A copy number gain on 18q present in primary prostate tumors is associated with metastatic outcome

Abstract

Background:

Most prostate cancers (CaPs) grow slowly and remain indolent, yet some become aggressive and metastasize. Clinical decision-making requires prognostic markers that can be utilized at the time of diagnosis to identify aggressive tumors. Previous studies have shown a correlation between genomic alterations on the long arm of chromosome 18 (18q) and metastatic CaP.

Objective:

The goal of this study was to comprehensively profile copy number alterations found on 18q in prostate tumors with varying outcomes to identify putative biomarkers associated with more aggressive disease

Methods:

A custom comparative genomic hybridization array was created composed of high-density tiling of 18q sequences. Primary prostate tumor tissues were gathered from men who underwent radical prostatectomy and were categorized based on the patient’s long-term clinical outcome as either metastatic disease (MET) or no evidence of disease (NED). DNA was isolated from formalin-fixed, paraffin-embedded prostatectomy tumor tissues, and analyzed for copy number variations (CNVs). Protein levels of genes found within the region of CNVs were analyzed using immunohistochemistry.

Results:

Thirty-Four primary prostate tumors were analyzed: 17 NEDs and 17 METs. Two significant regions of copy number gains were found on 18q associated with outcome. One gain located at 18q11.2 was found exclusively in NED outcome tumors while another gain, located at 18q21.31, was found exclusively in MET outcome tumors (P -value< 0.0076). Immunohistochemistry analysis of protein levels showed more protein associated with copy number gain in the MET samples vs. those without the gain as indicated by H-scores of 184.7 and 121.0 respectively.

Conclusions:

The latter of these CNVs represent a putative biomarker for aggressive disease and highlights a putative metastasis promoting gene. Further study of known connections to CaP suggests that the paracaspase MALT1 is the most likely target of the copy number gain and represents a potential therapeutic target. Future studies would be of interest to determine MALT1’s role in aggressive CaP and the ability of this CNV region to differentiate CaP that will eventually metastasize.

1. Introduction

Prostate cancer (CaP) is the most common non-cutaneous malignancy in American men and will account for approximately 33,000 deaths in 2020 [1]. The use of prostate specific antigen (PSA) screening has dramatically increased diagnosis of CaP with. However, the typically slow-growing nature of many prostate tumors has led to the concern that PSA testing resulted in over treatment of tumors that would have remained indolent [2]. Thus, there is a need for biomarkers that can differentiate between indolent and aggressive tumors to improve clinical decision making. CaP is a heterogeneous disease with various underlying genetic causes. Copy number variations (CNVs) are one of the most important genetic aberrations within CaP [3,4]. CNVs involving chromosome 18 have been studied extensively in many types of cancer including colorectal and pancreatic often identifying specific genes involved in progression of these diseases [5,6]. The study of chromosome 18 genes in CaP has been fraught with an inability to pinpoint their exact role in aggressive disease. CNV studies in CaP identified regions of loss of heterozygosity on 18q while others identified regions of gain on chromosome 18 [7,8,9]. Our previous work identified various regions of allelic imbalance on chromosome 18 that correlated with metastatic CaP suggestive of a CNV that encodes genes involved in progression. Due to the limitations of the allelic imbalance studies, no 18q region or gene has been identified as having prognostic value or a primary function in metastatic CaP. Using tissues obtained from primary prostate tumors, we sought to re-evaluate the role of 18q using higher resolution methodologies to identify CNVs that could predict outcome at the time of CaP diagnosis. The loci identified through this analysis could also provide novel targets for CaP therapies.

2. Materials and methods

2.1. Sample collection

Prostatectomy specimens were banked at the University of Texas Health Science Center at San Antonio Genitourinary Tissue Biorepository with Institutional Review Board approval. Clinical records were reviewed by a urologist (JH) who categorized patient outcome as No Evidence of Disease (NED) at a minimum of 5 years; or Node positive or Metastatic (MET) (Fig. 1). Formalin-fixed, paraffin-embedded (FFPE) prostate tissues for use in array Comparative Genomic Hybridization (arrayCGH) and flash frozen tumor tissues for miRNA Array was provided without identifiers. Tumor foci were identified by a board-certified pathologist (DAT) after H&E staining. Detailed clinical information about the patients and tumors used in arrayCGH and miRNA can be found in Tables 1 and 2, respectively.

