Kallikrein-Related Peptidase 10 (KLK10) Expression and Single Nucleotide Polymorphisms in Ovarian Cancer Survival

Batra, Jyotsna PhD*; Tan, Olivia L. PhD*; O'Mara, Tracy BSc (Hons)*†; Zammit, Rebecca BSc (Hons)*; Nagle, Christina M. PhD‡; Clements, Judith A. PhD*; Kedda, Mary-Anne PhD*†; Spurdle, Amanda B. PhD‡

International Journal of Gynecological Cancer: May 2010 - Volume 20 - Issue 4 - pp 529-536
doi: 10.1111/IGC.0b013e3181d9273e
Ovarian Cancer

Introduction: Kallikrein-related peptidase 10 (KLK10) overexpression is a predictor of poor disease outcome in women with late-stage ovarian cancer. We aimed to identify whether KLK10 overexpression could be attributed to genetic variants, in particular, in hormone response elements or transcription factor binding sites.

Methods: Cox regression analysis was used to assess the association between 2 tag and 1 exonic KLK10 single nucleotide polymorphisms (SNPs) and the survival of 319 patients with ovarian cancer. Four different ovarian cancer cell lines were investigated for KLK10 expression after hormone stimulation, and sequence variation in the 3.6-Kb upstream of the KLK10 start site. In silico analyses of SNPs in cell lines and from published databases were undertaken to identify further research novel and potentially functional SNPs that are not covered by tag SNPs.

Results: The KLK10 SNPs investigated were not associated with ovarian cancer survival. However, steroid hormone treatment of ovarian cell lines showed KLK10 up-regulation in response to estrogen and estrogen plus progesterone treatments in the aggressive cell line PEO1 and affirmed a role for KLK10 in aggressive ovarian cancer. Potentially functional KLK10 SNPs were identified by cell line sequencing and bioinformatic analysis.

Conclusion: Potentially functional candidate KLK10 SNPs require investigation in future association studies of ovarian cancer risk and survival, including rs3760738 identified in aggressive ovarian cancer cell lines and predicted to affect transcription factor binding sites.

*School of Life Sciences, Hormone-Dependent Cancer Research Program, Institute of Health and Biomedical Innovation; †School of Public Health, Queensland University of Technology; and ‡Division of Genetics and Population Health, Queensland Institute of Medical Research, Queensland, Australia.

Received December 8, 2009, and in revised form February 8, 2010.

Accepted for publication February 14, 2010.

Address correspondence and reprint requests to Amanda B. Spurdle, PhD, Division of Genetics and Population Health, Queensland Institute of Medical Research, Queensland, Australia. E-mail: Amanda.Spurdle@qimr.edu.au.

Batra and Tan are equal first authors and Kedda and Spurdle are equal last authors.

This work was supported by funding from the National Health and Medical Research Council of Australia (NHMRC) grant 390123. ABS and JC are NHMRC senior and principal research fellows. CN is supported by NHMRC career award. JB and OT are supported by Institute of Health and Biomedical Innovation postdoctoral fellowships. TO is supported by Australian Postgraduate Award, IHBI PhD top-up and Queensland government Smart State PhD top-up award.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the Journal's Website (www.ijgc.com).

Article Outline

Ovarian cancer is the leading cause of death due to gynecological malignancies and the sixth most common cause of cancer death for women worldwide.1 Approximately 75% of women with ovarian cancer are diagnosed with advanced-stage disease (International Federation of Gynecology and Obstetrics [FIGO] stage III or IV) and, as a result, their prognosis is poor.2,3 Less than 30% of women with advanced stage disease survive beyond 5 years; however, when diagnosed with an early-stage disease (FIGO stage I), up to 90% of women can be effectively treated with conventional surgery and chemotherapy.3

