Chromosomal structural variations during progression of a prostate epithelial cell line to a malignant metastatic state inactivate the NF2, NIPSNAP1, UGT2B17, and LPIN2 genes

Abstract

Prostate cancer is the second highest cause of male cancer deaths in the United States. A significant number of tumors advance to a highly invasive and metastatic stage, which is typically resistant to traditional cancer therapeutics. In order to identify chromosomal structural variants that may contribute to prostate cancer progression we sequenced the genomes of a HPV-18 immortalized nonmalignant human prostate epithelial cell line, RWPE1, and compared it to its malignant, metastatic derivative, WPE1-NB26. There were a total of 34 large (>1 Mbp) and 38 small copy number variants (<100 kbp) in WPE1-NB26 that were not present in the precursor cell line. We also identified and validated 46 structural variants present in the two cell lines, of which 23 were unique to WPE1-NB26. Structural variants unique to the malignant cell line inactivated: (1) the neurofibromin2 (NF2) gene, a known tumor suppressor; (2) its neighboring gene NIPSNAP1, another putative tumor suppressor that inhibits TRPV6, an anti-apoptotic oncogene implicated in prostate cancer progression; (3) UGT2B17, a gene that inactivates dihydrotestosterone, a known activator of prostate cancer progression; and (4) LPIN2, a phosphatidic acid phosphatase and a co-factor of PGC1a that is important for lipid metabolism and for suppressing autoinflammation. Our results illustrate the value of comparing the genomes of defined related pairs of cell lines to discover chromosomal structural variants that may contribute to cancer progression.

Keywords: :

• prostate cancer
• high throughput sequencing
• HYDRA
• AbCNV
• complex rearrangements
• metastasis
• NF2
• NIPSNAP1

Introduction

Prostate cancer is one of the most widely diagnosed cancers among males worldwide. According to the NCI it remains one of highest contributors to cancer-related deaths among males in the US. Most of the patients who die from the cancer do so from metastasis of the cancer.

Genetic variants such as single nucleotide polymorphisms, chromosomal rearrangements, and copy number variants have long been associated with cancer. The identification of the BCR-ABL gene fusion in chronic myeloid leukemia (CML)¹ and the success of therapies that target BCR-ABL indicate that such genetic variants contribute to cancer and provide critical targets for therapy. With the emergence of the massively parallel sequencing technologies, several studies have focused on identifying structural variants across the genome using the paired-end and single read sequencing methods in various cancers.^2-8 Several recent studies have started to catalog genetic variants in prostate cancer. Using transcriptome-sequencing approaches several gene rearrangements have been reported in prostate cancer,^9-11 including ETS family gene fusions (with TMPRSS2-ERG as the most commonly reported fusion). However genomic structural variants acquired as a prostate cancer progresses to advanced, metastatic, androgen-deprivation-refractory disease have not been completely cataloged.

Because genetic studies with prostate tumors have been begun by others and are complicated by intra-tumor heterogeneity and contamination of the malignant cells by normal stromal cells, we took a different and complementary approach. We chose RWPE1, a human papilloma virus 18 (HPV-18) immortalized non-tumorigenic prostate epithelial cell line, and its derivative, WPE1-NB26, which has been mutagenized by N-methyl-N-nitrosourea and selected for high metastatic and invasive potential. These two human cell lines provide an in vitro model for studying prostate carcinogenesis and progression.¹² We performed massively parallel paired-end sequencing to systematically identify and characterize structural variants across the genome (Fig. 1A). We applied two independent bioinformatic pipelines, HYDRA¹³ and AbCNV (Aberrant Copy Number Variations) (Fig. 1B) to identify structural variants (Fig. 1C) that are associated with the phenotypic progression of immortalized prostate epithelial cells to metastatic and invasive prostate cancer cells.

Figure 1. (A) Overview of the protocol followed to extract genomic DNA and prepare it for sequencing on the Illumina platform. (B) Flowchart depicting the data flow and analysis performed. (C) Different kinds of structural variants, and the logic for calling them.

