5%, dependent on the cell line. Preferably, the intra- and inter-assay variability should be known when selecting thresholds and determining the level that can reliably considered positive versus negative, and particularly, according to particular aspects, where the assay is to be used for monitoring treatments where the upward or downward trend is important. The present CVs are consistent with those reported by others for RT-PCR or PCR assays (15, 16).
Plasma DLC-1 DNA Methylation. For a subset of 15 patients with B-CLL/SLL, FL, or DLBCL, paired tumor and plasma samples were available. Of these, 12/15 samples demonstrated concordant results, with 10/12 samples showing methylation in both the tumor and in plasma and 2/12 did not show methylation in either the tumor or in plasma. The 3 discordant samples all demonstrated tumor methylation, but none was detected in the plasma samples. Two of the 3 were from patients with localized stage I FL. Plasma was selected as the sample based on preliminary observations that serum may be less reliable for this purpose. Although both serum and plasma have been examined for total DNA levels, and generally higher levels are reported in serum (17, 18), Boddy, et al (19) (incorporated by reference herein) demonstrated that a 2-spin method of separating plasma from cellular elements provided the most consistency and reliability. This 2-spin method was also used in our study. For all these samples, we examined DLC-1 methylation not only in the tumor and in plasma, but also from buffy coat preparation of peripheral blood cells. In all cases of B-CLL/SLL and FL where methylation was present in the tumor, it was also present in buffy coat cells. However, in the case of DLBCL, methylation was present in the tumor and plasma, but not in buffy coat cells, which is consistent with the fact that most patients with DLBCL (other than those with advanced disease) do not have detectable circulating tumor cells in blood.
Assay Sensitivity of Detecting Low Levels of DNA Methylation. The assay sensitivity was determined by using various amounts of input DNA and, following treatment with sodium bisulfite, determining the least amount of methylated DLC-1 that could be detected in the assay. A standard curve was produced at multiple levels of input DNA from the lymphoma cell line RL ranging from 1 ng to 500 ng (
Treatment of DNA with sodium bisulfite in known to result in destruction of as much as 90% of DNA (20). Thus, at very low levels of DNA, such as that found in plasma, it is quite possible to destroy enough that the assay becomes insensitive and quite variable. One potential way to improve this situation is to add carrier DNA to the extracted DNA prior to bisulfite treatment. The standard curve was compared at multiple levels of input DNA (ranging from 1 ng to 500 ng) in the presence and absence of 1 μg of salmon sperm DNA added prior to treatment. As shown in
Additional markers. According to addition aspects of the present invention, GSTP1, CDKN1A, RASSF1A and DAPK methylation markers have substantial utility as biomarkers of cancer (e.g., non-Hodgkin's Lymphoma).
Non-Hodgkin's Lymphoma (NHL) is a group of malignancies of the immune system that encompasses subtypes with variable clinical behaviors and diverse molecular features. Small B-cell lymphomas (SBCL) are low grade NHLs including mantle cell lymphoma, B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma, and grades I and II follicular lymphoma. Despite the progress made in classification of NHLs based on histological features, cell surface markers and cytogenetics, and despite identification of DNA hypermethylation of some genes such as p57(KIP2), p15(INK4B) (6, 7), DAPK (8) and p73 (9) as being frequent in lymphoid malignancies, there is a substantial need in the art for novel compositions and methods for molecular classification.
Experimental design. Expression profiling is known to be useful for precise classification of different tumor types and subtypes, and expression microarray studies can provide information to assess clinical aggressiveness and to guide the choice of treatment in FL (12). Alizadeh et al (13) used a lymphochip to monitor gene expression signatures of diffuse large B cell lymphoma subgroups derived from distinct stages of B cell differentiation, and several groups have demonstrated that tumor classification can also be achieved by microarray based DNA methylation profiling (14, 15). By contrast, few published reports have focused on the identification of genes whose methylation profiles differ between currently recognized SBCLs.
Results. A high-throughput array-based technique called differential methylation hybridization was used in this Example to study SBCL subtypes based on a large number of potential methylation biomarkers. A total of 43 genomic DNA microarray experiments were analyzed. From these microarrays, several statistical methods were used to generate a limited set of genes for further validation by methylation specific PCR (MSP). Hierarchical clustering of the DNA methylation data was used to group each subtype on the basis of similarities in their DNA methylation patterns, revealing, as disclosed herein, that there is diversity in DNA methylation among the different subtypes.
In particular, differential methylation of LHY2, POU3F3, HOX10, NRP2, PRKCE, RAMP, MLLT2, NKX6-1, LPR1B, and ARF4 markers was validated in NHL cell lines and SBCL patient samples, and demonstrated a preferential methylation pattern in germinal center-derived tumors compared to pre- and post-germinal center tumors.
According to particular aspects of the present invention, these markers define molecular portraits of distinct sub-types of SBCL that are not recognized by current classification systems and have substantial utility for detecting and characterizing the biology of these tumors.
Lymphoma Cell Lines. Six common NHL cell lines were used to study methylation patterns across different subtypes of lymphoma; RL, Daudi, DB, Raji, Granta 519 and Mec-1. RL is a germinal center cell line of FL derivation from a male patient with the t (14; 18) gene rearrangement (16). The Daudi cell line is a derived from CD77+ Burkitt's lymphoma and is often used as a model of germinal center function (17). DB is a DLBCL cell line that has undergone isotype switching (17) and Raji cells are of germinal cell derivation (18). The cell surface marker CD10 is expressed on RL, Raji, DB, and DLBCL, therefore suggesting a germinal center relationship among theses cell lines. Granta 519 is a pre-germinal center cell line derived from a MCL patient (19). The Mec-1 cell line is derived from the peripheral blood of a patient with transformed B-CLL/SLL (20). Granta 519 and Mec-1 do not express CD10. These cells were acquired through the American Type Culture Collection (ATCC) or the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ), and all were maintained in RPM1 1640 medium supplemented with 10% fetal bovine serum.
Patient Samples. Tissue and blood samples were obtained from patients following diagnostic evaluation at Ellis Fischel Cancer Center in Columbia, Mo., in compliance with the local Institutional Review Board. DNA was isolated from a total of 43 patient samples and control DNA was isolated from peripheral blood collected from volunteers whose mean age was <30 years using the QIAamp™ DNA Blood Minikit (Qiagen, Valencia, Calif.). Samples from 16 patients with FL, 12 with MCL, and 15 with B-CLL/SLL were used in this study. All cases of B-CLL/SLL had peripheral blood and bone marrow involvement, therefore were technically categorized as chronic lymphocytic leukemia, and are referred to herein as B-CLL/SLL. All specimens contained >80% neoplastic cells as determined by flow cytometry. Flow cytometry reports were available for 11 of 15 B-CLL/SLL patients used in this study; 5 patient samples were CD38+ and 6 CD38−. Cells from 3 patients with benign follicular hyperplasia (BFH) were also obtained.
Preparation of CGI Island Microarray. PCR products (on average 500 bp) of a microarray panel containing 8,544 sequenced CGI clones were prepared as previously described (21, 22). A pin-and-ring microarrayer GMS 417 (Genetic MicroSystems, Boston, Mass.) was used to spot unpurified PCR products as microdots on Corning UltraGAP II™ (Corning Life Science, Acton, Mass.) slides coated with amino-silane. The slides were then processed using the Corning Pronto Microarray™ (Corning Life Science, Acton, Mass.) reagents according to the manufacturer's recommendations.
Amplicon Preparation and Microarray Hybridization. DNA samples were prepared for hybridization via the DMH protocol (12). Succinctly, 2 μg of genomic DNA was restricted with MseI, a 4-base TTAA endonuclease that restricts bulk DNA down to less than 200 base pairs while preserving the GC-rich CGIs. The resulting sticky ends of the restriction digest are ligated using 0.5 nmol of the PCR linkers H24/H12 (H24: 5′-AGG CAA CTG TGC TAT CCG AGG GAT-3′ (SEQ ID NO: 6) and H12: 5′-TAA TCC CTC GGA-3′) (SEQ ID NO: 7). After a test PCR for successful ligation, DNA was directly digested with the methylation-sensitive endonucleases BstUI and HpaII, respectively (New England Biolabs, Beverly, Mass.). The amplicons were purified after a 20-cycle PCR reaction with QIAquick™ (Qiagen) columns and used for aa-dUTP (amino-allyl dUTP) incorporation using the BioPrime™ labeling kit (Invitrogen, Carlsbad, Calif.). Fluorescence amplicons representing pools of methylated NHL DNA (Cy5) relative to normal DNA (Cy3) were combined in a sex-matched manner and each mixture was co-hybridized to the CGI microarray chip as described (23-25). In females, 1 copy of the X chromosome is largely inactivated by DNA methylation. Therefore, women are expected to exhibit methylation of 1 allele of certain genes, such as the androgen receptor (AR) gene, whereas this occurs only in malignancy in males (26).