Kallikrein-related peptidases (KLKs) have been identified as potential diagnostic and prognostic cancer biomarkers because of their altered expression in hormone-related cancers.4,5 The human kallikein 10 gene (KLK10; also known as normal epithelial cell-specific 1 gene [NES1]) has been identified as a diagnostic marker for late-stage ovarian cancer and has potential as a marker for poor prognosis.6-8 High KLK10 expression has been associated with advanced ovarian cancer stage, serous histological subtype, suboptimal debulking, large tumor size, an increased rate of relapse, advanced tumor grade, no response to chemotherapy, and worse survival.9 A rapid increase in KLK10 protein in response to steroid hormone stimulation suggests that steroids up-regulate KLK10 expression through interaction between hormone-receptor complexes and hormone response elements (HREs).10,11 Although the presence of functional estrogen and androgen receptors have not been found to increase its constitutive activity in vitro, it is speculated that indirect regulation of hormone-mediating effects via other transcription factors (TF), and the presence of HRE in other regions of the gene is possible.11,12 Altered expression of KLK10 could be attributed in part to single nucleotide polymorphisms (SNPs) present in gene regulatory sites and affecting gene regulation by altering the binding specificity of hormone receptor complexes to HREs and/or TFs to transcription factor binding sites (TFBS).13-16 Similarly, SNPs in intron/exon boundaries could result in alternative transcripts being produced and thereby resulting in protein or isoforms exhibiting altered function.17,18 Further, SNPs located at microRNA-binding sites (miRNA-binding SNPs) are likely to affect the expression of the miRNA target.19 All these changes may contribute to prognosis and survival of ovarian cancer.

The elucidation of haplotype blocks has provided a powerful tool for the detection of SNPs associated with disease risk and outcome.20 Markers for haplotypes (tag SNPs) provide a cost- and time-effective method to identify regions of DNA that may contain functional SNPs associated with disease risk, progression, and survival.17,18 In this study, KLK10 tag SNPs were investigated for association with ovarian cancer survival. Also, KLK10 expression after hormone stimulation was investigated in 4 different ovarian cell lines. Furthermore, in silico SNP analysis from the published databases, and DNA sequencing of the 3.6 kilobase (kb) upstream of the KLK10 start site in the ovarian cell lines were undertaken to identify further research novel and potentially functional SNPs that are not covered by tag SNPs.

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Study Participants, DNA Extraction, and Genotyping

Three hundred and nineteen women aged 18 to 80 years with ovarian cancer were recruited through gynecology-oncology treatment centers located throughout Queensland, New South Wales, and Victoria, between 1990 and 1993, and from the Royal Brisbane Hospital, Queensland, between 1985 and 1997. Full details of the study recruitment and data collection have been described previously.14 All participants provided informed consent. Women were followed up for survival data (maximum follow-up, 20 years) using the National Death Index and state-based cancer registries; clinical information (including FIGO stage, grade, and histology) was abstracted from the women's medical records. A summary of clinical data for sample set analyzed is shown in Table 1. DNA was extracted as described in previous studies,14,16 and the Sequenom MassArray platform (San Diego, CA) was used to genotype 3 KLK10 SNPs in all patients: 2 HapMap tag SNPs rs2075695 and rs7259451 (July 06 data release) and an additional nonsynonymous exonic SNP, rs3745535, initially identified in tumor tissue.21

Institutional ethics approval for the research was obtained from the Royal Brisbane Hospital, relevant metropolitan hospitals, the Queensland Institute of Medical Research, and the Queensland University of Technology.

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Steroid Treatment and Quantitative Real-Time Polymerase Chain Reaction

The serous epithelial ovarian carcinoma cell lines OVCA432 (late-stage serous),21 SKOV3 (with moderate differentiation),22 PEO1 (differentiated after chemo resistance developed),23 and one normal ovarian cell line, HOSE17.1,24 were grown for 72 hours in phenol red free RPMI media with 2% charcoal-stripped fetal calf serum followed by steroid treatment for 24 hours (0.1 nmol/L of the synthetic androgen R1881 [PerkinElmer, Waltham, Mass], 10 nmol/L β-estradiol, 100 nmol/L progesterone, for E/P treatment 10 nmol/L β-estradiol for 24 hours followed by 100 nmol/L progesterone for 24 hours to up-regulate the progesterone receptor [Sigma, Castle Hill, NSW, Australia] or vehicle control [0.1% ethanol]). RNA was extracted and reverse transcribed. Real-time polymerase chain reaction was performed using primers, which are detailed in Table 1, Supplemental Digital Content 1, http://links.lww.com/IGC/A7, on an ABI7300 thermal cycler using SYBR Green Chemistry (Applied Biosystems).

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In Silico Analysis of KLK10 and Sequencing of KLK10 Promoter Region

A region comprising of 6 kb upstream of the transcription start site (chr19:56,214,198) to 1.5 kb downstream of the stop site (chr19:56,209,866) of the KLK10 gene sequence (ENSG0009129451) was used as a reference for all in silico analyses except where stated otherwise. Details on in silico modeling are given in the Supplemental Digital Content 2, Table 2 (Table 2), http://links.lww.com/IGC/A8.