Results

Genomic DNA from each cell line was isolated and nebulized to obtain a library of 500 bp fragments (Fig. 1A). This fragment library was then subjected to paired-end DNA sequencing with the Illumina GAII sequencing system, using 38–40 bp reads. A total of 308 234 420 chromPETs were obtained for RWPE1 and 303 838 292 chromPETs for WPE1-NB26 (Table 1A). The chromPETs were mapped back to the current reference genome assembly (hg19) using Novoalign¹⁴ (see Materials and Methods) with default parameters. This resulted in 279 001 461 (90.52%) and 272 883 446 (89.8%) chromPETs with both ends mapping back uniquely and within the expected distance for the immortalized RWPE1 and tumorigenic WPE1-NB26 cell lines, respectively (Table 1). The mapped distances between paired-tags for all intra-chromosomal chromPETs (both tags mapping to the same chromosome) yielded a median insert size of 510 bp for RWPE1 and 511 bp for WPE1-NB26 with a median absolute deviation of 31 bp. This gives us approximately 50× physical coverage for both genomes. All chromPETs with insert sizes within the median ± 6*MAD are classified as normal chromPETs and the rest as discordant chromPETs. The schematic in Figure 1C shows the different classifications that could give rise to discordant chromPETs. Using the data analysis workflow shown in Figure 1B, normal chromPETs were then used to estimate copy number using depth of coverage and the discordant chromPETs were used to identify genomic rearrangements.

Table 1A. Raw sequencing and variant numbers

	Cancer	Immortalized
chromPETs sequenced	303 838 292	308 234 420
Concordant chromPETs	272 883 446	279 001 461
Discordant chromPETs	2 527 047	2 080 954
Spanning coverage	~50×	~51×
Physical coverage	~8×	~8×

Number of chromPETs sequenced, and coverage for both cell lines, Cancer (WPE1-NB26) and Immortalized (RWPE1).

Genome rearrangements

As shown in Table 1A we had 2 527 047 discordant chromPETs in WPE1-NB26 and 2 080 954 discordant chromPETs in RWPE1. To identify putative breakpoints we used the HYDRA pipeline as described in Quinlan et al.¹³ HYDRA has several key strengths. First, it can identify breakpoints in both unique and repetitive genomic regions which allows us to assess structural variant (SV) within the most structurally dynamic parts of the genome, namely at transposons and low copy repeats (LCRs). Second, HYDRA does not require assumptions about variant structure, which allows us to identify complex events often missed by other methods. HYDRA called a total of 1598 rearrangements (with the filtering scheme described in the Materials and Methods) in the two cell lines, with 88 predicted to be somatic breakpoints specific for WPE1-NB26 (Table 2). To validate these potential breakpoints we performed PCR on genomic DNA from the two cell lines and normal lung tissue using forward and reverse primers designed within the footprints of each breakpoint call. 50/88 (56.8%) of the predicted breakpoints were detected in at least one of the genomes tested. 23/50 (~46%) of the validated breakpoints were specific for the WPE1-NB26 malignant cell line.

Table 2. HYDRA calls and validation status

An independent measure of the HYDRA breakpoint calls was obtained by evaluating how many of the deletion breakpoints from the HYDRA pipeline were seen as germline deletions in the Thousand Genomes project.¹⁸ 569/878 (~65%) of the deletion breakpoints predicted in both RWPE1 and WPE1-NB26 cells overlapped with deletion breakpoints identified in the Thousand Genomes project. Such a high amount of overlap indicates that the variants that are common to both cell lines in our pipeline are likely germline variants present in the population and provides additional confidence in the HYDRA algorithm.

A Circos plot¹⁹ visualizing the 46 structural variants seen in WPE1-NB26 malignant cells (23 of which are present in the precursor RWPE1 cells) is shown in Figure 2.

Figure 2. Circos plot for 46 structural variants seen in the WPE1-NB26 malignant cells. Innermost lines depict the breakpoints (intra-chromosomal in blue, inter-chromosomal in red). The next track in green depicts the log ratio of read depth (WPE1-NB26:RWPE1) across the genome. The next track is the ideogram of the chromosomes, followed by the validated copy number changes across the genome (large changes inner to the small changes, amplifications in blue and deletions in red).

The 46 somatic breakpoints identified in WPE1-NB26 intersected with 34 refseq transcripts (Table 2). The genes involved (with a positive validation by PCR analysis) include, NF2, NIPSNAP1, FAM118B, WWP2, TBX15, PDE4DIP, SLC2A5-BTBD7, COL24A1-C9orf156, LRP1B, FMNL2, SCAP, ITPR1 (Fig. 3B), and RHOH. We used g:Profiler^20,21 to look for a functional enrichment of the genes with breakpoint in a particular functional group. We only found an enrichment of Rho GTPase binding gene ontology (Molecular Function—GO:0017048, ROCK1, RHOH, FMNL2, and DIAPH2) with a P value of 1.14e−03 (or ROCK1, RHOH, and DIAPH2 with a P value of 9.28e−03). Although not highly significant statistically, the enrichment suggests that breaks in genes regulating Rho GTPase are selected for during progression to malignancy, metastasis, and invasion. We also found that the genes with breakpoints were enriched in a network of interacting proteins from BioGRID interaction database: NF2, FAM118B, GNB2, PRKG1, and ITPR1 with a P value of 7.72e−04 (Fig. S2A). The same pathway is also found enriched among the genes that have breakpoints only in WPE1-NB26: NF2, GNB2, and FAM118B, with a P value of 3.07e−02 as shown in Figure S2B.