Microarray Data Analysis. Each locus on the slide appears as a colored dot comprised of red (from Cy5) and green (Cy3). The intensity levels of red and green in each spot signify the amount of methylation found in cancer (red) and normal (green) cells. Both were background-corrected and a global normalization applied with the assumption that the methylation level of both cancer and normal cells is similar in most loci (red/green≈1). Those loci with (red+green)≧T (where T=700) were flagged as good quality spots and sorted based on their log ratio of fluorescence. The normalization ratio was defined between the 20th and 80th percentile of that sorted list in an effort to minimize extreme ratio values caused by extremely small red or green values. Spots that were too low in intensity or disturbed by artifacts (along with all known housekeeping genes and repeat sequences) were assigned a normalized ratio of 1. After array normalization, an across-array analysis was performed for each locus. Only those loci with at least 25% of their between-array samples having a true normalized ratio (not artificially assigned to 1) were selected for analysis. These filtered loci were then subjected to further statistical testing to determine those loci that were differentially methylated across subtypes of NHL. The Kruskal-Wallis test, because of its ability to compare more than two data distributions and is a nonparametric method that does not assume normalcy in the data, was performed on the group of samples at each locus. The p-value threshold was calculated using the Benjamini and Hochberg method (27). The p-values of all loci were sorted in ascending order, p(1)≦p(2)≦ . . . ≦p(G), where G is the number of across-array filtered loci. Let J be the largest index j for which:
Then, the loci corresponding to the P-values p(1)≦p(2)≦ . . . ≦p(J) were classified as differentially methylated. Nucleotide sequencing results came from the Der Laboratory, Toronto, Canada (http://derlab.med.utoronto.ca/CpGIslandsMain.php). Sequence identification information was obtained by the BLAST™ method.
Methylation Confirmation Analysis by MSP. The DNA methylation status of selected candidate genes from specific regions of the microarray clusters was confirmed using MSP. Each selected gene was first analyzed on cell line DNA and secondly on patient DNA. The following ten selected genes were examined; MLLT2, LHX2, LRP1B, HOX10, NKX6-1, ARF4, NRP2, RAMP, NRP2, and POUF3. One μg of genomic DNA was treated with sodium bisulfite to induce a chemical conversion of unmethylated (but not methylated) cytosine to uracil according to the manufacturer's instructions (EZ DNA Methylation Kit; Zymo Research, Orange, Calif.). For positive controls, normal lymphocyte DNA was treated with SssI methyltransferase (New England Biolabs), which methylates all the cytosines in the genome. The primer sequences used to confirm selected genes are listed in TABLE 1 and the MSP protocol was as described (25, 26). Methylated and unmethylated primers were designed using MethPrimer™ (wwW.urogene.org/methprimer/index.html). Products (5-9 μl) were directly loaded on a 2.5-3% agarose gel stained with SYBR Green (Cambrex Bio Science Rockland, Me.) visualized under UV light and quantified using Kodak gel documentation system.
Statistical analysis. For comparisons of gene promoter methylation between classes of NHLs, the chi-square statistic, as implemented in SAS (Cary, N.C.) software, was employed.
Segregation of SBCL subtypes by hierarchical clustering. Genomic DNA methylation microarray technology was used to characterize the three SBCL subtypes; MCL, B-CLL/SLL and FL. The cell of origin in each of these lymphomas is related to progressive stages of normal lymphoid cell differentiation activated in association with, or without, antigen in peripheral lymphoid tissues. This investigation included a total of 16 de novo patient samples from those with FL, 15 B-CLL/SLL, 12 MCL and 3 samples of BFH that were all probed for the presence of methylated DNA, mainly in the promoter and 1st exon regions of genes and initially analyzed by hierarchical clustering. The relationship between the experimental results and patient samples of each type of SBCL is shown in
Cells from FL are similar in their biological characteristics to cells found in reactive secondary follicles or germinal centers of lymph nodes. From a quantitative standpoint there appear to be more CGI loci hypermethylated in FL patients than the MCL and a subset of B-CLL/SLL samples (
TABLE 2: Information on genes selected from various regions of all differentially methylated clusters from
Confirmation of Microarray findings by MSP. Microarrays are excellent discovery tools, but additional confirmation of selected results is prudent to have full confidence in the findings. In order to independently confirm the DNA methylation status of 10 known genes (NKX6-1, LRP1B, MLLT2, LHX2, ARF4, HOX10, RAMP, NRP2, POU3F3, PRKCE) selected to represent each region of the hierarchical clusters, MSP primers were produced and used to test a series of NHL cell lines (
Analysis of CGI Methylation patterns in de novo SBCL samples. The methylation patterns of cancer cell lines do not always reflect the presence of methylation in primary tumors. There is evidence that CGI methylation in several tissue-specific genes is secondary to intrinsic properties of cell lines (28). However, in this study consistency was found between promoter methylation of the selected genes in NHL cell lines and primary NHLs. The nine genes confirmed as above were examined in 42 NHL and 3 BFH samples using MSP (
Relationships between SBCL classes, the percentage of patient samples methylated in each gene promoter, and the statistical significance of these observations using the chi-square test are presented in TABLE 3.
For instance, in the comparison of B-CLL/SLL (n=15) with MCL (n=12), of the 9 gene promoters examined, only ARF4 (p=0.001) and HOX10 (p=0.05) revealed differences at p=/<0.05. The others were not statistically different between the 2 classes. The greatest differences were seen when comparing FL (n=15) to either B-CLL/SLL or MCL. For the comparison of FL to B-CLL/SLL, only 3 gene promoters were not significantly different at p=/<0.05; LHX2, HOX10, and PRKCE. In comparison of FL to MCL, only 4 gene promoters, LRP1B, BLK, POU3F3, and NRP2 were statistically different. In the case of POU3F3, while all 3 classes revealed DNA methylation, they were all similar in proportion. Therefore, we were able to confirm that promoter DNA methylation, as discovered in the microarray experiments, was present in 9 of the 10 genes tested in de novo NHL samples, while all 10 were methylated in NHL cell lines.
Non-Hodgkin's Lymphoma (NHL) is the 5th most common malignancy in the U.S., accounting for approximately 56,390 new cases in 2005 (1). Mature B-cell NHLs including B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma (B-CLL/SLL), mantle cell lymphoma (MCL), follicular lymphoma (FL), and diffuse large B-cell lymphoma (DLBCL) comprise the majority of all NHL cases (2) and each of these diseases is closely related to a normal counterpart in B-cell differentiation (3) (
A CpG island microarray-based technique was previsouly developed for genome-wide methylation analysis in breast and ovarian cancer (10, 11). In this Example, applicants used this approach to identify a group of genes silenced by DNA methylation in 6 NHL cell lines that are derived from different subtypes of NHL. A sub panel of the novel methylated genes was further examined in primary NHL samples and stage-related methylation in NHLs was discovered.
More specifically, 30 novel methylated genes were identified in these cell lines and ten of them were independently confirmed. Methylation of six of these genes was then further examined in 75 primary NHL specimens comprised of four subtypes representing different stages of maturation. Each gene (DLC-1, PCDHGB7, CYP27B1, EFNA5, CCND1 and RARβ2) was frequently hypermethylated in these NHLs (87%, 78%, 61%, 53%, 40%, and 38% respectively), but not in benign follicular hyperplasia. While some genes were methylated in almost all cases, others were differentially methylated in specific subtypes. Particularly, tumor suppressor candidate gene DLC-1 methylation was detected in a large portion of primary tumor and plasma DNA samples by using quantitative methylation specific PCR analysis. This promoter hypermethylation inversely correlated with DLC-1 gene expression in primary NHL samples. Thus, according to aspects of the present invention, CpG island microarray was used to identify novel methylated gene markers relevant to molecular pathways in NHLs, and having substantial utility as biomarkers of disease, and subtypes thereof.
Cell Lines and Drug Treatments. Human NHL lines RL, Daudi, DB, Raji, Granta 519 and Mec-1 were maintained in RPMI 1640 media. The germinal center related cell line RL is derived from a male patient with FL and the t(14,18) gene rearrangement (12), and Daudi and Raji cells are of germinal center derivation. The postgerminal center cell line DB is a DLBCL cell line that has undergone isotype switching (12). All four of these cell lines expressed surface CD10, thus suggesting a germinal center relationship (9). Granta 519 is an MCL cell line over-expressing cyclin D1 (13) and Mec-1 is a transformed B-CLL/SLL cell line (14). For gene reactivation experiments, cells were cultured in the presence of vehicle (PBS) or DAC (1.0 μM; medium changed every 24 h). After 4 days, cells were either harvested or further treated with TSA (1.0 μM) for 12 h and then harvested. Some cells were also treated with TSA alone for 12 h before harvest. Genomic DNA or total RNA was isolated using Qiagen™ kits (Qiagen, Valencia Calif.) and used for methylation and gene expression analysis, respectively.
