Ron Tongbai12/18/09

Research Summary: Transcriptional Regulatory Network Analysis of Uveal Melanoma

Abstract:Uveal melanoma is the most common form of intraocular melanoma in adults and metastasis to the liver and lungs is associated with a worse prognosis (1). A study by Onken et al utilized microarrays to characterize the gene expression profiles of tumors which metastasized and compared them with the gene expression profiles of tumors which did not metastasize. The study identified specific genetic signatures which discriminated between tumors that do and do not metastasize. The study provides an important insight into the molecular pathways and biological processes that govern the establishment and growth of these metastases. Promoter region sequences from a 600 bp region (-500 bp upstream to +100 bp downstream) were obtained for each gene in each of the gene lists using the ProSpector free web-based promoter annotation tool (4). The promoter regions of each of the gene signatures were analyzed for matches to approximately 300 position weight matrices (TFBS) using the MatInspector module of the GEMS LauncheR 4.1 (Genomatix, Munich, Germany). A regulatory profile was generated based on the methods described above. Each of the gene lists were analyzed to generate p-values representing the degree of statistical enrichment for each of the approximately 300 TFBS in the promoter regions of these genes as described in the methods.

Background: Uveal melanoma is the most common form of intraocular melanoma in adults and metastasis to the liver and lungs is associated with a worse prognosis (1). A study by Onken et al (2) utilized microarrays to characterize the gene expression profiles of tumors which metastasized and compared them with the gene expression profiles of tumors which did not metastasize. The study identified specific genetic signatures which discriminated between tumors that do and do not metastasize. The study provides an important insight into the molecular pathways and biological processes that govern the establishment and growth of these metastases.

Data:

The data analyzed including the gene expression profiles from Onken et al (2) as well as gene expression profiles that were generated in-house based on structural chromosomal differences between tumors. The data from Onken et al were divided into 6 unique gene lists based on the following 2 criteria:

First, 34,382 probe sets were filtered to exclude those with a median significance level P > .05 resulting in 8750 significant genes. These 8750 genes were filtered for a mean expression difference > 1.5 fold, t test P< .05 to yield 1605 genes.

Second, 34,382 probe sets filtered to exclude those with a median signaficance level P > .005 resulting in 3075 highly significant genes. Median expression for the 3075 genes were compared between class 1 and class 2 using a signal to noise algorithm and genes were filtered for a signal-to-noise score > 0.25 and a significance level p < 0.01, yielding 62 highly discriminating genes

These genes were subdivided into 6 groups as follows:

Class 1 Gene List: Of the 62 genes, these 42 genes were upregulated in class 1 tumors and downregulated in class 2 tumors

Class 2 Gene List: Of the 62 genes, these 20 genes were upregulated in class 2 tumors and downregulated in class 1 tumors

Class 1 Low Stringent Gene List: Of the 1605 genes, 662 were upregulated in class 1 tumors

Class 2 Low Stringent Gene List: Of the 1605 genes, 943 were upregulated in class 2 tumors

Class 1 3fold Gene List: same conditions as above, but with a mean difference > 3 fold, t test P <.05 -> 64 genes upregulated in class 1 tumors

Class 2 3fold Gene List: same conditions as above but with a mean difference > 3 fold, t test P < .05 -> 86 genes upregulated in class 2 tumors

The data that was generated in-house was subdivided into 2 unique gene lists which were generated by taking the 173 unique genes that were found to be upregulated in chromosome 3 loss tumors compared to chromosome 6p gain tumors (will be referred to as chrom3_up gene list in this paper) and 133 unique genes that were found to be downregulated in chromosome 3 loss tumors compared to chromosome 6p gain tumors (will be refered to as chrom3_down gene list in this paper).

Methods:

The methods utilized in this study will be similar to the methods in a previous study which examined the promoter composition of gene lists representing 5 different molecular subtypes of breast cancer known to be associated with distinct clinical outcomes (3). Please see reference for a more detailed description of the methods. Briefly, promoter region sequences from a 600 bp region (-500 bp upstream to +100 bp downstream) were obtained for each gene in each of the gene lists using the ProSpector free web-based promoter annotation tool (4). The promoter regions of each of the gene lists were analyzed for matches to approximately 300 position weight matrices (TFBS) using the MatInspector module of the GEMS LauncheR 4.1 (Genomatix, Munich, Germany). A p-value for each of the approximate 300 matrices or TFBS for a gene list were calculated by comparing the number of matches per 1-kb promoter region in each of the gene lists against the number of matches per 1-kb promoter region in a reference background model using a complemented Poisson distribution in the Perl math library. The reference background model was derived from extracting all unique RefSeq IDs (24,704) from the UCSC genome browser (5) and mapping them to 15,318 IDs using Prospector followed by promoter TFBS annotation using MatInspector as described

above. A Perl script was used to calculate p-values for each of the approximately 300 position weight matrices for each of the gene lists- this resulted in what will be referred to as a regulatory profile that is specific for that particular gene list. Hierarchical clustering was performed with the Expander tool (6). Network analysis will be done with the Ingenuity software package.

Results:

For each of the 8 gene lists, a regulatory profile was generated based on the methods described above. Briefly, each of the gene lists were analyzed to generate p-values representing the degree of statistical enrichment for each of the approximately 300 TFBS in the promoter regions of these genes as described in the methods. The tables contain all the TFBS with a p-value < 0.1 for each of the gene lists.