A region 3.615 kb upstream of the KLK10 transcription start site to 0.068 kb downstream of the start site was sequenced in the ovarian cell lines to identify new SNPs or sequence variants. Primer sets were designed to amplify 6 overlapping regions and sequenced as described in Supplemental Digital Content 1, Table 1, http://links.lww.com/IGC/A7.

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Statistical Analysis

The Statistical Package for Social Science (SPSS) version 14.0 (2006; SPSS, Inc., Chicago, IL) was used for all analyses, unless otherwise specified. The Kaplan-Meier technique was used to plot crude survival curves and estimate crude 5-year survival probabilities. Cox regression models were used to estimate adjusted hazard ratios (HR) and 95% confidence intervals. Hazard ratio was adjusted for age (10-year age groups), FIGO stage, histological subtype, and grade. Haplotypes were inferred for each individual using PHASE, which uses a Bayesian approach incorporating a priori expectations of haplotypical structure from population genetic and coalescent theory.25 Haplotypes with a frequency of less than 5% were pooled, and per-haplotype HR was estimated relative to the most common haplotype. For KLK10 mRNA expression, an analysis of variance post hoc t-test was used to calculate the significance between the treated and vehicle samples and between different cell lines. P < 0.05 was considered statistically significant.

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Association of KLK10 SNPs and Haplotypes With Ovarian Cancer Survival

Because HapMap tag SNPs are an excellent tool for broad assessment of SNP association, we first assessed the proximal promoter and gene coding regions of KLK10 using 2 available HapMap tag SNPs. These SNPs, rs2075695 and rs7259451, cover the region ch19:56209866 to 56214765, including 0.567 kb upstream of the transcription start site to the transcription stop site of the KLK10 gene. We also genotyped the functional SNP, rs3745535, located within exon 2 (chr:19:56212299). None of the KLK10 SNPs or the 3 locus haplotypes were found to be associated with ovarian cancer survival.

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KLK10 mRNA Expression in Response to Steroid Treatment in Ovarian Cancer Cell Lines and DNA Sequencing

We wished to further explore the notion that SNPs that may regulate KLK10 expression are more likely to be found further upstream and/or associated with hormonally regulated regions; we assessed the regulation of KLK10 under steroid hormone treatment and also sequenced the ovarian cell lines. Our cell lines of choice were the normal ovarian cell line HOSE17.1 and 3 serous epithelial ovarian carcinoma cell lines, OVCA432, SKOV3, and PEO1, that represent a spectrum of disease from less invasive to more aggressive. First, we quantified the baseline and hormone-induced messenger RNA (mRNA) expression of KLK10 in these lines using primers in exon 4-5 designed to capture most of the transcript variants (Fig. 1A). No significant differences were seen between the cell lines for baseline expression (data not shown). However, KLK10 mRNA expression was significantly increased in response to estrogen plus progesterone treatment compared with the other treatments in the PEO1 cancer cell line, the most aggressive tumor phenotype (P = 0.007; Fig. 1B), demonstrating a synergistic effect of the combination of estrogen and progesterone (Fig. 1B). The KLK10 mRNA expression level in the other 3 cell lines were not significantly changed with steroid treatment (P > 0.05; Fig. 1B). Comparing the effect of each treatment across the different cell lines, the PEO1 cell line had higher KLK10 mRNA expression compared with HOSE 17.1, OVCAR432, and SKOV3 in response to treatment with estrogen alone (P = 0.015, P = 0.041, and P = 0.140, respectively) and in response to estrogen and progesterone combined (P < 0.001 for all comparisons).

Sequencing of genomic DNA 3.615 kb upstream of the KLK10 transcription start site to 0.068 kb downstream of the start site (region: chr19:56217813-56214130) in these 4 lines did not identify any novel sequence variants but confirmed 2 previously reported SNPs: rs3760738 and rs3760737 (Supplemental Digital Content 3, Table 3 (Table 3), http://links.lww.com/IGC/A9, which shows KLK10 sequence variants upstream of the transcription start site identified in ovarian cancer cell lines).

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In Silico Analysis of the KLK10 Gene and Flanking Regions

Prediction of Elements

Given the aforementioned association of estrogen/progesterone regulation of KLK10 with a more aggressive phenotype, we next wished to determine whether putative estrogen and/or androgen/progesterone response elements could be found within the KLK10 gene flanking regions. In silico modeling predicted 4 estrogen response elements (EREs) and 15 androgen response elements (AREs) clustered upstream of the KLK10 5′ UTR, 11 AREs clustered between exons 2 and 3 and 2 AREs clustered within the 3′ UTR of exon 5 (Fig. 2A). In addition, 11 miRNA sites were identified, and these were scattered throughout the gene (Fig. 2A). Six overlapping miRNA sites were clustered within exon 5. Multiple other TFBS were found throughout the KLK10 gene as shown in Supplemental Digital Content 4, Table 4, http://links.lww.com/IGC/A10.