Figure 3. Example of events predicted by our analysis. (A) AbCNV results from chr18. UCSC browser snapshot of chr18. Topmost plots (PCancer Big Del, PCancer Big Ampl, PCancer Small Ampl, and PCancer Small Del) show the segments with copy number variants flagged by AbCNV. Ratio_C_N_5000 is for the log2 ratio of the read depths (WPE1-NB26:RWPE1) over 5000 base windows. The final two plots show the read depth for Cancer (WPE1-NB26) and the Immortalized (RWPE1) cell lines. (B) WPE1-NB26 specific deletion predicted by HYDRA. This deletion on chr3 removes the 3′ end of the ITPR1 gene.

Copy number variations

The uniquely mapped chromPETs that were classified as reporting non-rearranged genomic architecture were used to estimate copy number variant of genomic segments using depth of coverage.^15,17,22,23 A total of 279 001 461 and 272 883 446 concordant chromPETs were obtained for RWPE1 and WPE1 NB26, respectively. We used a window size of 15 000 bp to analyze the data, and this determines the minimum resolution for our CNV calls.

We developed the AbCNV program to identify regions with copy number variants (Fig. 1B). This involves (1) creating a normalized profile of coverage across the genome, (2) calculating the log2 ratio of sequence coverage between the WPE1-NB26 and the RWPE1 cell lines (Ratio_C_N), and (3) using a segmentation algorithm to identify regions with high/low Ratio_C_N. For each of these regions we compared the raw values of coverage relative to adjoining regions in RWPE1 cells to eliminate regions that were amplified or deleted in these control cells. This eliminated false positives stemming from a change in gene copy number that was specific to RWPE1 cells.

In total we had 20 large (>1 Mbp) amplifications and 14 large (>1 Mbp) deletions that were specific for WPE1-NB26 (Table 1B). Similarly we also observed 9 small amplifications and 29 small deletions specific for WPE1-NB26. We used quantitative PCR (qPCR) of genomic DNA from the two cell lines and normal lung to validate these copy number changes. Table 3 lists all the sites and shows that our validation rates were 83% (10/12 regions tested) for the large CNVs and 66.7% (6/9 regions tested) for the small CNVs. For the small CNVs, we only validated the sites that involved known genes. Copy number variant (CNV) data for the whole genome is also shown in the Circos plot in Figure 2.

Table 1B. Raw sequencing and variant numbers

Events
Structural variants	All	1598
	Cancer specific	88
	Inter-chromosomal	29
	Intra-chromosomal	59
	Validated	49/88 (55.7%)
CNV	Large amplifications	20
	Large deletions	14
	Small amplifications	9
	Small deletions	29
	Validation % (large CNV)	10/12 (83.3%)
	Validation % (small CNV)	6/9 (66.7%)

Number of structural variants called and validated.

Examples of our analysis are shown in Figure 3 and 4. The Ratio_C_N identifies a 50 Mb region that is enriched in WPE1-NB26 relative to RWPE1 (Fig. 3A). Examination of the raw sequence coverage of this region relative to its neighbors in both cell lines (tracks marked Imm_Sk or Cancer_Sk) confirms that the copy number is normal in RWPE1 but is amplified in WPE1-NB26 cells. This is confirmed by q-PCR of genomic DNA (Table 3A, big amplifications, line 1)

Figure 4. Deletion of 11q24–25. (A) UCSC genome browser snapshot of 11q24–25 locus. The top track shows the deletion predicted by AbCNV. The next two tracks show the normalized chromPET coverage from Cancer (WPE1-NB26) and Immortalized (RWPE1) cell lines. Ratio_C_N_5000 is as described in Figure 3A. (B) qPCR validation of the deletion. (C) UCSC genome browser snapshot depicting the small deletion in the UGT2B17 locus on chr4. The tracks are as indicated in Figure 3A and (A).

