Effects of Cyclooxygenase Inhibitors in Combination with Taxol on Expression of Cyclin D1 and Ki-67 in a Xenograft Model of Ovarian Carcinoma
2012

Wei Li
Department of Gynecology
Nanjing Medical University of Hangzhou Hospital
261 Huansha Road310006Hangzhou, ZhejiangChina

Jia-Hui Cai
Department of Gynecology
Nanjing Medical University of Hangzhou Hospital
261 Huansha Road310006Hangzhou, ZhejiangChina

Jun Zhang
Department of Gynecology
Nanjing Medical University of Hangzhou Hospital
261 Huansha Road310006Hangzhou, ZhejiangChina

Yun-Xian Tang
Department of Gynecology
Nanjing Medical University of Hangzhou Hospital
261 Huansha Road310006Hangzhou, ZhejiangChina

Liang Wan
Department of Gynecology
Nanjing Medical University of Hangzhou Hospital
261 Huansha Road310006Hangzhou, ZhejiangChina

Effects of Cyclooxygenase Inhibitors in Combination with Taxol on Expression of Cyclin D1 and Ki-67 in a Xenograft Model of Ovarian Carcinoma

Int. J. Mol. Sci
13201210.3390/ijms13089741Received: 18 June 2012; in revised form: 22 July 2012 / Accepted: 27 July 2012 /International Journal of Molecular Sciences * Author to whom correspondence should be addressed; E-Mail: email@example.com; OPEN ACCESS Int. J. Mol. Sci. 2012, 13 9742ovarian carcinomaSC-560celecoxibtaxolcyclin D1cell proliferation
The present study was designed to investigate the effects of cyclooxygenase (COX) inhibitors in combination with taxol on the expression of cyclin D1 and Ki-67 in human ovarian SKOV-3 carcinoma cells xenograft-bearing mice. The animals were treated with 100 mg/kg celecoxib (a COX-2 selective inhibitor) alone, 3 mg/kg SC-560 (a COX-1 selective inhibitor) alone by gavage twice a day, 20 mg/kg taxol alone by intraperitoneally (i.p.) once a week, or celecoxib/taxol, SC-560/celecoxib, SC-560/taxol or SC-560/celecoxib/taxol, for three weeks. To test the mechanism of the combination treatment, the index of cell proliferation and expression of cyclin D1 in tumor tissues were determined by immunohistochemistry. The mean tumor volume in the treated groups was significantly lower than control (p < 0.05), and in the three-drug combination group, tumor volume was reduced by 58.27% (p < 0.01); downregulated cell proliferation and cyclin D1 expression were statistically significant compared with those of the control group (both p < 0.01). This study suggests that the effects of COX selective inhibitors on the growth of tumors and decreased cell proliferation in a SKOV-3 cells mouse xenograft model were similar to taxol. The three-drug combination showing a better decreasing tendency in growth-inhibitory effect during the experiment may have been caused by suppressing cyclin D1 expression.

Introduction

Ovarian cancer represents the leading cause of death among gynecological malignances, and because of its insidious onset, most women with this disease present with advanced stage disease at the time of diagnosis. Although the quality of cytoreductive surgery, as well as development of novel drugs and new chemotherapy regimens for ovarian cancer therapy have been improved, long-term survival rates for patients with advanced epithelial ovarian carcinoma remain disappointing, and ongoing efforts have aimed to develop more effective primary therapy [1]. Since the early 1990s, taxol has been used to treat ovarian cancers [2]. Taxol belongs to a family of microtubule-targeting drugs called the taxanes [3], which work by promoting assembly and stabilization of microtubules preventing depolymerization.