Tissue Samples. Tissue and blood samples were obtained from patients after diagnostic evaluation for suspected lymphoma at the Ellis Fischel Cancer Center (Columbia, Mo.) and the Holden Comprehensive Cancer Center (Iowa City, Iowa) in compliance with local Institutional Review Boards. DNA was isolated from a total of 126 specimens; 8 from peripheral blood of healthy volunteers, 5 from patients with benign follicular hyperplasia (BFH1), 13 MCL (mean age, 52.7 years; range, 39-87 years), 30 with B-CLL/SLL (mean age, 66.9 years; range, 56-84 years), 30 from FL (mean age, 62.0 years; range, 50-75 years), and 29 DLBCL (mean age, 57.0 years; range, 45-75 years). All cases of B-CLL/SLL had peripheral blood and bone marrow involvement, and thus were technically categorized as CLL. These are all referred to herein as B-CLL/SLL. Retrospective analysis of flow cytometric data collected at the time of diagnosis for a subset of cases revealed that FL specimens comprised 75% neoplastic B-cells (n=9, range 36-90%), MCL specimens comprise 88% neoplastic cells (n=4, range 85-91%), CLL specimens comprise 80% neoplastic cells (n=12, range 39-94%), and DLBCL specimens comprise 75% neoplastic cells (n=7, range 38-99%). Total RNA was extracted from 2 samples of normal peripheral blood lymphocytes, 3 normal lymph nodes, 9 DLBCL, 10 FL, 11 CLL, and 9 MCL patient samples using the RNeasy™ kit (Qiagen, Valencia, Calif.). A 2-spin method of separating plasma from cellular elements (15) was used in our study. Plasma DNA was isolated from peripheral blood of 15 NHL patients using the QiaAmp™ Blood kit.
Preparation of CpG Island Microarray. The production of microarray panel containing 8,640 CpG island clones was prepared as described (11). Amplified PCR products were spotted, in the presence of 20% DMSO, on UltraGap™ slides (Corning Life Science, Acton, Mass.). The slides were post-processed immediately before the hybridization using Pronto Universal Microarray Reagents (Corning Life Science, Acton, Mass.). In addition, sequences from CpG islands of 42 known tumor suppressor genes were PCR amplified and printed on the same slides. The whole CGI library was recently sequenced by the Microarray Centre of University Health Network, Toronto, Canada and the sequences can be viewed at htt://s-der10 med.utoronto.ca/CpGIslands.htm. Out of the 8640 CpG island fragments, 4564 unique genomic loci were identified.
Preparation of Amplicons for Methylation Analysis. Amplicon preparation for methylation analysis was performed as previously described (16, 17). Briefly, 2 μg genomic DNA was digested with MseI and then ligated to a PCR-linker. The ligated DNA was then directly digested with methylation-sensitive endonucleases, HpaII and BstUI, and amplified with a linker primer by PCR (11). The amplified products (or amplicons) were purified for fluorescence labeling. Incorporation of aa-dUTP1 into amplicons (5 μg) was conducted using the Bioprime DNA Labeling System (Invitrogen, Carlsbad, Calif.). Cy5 and Cy3 fluorescence dyes were coupled to aa-dUTP-labeled test and reference amplicons, respectively, and co-hybridized to the CpG island microarray panel. Hybridization and the post-hybridization washing were done according to the manufacturer's procedures (Corning Life Sciences, Acton, Mass.). Hybridized slides were scanned with the GenePix™ 4200A scanner (Axon, Union City, Calif.) and the acquired images were analyzed with the software GenePix™ Pro 5.1
Microarray data analysis. The Cy3 and Cy5 fluorescence intensities were obtained for each hybridized spot. Array spots with fluorescence signals close to the background signal, reflecting PCR or printing failures, were excluded from the data analysis. Because Cy5 and Cy3 labeling efficiencies varied among samples, the Cy5/Cy3 ratios from each image were normalized according to a global mean method in Genepix™ Pro 5.1. This internal control panel included 20 Mse I fragments that have no internal Bst UI and Hpa II restriction sites spotted at several concentrations on each array. The adjusted Cy5/Cy3 ratio for each CGI locus was then calculated and data were exported in a spreadsheet format for analysis. The hybridization experiments were repeated and only those reproducible spots were chosen for analysis.
Methylation Specific PCR (MSP) and Combined Bisulfite and Restriction Analysis (COBRA). 2 μg of genomic DNA was treated with sodium bisulfite according to the manufacturer's recommendations (Ez™ DNA methylation kit; Zymo Research, Orange, Calif.). For the preparation of 100% methylated DNA, a blood DNA sample was treated with M. SssI methyltransferase (New England Biolabs, Beverly, Mass.) that methylated all cytosine residues of CpG dinucleotides in the genomic DNA. Sodium bisulfite modification of the test and SssI-treated DNA samples was then performed as described above. Bisulfite-treated genomic DNA was used as a template for PCR with specific primers located in the CpG island regions of each selected gene. For MSP, allele specific primers which cover 2-3 CpG dinucleotides were designed to differentiate methylated and unmethylated sequences. Amplification was performed using AmpliTaq™ Gold polymerase (Applied Biosystems, Foster City, Calif.). For COBRA, after amplification, PCR products were digested with the restriction enzyme BstUI (New England Biolabs, Beverly, Mass.), which recognizes sequences unique to the methylated and bisulfite-unconverted alleles. The digested DNA samples were separated in parallel on 3% agarose gels, stained with SYBR green and quantified using a Kodak gel documentation system. The additional COBRA primers used are: CCND1, 5′GGTTTGGGTAATAA GTTGTAGGGA (sense strand) (SEQ ID NO:52) and 5′-CAACCATAAAACA CCAACTCCTATAC (antisense strand) (SEQ ID NO:53); EFNA5, 5′-TTTAAGGAGGGAAAGAGGAGTAGTT (sense strand) (SEQ ID NO:54) and 5′-AAATC CCTCCAACTCCTAAAT AAAC (antisense strand) (SEQ ID NO:55); PCDHGB7, 5′-TGGGGTAGAATAAA GGTAGTAGTAAAGGAA (sense strand) (SEQ ID NO:56) and 5′-ACAATCCCACACAAAACCTCTAAAC (antisense strand) (SEQ ID NO:57); NOPE, 5′-TTTTTTGTTTTATTTATTTTAGTTTTAGTT (sense strand) (SEQ ID NO:58) and 5′-AAAACCCATCTCCACAAATATCAT (antisense strand) (SEQ ID NO:59); RPIB9, 5′-ATTGGAATTGATATA AAG TTT AGG GTT (sense strand) (SEQ ID NO:60) and 5′-ACCCCCTTAAACAAATATAAAAAAC (antisense strand) (SEQ ID NO:61); PON3, 5′-TTTTTGGGTAGAGGTTAAGGTTTAA (sense strand) (SEQ ID NO:62) and 5′-CCCCAAATCCTAAAAAAAATAAATTA (antisense strand) (SEQ ID NO:63); FLJ39155, 5′-GGTTTTTGTTTTTGGTTTTTAGTTT (sense strand) (SEQ ID NO:64) and 5′-ATCTAAAAAATTAATCATTCTTTTAATAAA (antisense strand) (SEQ ID NO:65).
DLC-1 Quantitative Real Time MSP Assay. The real-time MSP uses two amplification primers specific for methylated sequences and an additional, amplicon-specific, and fluorogenic hybridization probe (Probe: FAM/AAG TTC GTG AGT CGG CGT TTT TGA/BHQ1 (SEQ ID NO:5) whose target sequence is located within the amplicon. The probe was labeled with two fluorescent dyes, with FAM at the 5′-end and BHQ1 at the 3′-end, and synthesized by IDT (Coralville, Iowa). The bisulfite treated DNA was used for PCR amplification with appropriate reagents in QPCR mix (ABgene, Rochester, N.Y.) as recommended by the manufacturer. The reaction was carried out in 40-45 cycles using a SmartCycler™ real-time PCR instrument (Cepheid, Kingwood Tex.).
Real-time RT-PCR. Total RNA (2 μg) was pre-treated with DNase I to remove potential DNA contaminants and reverse-transcribed in the presence of SuperScript III™ reverse transcriptase (Invitrogen, Carlsbad, Calif.). The generated cDNA was used for PCR amplification with the system described above. The Taqman™ probe and primer sets for real time PCR were purchased from Applied Biosystems (Foster City, Calif.). Separate parallel reactions were run for GAPDH cDNA using a series of diluted cDNA samples as templates to generate standardization curves. The mRNA levels were derived from the standardization curves and expressed as relative changes after normalization to those of GAPDH.
