Farahnaz Rahmatpanah

Applications of CpG Island Microarrays for High-Throughput Analysis of DNA Methylation

Differential methylation hybridization (DMH) is a high-throughput microarray technique designed to identify changes in DNA methylation patterns commonly observed in cancer and other disease states. The DMH methodology comprises three fundamental components: the arraying of CpG island clones on glass slides, the preparation of the sample amplicons under investigation, and the hybridization of amplicon targets onto the CpG island microarray. Herein, we outline the DMH protocol and illustrate its utility and the validation approaches used in analyzing the hypermethylation profile of breast tumor and normal samples.

Differential DNA methylation patterns of small B-cell lymphoma subclasses with different clinical behavior.

Non-Hodgkins lymphoma (NHL) is a group of malignancies of the immune system with variable clinical behaviors and diverse molecular features. Despite the progress made in classification of NHLs based on classical methods, molecular classifications are a work in progress. Toward this goal, we used an array-based technique called differential methylation hybridization (DMH) to study small B-cell lymphoma (SBCL) subtypes. A total of 43 genomic DMH experiments were performed. From these results, several statistical methods were used to generate a set of differentially methylated genes for further validation. Methylation of LHX2, POU3F3, HOXC10, NRP2, PRKCE, RAMP, MLLT2, NKX6.1, LRP1B and ARF4 was validated in cell lines and patient samples and demonstrated subtype-related preferential methylation patterns. For LHX2 and LRP1B, bisulfite sequencing, real-time reverse transcriptase-polymerase chain reaction and induction of gene expression following treatment with the demethylating agent, 5′-aza-2′-deoxycytidine, were confirmed. This new epigenetic information is helping to define molecular portraits of distinct subtypes of SBCL that are not recognized by current classification systems and provides valuable potential insights into the biology of these tumors.

Large-scale analysis of DNA methylation in chronic lymphocytic leukemia

AIMS B-cell chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy that clinically ranges from indolent to rapidly progressive. CLL, like other cancers, can be affected by epigenetic alterations. MATERIALS & METHODS A microarray discovery-based study was initiated to determine DNA methylation in CLL cases with a range of CD38 expression (1–92%). RESULTS Many loci were either methylated or unmethylated across all CD38 levels, but differential methylation was also observed for some genes. Genomic sequencing of DLEU7 confirmed extensive cytosine methylation preferentially in patient samples with low CD38 expression, whereas NRP2, SFRP2 and ADAM12 were more commonly methylated in those with high CD38 expression. CONCLUSION This study demonstrates that CLL is affected by CpG island methylation in some genes that segregate with CD38 expression levels, while most others show similar methylation patterns across all levels. The CpG island methylation in certain functional gene groups and pathway-associated genes that are known to be deregulated in CLL provides additional insights into the CLL methylome and epigenetic contribution to cellular dysfunction. It will now be useful to investigate the effectiveness of epigenetic therapeutic reversal of these alterations to develop effective treatments for the disease.

Diagnosis of prostate cancer using differentially expressed genes in stroma.

More than one million prostate biopsies are performed in the United States every year. A failure to find cancer is not definitive in a significant percentage of patients due to the presence of equivocal structures or continuing clinical suspicion. We have identified gene expression changes in stroma that can detect tumor nearby. We compared gene expression profiles of 13 biopsies containing stroma near tumor and 15 biopsies from volunteers without prostate cancer. About 3,800 significant expression changes were found and thereafter filtered using independent expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier for nearby tumor was constructed on the basis of 114 candidate genes and tested on 364 independent samples including 243 tumor-bearing samples and 121 nontumor samples (normal biopsies, normal autopsies, remote stroma, as well as stroma within a few millimeters of tumor). The classifier predicted the tumor status of patients using tumor-free samples with an average accuracy of 97% (sensitivity = 98% and specificity = 88%) whereas classifiers trained with sets of 100 randomly generated genes had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing the presence of tumor in patients when a prostate sample is derived from near the tumor but does not contain any recognizable tumor.

Expression differences between African American and Caucasian prostate cancer tissue reveals that stroma is the site of aggressive changes

In prostate cancer, race/ethnicity is the highest risk factor after adjusting for age. African Americans have more aggressive tumors at every clinical stage of the disease, resulting in poorer prognosis and increased mortality. A major barrier to identifying crucial gene activity differences is heterogeneity, including tissue composition variation intrinsic to the histology of prostate cancer. We hypothesized that differences in gene expression in specific tissue types would reveal mechanisms involved in the racial disparities of prostate cancer. We examined 17 pairs of arrays for AAs and Caucasians that were formed by closely matching the samples based on the known tissue type composition of the tumors. Using pair‐wise t‐test we found significantly altered gene expression between AAs and CAs. Independently, we performed multiple linear regression analyses to associate gene expression with race considering variation in percent tumor and stroma tissue. The majority of differentially expressed genes were associated with tumor‐adjacent stroma rather than tumor tissue. Extracellular matrix, integrin family and signaling mediators of the epithelial‐to‐mesenchymal transition (EMT) pathways were all downregulated in stroma of AAs. Using MetaCore (GeneGo) analysis, we observed that 35% of significant (p < 10−3) pathways identified EMT and 25% identified immune response pathways especially for interleukins‐2, ‐4, ‐5, ‐6, ‐7, ‐10, ‐13, ‐15 and ‐22 as the major changes. Our studies reveal that altered immune and EMT processes in tumor‐adjacent stroma may be responsible for the aggressive nature of prostate cancer in AAs.