Fig. 1.

Primary prostate tissues were obtained from the University of Texas Health Science Center Genitourinary Tissue Bank. A minimum of 5-year clinical follow up data was collected for each patient with non-progressing disease. Primary tumor tissues were characterized on outcome as No evidence of disease (NED) or confirmed Metastasis (MET) following prostatectomy. Final analysis including a total of 17 METs and 17 NEDs.

Table 1.

Clinical characteristics of prostate cancer patients selected for comparative genome hybridization array analysis.

		NEDa	METb
Clinical	Avg. age (Range)	64.3 (54–74)	61.8 (41–72)
	Avg. Gleason	6.8	8.3*
	Clinical follow-up (years)	9.2 ± 2.3	5.5 ± 3.0
	Time to confirmed metastasis (years)	N/A	5.5 ± 3.6
	Presented with +Lymph node	N/A	5
Stage	T2	17	6
	T3	0	10
	T4	0	1
Site of metastasis	Lymph node	N/A	7
	Bone		10
	Brain		3

^aNED-No Evidence of Disease Recurrence Outcome

^bMET- Confirmed Metastasis Outcome

^*P <0.001

Table 2.

Clinical characteristics of prostate cancer patients selected for miRNA array

		NEDa	METb
Clinical	Avg. Age (Range)	61.0 (54–71)	60.2 (46–80)
	Avg. Gleason	6.6	7.7*
	Clinical Follow-up (years)	13.4 ± 3.9	6.6 ± 5.3
	Time to Confirmed Metastasis (years)	N/A	4.3 ± 4.3
Stage	T1	0	1
	T2	11	3
	T3	0	4
	N/A	0	4
Site of metastasis	Lymph Node	N/A	4
	Lung		2
	Liver		1
	Bone		7
	Brain		1

^aNED- No Evidence of Disease Recurrence Outcome

^bMET- Confirmed Metastasis Outcome

^*P <0.05

2.2. DNA isolation

DNA was isolated from FFPE rolls of primary prostate tumor tissue and normal prostate tissue using a modified protocol from the DNeasy DNA kit per Agilent’s recommendations (Qiagen; Germantown, M.D). DNA was isolated from 65 prostatectomy tissues with varying patient outcomes: 41 NED outcome and 25 MET outcome. Five normal prostate tissues comprised of non-cancerous prostate tissue recovered from bladder cancer cystectomy patients were pooled for reference DNA. Paraffin was removed from the tissues using Tween 20 then treated with Proteinase K after which DNA was extracted using the DNeasy Mini spin column. DNA was assessed using a NanoDrop 1000 Spectrophotometer (ThermoScientific; Wilmington, DE). DNA samples that did not meet a 260/ 280 ratio of >1.5 or 260/230 ratio of >1.1 were excluded from analysis giving a total of 23 NED and 20 MET DNAs for hybridization (Fig. 1).

2.3. RNA isolation

RNA isolation from flash frozen prostatectomy tissues was performed using the Zymo Direct-zol RNA Miniprep kit (Irvine, CA). Quality of RNA was measured using an Agilent 2100 Bioanalyzer (Agilent; Santa Clara, CA). RNA from 12 MET tumors and 12 NED tumors were selected for analysis.

2.4. Agilent custom arrayCGH design

Using SureDesign software (Agilent; Santa Clara, CA), a custom CGH array with 60,000 probes was created consisting of high-density tiling of 18q and control probes. A total of 44,000 probes were tiled along 18q at a median density of 1 probe per 1,163 bases. 10,148 probes were placed throughout the genome by Agilent for Control/Normalization purposes. The rest of the array was tiled against other genes (results not included).