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SNPs Identified Within KLK10

Next, we initiated a comprehensive in silico search of the SNP databases to determine if any were located within these putative HREs/TFBS. We identified 81 SNPs, from 6 kb upstream to 1.5 kb downstream of the KLK10 gene. Single nucleotide polymorphism IDs, tag, validation status, and additional information is detailed in Supplemental Digital Content 4, http://links.lww.com/IGC/A10. Twenty-four SNPs were located upstream of the KLK10 5′ UTR (chr19:56220198 to 56215244), with 12 of these validated by a noncomputational method (cluster, frequency, submitter, or doublehit methods; Fig. 2B). Two validated SNPs were located within the 5′ UTR (exons A, B, and C) and 8 SNPs (5 validated) were located in the coding exons of KLK10 (Exon 2, 1 SNP; Exon 3, 6 SNPs; Exon 4, 1 SNP). Four SNPs resulted in amino acid changes: rs3745535 (typed in this study) is located in exon 2 and results in an alanine to serine substitution; the other 3 SNPs are located within exon 3: rs34070620 is a deletion resulting in a frameshift and encodes a proline, rs3097885 results in a methionine to arginine substitution, and rs2075690 results in a proline to leucine substitution. The 3′ UTR of exon 5 contained 16 SNPs (8 validated). There were 31 SNPs (19 validated) located within the introns of KLK10 (Intron B, 1 SNP; Intron C, 1 SNP; Intron 1, 15 SNPs; Intron 2, 8 SNPs; Intron 3, 1 SNP; and Intron 4, 5 SNPs).

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Predicted Alterations to Elements With SNPs

Multiple SNPs were identified to fall in predicted HREs/TFBS. These are detailed in Supplementary Table 4, http://links.lww.com/IGC/A10. Briefly, effects on HREs were predicted for 12 of 24 SNPs upstream of the 5′ UTR of KLK10: 4 SNPs gained an ERE (rs2691207, rs11368775, rs3745538, and rs10413969), 6 SNPs lost an ARE (rs28673131, rs10413969, rs34929149, rs2691209, rs3760737, and rs10426480) and 2 SNPs (rs1048344 and rs4417638) gained an ARE (Fig. 2B). No exonic SNPs had an effect on HREs. Three intronic SNPs (rs35125447, rs10425377, and rs2304157, located within introns B, C, and 1, respectively) caused a loss of an ARE. No SNPs had an effect on potential miRNA sites. Multiple effects on TFBS were also seen for 14 SNPs upstream of the KLK10 promoter and for numerous SNPs located within the introns and exons of KLK10 (Supplementary Table 4, http://links.lww.com/IGC/A10). The 3′ UTR defined for the alternative KLK10 transcript accession number AY561634 contains 5 SNPs (rs1802056, rs10426, rs9524, ENSSNP1544478, and rs1698), with 3 of these SNPs being validated. The SNPs rs9524, ENSSNP1544478, and rs1698 were predicted to have an effect on mRNA folding (Supplemental Digital Content 5, Fig. 1, http://links.lww.com/IGC/A11 shows KLK10 mRNA folding and 3′ UTR sequence variants predicted by MFOLD). With respect to the 2 previously reported SNPs identified in the cell lines, the rs3760738 SNP predicted the gain of one TFBS, and the loss of another was identified in a heterozygous state in OVCA432 and in a homozygous state in PEO1. The rs3760737 SNP was found in the heterozygous state in both OVCA432 and PEO1 and was predicted to result in the loss of an ARE and introduction of 3 different TFBS (Supplemental Digital Content 3, http://links.lww.com/IGC/A9).