Methylation profiling in NHL cell lines. The microarray (16) was used to identify hypermethylated CpG island loci in the 6 NHL cell lines. Cy5- and Cy3-labeled amplicons, representing differential pools of methylated DNA in NHL cell lines relative to normal lymphocyte samples in a sex matched manner, were used as targets for microarray hybridization. Genomic DNA fragments containing methylated restriction sites were protected from the digestion and could be amplified by linker-PCR, whereas the equivalent allele fragments containing the unmethylated restriction sites were digested and thus could not be amplified in the normal lymphocytes. As similar to cDNA microarray experiments, the significance of methylation changes is determined by the comparison of the ratio of two reporters, Cy5 and Cy3. These hypermethylated CpG island loci appeared as “red” spots after microarray hybridization because greater signal intensities were obtained from the Cy5-labeled (red) NHL amplicons, than from those of the Cy3-labeled (green) control amplicons. When a cut-off value of the normalized Cy5/Cy3 ratio was set at >2 for the positive loci, a total of 86 methylated CpG loci (1.88% of 4564 CpG island fragments) were identified in Raji, 74 (1.62%) in Daudi, 68 (1.49%) in RL, 71 (1.55%) in DB, 51 (0.87%) in Mec-1 and 26 (0.56%) in Granta 519. Fifty two loci (1.14%) were found commonly methylated in at least 4 of the 6 NHL cell lines. This same cut-off ratio was effective in identifying hypermethylated CpG islands in breast tumors in applcants' previous study (11). Using the methylation microarray data of 83 named genes that are methylated in at least two cell lines, cluster analysis was conducted. Clustering of the pattern of methylation yielded a profile that allowed discrimination between germinal center derived lymphomas DB and RL, and non-germinal center lymphoma Granta 519 and Mec-1 (
Independent Verification of Methylation. Among the 30 most interesting genes based on review of literature (TABLE 4), the microarray findings of 10 known genes (PCDHGB7, EFNA5, CYP27B1, CCND1, DLC-1, NOPE, RPIB9, FLJ39155, PON3 and RARβ2) whose function might relate to cancer were selected for independent confirmation by COBRA and MSPCR analyses. Hypermethylation of these genes was found in the 6 NHL cell lines (
aSequences of the clones can be obtained from http://s-der10.med.utoronto.ca/CpGIslands.htm.
Reactivation of methylated genes by a demethylating agent and HDAC inhibitor. Real time RT-PCR was performed on 4 of these 10 genes in the cell lines treated with DAC and TSA (
Hypermethylation in primary NHLs. The methylation profile of cancer cell lines does not always reflect the pattern of methylation in primary tumors. Therefore, the promoter methylation of 6 gene subset was selected and confirmed in a larger panel of NHLs (75 cases) including B-CLL/SLL, MCL, FL and DLBCL by COBRA and MSP analysis. Representative COBRA results of four of the genes are illustrated in
Overall, simultaneous promoter methylation in ≧3 genes occurred in 9/14 (64%) of B-CLL/SLL, 2/10 (20%) of MCL, 15/15 (100%) of FL and 12/13 (92%) of DLBCL. As shown in
Down-regulation of DLC-1 gene expression in primary NHLs. The mRNA expression level of DLC-1 was quantified by real time RT-PCR in 5 normal controls and 39 primary NHL samples. As shown in
Quantitative analysis of DLC-1 methylation in tumor and plasma samples of NHL patients. To test the idea of utilizing DLC-1 as a biomarker, a real time quantitative MSP assay was designed and expanded the methylation analysis from all the samples described above to now include additional samples from patients with MCL, CLL, FL and DLBCL. When a cut off ratio of DLC-1: β-actin×1000 was set as 15, the DLC-1 methylation frequencies were 71%, 62%, 83%, and 83%, respectively (
For a subset of 15 patients with B-CLL/SLL, FL, or DLBCL, paired tumor and plasma samples were available. Of these, 12/15 samples demonstrated concordant results, with 10/12 samples showing methylation in both the tumor and in plasma and 2/12 did not show methylation in either the tumor or in plasma. The 3 discordant samples all demonstrated tumor methylation, but none was detected in the plasma samples. Two of the 3 were from patients with localized stage I FL. For all these samples, we examined DLC-1 methylation not only in the tumor and in plasma, but also from buffy coat preparations of peripheral blood cells. In all cases of B-CLL/SLL and FL where methylation was present in the tumor, it was also present in buffy coat cells. However, in the case of DLBCL, methylation was present in the tumor and plasma, but not in buffy coat cells, which is consistent with the fact that most patients with DLBCL (other than those with advanced disease) do not have detectable circulating tumor cells in blood.
Experimental design. Expressed CpG Island Sequence Tags (ECISTs) microarray (14), is an integrated microarray system that allows assessing DNA methylation and gene expression simultaneously, and provides a powerful tool to further dissect molecular mechanisms in MMs, and to assess related pharmacologic interventions by differentiating the primary and secondary causes of pharmacological demethylation. This innovative microarray profiling of DNA methylation was used in this Example to define Epigenomic Signatures of Myelomas. Novel epigenetic biomarkers were identified that have substantial utility for diagnosis and prognosis.
Results. In this Example, methylation microarray profiling was conducted in the context of 4 multiple myeloma (MM) cell lines, 18 MM primary tumors and 2 normal controls. Multiple novel methylated genes were identified, and a subset of these were confirmed in MM cell lines and in primary MM samples (20 primary MM samples from our cell bank, from which DNA was isolated). Additionally, a real time methylation-specific PCR assay was developed for the tumor suppressor gene DLC-1, and was optimized in terms of sensitivity and variability. Furthermore, four MM cell lines were treated with a demethylating agent and histone deacetylase inhibitor, and RNA was isolated from the drug-treated cell lines.
Cultured B-cell lines and drug treatment. Myeloma lines U266, NCI-H929, RPMI 8226 and KAS 6/1 were maintained in RPMI 1640 media supplemented with 10% fetal bovine serum (FBS). KAS 6/1 cells were supplemented with IL-6 at a concentration of 10 ng/mL. For ‘gene reactivatio’ experiments, cells were cultured in the presence of vehicle (PBS) or 5-aza-2′-deoxycytidine (1.0 μM; medium changed every 24 h). After 4 days, cells were either harvested or further treated with TSA (1.0 μM) for 12 h and then harvested. Some cells were also treated with TSA alone for 12 h before harvest. Genomic DNA or total RNA was isolated using Qiagen™ kits (Qiagen, Valencia Calif.) and used for methylation and gene expression analysis, respectively.
Tissue sample preparation. Plasma cells were enriched by immunomagnetic separation. Cell suspensions were incubated with an anti-CD138 (Beckman Coulter, Fla.) respectively at 4° C. for 30 min, washed twice in PBS containing FCS (0.5%), and incubated in the cold for 15 min with magnetic beads coated with α-mouse IgG (Dynal, N.Y.). CD138 is known as Syndecan-1 and is expressed on normal and malignant plasma cells but not on circulating B-cells, T-cells and monocytes. B-cell subsets were examined by flow cytometry analysis.
Methylation microarray analysis. The approach was adapted from a previously described protocol (15). Briefly, 2 μg genomic DNA was restricted with MseI, a 4-base TTAA endonuclease that restricts bulk DNA into small fragments (<2000-bp), but retains GC-rich CpG islands. The ‘sticky ends’ of the digests were ligated with 0.5 nmol PCR linkers H-24/H-12 (H-24: 5′-AGG CAA CTG TGC TAT CCG AGG GAT (SEQ ID NO:6), and H-12: 5′-TAA TCC CTC GGA (SEQ ID NO:7)). Linker-ligated DNA was digested by McrBC, a restriction enzyme that only cuts methylated DNA sequences (16). About 20 ng of the linker-ligated-uncut samples and 20 ng linker-ligated-McrBC-cut DNA were amplified by PCR. The amplified products (or amplicons) were purified for fluorescence labeling. Incorporation of aa-dUTP into amplicons was conducted using the Bioprime™ DNA Labeling System (Invitrogen, Carlsbad, Calif.). Cy5 and Cy3 fluorescence dyes were coupled to aa-dUTP-labeled McrBC-cut and uncut amplicons respectively, and co-hybridized to the 12K CpG island microarray panel. Hybridization and the post-hybridization washing were done according to the manufacturer's procedures (Corning Life Sciences, Acton, Mass.). Hybridized slides were scanned with the GenePix™ 4200A scanner (Axon, Union City, Calif.) and the acquired images were analyzed with the software GenePix™ Pro 5.1.
Microarray data analysis. The hybridization output is the measured intensities of the two fluorescent reporters, Cy3 and Cy5, false-colored with green or red and overlaid one on the other. The fluorescence ratios calculated for each CpG island (digested/undigested) reflect the degree of DNA methylation for each CpG island locus. Mitochondrial DNA is unmethylated (17), therefore signals intensities of both channels coming from mitochondrial clones are expected to be equal. Data from arrays analyzing methylation were normalized based on signals of 60 spots containing mitochondrial clones. These spots were spotted in each of 48 blocks. Their pixel intensities covered the whole signal range of the microarray. After normalization, a ratio that approaches 0 indicates a methylated CpG island—no production of labeled PCR product following McrBC digestion, while the undigested reference will yield labeled PCR product. A ratio approaching 1 indicates an unmethylated CpG island—fluorescently labeled PCR product will be obtained in both the McrBC digested test sample and the undigested reference. The average Cy5/Cy3 ratio of two experiments (dye-swapped) was used for comparison.