2.5. ArrayCGH sample labeling and array processing

Four hundred nanograms of tumor DNA and normal prostate DNA were labeled with Cy5 and Cy3 dyes, respectively, following the Genomic DNA Universal Linkage System labeling kit protocol (Agilent; Santa Clara, CA). Excess fluors were removed via Agilent Kreapure columns. Following the protocol for Agilent Array-Based CGH for Genomic DNA Analysis, the labeled probes were blocked and hybridized to the microarrays for 40 hours at 65°C while rotating at 20 rpm, utilizing COT1 as competitor DNA. Arrays were washed and scanned using an Agilent SureScan microarray reader at 3μm resolution and data was retrieved using Agilent’s Feature Extraction Software. Quality control (QC) measurements were assessed based on array scans. Samples that did not meet the QC value of 1.00 in the Good Grid category indicating an issue with control probes were excluded from analysis. After QC eliminations, the copy number of 17 NED outcome and 17 MET outcome tumor DNAs were analyzed using Agilent Cytogenomics Software.

2.6. MicroRNA array

MicroRNA expression was analyzed utilizing the Agilent Sureprint G3 Human MicroRNA r21 array (G4870C). RNA samples were labeled using Agilent’s microarray labeling and hybridization protocol. Briefly, 100 nanograms of total RNA was dephosphorylated followed by denaturation with DMSO at 100°C. Cy3 dye was ligated to the sample using T4 RNA ligase in 16°C incubation, followed by drying in a vacuum concentrator at 45°C. The probes were resuspended in water/hybridization buffer mix and incubated at 100°C. The hybridization mixture was loaded onto the arrays and hybridized in a rotating rack at 55°C for 20 hours. Slides were subsequently washed and scanned using an Agilent SureScan microarray scanner.

2.7. Immunohistochemistry

Due to limitations from the original source of tumor, 6 MET outcome patients included in the arrayCGH were chosen for immunohistochemical analysis (IHC) based on the availability of slides from the same section, as well as copy number status at 18q. This included 3 tumors with gain and 3 without gain. Five μm slides were cut and prepared for IHC. The tissue sections were deparaffinized and hydrated through graded alcohols to water. Antigen retrieval was performed using 0.01 M EDTA pH 8 at 100°C in a water bath for 40 minutes followed by a 20 minutes cool down. Tris Buffered Saline was used for rinses between all steps. Primary antibody MALT1 was diluted 1:25 and incubated with the tissues for 2 hours at room temperature. The detection method was a Hapten detection system (Leica), Mouse Powervision horseradish peroxidase was used for 30 minutes. DAB chromagen was used to produce the brown color at the antigen site. Images were captured using an Aperio Versa 200 (Leica) digital pathology system and analyzed using Imagescope ver. 12.3. Individual cancerous glands were circled using the pen tool to exclude stroma, and then analyzed using Cytoplasmic ver. 2 algorithm to determine the H-score.

2.8. Statistical analysis

Clinical data and CNV data were analyzed using Student’s t-test to determine differences in groups of samples. Statistical analysis of CNV data was performed using the Agilent Cytogenomics software. The H-score is the weighted sum of individual percentage of cells staining at each intensity level using the formula H-score = [1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+)].