Differential DNA methylation of gene promoters in small B-cell lymphomas

Improved care of patients with small B-cell lymphomas (SBCLs) is likely to result from the ongoing discovery of molecular markers that better define these malignant neoplasms. We identified multiple gene loci whose DNA methylation patterns differed between 3 types of SBCL: B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma, mantle cell lymphoma, and grades I and II follicular lymphoma. This analysis was performed using an oligonucleotide microarray that allowed determination of the DNA methylation status of 156 loci in 38 genes. Combined bisulfite restriction analysis and methylation-specific polymerase chain reaction were used to validate the differential methylation of 6 of these genes. By using non-Hodgkin lymphoma cell lines as models, these genes were examined further for methylation and gene expression relationships. This study illustrates nonrandom epigenetic alterations in SBCLs that seem to preferentially involve lymphomas of germinal center derivation.

TGF-β mediated DNA methylation in prostate cancer

Almost all tumors harbor a defective negative feedback loop of signaling by transforming growth factor-β (TGF-β). Epigenetic mechanisms of gene regulation, including DNA methylation, are fundamental to normal cellular function and also play a major role in carcinogenesis. Recent evidence demonstrated that TGF-β signaling mediates cancer development and progression. Many key events in TGF-β signaling in cancer included auto-induction of TGF-β1 and increased expression of DNA methyltransferases (DNMTs), suggesting that DNA methylation plays a significant role in cancer development and progression. In this review, we performed an extensive survey of the literature linking TGF-β signaling to DNA methylation in prostate cancer. It appeared that almost all DNA methylated genes detected in prostate cancer are directly or indirectly related to TGF-β signaling. This knowledge has provided a basis for our future directions of prostate cancer research and strategies for prevention and therapy for prostate cancer.

Relational Analysis of CpG Islands Methylation and Gene Expression in Human Lymphomas Using Possibilistic C-Means Clustering and Modified Cluster Fuzzy Density

Heterogeneous genetic and epigenetic alterations are commonly found in human non-Hodgkins lymphomas (NHL). One such epigenetic alteration is aberrant methylation of gene promoter-related CpG islands, where hypermethylation frequently results in transcriptional inactivation of target genes, while a decrease or loss of promoter methylation (hypomethylation) is frequently associated with transcriptional activation. Discovering genes with these relationships in NHL or other types of cancers could lead to a better understanding of the pathobiology of these diseases. The simultaneous analysis of promoter methylation using Differential Methylation Hybridization (DMH) and its associated gene expression using Expressed CpG Island Sequence Tag (ECIST) microarrays generates a large volume of methylation-expression relational data. To analyze this data, we propose a set of algorithms based on fuzzy sets theory, in particular Possibilistic c-Means (PCM) and cluster fuzzy density. For each gene, these algorithms calculate measures of confidence of various methylation-expression relationships in each NHL subclass. Thus, these tools can be used as a means of high volume data exploration to better guide biological confirmation using independent molecular biology methods.

Airway Epithelial Cells Enhance the Immunogenicity of Human Myeloid Dendritic Cells under Steady State

Dendritic cells (DCs) and airway epithelial cells (AECs) are in close proximity, and AECs secrete factors such as retinoic acid which induce tolerance in DCs at homeostasis. However, the question remains as to how DCs in the lung are able to respond to pathogens in the immunosuppressive environment. Using an in vitro human myeloid DC (mDC)‐AEC co‐culture system, we demonstrate that AECs induced several gene changes in the mDCs cultured with AECs compared to the mDCs not cultured with AECs. Analysis revealed that several chemokine genes were altered. These chemokine genes could serve to attract neutrophils, natural killer (NK) T as well as T helper type 1 (Th1)/Th2 cells to the airways. Genes priming lipid and major histocompatibility complex (MHC) class II antigen presentation were also up‐regulated, along with certain anti‐microbial protein genes. In addition, the expression and function of pathogen‐sensing Toll‐like receptors (TLRs) as well as Nod‐like receptors (NLRs) and their downstream signalling molecules were up‐regulated in mDCs cultured with AECs. Moreover, murine mucosal DCs from the lung expressed significantly higher levels of TLRs and NLRs compared to peripheral DCs from the spleen. These results indicate that AECs prime mDCs to enhance their immunogenicity, which could be one of the mechanisms that compensates for the immunosuppressive mucosal environment.

Algorithmic Discovery of Methylation "Hot Spots" in DNA from Lymphoma Patients

Summary The computational aspects of the problem in this paper involve, firstly, selective mapping of methylated DNA clones according to methylation level and, secondly, extracting motif information from all the mapped elements in the absence of prior probability distribution. Our novel implementation of algorithms to map and maximize expectation in this setting has generated data that appear to be distinct for each lymphoma subtype examined. A “clone” represents a polymerase chain reaction (PCR) product (on average ~500 bp) which belongs to a microarray of 8544 such sequences preserving CpG-rich islands (CGIs) [1]. Accumulating evidence indicates that cancers including lymphomas demonstrate hypermethylation of CGIs “silencing” an increasing number of tumor suppressor (TS) genes which can lead to tumorigenesis. Availability Algorithms are available on request from the authors Contact papageorgioc@health.missouri.edu Supplementary Information available on page 453.