Table 3A. AbCNV predictions and validations

PCancer big deletions
Chrom	Start	End	Validated
chr11	126140000	134950000	+
chr15	20935000	27180000	*
chr1	104175000	105640000	NT
chr1	105830000	110225000	NT
chr1	110250000	119145000	+
chr1	119455000	121475000	NT
chr1	147935000	149800000	NT
chr1	187780000	191035000	*
chr7	100380000	108980000	NT
chr7	109000000	127585000	+
chr9	11865000	13050000	NT
chr9	22460000	24640000	NT
chr9	28500000	32725000	NT
chr9	38830000	40325000	NT
PCancer big amplifications
Chrom	Start	End	Validated
chr11	62180000	126100000	+
chr18	145000	2910000	+
chr18	18710000	27415000	+
chr18	76515000	78020000	NT
chr20	195000	6400000	+
chr20	8960000	11090000	+
chr20	11280000	19385000	NT
chr20	20240000	24495000	NT
chr3	4750000	75725000	NT
chr4	80000	4130000	NT
chr4	4160000	9105000	NT
chr5	50000	14995000	+
chr5	15720000	17340000	NT
chr5	31875000	34220000	NT
chr5	34330000	38045000	NT
chr5	40115000	42200000	NT
chr5	43365000	46320000	NT
chr8	94670000	96650000	+
chr8	98610000	104445000	NT
chr8	113840000	122040000	NT

Large amplifications and deletions. AbCNV predicted amplifications and deletions specific for WPE1-NB26 and validation of the change in copy number by qPCR of genomic DNA from the two cell lines. +, copy number change specific for WPE1-NB26. NT, not tested. *The copy number was amplified in RWPE1 and normal in WPE1-NB26

A similar analysis is shown for a 200 kb deletion involving the ITPR1 gene (Fig. 3B). Here we also have independent validation of the deletion from the HYDRA calls, because a breakpoint is identified spanning the deletion segment.

A 9 Mb deletion in 11q24–25 is identified in Figure 4A (Table 3A, big deletions, line 1). Here we also show q-PCR validation of the decrease in DNA copy number in WPE1-NB26 relative to RWPE1 or normal lung genome (Fig. 4B).

Finally, we highlight a 50 kb deletion across the UGT2B17 gene (Fig. 4C). This deletion was validated by PCR of genomic DNA (Table 3B). For independent validation we show a reverse-transcription and PCR of RNA from RWPE1 and WPE1-NB26 cells (Fig. 5D). As expected, the UGT2B17 mRNA is absent in WPE1-NB26. Similar to this we saw several small deletions in the genic or promoter regions of several genes, such as NF2, NME7, FILIP1L, UGT2B14/17, and DIAPH2 (Table 3B).

Table 3B. AbCNV predictions and validations

PCancer AbCNV small deletions
Chrom	Start	End	Validated	Gene affected
chr12	9635000	9735000	NT
chr13	19330000	19380000	NT
chr13	38070000	38090000	NT
chr13	83165000	83180000	NT
chr18	60405000	60440000	NT
chr18	60745000	60825000	NT
chr18	71060000	71075000	NT
chr18	72005000	72105000	NT
chr18	72860000	72900000	NT
chr18	74970000	75045000	NT
chr18	75820000	75835000	NT
chr18	76130000	76180000	NT
chr1	56245000	56330000	NT
chr1	105665000	105685000	NT
chr1	169225000	169245000	+	NME7
chr22	29990000	30075000	+	NF2
chr22	49310000	49335000	NT
chr3	99745000	99785000	+	FILIP1L
chr4	69380000	69435000	+	UGT2B17
chr6	60000	85000	NT
chr6	18875000	18895000	NT
chr9	41190000	41235000	NT
chr9	41970000	41985000	NT
chr9	44245000	44310000	NT
chr9	44485000	44580000	NT
chr9	45180000	45255000	NT
chr9	66660000	66675000	NT
chrX	96720000	96815000	+	DIAPH2
chrX	146190000	146205000	NT
PCancer AbCNV small amplifications
Chrom	Start	End	Validated	Gene affected
chr11	610000	660000	-
chr12	2245000	2260000	*
chr16	78675000	78705000	*
chr17	81160000	81195000	NT
chr1	228750000	228785000	NT
chr3	197900000	197915000	NT
chr7	100210000	100275000	+
chr8	140455000	140490000	NT
chr9	141020000	141035000	NT

Small amplifications and deletions along with associated gene (direct overlap with a refseq transcript). *The copy number was low in RWPE1 and normal in WPE1-NB26

Figure 5. Complex rearrangements delete the NF2 gene. (A) UCSC genome browser snapshot of the NF2/NIPSNAP1 locus depicting the read depths for Cancer (WPE1-NB26) and Immortalized (RWPE1) and Ratio_C_N_5000 that shows the small deletion in the promoter region of NF2. The AbCNV deletion is shown at the bottom followed by the two HYDRA predicted inversion breakpoints. A1, A2 and A3 are the primer pairs used in (B). (B) PCR validation of the NF2 copy number deletion in genomic DNA. M:MW marker, L:normal lung, R:RWPE1, W:WPE1-NB26. PCR primer pairs shown at the top and (A). (C) Western blot for Merlin (NF2) in R (RWPE1) and W (WPE1-NB26). The * show the two different isoforms of Merlin. The uppermost band is a cross-reactive band that serves as loading control. (D) RT-PCR to show loss of expression of NIPSNAP1 and UGT2B17 mRNA in WPE1-NB26 (W) compared with RWPE1 (R). Actin shows that equal amounts of mRNA were input from the two cell lines. -RT: Actin PCR without the reverse-transcription step.