3. Results

3.1. CNVs in chromosome 18

To identify CNVs that correlate with outcome, primary prostate tumors from 17 patients classified as MET outcome were compared with 17 NED outcome patients. NED patients were classified after a minimum of 5 years follow up. A small subset of the MET patients (6) presented with lymph node positive disease and were classified as metastatic at time of prostatectomy. Two of these patients developed further metastases while 4 did not show further progression in the subsequent follow-up. Gleason scores between the NED and MET outcome samples were significantly different (P <0.001). This was expected as Gleason score is a known marker of prognosis. Clinical data for these patients and tumors is presented in Table 1. Two significant CNVs on 18q were identified from arrayCGH when comparing NED outcome to MET outcome tumors. One alteration was a gain covering »441kb on the proximal region of 18q11.2 (19059163–19500727:hg19) that was present in 29% (5/17; P -value = 0.0184) of the NED samples, but not in any of the 17 MET outcome tumors (Fig. 2A). The other alteration was a region of gain present in 35% (6/17; P -value = 0.0076) of the MET outcome tumors, but not in any of the 17 NED outcome tumors. This region spans »755kb at 18q21.31 (55713005–56468680: hg19) (Fig. 2B). Other regions of gain and loss on 18q were identified, but none were statistically significantly different between NED and MET outcome tumors. As biomarkers are most effective when they can be used to predict more aggressive disease, we focused on the CNV located at 18q21.31 that was found exclusively in MET outcome samples. This region contains 3 protein-coding genes and 1 miRNA whose products could potentially be increased. The genes found in this region are: Neural Precursor Cell Expressed Developmentally Down-Regulated 4-Like E3 Ubiquitin Protein Ligase (NEDD4L), Alpha Kinase 2 (ALPK2), and Mucosa- Associated lymphoid tissue Lymphoma Translocation 1 (MALT1) as well as microRNA 122 (miR-122). A description and function of these genes in the context of cancer is summarized in Table 3. To further test the potential of miR-122 as the target of copy gain, miR-122 expression was determined by microRNA array profiling. A subset of these samples was from the same patients as those used in the CNV analysis, but the chromosome 18 status was unknown for 14 of the 23 samples. Expression of miR-122 was not detected in any of the samples independent of outcome status suggesting miR-122 is not the target of gain on chromosome 18. However, we are unable to completely rule out miR-122 as the target of copy number gain.

Fig. 2.

Two significant copy number gains on 18q associated with patient outcome were identified by arrayCGH. (A) In NED outcome tumors (5 of 17 samples) copy number gain at chromosomal location 18q11.2 was statistically associated with good outcome as analyzed via the Default Analysis Method-CGH v2 Agilent Cytogenomic software. (B) In MET outcome tumors (6 of 17 samples), copy number gain at 18q21.32 was statistically associated with poor outcome.

Table 3.

Genes located in the region of gain at cytoband 18q21.31 presenting in exclusively MET outcome samples.

Gene	Function	Association with cancer	Association with CaP
MALT1	Paracaspase-Forms complex to activate NF-kB	-First identified in B-cell Lymphoma28 -Upregulated in Breast and Lung20,22	Increase associated with lower survival long-term16,17
NEDD4L	E3 ubiquitin Ligase	Downregulated in Colon Cancer14	N/A
ALPK2	Kinase Negative regulator of WNT signaling in cardiogenesis27	Downregulated in Colon Cancer5	N/A
miRNA-122	Highly expressed in Liver	Downregulated in Liver Cancer29	Upregulated in high Gleason cancer15

3.2. Immunohistochemistry in primary tumors

To confirm that the copy number gain correlated with increased protein expression, we performed IHC analysis on 3 of the MET samples with the CNV, and 3 MET samples without this CNV to evaluate the expression of the 3 genes in the region of gain. ALPK2 showed no differential pattern of staining, while NEDD4L and MALT1 exhibited some differential staining within certain samples between the 2 sets. MALT1 showed the most obvious differential expression with all 3 samples that contained the gain having more intense staining within the tumor (Fig. 3 A and B). An H-score was calculated using percent of cells staining at each intensity. This is useful as a way of giving more weight to darker staining cells. The H-scores show a clear difference in intensity of staining of MALT1 between those tumors with the gain vs. those with no gain. Average H-score in the tumors with gain was 184.7, whereas the tumors without gain had a score of 121. NEDD4L showed a similar though smaller increase in H-score, although that seemed to be driven by 1 intensely staining tumor. ALPK2 exhibited the opposite staining pattern as tumors without the gain had an increased level of protein expression compared to those with the gain (H-score 233.3 vs. 194.8, respectively) (Fig. 3C). Due to the small number of tumors scored in this analysis none of these differences reached significance, but the results are supportive of MALT1 being the target of the increased copy number.

Fig. 3.