Confirmation of methylation analysis by MSP and COBRA. Methods for bisulfite modification of DNA and subsequent PCR techniques used in this study are as described earlier (14). 1 μg of genomic DNA was treated with sodium bisulfite according to the manufacture's recommendations (Ez™ DNA methylatin kit; Zymo Research, Organe, Calif.). This treatment converts unmethylated, but not methylated, cytosine to uracil in the genome. For the preparation of 100% methylated DNA, a blood DNA sample was treated with M. SssI methyltransferase that methylates all cytosine residues of CpG dinucleotides in the genome. Sodium bisulfite modification of the test and SssI-treated DNA samples were then performed as described above. Bisulfite-treated genomic DNA (100-200 ng) was used as a template for PCR with specific primers located in the CpG island regions of multiple genes. For MSP, allele specific primers were designed to differentiate methylated and unmethylated sequences. Amplification was performed using AmpliTaq Gold™ polymerase. For COBRA, after amplification, PCR products were digested with the restriction enzyme BstUI (New England Biolabs), which recognizes sequences unique to the methylated and bisulfite-unconverted alleles. The digested and undigested control DNA samples were separated in parallel on 3% agarose gels, stained with SYBR green and quantified using Kodak gel documentation system.
Development of real time methylation specific PCR. Bisulfite treatment of the DNA was performed as described above. The real time methylation specific PCR uses two amplification primers and an additional, amplicon-specific, and fluorogenic hybridization probe whose target sequence is located within the amplicon. The published primers (M(+): 5′-CCC AAC GAA AAA ACC CGA CTA ACG-3′ (SEQ ID NO:1); M(−): 5′-TTT AAA GAT CGA AAC GAG GGA GCG-3′ (SEQ ID NO:2); U(+): 5′-AAA CCC AAC AAA AAA ACC CAA CTA ACA-3′ (SEQ ID NO:3); U(−): 5′-TTT TTT AAA GAT TGA AAT GAG GGA GTG-3′ (SEQ ID NO:4)) for DLC-1 were used for the PCR amplification of methylated and unmethylated alleles in two separate reactions. The real-time methylation specific PCR uses the same two amplification primers specific for methylated sequences and an additional, amplicon-specific, and fluorogenic hybridization probe (Probe: FAM/AAG TTC GTG AGT CGG CGT TTT TGA/BHQ—1 (SEQ ID NO:5)) whose target sequence is located within the amplicon. The probe was labeled with two fluorescent dyes, with FAM at the 5′-end and with BHQ1 at the 3′-end. The primers/probe set for real-time methylation specific PCR were synthesized by IDT. The bisulfite treated DNA was used for PCR amplification with appropriate reagents in QPCR mix (ABgene) as recommended by the manufacturer. The reaction was carried out in 40-45 cycles using a SmartCycler™ real-time PCR instrument (Cepheid).
Methylation profiling of four myeloma cell lines. The microarray was first used to identify hypermethylated CpG island loci in four MM cell lines. Cy3- and Cy5-labeled amplicons, representing differentially methylated pools of genomic DNA were co-hybridized on the 12K CpG island microarray. Genomic DNA fragments containing methylated CpG sites in the McrBC-cut sample were digested by McrBC and can not be amplified by linker-PCR, whereas the equivalent allele can be amplified in the uncut sample (
Methylation profiling of 18 cases of primary myelomas. Primary myeloma samples from 18 cases were then studied using the microarray strategy described above. The Cy5/Cy3 ratios ratios, which represent the level of methylation of each CpG island locus from 3,962 annotated genes were used for initial analyses. The methylation index ratio for each CpG island locus in each tumor samples was calculated as described above. The ratios were then used for cluster analyses (
Confirmation Study in Cell Lines. As an initial test, the microarray findings of 10 known genes (PCDHGB7, CYP27B1, DLC-1, NOPE, FLJ39155, PON3, PITX2, DCC, FTHFD and RARβ2) whose function might relate to cancer were independently confirmed by COBRA and MSP analyses. Hypermethylation of these genes was confirmed in the 4 MM cell lines (
Confirmation Studies in Primary Myelomas. A subset of 3 of the above-identified genes was selected and the promoter methylation was confirmed in 10 cases of primary MMs. Representative COBRA results of the three genes are illustrated in
A real-time methylation-specific PCR assay with high sensitivity and reproducibility was developed. As disclosed herein, DLC-1, a candidate tumor suppressor gene (19), was methylated in a large portion of leukemia and lymphoma. A real time quantitative methylation specific PCR (qMSP) assay was therefore developed for DLC-1 gene. To quantify the methylation level of DLC-1 in each sample analyzed, a probe was designed to include the CpG island in the DLC-1 promoter, the hypermethylation of which is known to be correlated with a lack of DLC-1 gene expression. The relative methylation levels in a particular sample are measured by the ratio of DLC-1 ACTIN×1000. To reliably determine a quantitative cut-off for positivity, the intra-assay and inter-assay variability was examined. Three lymphoma cell lines were used, and each was divided into 5 separate aliquots and treated with sodium bisulfite in preparation for qMSP analysis. All 5 samples were analyzed in the same group on the same day to represent the variation that might be expected within a single analytical run. The intra-assay co-efficient of variation (CV) ranged from 0.422-0.644 when the variable was the qMSP cycle number (Ct). For the β-actin internal control, the range of CV was 0.346-0.746. When the ratio of DLC-1 methylation: β-actin was plotted on the standard curve, the CV increased to a range of 9.92-16.6, dependent on the cell line. To test the inter-assay variability, 5 aliquots of each cell line were independently treated and assayed on 5 separate days. The inter-assay CV for DLC-1 ranged from 0.820-2.31 when the variable was the Ct. For the β-actin internal control, the range of CV was 0.709-1.92. When the ratio of DLC-1 methylation: β-actin was plotted on the standard curve, the CV increased to a range of 5.71-17.5, dependent on the cell line. The assay sensitivity was determined by using serial dilutions of Raji cell DNA before bisulfite treatment and determining the least amount of methylated DLC-1 that could be detected in the assay. In this case, tumor DNA could be detected at a dilution of 1:10,000. As show in
Quantitative analysis of DLC-1 methylation in primary MMs. 15 primary MM samples were analyzed using the qMSP assay developed above (
Rationale and experimental design. The intent of this Example was to determine whether genomic methylation profiling could be used to distinguish between clinically recognized subtypes of acute myelogenous leukemia (AML). Aberrant DNA methylation is believed to be important in the tumorigenesis of numerous cancers by both silencing transcription of tumor suppressor genes and destabilizing chromatin. Previous studies have demonstrated that several tumor suppressor genes are hypermethylated in AML, suggesting a roll for this epigenetic process during tumorigenesis. However, it is unknown how the genomic methylation profiles differ among AML variants, or even whether AML can be distinguished on this basis from normal bone marrow or other hematologic malignancies. In this Example, the epigenomic microarray screening technique called Differential Methylation Hybridization (DMH) was applied to the analysis of 23 bone marrow samples from patients having the AML granulocytic subtypes M0 to M3 as well as normal controls.
Results. With this method, a unique genomic methylation profile was created for each patient by screening sample DNA amplicons with an array of over 8600 CpG-rich DNA tag sequences. Cluster analysis of methylation features was then performed that demonstrated these disease subtypes could be sorted according to methylation profile similarities. From this screening, over 70 genomic loci were identified as being hypermethylated in all four examined AML subtypes relative to normal bone marrow. Three hypermethylated loci in M0 samples were found to distinguish this class from all others. Sequence analysis of these loci was performed to identify their encoded genes. Confirmation of their methylation status in AML was conducted using MS-PCR and COBRA analyses.
Results of this Example indicate that genomic methylation profiling has substantial utility not only for diagnosing AML and subtypes thereof, but also in distinguishing this disease from other hematopoietic malignancies. Moreover, analysis of the impact of methylation on the expression of the identified genes will facilitate understanding the underlying molecular pathogenesis of AML.
Differential Methylation Hybridization (DMH). Differential Methylation Hybridization screening was applied, essentially as described elsewhere herein above, to the analysis of 23 bone marrow samples from patients having the AML granulocytic FAB subtypes M0 to M3 as well as disease-free bone marrow samples. MS-PCR, COBRA and Cluster analysis was performed essentially as described herein above.
DMH screening of 23 bone marrow samples identified over 70 genomic loci as being hypermethylated in all four examined AML subtypes relative to normal bone marrow, and particular loci are listed in TABLE 5.
Sequence analysis of these loci (DNA tags) was performed to identify their encoded genes, revealing several genes not previously associated with abnormal methylation in AML, including the dual-specificity tyrosine phosphorylation regulated kinase 4, structural maintenance of chromosome 2-like-1, and the exportin 5 genes. In particular aspects, three hypermethylated loci in M0 samples were found to distinguish this class from all others.
Confirmation of their methylation status in AML was conducted using MS-PCR and COBRA analyses (
Cluster analysis of methylation features from each sample was then performed, demonstrating that the FAB M0-M3 subtypes could be discriminated on the basis of their methylation profile patterns (
Distinguishing between AML and ALL.
In additional experiments, differential methylation of 508 chromosomal loci in ALL and AML was evaluated and used to differentiate these two diseases. The cluster image created from the DMH experiments demonstrated a clear delineation between ALL and AML samples of various subtypes. Furthermore, the cluster illustrated numerous hypermethylated and hypomethylated loci. For example, a prominent cluster of hypermethylated loci in AML is seen in one region of an array and a similar cluster is seen including hypomethylated loci in ALL samples. The following genes were found to be hypermethylated in AML and may be possible tumor suppressor genes: DPYSL5, ARL61P2, SLIT2, HSPA4L, HOXB13, and CKS2.