Complex rearrangements

A striking complex rearrangement in WPE1-NB26 cells is on 22q12.2. We found three HYDRA breakpoint calls in this region, suggesting deletions spanning from the 3′ region of NIPSNAP1 (non-neuronal SNAP25-like protein homolog 1) gene to the 5′ region of NF2 (neurofibromatosis type 2) gene, and also an inversion of the NIPSNAP1 gene (Fig. 5A). By the AbCNV analysis we also found a small deletion of 45 Kb that removes the promoter and the 5′ UTR of the tumor suppressor NF2 gene in WPE1-NB26 cells (Fig. 5). As our model in Figure 5E suggests, in the first step segments B and C containing the NIPSNAP1 gene were inverted to create the junction (reported by HYDRA breakpoints 158362 and 158424) joining A to C. In the second step the B and D segments containing the 5′ ends of NIPSNAP1 and NF2, respectively, are deleted to create the junction joining C to E. PCR with primer pairs located in the deletion (A1 and A2) and outside (A3) confirmed the deletion involving NF2 in WPE1-NB26 (Fig. 5B). This model suggested that NIPSNAP1 and NF2 gene expression should be suppressed in WPE1-NB26. Western blot shows that the NF2 protein is indeed not expressed in the WPE1-NB26 malignant cells (Fig. 5C). Reverse-transcription PCR on cellular RNA confirms that NIPSNAP1 mRNA is also not expressed in WPE1-NB26 cells (Fig. 5D).

We identified another complex rearrangement event involving the EMILIN2/LPIN2 and ROCK1 genes on chr18 (Fig. 6A). The individual breakpoints predicted by HYDRA (ID: 104993, 104965, and 104992 in Table 2 and Fig. 6A) have been confirmed by PCR across the breakpoints (Fig. 6A). To elucidate the genomic structure of this locus, we sequenced the PCR amplified fragments (Fig. 6B) and also measured the DNA copy number at selected sites across the entire region (results summarized in Fig. 6A). Sequencing of the PCR fragments across the breakpoints indicated that HYDRA ID:104993 and ID:104965 were reporting on the same breakpoint. A deletion of 19 005 bases between chr18:2 908 934 (end of segment A) and 2 927 939 (beginning of segment E) creates the A–E adjacency reported by HYDRA ID:104965. Unexpectedly, a 245 base DNA fragment G from chr18:18 691 959–18 692 204 was inserted between fragments A and E to create the A–G adjacency reported by HYDRA ID:104993 and confirmed by the sequencing of the PCR products in Figure 6B. Intriguingly, part of the BCD segment is not lost completely as evidenced both by the copy number measurements (Fig. 6B) and the C–H adjacency reported by HYDRA ID:104992.

Figure 6. Complex rearrangements in EMILIN2-LPIN2-ROCK1 locus on chr18. (A) Cartoon showing the chr18: 2 900 000–19 500 000 locus divided into named segments from A–H. The track underneath shows the three genes of interest (EMILIN2, LPIN2, and ROCK1). The next tracks show the location of the footprint of HYDRA breakpoint IDs: 104992, 104993, and 104965. The next track shows the measured copy number across this whole region. (B) Cartoon showing the sequencing results obtained for the three HYDRA breakpoints (IDs: 104992, 104993, and 104965) in the terms of the same named segments as defined in (A). (C) Model depicting our hypothesis for the complex event that takes place on chr18 to create the deletion and the HYDRA breakpoints reported in (A).

We suggest that initially there was an increase of one copy of the entire region, accounting for the 1.5× copy number of the ends of the region (Fig. 6C). Two deletions occur in two different copies of the locus: one removes DEFG to create the C–H junction, while the other removes BCD, but incorporates G from the first deletion as a genome shard in the A–E junction. The net result will be that WPE1-NB26 will have 3 copies of G (1.5×), 1 copy of D (0.5×), and 2 copies of B, C, E, and F (1×). However, since there are >2× copies of B, this segment may have amplified independently or inserted elsewhere in the genome as genome shards. The loss of segment G in one allele and its insertion as a genome shard in a deletion in another allele raises the possibility that these alleles were close together (perhaps due to binding to a transcription factory) when the break and joining events occurred in the two alleles. The allele containing the C–H junction may lack a centromere and therefore might be associated with another chromosome.