Immunohistochemical quantification of protein expression of MALT1, ALPK2, and NEDD4L genes located within the gain at 18q21.31. (A) Tumor tissue from MET outcome patients that showed no copy number gain at 18q21.31 show positive stain for MALT1, ALPK2, and NEDD4L. (B) Tumor tissue from patients with the gain show increased staining of MALT1 and NEDD4L, while ALPK2 shows no clear increase in stain. (C) Average H-scores based on intensity of the staining of cancerous glands indicate higher expression of both MALT1 and NEDD4L in samples that contain the chromosomal gain, while ALPK2 shows the decreased expression.

4. Discussion

4.1. Chromosome 18 in prostate cancer

Chromosome 18 has been previously implicated in CaP. Databases, such as the metastatic CaP database from University of Michigan have identified loss of chromosome 18 in tissues at the site of metastasis [10]. The Cancer Genome Atlas (TCGA) is a valuable tool when trying to validate findings in smaller datasets. Looking at the region of gain (18q21.31) found in our MET outcome tumors in TCGA, the TCGA data are variable in terms of copy number alterations with many studies identifying both losses and gains within this region of chromosome 18. Interestingly, a study looking at epigenetic and genetic changes in metastatic tumors from castration resistant CaP with or without neuroendocrine characterization showed the highest percentage of gain within this region of chromosome 18 in all of the TCGA datasets with almost 10% of these metastatic tumors showing gain at this specific region [11].

4.2. Genes within region of gain at 18q21.31

The 4 genes in the region of gain at 18q21.31 are NEDD4l, ALPK2, MALT1, and miR-122. Of these, both NEDD4L and ALPK2 are associated with different types of cancer. However, it is decreased expression that is correlated with either aggressive disease or progression. NEDD4L is an E3 ligase that inhibits canonical WNT signaling and has been shown to be downregulated in colon cancer [12]. ALPK2 has been implicated in apoptosis and DNA damage in colon cancer cells and its downregulation was associated with colorectal cancer [13]. Only MALT1 and miR-122 have been associated with cancers through upregulation of expression. The miR-122 is an interesting target as it has been shown to be expressed at higher levels in Gleason 8 and above CaP [14]. However, our results do not confirm this, as none of our tumors showed expression of miR-122.

4.3. MALT1 in cancer

Due to our protein expression data, as well as publicly available data, we believe MALT1 is the most likely driver of this increase in 18q copy number with metastatic disease outcome. Analysis of the gene expression data in a long-term study of CaP patients on the publicly available database Prognoscan, shows that patients on a watchful waiting (now commonly referred to as active surveillance) treatment plan with high MALT1 expression had significantly reduced long term survival after 10 years compared to those with low expression [15,16]. Unfortunately, of the 3 genes in the region of gain, only MALT1 was included in the analysis of those patients, so no conclusion can be made about the other genes. MALT1 is a paracaspase whose primary function is to facilitate activation of the NF-κB pathway through the CARD-BCL10-MALT1 complex. Recently it was shown that activation of the MALT1-driven NF-kB pathway increased aggressiveness of Angiotensin II receptor (AGTR1) positive breast cancer [17]. In CaP, inactivation of NF-kB signaling pathway in C4-2 and PC3 CaP cell lines inhibited their ability to metastasize to bone and activation of the NF-kB pathway in LNCaP CaP cells increased osteoclastogenesis, which is a prominent feature of bone metastasis [18]. Further study of MALT1 and its role in activating NF-kB signaling and its response to androgen within CaP is needed. If MALT1 proves to play a major role in NF-kB signaling in CaP, it could become a therapeutic target for these subsets of cancers with increased MALT1 expression.

5. Conclusion

The data presented show a copy number gain identified on 18q that is associated with metastatic disease as well as identified potential putative therapeutic targets for aggressive CaP. However, due to the small sample size more tumors with MET outcome are needed to elucidate the role of this CNV in metastatic disease. This does, however, provide evidence that elevated MALT1 expression could play a role in advanced CaP.