Therefore, the present compositions and methods enable discrimination between ALL and AML using differential methylation patterns, and methylation patterns in ALL and AML provide a blueprint for the behavior of this heterogeneous disease. The methylation patterns identified in ALL and AML have substantial diagnostice prognostic utility.
Rationale and experimental design. Previous studies investigating the aberrant methylation of gene promoters in ALL have associated hypermethylated promoters with prognosis (Roman-Gomez et al. 2004), cytogenetic alterations (Shteper et al. 2001; Maloney,et al. 1998), subtype (Zheng et al. 2004) and relapse (Matsushita et al. 2004). However, elucidaticdation of the aberrant methylation profiles in ALL is limited by the small number of CGIs analyzed to date, The intent of this Example was to determine whether genomic methylation profiling could be used to identify and distinguish Acute Lymphoblastic Leukemia (ALL). Aberrant DNA methylation is believed to be important in the tumorigenesis of numerous cancers by both silencing transcription of tumor suppressor genes and destabilizing chromatin. Until the present work, it was unknown whether ALL could be distinguished from normal bone marrow on this basis. In this Example, the epigenomic microarray screening technique called Differential Methylation Hybridization (DMH) was applied to the analysis of bone marrow samples from patients having ALL, as well as from normal controls.
Results. In this Example, to attain a global view of methylation within the promoters of genes in ALL patients and to identify a novel set of hypermethylated genes associated with ALL, methylation profiles for 16 patients were generated using DMH and a CpG island array that contains clones representing more than 4 thousand unique genes spanning all human chromosomes. From the generated profiles, 49 candidate genes were identified to be differentially methylated in at least 25% of patient samples. The presence of methylation in DCC, DLC-1, DDX51, KCNK2, LRP1B, NKX6-1, NOPE, PCDHGA12, RPIB9/ABCB1(MDR1) and SLC2A14 was verified by COBRA, MSP or qMSP. We examined the expression of these genes in 2 ALL cell lines (Jurkat, NALM-6) pre- and post-treatment with 5-aza and TSA by semi-quantitative real-time RT-PCR. In all cases, methylation corresponded to the down-regulation or silencing of the gene and up-regulation of gene expression was achieved after treatment.
Therefore, particular aspects of the present invention provide ALL-specific epigenetic profiles having substantial utility for subtype classification, prognosis and treatment response in ALL patients.
Tissue specimens. Bone marrow samples of patients diagnosed with leukemia at the Ellis Fischel Cancer Center (Columbia, Mo.) were obtained with the Institutional Review Board approval. DNA was isolated using the QIAamp™ DNA Mini Kit (Qiagen, Valencia, Calif.) according to the manufacturer's specifications from 16 specimens: 6 from patients diagnosed with T-ALL and 10 from patients diagnosed with pre B-ALL (TABLE 6).
Amplicon development and differential methylation hybridization (DMH). Amplicons were generated and DMH was performed as previously described (Huang et al 1999; incorporated by reference herein). Briefly, 2 μg of genomic DNA from malignant and non-malignant cells were digested with MseI followed by ligation of PCR linkers and digestion with methylation sensitive endonucleases (HpaII and BstUI). PCR was then performed amplifying only methylated fragments or fragments containing no internal HpaII or BstUI sites. The amplicons from the malignant and normal sample were labeled with Cy5 or Cy3 fluorescence dye respectively and cohybridized to a panel of 8,640 short CpG island tags arrayed on a glass slide. The slides were scanned with GenePix™ 4200a scanner and signal intensities of hybridized spots were analyzed with the GenePix™ 4.0 software program (Molecular Devices Corporation, Sunnyvale, Calif.).
To determine which clones were differentially methylated in the tumor versus the normal samples, we used global normalization for each array then performed across-array analysis for each spot. The Kruskal-Wallis non-parametric test was then used to identify clones that were differentially methylated in ALL and non-malignant samples.
Clone sequences. Sequences from differentially methylated CpG clones were extracted from the Der laboratory website (http://derlab.med.utoronto.ca/CpGIslands/). BLAST searches were performed to determine if these clone sequences were associated with the promoter region of known genes and if these regions contained CpG islands. Finally, we used these sequences were used to develop primers for RT-PCR and PCR using MethPrimer™ and Primer3™ respectively.
Methylation specific PCR (MSP) and combined bisulfite and restriction analysis (COBRA). Two μg of DNA was treated with sodium bisulfite according to the manufacturer's recommendations (Ez™ DNA methylation kit; Zymo Research, Orange, Calif.). Bisulfite treated DNA was used as a template for PCR with specific primers designed using Primer3™ and that were located in the CpG island regions of each tested gene (TABLE 7).
1All COBRA amplicons were digested with BstUI except for DDX51 (TaqaI) and KCNK2 (HpyCH4IV).
2Product sizes are approximate.
3HPRT1 primer sequence from Vandesompele et al. (2002).
The purified PCR products were restricted with BstU1, TaqaI or HpyCH4IV according to manufacturer's recommendations (New England Biolabs). The MSP primers (M(+): 5′-AAT AAC ATT TAT AAA TAC CGC CGT T-3′ (SEQ ID NO:25); M(−): 5′-AGT TTG CGT TGG AGA TTG TTC-3′ (SEQ ID NO:24); U(+): 5′-CCA ATA ACA TTT ATA AAT ACC ACC ATT-3′ (SEQ ID NO:27); U(−): 5′-AAG TTT GTG TTG GAG ATT GTT TG-3′) (SEQ ID NO:26) were used in PCR to differentiate methylated and unmethylated sequences in LRP1B. Electrophoresis was performed using a 3% agarose gel stained with SYBR green or a 1.5% agarose gel stained with ethidium bromide to visualize COBRA and MSP products respectively.
Quantitative real time methylation specific PCR (qMSP). qMSP was performed as described previously (Lehmann et al 2002). Briefly, 100 ng of bisulfite treated DNA and the DLC-1 primers (M(+): 5′-CCC AAC GAA AAA ACC CGA CTA ACG-3′(SEQ ID NO:1); M(−): 5′-TTT AAA GAT CGA AAC GAG GGA GCG-3′ (SEQ ID NO:2); U(+): 5′-AAA CCC AAC AAA AAA ACC CAA CTA ACA-3′ (SEQ ID NO:3); U(−): 5′-TTT TTT AAA GAT TGA AAT GAG GGA GTG-3′ (SEQ ID NO:4)) and probe (FAM/AAG TTC GTG AGT CGG CGT TTT TGA/BHQ—1 (SEQ ID NO:5)) were used for the PCR amplification of methylated and unmethylated alleles in two separate reactions. ABgene QPCR mix was used, and the reaction was performed for 40-45 cycles using a SmartCycler™ real-time PCR instrument (Cepheid).
Cell line treatment. ALL cell lines, Jurkat and NALM-6 were purchased from DSMZ (Braunschweig, Germany) and were grown in flasks with RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), L-glutamine and gentamicin. Treatment was conducted during the log phase of growth with 5-aza-2-deoxycytidine (5-aza) and trichostatin A (TSA) and the control cells were not treated. Jurkat cells were seeded at 8×106 cells/mL and NALM-6 cells were seeded at 5×106 cells/mL. In culture, TSA was added at a 1 μM concentration and incubated for 6 hr, while 5-aza was added at a 1 μM concentration and incubated for 54 and 78 hr in Jurkat and NALM-6 respectively with a media change every 24 hr. The cell culture that received both TSA and 5-aza treatment was first incubated with 5-aza as previously described, followed by an additional 6 hr of incubation with TSA. RNA and DNA from the cultured cells were extracted for use in RT-PCR and COBRA respectively using the previously mentioned kits.
Semiquantitative real time PCR. Total RNA (2 μg) from cell line treatments was pre-treated with DNase I to remove potential DNA contaminants and was then reverse-transcribed in the presence of SuperScript™ II reverse transcriptase (Invitrogen). The generated cDNA was used for PCR amplification with appropriate reagents in the reaction mix with SYBR Green and fluorescein (ABgene) as recommended by the manufacturer. GAPDH and HPRT1 were used as the housekeeping genes in the Taqman™ and SYBR Green real time assays, respectively. The DLC-1 and GAPDH Taqman™ probe and primer set for real-time PCR were purchased from Applied Biosystem's Assay-on-Demand services. The reaction was carried out using a SmartCycler™ real-time PCR instrument (Cepheid). The cycling conditions included an initial 15 min hot start at 95° C. followed by 45 cycles at 95° C. for 15 sec and 60° C. for 1 min. Primers were developed for SYBR Green assays using Primer3 (TABLE 7). The reactions were carried out using the iCycler™ (Biorad). The cycling conditions included an initial 15 min hot start at 95° C. followed by 50 cycles at 95° C. for 15 sec, 58° C. for 30 sec and 72° C. for 30 sec. All samples were run in triplicate and fold changes were determined using the 2−ΔΔCT method (Livak & Schmittgen 2001).