Table 1: A. Class 1 3fold Gene List

B. Class 2 3fold Gene List

C. Class 1 Low Stringent Gene List

D. Class 2 3fold Gene List

E. Chrom3_Up Gene list

F. Chrom3_down Gene List

Figure 1. Hierarchical clustering was performed on the 8 regulatory profiles to not only look for similarities and differences between each of the regulatory profiles based on differences in statistical enrichment of TFBS (A) but to also look for clusters of TFBS that could potentially be co-regulated (B).

Ingenuity network analysis allows us to understand the impact of a set of genes on well-characterized pathways. We can combine the genes in each gene list with the transcription factor gene cognate for the most significantly enriched TFBS for that gene list. This in essence creates a composite list of the regulated and regulator which will be referred to as a gene set. We can then input these sets of genes into the Ingenuity network analysis which overlays a set of genes onto a global molecular network based on information in the Ingenuity Pathways knowledge base. Each edge represents a biological relationship between 2 genes and all edges are supported by at least 1 reference from

literature, from a textbook, or from canonical information stored in Ingenuity knowledge base. For each of these gene sets, a table indicating the top networks for that gene set was created and a few select networks are shown. For Ingenuity network analysis, only genes from class 1 gene list and class 2 gene lists were used. The rest of the tables and figures are derived from data generated by the Ingenuity network analysis.

Table 2. Ingenuity network analysis of class 2 gene set.

Figure 2. Two networks (A and B) for the class 2 gene set are shown.

Table 3. Ingenuity network analysis of class 1 gene set.

Figure 3. Three networks (A,B, and C) for the class 1 gene set are shown.

Table 4: The networks generated by the Ingenuity Network Analysis for each of the classes are compared. Genes that are common in both the class 1 and class 2 gene sets are highlighted blue. Genes that are specific to only class 1 are highlighted red and genes that are specific to only class 2 are highlighted yellow.

TP53, TNF, progesterone, TP73, ENPP2, dihydrotestosterone

Network 3

TFAP2A, TP53, TP73, SP1, SP3, TGFB1

Beta-estradiol, HNF4ANetwork 2

Stat3, ERK, NFkB, ATF1, ATF4, Creb, Akt, SREBF1, MAPK, insulin, SHC1, ARNT, PAX5, PDGFBB

CEBPB, SMAD3, NFkB, MAPK, Akt, Erk, TFAP2A, ERBB3, Jnk, SPP1, ARNT, DBP, insulin, ERBB3,

Network 1Gene Signatures + Regulators

Class 2Class 1NetworkGene Sets

Table: Network Comparisons

TP53, TNF, progesterone, TP73, ENPP2, dihydrotestosterone

Network 3

TFAP2A, TP53, TP73, SP1, SP3, TGFB1

Beta-estradiol, HNF4ANetwork 2

Stat3, ERK, NFkB, ATF1, ATF4, Creb, Akt, SREBF1, MAPK, insulin, SHC1, ARNT, PAX5, PDGFBB

CEBPB, SMAD3, NFkB, MAPK, Akt, Erk, TFAP2A, ERBB3, Jnk, SPP1, ARNT, DBP, insulin, ERBB3,

Network 1Gene Signatures + Regulators

Class 2Class 1NetworkGene Sets

Table: Network Comparisons

Table 5. Canonical Pathways are core pathway established for a given molecule in the cell in which molecular interactions occur in a linear and stepwise manner. Highly significant canonical pathways for each of the gene lists are generated below.

Discussion

AP2 is enriched predominantly in the class 2 gene signature. AP-2 found on the short arm of chromosome 6 near the HLA locus and it is implicated in the regulation of cell proliferation, differentiation, apoptosis, and carcinogenesis. AP2 known to play a role in metastasis of cutaneous melanoma. Studies by Bar-Eli et al have shown progression of cutaneous melanoma associated with loss of expression of AP-2, resulting in the overexpression of MCAM/MUC18 and MMP-2, and lack of expression of c-KIT. Additionally, they found AP2 regulates additional genes involved in melanoma development and progression, including E-cadherin, p21, HER2, Bcl-2, FAS/APO-1, IGF-R-1, and VEGF (7). However, the role of AP2 in uveal melanoma is not well known

Table 6. Enrichment of AP2 in each of the gene lists (highlighted yellow). POM_up refers to Class 2 Low Stringent Gene List and POM_down refers to Class 1 Low Stringent Gene List. 62_class1 refers to Class 1 Gene List and 62_class2 refers to Class 2 Gene List.

ATF has also been found to be significantly enriched in the class 2 tumor gene lists (see above tables and figures). ATF has also been implicated in cutaneous melanoma progression, particularly the transition of melanoma cells from radial growth phase to vertical growth phase is associated with overexpression of CREB and ATF1. Additionally, Anti-ATF-1 inhibited tumorigenicity and metastatic potential of cutaneous melanoma cells in nude mice (7). However, the role of ATF in uveal melanoma has never been well described.

According to Tables 4 and 5, the MapK/ERK signaling pathway seems to play a role in both class 1 and class 2 phenotypes. A study by Zuidervaart et al (8) suggests that activation of MAPK pathway is commonly involved in the development of uveal melanoma, but rarely occurs through mutation of BRAF or RAS. According to Table 5, estrogen receptor signaling could potentially play an important role in the class 2 phenotype. Tamoxifen has long been a mainstay of treatment for cutaneous melanoma. The role of tamoxifen in uveal melanoma has never been well studied, although a small study by Macneil et al found that tamoxifen inhibited ocular melanoma cell attachment to matrix proteins (9) and could therefore potentially inhibit metastatic spread.

References:

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9. Macneil S, Wagner M, Rennie IG. Tamoxifen inhibition of ocular melanoma cell attachment to matrix proteins. Pigment Cell Res. 1994; 7:222-226.