Conclusion

There have been several previous studies that have studied gene rearrangements in prostate cancer. In 2006, Tomlins et al.²⁴ found gene fusions between TMPRSS2 and ETS family members ETV and ERG, resulting in an overexpression of the resulting transcript. Since then the TMPRSS2:ERG gene fusion has been the found out to be one of the most prevalent gene fusion found in prostate cancers.²⁵ Chinnaiyan et al. and others have shown, using microarray data and exome sequencing data that 50–70% of cancer samples have members of the ETS and ETV family of transcription factors involved in gene rearrangements.^24-29 Pflueger et al.⁷ also discovered N-Myc downstream regulated gene 1 (NDRG1) to be fused with ERG in prostate cancers. However, recent transcriptome sequencing experiments have also discovered gene fusions not involving any ETS family of transcription factors. Using ETS rearrangement negative prostate cancers, Palanisamy et al.³⁰ found RAF kinase family of genes—BRAF and RAF1 to be involved in gene fusions. They also screened a large cohort of patients and found RAF pathway genes were involved in gene fusions in advanced prostate cancers. In 2011, Berger et al.³¹ sequenced and analyzed tumor and matched normal DNA from seven patients with high-risk primary prostate cancer. Only 3 of the patients harbored the TMPRSS2-ERG gene fusion. On an average they found ~90 arrangements per genome, which compares favorably with the 46 rearrangements found in WPE1-NB26 in our study. In total they found 16 genes that had rearrangements in more than one patient sample, including ZNF407, CHD1, PTEN, C21ORF45, CSMD3, CADM2, ERG, and TMPRSS2.

Prostate cancer is indolent in most patients, but progresses to a more invasive and metastatic stage in a small fraction of the patients. In this paper we attempted to identify rearrangements and copy number variants in the context of prostate cancer progression. By using a prostate epithelial cell line and its metastatic and invasive form and comparing it to an immortalized but non-metastatic precursor, we hope to identify genetic changes that contribute to prostate cancer progression. Although we did not detect the more widely known ETS family of gene DNA fusions seen in prostate cancer, it is entirely possible that such fusions can be achieved in the cancer cells by trans-splicing of RNAs.³²

Interestingly, several of the predicted regions containing a CNV have also been implicated in either prostate cancer or tumorigenesis in general. For example, AbCNV predicted a deletion at 11q24–25. By qPCR we discovered that this 8.8 Mbp deletion has normal copy number in immortalized cell line (RWPE1) and only half the copy number in cancer cell line (WPE1-NB26). This region harbors the OPCML gene that has been demonstrated to act as a broad tumor suppressor in prostate cancer cell lines.³³ The authors showed the gene is epigenetically silenced by CpG methylation. Thus the presence of this deletion in WPE1-NB26 is consistent with deletion of the tumor suppressor OPCML during progression.

Similarly, a deletion in a suspected tumor suppressor locus chromosome 7q31, and a known fragile site (FRA7G) has been identified as a loss of heterozygosity region (LOH) in primary prostate cancer and is associated with tumor aggressiveness and progression.³⁴ Caveolin-1 (Cav-1) is one of candidate tumor suppressor genes in this locus and it has been shown to be downregulated in prostate tumors.³⁵ Cav-1 null mice develop a carcinogen-induced tumor susceptibly.³⁶ Also exogenous expression of Cav-1 in MCF7 human breast adenocarcinoma cells inhibited anchorage-independent growth and matrix invasiveness.³⁷

We discovered a complex event affecting two significant genes, NF2 and NIPSNAP1. NF2 gene was identified as mutated in neurofibromatosis type 2 and the encoded protein belongs to the Band 4.1 superfamily. Inherited NF2 mutation predisposes the patient to schwannomas, meningiomas, and ependymomas. Somatic NF2 mutations have also been found in other types of sporadic tumors. In several prostate tumor cell lines, NF2 expression was low or its activity was suppressed by PAK-mediated constitutive phosphorylation.^38,39 Many lines of evidence suggest its tumor suppressor properties involved in integrating and regulating the extracellular and intracellular signaling pathways that regulate cell proliferation, and survival. These studies, along with our results, indicate a potential role of NF2 as a tumor suppressor that is inactivated in prostate cancer progression.