To generate epigenetic profiles of selected ALL patients, DNA was extracted from bone marrow aspirate from patients collected at the time of diagnosis and from 4 healthy donors and the samples were compared to a pooled sample of DNA from peripheral blood leukocytes by dual hybridization to a CpG island array. After global normalization, the Kruskal-Wallis non-parametric statistical test was used in an across-array analysis to identify those genes differentially methylated in the patient samples but not in the normal bone marrow controls when compared to the pooled normal DNA. From this analysis, we identified a set of candidate diagnostic genes which were hypermethylated in at least 25% of the patient samples and in none of the normal control bone marrow samples, and which had at least a 1.8-fold difference in methylation between patient and pooled normal DNA (TABLE 8, below). This set of candidate genes includes the ATP-binding cassette, subfamily B member 1 (ABCB1/MDR1), which has previously been shown to be aberrantly methylated in ALL patients (Garcia-Manero et al. 2003) and genes associated with aberrant methylation in other malignancies including deleted in liver cancer 1 (DLC-1), deleted in colorectal cancer (DCC) and the low density lipoprotein receptor-related protein 1B (LRP1B).
We validated the results from the CpG island array experiment in the patient samples and 4 ALL cell lines using COBRA, MSP or qMSP for 10 of the genes found to be methylated in at least 50% of the studied patients (
Despite the small sample size, we detected some interesting methylation patterns. For example, the NK6 transcription factor related locus 1 (NKX6-1) gene was methylated in 100% of the examined patients and cell lines and the DEAD box polypeptide 51 (DDX51) gene was methylated in 70% of the B-ALL and in none of the T-ALL patients which indicates the utility of these genes as a biomarkers for ALL and for distinguishing between B-ALL and T-ALL cases.
Examination of the effects of gene promoter methylation in vitro by real-time reverse transcription-PCR. To determine whether the promoter methylation detected in the validated gene set was responsible for the down-regulation of these genes in ALL, the in vitro effects of treatment with a demethylating agent, 5-aza-2-deoxycytidine (5-aza), and a histone deacetylase inhibitor, trichostatin A (TSA), was examined both individually and in combination using a B-ALL cell line (NALM-6) and a T-ALL cell line (Jurkat) by real-time reverse transcription PCR. At the baseline, detection of mRNA for 8 of the 10 genes was negative or weak in the untreated (control) cell lines. However, the mRNA expression patterns of ABCB1, DCC, DLC-1, PCDHGA12 and RPIB9 were all increased by at least 10-fold post-treatment (
Additionally, while DDX51 and SLC2A14 were moderately expressed in the control cell lines, approximately a 2-fold increase in mRNA expression post-treatment was observed. Finally, only a slight increase (<2-fold) in the transcript levels of LRP1B and NKX6-1 was observed after one or more treatments. These data indicate that the expression of these genes is controlled at some level by methylation and/or deacetylation.
Example Summary. To attain a global view of the methylation present within the promoters of genes in ALL patients and to identify a novel set of methylated genes associated with ALL, methylation profiles were generated for 16 patients using a CGI array consisting of clones representing more than 4 thousand unique CGI sequences spanning all human chromosomes. This is the first time, to applicants' knowledge, that a whole genome methylation scan of this magnitude has been performed in ALL. From the generated profiles, 49 candidate genes were identified that were differentially methylated in at least 25% of the patient samples. Many of these genes are novel discoveries not previously associated with aberrant methylation in ALL or in other types of cancers. Methylation in ten genes found by the CGI array to be differentially methylated in at least 50% of the patients was verified by COBRA, MSP or qMSP. The observations were concordant with the methylation arrays, and the independent verifications indicated that between 10 and 90% of these genes were methylated in every patient. The genes identified in TABLE 7 are involved in a variety of cellular processes including transcription, cell cycle, cell growth, nucleotide binding, transport and cell signaling. In conjunction with the detection of promoter methylation in the ALL samples but not in the normal controls, this indicates that these genes act as tumor suppressors in ALL.
NKX6-1
NM_006168
Regulation of transcription
100
KCNK2
NM_001017424
Potassium ion transport
87.5
DCC
NM_005215
Induction of apotptosis
81.25
LRP1B
NM_018557
Protein transport
75
RP1B9/ABCB1
NM_138290/NM_000927
Unknown/Multidrug resistance
75
DLC-1
NM_182643
Negative regulation cell growth
68.75
NOPE
NM_020962
Cell adhesion
68.75
PCDHGA12
NM_003735
Cell adhesion
62.5
SLC2A14
NM_153449
Carbohydrate transport
62.5
DDX51
NM_175066
Nucleic acid binding
50
1Methylation % is the percentage of ALL patients with methylation at a particular locus.
2No CpG island present in clone. These clones do contain CG dinucleotides. Bolded entries were chosen for validation studies and percentage methylation refers to results from validation studies.
It was determined herein that the 10 validated genes were silenced or down-regulated in NALM-6 and Jurkat ALL cell lines and that their expression could be up-regulated after treatment with a demethylating agent alone or in combination with TSA. Of the validated genes, the greatest post-treatment increase in mRNA expression was for ABCB1, RPIB9 and PCDHGA12 and these appear to be functional genes involved in the development or progression of ALL, and, according to particular aspects, have substantial utility for distinguishing development or progression of ALL. RPIB9 and ABCB1 are genes transcribed in opposite directions with overlapping CGI containing promoters. It has recently been shown that hypomethylation of the ABCB1 promoter leads to multi drug resistance (Baker et al. 2005) and that methylation of the ABCB1 promoter is linked to the down-regulation of gene expression in ALL (Garcia-Manero et al. 2002). This suggests that individuals with methylation in the ABCB1 promoter may better respond to chemotherapeutic treatment than individuals lacking methylation. Although the function of RPIB9 has yet to be confirmed, it likely functions as an activator of Rap which allows B-cells to participate in cell-cell interactions and contributes to the ability of B-lineage cells to bind to bone marrow stromal cells, a requisite process for the maturation of B-cells (McLeod 2004). Therefore, if methylation of the RPIB9 promoter suppresses its transcription, the ability of B-lineage cells to bind to bone marrow stromal cells will likely be inhibited causing the progression of B-lineage cells to halt and resulting in the proliferation of immature cells, a hallmark of ALL. Finally, PCDHGA12 is disclosed herein as an interesting functional gene for ALL in light of a recent report connecting promoter methylation and silencing of PCDHGA11 in astrocytomas and the suggestion that the inactivation of PCDHGA11 is involved in the invasive growth of astrocytoma cells into the normal brain parenchyma (Waha et al. 2005).
In summary, the methylation status of novel genes associated with ALL including NKX6-1, KCNK2, RPIB9, NOPE, PCDHGA12, SLC2A14 and DDX51 was validated Additionally, after treatment with a demethylating agent, mRNA expression was increased in vitro for all 10 genes validated, with the greatest increases occurring for ABCB1, RPIB9, and PCDHGA12. Although the precise role of these genes in ALL progression is unknown, the epigenetic profiles generated in this study, according to particular aspects of the present invention, provide insights to improve our understanding of ALL, provide both novel and noninvasive diagnostic (and/or prognostic, staging, etc.) tools, and novel therapeutic methods and targets for the treatment of ALL.
This Example illustrates a novel ‘goal driven’ approach and methods for the identification of differentially methylated genes in DNA microarray data. The goal driven method is applied in this exemplary embodiment to small B-cell lymphoma (SBCL), and permits an accurate discrimination between three types of SBCL and normal patients. Various steps of the algorithm (e.g., data normalization and gene finding) are ‘tuned’ such that final sample clustering optimally matches corresponding pathologically-determined lymphoma diagnoses. More specifically, the gene-finding step comprises two methods, the results of which are fused to reduce the frequency/amount of ‘false positives.’ The output of the fusion step consists in three lists of differential methylated genes (marker candidates). At least one methylation assay (e.g., a combination of bisulfite restriction analysis (COBRA), and methylation-specific PCR (MSP)) is then used (e.g., by pathologists) to validate the differential methylation of these genes (i.e., to validate the candidate differentially methylated markers). Optionally, to further assist in validation, the candidate genes obtained in the gene-finding step are ranked, based on their frequency of appearance in a suitable literature database (e.g., Medline abstracts). For example, in the instant Example, some of the identified genes (e.g., validated differentially methylated genes) are known to be involved in critical pathways such as apoptosis and proliferation while others function as tumor suppressor genes or oncogenes.
There are many papers devoted to two-color cDNA microarray processing algorithms. In general, the cDNA microarray processing has four steps: preprocessing, normalization, expression analysis (or feature extraction) and data classification (or pattern discovery).
In spotted cDNA arrays, probes from a cDNA library are deposited as a solution on the surface of the support (plastic or glass) using a set of pins. The RNAs from the test and the reference samples are labeled with different fluorescent dyes (Cy5-red and Cy3-green, respectively) and then hybridized on the array. The expression (methylation) level of individual genes corresponds to the intensity levels of each dye measured at each spot.