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Human kallikreins are attractive targets as diagnostic/prognostic marker/s for ovarian cancer progression.4,5 Previous research identified KLK10 as an unfavorable prognostic biological marker for late-stage ovarian cancer.6-10 Luo and colleagues found no association between ovarian tumor KLK10 levels and survival, but when they stratified by stage of disease, they found that high KLK10 levels (≥1.35 ng/mg) were associated with significantly worse progression-free survival in 120 women with late-stage disease with 2-fold HR.8 In another study of 146 patients with ovarian cancer, very high serum KLK10 levels were associated with worse overall survival (HR, 3.43; 95% CI, 1.23-5.54), but no association was found with progression-free survival.9 These contradictory findings prompted us to assess the association of selected KLK10 SNPs with ovarian cancer survival and also assess its transcription expression levels in a range of ovarian cell lines. KLK10 SNPs genotyped in our study were not found to be associated with ovarian cancer survival. Although mechanisms such as hypermethylation of the KLK10 gene26,27 or regulation by miRNA19 may be involved in the regulation of KLK10 expression rather than inherited genetic variation, another possibility is that the tag SNPs investigated in this study did not capture the effect of other KLK10 regions that may be associated with survival. Whereas the tag SNPs investigated did cover the promoter and exonic regions considered most likely to be associated with altered mRNA expression levels, they did not cover the regions upstream of the 5′ UTR or the 3′ UTR, which may influence mRNA expression or stability. Moreover, our bioinformatic analysis indicates that a considerable proportion of the SNPs, even those in the promoter and exonic regions, are apparently not tagged by the SNPs analyzed in this study (Supplemental Digital Content 4, http://links.lww.com/IGC/A10). Further rationale for undertaking a more comprehensive study on SNPs associated with KLK10 gene regulation and/or ovarian cancer risk and survival is supported from our analysis of KLK10 transcripts in a range of different ovarian cell lines after hormone stimulation. Our study showed that the aggressive PEO1 cell line had a significantly increased response to estrogen plus progesterone treatment compared with other treatments. Our results support those of a previous study, where KLK10 was demonstrated to be up-regulated mainly by estrogens, androgens, and progestins11 or by their combination28 in several breast cancer cell lines (BT-474, MCF7, and T-47D) at both the transcriptional (mRNA) and translational (protein) levels. Furthermore, our in silico analyses identified a considerable number of potentially functional SNPs affecting HRE and TFBS upstream of the transcription start site.

Based on our in silico analyses, we were able to define potentially important KLK10 regulatory regions and selected these regions for sequencing in ovarian cell lines. We identified 2 putatively functional SNPs, rs3760737 and rs3760738, and established that these SNPs are not represented by currently defined tag SNPs and have not been previously examined for their association with risk or survival. SNP rs3760738 found in homozygous state in the most aggressive ovarian cancer cell line PEO1, is predicted by in silico analysis to introduce a v-maf binding site and lose a FOXD1 binding site. V-maf dimerizes to Nrf2, which regulates various genes involved in cellular protection of the genome from xenobiotic and oxidative stress and consequently are important in carcinogenesis.29 FOXD1 integrates hormonal signals through protein kinase B and cyclic adenosine monophosphate response element binding domains.30 The cyclic adenosine monophosphate response element binding domains (present in KLK10) induce cell survival responses to peptide hormones and growth factors in normal tissues and have been implicated in tumorigenesis30 and therefore supports the theory that the loss of a FOXD1 transcription factor binding site may be involved in ovarian cancer progression. Because in silico predictions are error-prone, these predictions need to be experimentally validated. Because SNP rs3760737 was found in the heterozygous state in both the least aggressive OVCA432 and most aggressive PEO1 epithelial cell lines, it is most likely not associated with ovarian cancer progression and/or survival, but a role in ovarian cancer predisposition cannot be discounted.

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This study has laid the foundation for further research into the functional role that SNPs may play in the expression of KLK10 and their association with ovarian cancer risk and survival. The SNP rs3760738, identified in particular in aggressive ovarian cancer cell lines and predicted to affect TFBS, could be associated with ovarian cancer risk/survival/prognosis and thus could have clinical value for ovarian cancer prognosis. Further investigation is required to confirm if this SNP is associated with KLK10 expression in a range of cell lines or tissues, and/or with survival or other prognostic features, so justifying detailed study of the possible functional effect of this SNP on KLK10 gene expression. In addition, other potentially functional SNPs identified from bioinformatic analysis have been identified for future association studies assessing ovarian cancer risk and/or survival.

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The authors thank all the participating women and people who supported the recruitment. The authors also thank Georgia Chenevix-Trench for access to the DNA and associated data for the ovarian cancer and SNP association studies, Professor Samuel Mok (Harvard University, Massachusetts), who kindly provided the HOSE17.1 and OVCA432 ovarian cell lines, and Srilakshmi Srinivasan for her assistance in formatting the article.

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Kallikrein-related peptidase 10; Ovarian cancer; Survival; Steroid hormone regulation

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