NIPSNAP1 belongs to a highly conserved family of proteins with unknown function. This protein was suggested to interact with the transient receptor potential vanilloid channels 5 and 6 (TRPV5/6) and inhibit their activity.⁴⁰ TRPV6 was demonstrated to confer resistance to apoptosis induced via Ca²⁺/NFAT-dependent pathways.⁴¹ Therefore, NIPSNAP1 might inhibit TRPV6 to promote apoptosis and thus have tumor suppressor activity. NIPSNAP1 deletion during the transition to malignancy will now allow TRPV6 to confer resistance to apoptosis.

UGT2B17 is of interest, because several previous studies have suggested that a deletion polymorphism in UGT2B17 may significantly contribute to prostate cancer susceptibility in men. UGT2B17 catalyzes the transfer of glucuronic acid from uridine diphosphoglucuronic acid to substrates. In the human prostate, natural androgen, dihydrotestosterone (DHT) has the highest affinity for the androgen receptor. UGT2B17 class of enzymes is responsible for DHT glucuronidation and inactivation.⁴² In other words, downregulation of UGT2B17 will increase the levels of functional DHT. Thus, the mutation of UGT2B17 may explain why WPE1-NB26 cells are much less sensitive to androgen depletion than the parental cell line RWPE1.

Not all deletions could be construed as a simple loss of a tumor suppressor. ROCK1 (and ROCK2) is a serine/threonine kinase that functions downstream of RhoA and RhoC. It phosphorylates a myosin light chain (MLC) and regulates acto-myosin contractility,⁴³ which contributes to invasive and metastatic behavior in cancer. A preclinical study has shown ROCK inhibition suppressed both Rho-mediated activation of actomyosin and invasive activity of rat MM1 hepatoma cells implanted into the peritoneal cavity of syngeneic rats.⁴⁴ Thus, it is difficult to explain why WPE1-NB26 would select for complex rearrangement that is accompanied by deletion of this oncogene.

In summary, our results with pure populations of cells present as cancer cell lines underlines the complexity of the genetic changes that one can observe during progression of prostate cancer. Not all the genetic changes are likely to be driver mutations, but prior knowledge about some of the tumor suppressors and oncogenes allows us to make hypotheses about why some of the genes are lost or amplified as the immortalized RWPE1 prostate epithelial cells progress to the highly malignant and metastatic WPE1-NB26 cells. Our results suggest, for example, that loss of tumor suppressors OPCML, Cav-1, NF2, and UGT2B17 by genetic or epigenetic mechanisms can be anticipated as prostate cancers progress from the indolent to the more aggressive form of the disease. The large number and variety of structural variants seen in this relatively simple model of prostate cancer also indicates that many more genetic changes are waiting to be discovered in clinical prostate cancer as it progresses to the aggressive disease.

Materials and Methods

Reagents

Reagents used were DNAZol reagent (Invitrogen, 10503-027), End-It DNA End Repair Kit (Epicenter, ER0720), human adult normal lung genomic DNA (BioChain, D1234152), MinElute Reaction Cleanup Kit (Qiagen, 28204), QIAquick Gel Extraction Kit (Qiagen, 28704), QIAquick PCR Purification Kit (Qiagen, 28104), Quick Ligation Kit (NEB, M2200S), Phusion High-Fidelity DNA Polymerase (Finzymes, F530), Taq DNA Polymerase (Roche, 11146165001), and TaKaRa Ex Taq DNA Polymerase (Takara, TAK RR001A).

Cell lines

RWPE1 cells (CRL-11609) and WPE1-NB26 cells (CRL-2852) were purchased from ATCC and cultured according to ATCC instructions.

Paired-end sequencing

All chromPET libraries were constructed according to the protocol supplied by Illumina with minor modifications. Genomic DNA was extracted with DNAZol reagent and 2 μg of DNA was sheared by a Nebulizer. The ends of DNA fragments were polished by an End-It DNA End Repair Kit and A-tail added to the 3′ end by Taq DNA polymerase. The Y-shaped adaptor was ligated to both ends of DNA fragments by a Quick Ligation Kit and 600–700 bp DNA fragments were purified by 2.0% agarose gel electrophoresis and a QIAquick Gel Extraction Kit. Y-shaped adaptor ligated DNA was amplified by PCR primer PE1.0 and 2.0 and was again purified. Paired-end high-throughput sequencing was performed according to the manufacturer’s protocol (Illumina).

Correction of sequencing artifacts

PCR bias may lead to artifacts that result in duplication of the chromPETs that can contribute to false positives in our analysis pipeline. Since such chromPETs would map to the same genomic location, we removed all but one of multiple chromPETs with the exact same mapping locations on both sides. Also all chromPETs that have both tags mapping into simple sequence repeats (SSRs) were removed.