The preprocessing consists in the extraction of the intensity values for the two channels, Cy3 (green) and Cy5 (red), and the background at each spot on the microarray. This involves various image processing techniques that we do not detail here. In the present work describe below, these values were provided by a GenePix™ 4000 microarray scanner (Axon Instruments, Union City, Calif.).
Next, one has to normalize the data to account for variability factors such as dye (green and red), pin number, spot location on the array, and array (sample). Among the most used normalization methods we mention: the loess method [Yang 2002], the ANOVA method, the quantile method [Bolstad 2003] and the variance stabilization method [Huber 2002].
The feature (gene) selection step consists in finding the subset of genes that can best discriminate between the different types of leukemia. Various methods can be used for this purpose such as “idealized expression pattern” [Golub 1999], chi-square, T-test, correlation based feature selection [Yeoh 2002], principal component analysis [Khan 2001], and permutation tests [Lee 2004].
Methods such as support vector machines [Furrey 2000, Yeoh 2002], K-nearest neighbor [Golub 1999], neural networks [Khan 2001], decision trees [Yeoh 2002], and fuzzy c-means [Asyali 2005] were used for classifying the samples based on the gene expression. For clustering the sample correlation matrix hierarchical clustering was used. An alternative approach was suggested by Claverie [Claverie 1999] that employs fuzzy c-means for the same task. Applicants have found that this method performs better that the hierarchical clustering for grouping the sample correlation matrix and, therefore, it was used in the method of this Example.
Finally, a group of methods are noteworthy that combine the feature selection with classification denoted as co-clustering (bi-clustering, two-way clustering) algorithms: CTWC [Getz 2000], Residue minimization [Cheng 2001], spectral graph [Cho 2004], marker propagation [Oyanagi 2001], fuzzy co-clustering [Oh 2001, Kummamuru 2003].
A diagram of the gene selection method used in this paper is presented in
1. Normalization: The normalization was performed using the loess method [Yang 2002]. [Ozy: xxx, the best came out to be: back-corrected, pin-based, order 1, span 0.2]. A normalization across samples was performed for each gene (locus) by subtracting the mean and dividing by the standard deviation.
2. and 3. Idealized Methylation Pattern. For the gene selection step we used two methods in order to reduce the number of genes that were not relevant to our search (to reduce false positives):
The first method employed was a modified version of the “idealized expression pattern” [Golub 1999]. The modified method is referred to herein as “idealized methylation pattern” (IMP), because methylation and not expression is detected in the present experiments. The IMP method is briefly explained in
In computing the correlation, the samples in each class are weighted by the cardinality of each class. Then the genes were ranked (from high to low) by their correlation value. For each class we selected the first 40 genes in the list.
The second gene selection method was based on a pair-wise t-test. The right tailed t-test was used to determine if the mean of the methylation values in one class is higher than the mean of the values in the other classes. For example, to determine if a gene gi was exclusively hypemethylated in HP (
4. Clustering of the sample (patients) correlation matrix. Each patient Pj, j=1 . . . 46, is characterized by a set of 8,640 methylation values {gjk}, k=1 . . . 8,640. The patient correlation matrix (“PCM”) is computed as:
The correlation matrix is a similarity matrix, that is, PCMij is 1 for very similar patients and is 0 for very dissimilar patients. If we consider the row i in PCM as a feature vector that describes how similar patient i is to the other patients [Clayerie 1999], then we can use fuzzy c-means [Bezdek 1981] for clustering. In applicants' experience, fuzzy c-means proved to produce better results than the hierarchical clustering on similarity matrices, and is thus preferred.
5. Multidimensional scaling (MDS) for cluster visualization. One of the most important goals in visualizing clustered data is to get a sense of how near or far points are from each other. Often, one can do this with a scatter plot. However, for some analyses, the data at hand might not be in the form of points (objectual) at all, but rather in the form of pair-wise similarities or dissimilarities between samples (relational). Moreover, even if one has the data in objectual form, if the feature dimensionality is higher than 3, the points cannot be represented in an easily understandable form (2D or 3D scatter plot). For this latter case, one could use some form of projection such as principal component analysis (PCA). However, for the case of the microarray experiments, PCA provides a very poor approximation because the number of sample (patients) is 2-3 orders of magnitude smaller than the number of features (genes). In our experience, one eigenvalue (one dimension) explains about 1/NP (NP, number of patients, 43 in our case) of the data, hence considering the first 3 highest eigenvalues results in an approximation error of about 100(NP−3)/NP % (93% in the present case).
Multidimensional scaling (MDS) [Cox 2001] is a set of methods that address the above problems. MDS allows the visualization of the sample distribution for many kinds of distance or dissimilarity measures and can produce a representation of the data in a small number of dimensions. MDS does not require raw data, but only a matrix of pair-wise distances or dissimilarities. MDS methods are grouped in Euclidean (considers that the sample space is Euclidean) and non-Euclidean (the sample space is non-Euclidean, for example the space of all the country capitals in the world). In our experiments, we used the Euclidean (Classical) MDS implemented in Matlab® (cmdscale from the Statistics package) and the patient correlation matrix, PCM. The approximation error obtained using the MDS dimensionality reduction is less than 1%.
MDS was employed to assess the clustering produced by the FCM (PCM). In addition, the obtained clusters were inspected for possible sub-clusters that will signal possible lymphoma sub-types.
6. Selected Gene Filtering by Result Fusion. The genes selected by the IMP and t-test methods were filtered using a two-out-of-two voting scheme (result fusion; voting). Only genes selected by both methods as being uniquely methylated in a given class were chosen for further validation with COBRA and methylation specific PCR. This particular fusion approach ignores the rank of a gene and the performance of each selection method. Alternatively, more selection algorithms could be used along with a rank and performance based fusion.
7. Literature Look-up of the Selected Genes. Both COBRA and methylation specific PCR are time consuming. For this reason, in particular embodiments by investigating another dimension of the selected genes was invested (the publishing dimension) to further assist (e.g., the pathologists) in choosing which genes to analyze first. To accomplish, the number of papers where each gene co-occurred with the term “lymphoma” were counted. The premise of this approach is that if a selected gene has been mentioned many times as being linked to lymphoma, then it has a higher chance to be differentially hypermethylated in one type of lymphoma than a gene that was not investigated yet. The search was conducted by matching the MeSH terms present in the article abstracts with our selected genes and the MeSH term “lymphoma”.
The follow results were obtained on a 46 patient dataset. The dataset consists in methylation microarrays from 3 patients diagnosed with hyperplasia (HP), but considered normals, 16 patients diagnosed with CLL, 15 patients diagnosed with FL and 12 patients diagnosed with MCL. Each array contains 8,640 loci that represent CpG islands (DNA regions rich in the Cytosine-Guanine pair) from the promoter and first exon regions of a number of genes. For a specific locus, one can find the related gene by searching the database provided by the Der Laboratory at the University of Toronto (http://s-der10. med.utoronto.ca/CpGIslands.htm).
The results of the IMP selection method are presented in
To cluster the samples, fuzzy C-means was used (instead of hierarchical clustering) on the cross-correlation matrix. By clustering the rows of the matrix (
Using MDS with the patient correlation matrix (
The result obtained using the t-test selection method is next presented. The number of genes selected this way was 213, respectively, 43, 73, 37 and 60. The methylation profile of the genes selected for each class are shown in
The patient correlation matrix from
It is obvious in
Fusion. To refine (remove false positives) we fused the selected gene sets obtained using the IMP method and the t-test method. Out of the 40 exclusively hypermethylated loci found for each class using the IMP selection method, only respectively 10, 30, 25 and 33 were confirmed as such by the t-test method. From the above 98 loci, only 49 were associated with genes (see TABLE 9).
To further assist (e.g., the pathologist) in the validation of the computational results presented in TABLE 9, Medline® was searched for abstracts that mention the genes in TABLE 2 in a lymphoma context. For example, the search for the abstracts that mentioned MEIS1 was performed using the strategy: “(lymphoma OR leukemia) AND MEIS1”. For the HP genes, the searched used only the gene name. The number of the abstracts retrieved for each lymphoma type is shown in TABLE 9 adjacent to the gene name.
Further embodiments provide a method for simultaneous gene selection in, for example, B-cell lymphoma from methylation and expression microarrays. The approach is analogous to that described above in this example, except that rank fusion (rank averaging) is between a differentially methylated gene ranking (IMP, t-test) and a differentially expressed gene ranking (IEP, t-test), resulting in a fused rank list, from which genes are optimally selected by computing patient correlation matrix, and clustering of the patient similarity matrix using C-means to select for an optimal number of genes that best match the pathologically determined lymphoma diagnoses (see
This application claims the benefit of priority to U.S. Provisional Application Ser. Nos. 60/731,040, filed 27 Oct. 2005, and 60/733,648, filed 4 Nov. 2005, both of which are incorporated herein by reference in their entirety.
Aspects of this disclosure were developed with funding from NIH grant CA097880-01. The United States government has certain rights in this invention.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US2006/041670 | 10/27/2006 | WO | 00 | 3/12/2009 |
| Number | Date | Country | |
|---|---|---|---|
| 60731040 | Oct 2005 | US | |
| 60733648 | Nov 2005 | US |