Mapping

The tags are mapped back to the hg19 version of the human genome (downloaded from the UCSC genome browser) using Novocraft’s Novoalign software.¹⁴ The hg19 human genome was first indexed using the Novoindex software (-k15 and -s1). The initial mapping is done using the default parameters for Novoalign (novoalign -r All -e 50 -c 3). The second mapping process was done using more sensitive parameters that also allows for multiple mappings (novoalign -c 12 -r E 25 -e 100 -t 150).

Correction of AT bias

Illumina genome analyzer GAII data has been shown to have an AT bias in sequencing coverage. To estimate the effect of AT content on sequence coverage, we divided the genome into 7500 bp non-overlapping windows. For each window, we calculated AT content and plotted it with coverage for that particular window. To correct for the AT bias, we binned the non-overlapping windows into 100 bins based on increasing AT content.¹⁵ For example, a window with AT content 45.5% goes into bin 45, and a window with AT content 48.6% goes into bin 48. We then convert the coverage scores for all windows in a bin to their respective Z-scores. This results in an average coverage score of zero in each window, thereby correcting for the AT bias.

HYDRA pipeline for detecting chromosomal breaks

After a second alignment of discordant chromPETs back to the human genome with more sensitive parameters, and excluding any “concordant” chromPET that map with the expected size and orientation, we processed all the resulting “discordant” chromPETs (including multiple mappings) using the HYDRA pipeline.^13,16 Duplicate alignments were removed and then fed to the HYDRA pipeline with default parameters. To filter for a high confidence set of breakpoint calls, we selected for breakpoints that were either inter-chromosomal, or intra-chromosomal with the following characteristics

• The two ends of the breakpoint call were separated by >1000 bp

• The chromPETs comprising the breakpoint call had, on average, <1000 mapping combinations between the two ends (if first read in a chromPET maps m number of times and second read maps n number of times, then m × n < 1000),

• The breakpoint call was supported by >2 discordant chromPETs, and

• The average number of mismatches in the sequence of the two ends comprising the call (relative to the reference genome) was <2.

AbCNV algorithm for detecting copy number variants

The AbCNV algorithm uses the depth of coverage to estimate the copy number for a genomic segment.¹⁷ The chromPETs that map within the expected distance are used to construct a coverage profile across the genome. We used non-overlapping windows across the chromosome and calculate coverage in each window. The binned data are then normalized to the total number of chromPETs by converting the average coverage per window to Tags per million (TPM) per window. We then calculate the log2 ratio of the TPM for each window in the WPE1-NB26 (C) vs. the RWPE1 (N) cell lines (Ratio_C_N).

The Ratio_C_N per window is then fed into a simple decision machine to determine the copy number of a window given all windows we have seen so far, as shown in Figure S1. Based on the parameters given, it converts the log2 ratio data into one of three states, normal, low or high connected together by edges. The different state transitions were governed by the following functions (C.N. = Log2 ratio copy number):

• Normal → High: C.N. > Median + Threshold

• Normal → Low: C.N. < Median − Threshold

• Low → Normal: C.N. > Median − Threshold + Tolerance * (Threshold)

• High → Normal: C.N. < Median + Threshold − Tolerance * (Threshold)

The threshold we used was twice the Median Absolute Deviations (MAD).

The tolerance we used was 75%.

The machine does not change state unless the criteria for a state transition (as shown in Fig. S1) are fulfilled for 3 consecutive probes. This allows the segmentation algorithm to overcome minor variances in the data that could arise from noise in the data. The genomic regions marked as low/high are regions where the Ratio_C_N was either in the High state or the Low state.

PCR validations

The copy numbers of target loci were measured by real time quantitative PCR using isolated genomic DNA from RWPE1, WPE1-NB26 and normal lung tissue cells. Genomic DNA from normal lung tissue is used as a control. For normalization we used the copy number of the Orc2 locus. qPCR values of target loci are normalized to the Orc2 locus and again normalized to qPCR value of the normal lung data.

Acknowledgments

AD, AM, and YS contributed to the conception of this project. YS prepared the chromPET libraries and validated predicted regions by PCR. AM performed the sequence alignments, designed a strategy of data analysis, wrote the algorithms and programs for AbCNV, and performed all the computational analysis for the project. AD devised and supervised the project. IH participated in discussions of analysis and coordination. All authors contributed to the drafting of the manuscript and the project was supported by P01CA104106. All authors read and approved the final manuscript.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Supplemental Materials

Supplemental materials may be found here: www.landesbioscience.com/journals/cbt/article/25329