However, tolerance to taxol in ovarian cancer cells has been observed [4]. Cyclooxygenase(COX)-2 overexpression has been found to be associated with chemotherapy resistance [5], and its overexpression might reduce the efficacy of taxol [6]. In addition, Subbaramaiah et al. also found that taxol could induce COX-2 mRNA expression and increase COX-2 protein levels in epithelial and tumor cell lines [6]. COX-2 overproduction induced by taxol may therefore cause undesirable effects. However, another study showed that overexpression of COX-2 predicts less susceptibility to platinum-based regimes but is not associated with response to platinum/paclitaxel [7]. COX-2 is one of the two isoforms of COX, which are the rate-limiting enzymes of the prostaglandins. It has been identified as being involved in the onset and progression of a variety of malignancies [8], including ovarian cancers [1]. Many studies found that selective COX-2 inhibitors could enhance the response to taxol in cancers [9], such as non-small-cell lung cancer [10] and ovarian cancer [11]. Another isoform of COX is COX-1, which is a constitutive form of the enzyme [12]. Gupta et al. [13] found that COX-1 was overexpressed in ovarian cancers in vivo and a later study showed its overexpression could be inhibited by COX-1 selective inhibitors in a mouse model of epithelial ovarian cancer [14]. These findings suggest that COX may play an important role in carcinogenesis and could be targeted for anti-tumor therapy. Nowadays, scholars have investigated the effects of COX inhibitors in combination with taxol on antiangiogenesis [9], apoptosis and proliferation [11]; however, the exact mechanism remains inconclusive.

Cyclin D1, a cell cycle protein, is a well-established human oncogene: A recent census concluded that there was substantial evidence for the involvement of cyclin D1 amplification and overexpression in cancers [15]. Moreover, in some studies the correlation between cyclin D1 expression and proliferation was echoed in carcinomas [16,17]. A recent study showed the deregulation of cyclin D1 expression could directly lead to some of the hallmarks of cancer by causing proliferation, and this could be a mechanism-based targeted therapy to treat human cancers [18]. In addition, it was previously reported that COX-1 [13], COX-2 [19] and cyclin D1 [20] were all up-regulated in ovarian cancer, and downregulation of cyclin D1 expression via a COX-2 dependent mechanism by celecoxib could be a potential mechanism to inhibit ovarian cancer growth [21]. Therefore, it is reasonable to believe that a decrease in cyclin D1 could be potentially effective in inhibiting proliferation of tumor cells. In this study, we hypothesized that the addition of COX inhibitors could enhance the antitumor effect of taxol on xenograft ovarian cancer by reducing the expression of cyclin D1 and decreasing cell proliferation.

Results and Discussion

Inhibition of Ovarian Cancer Growth

To test whether COX inhibitors or taxol could inhibit ovarian cancer growth, we used the human ovarian carcinoma cell line SKOV-3. The tumor growth in the control group increased throughout the period examined. Data in Figure 1 show the relative effect of SC-560, celecoxib or/and taxol treatment. At the end of the experiment, treatment with SC-560, celecoxib and taxol resulted in mean tumor volumes of 405.10 mm 3 , 394.75 mm 3 and 324.79 mm 3 , respectively, while the mean tumor volume in control mice was 713.51 mm 3 ; tumor growth was significantly reduced when treated with these drugs alone compared with the control group (p < 0.05). Under similar conditions, tumor volume in the three-drug combination group was reduced by 58.27% to 297.78 mm 3 compared with control mice (p < 0.01). The inhibitory effect of the three-drug combination group showed a better decreasing tendency in growth-inhibitory effect compared with the independent group. No toxicity was observed in any of the animals, as measured by weight gain/loss as well as gross pathological examination of the gastrointestinal tract of the animals at necropsy. The inhibitory of SC-560, celecoxib and taxol on tumor growth were determined in an ovarian cancer model using SKOV-3 cells. After 7 days to allow tumor establishment, mice were treated with SC-560, celecoxib and taxol. Treatment was continued for 21 days. The average tumor volume in all the drug-treated mice was significantly different from vehicle-treated mice at day 28. Statistical significance was determined using Least Significant Difference (LSD)-t test. * p < 0.05, # p < 0.01.

Cyclin D1 Expression in Tumors

To evaluate the expression of cyclin D1 in SC-560, celecoxib or/and taxol-treated mice, protein changes in drug-treated xenograft tumors were detected by immunohistochemistry analysis. The expression of cyclin D1 in tumor sections was substantially lower when the mice were exposed to SC-560, celecoxib or the combination of the three drugs, compared with the control (Figure 2A). The expression positive rate of cyclin D1 was 37.17% ± 10.24% in the control, while 18.5% ± 6.83%, 23.17% ± 7.02%, 18.67% ± 8.62%, 17.00% ± 7.21% and 19.17% ± 8.23% of cells in the group treated with SC-560, celecoxib, SC-560/celecoxib, SC-560/taxol, or the combination of the three drugs, respectively; they all showed significantly decreased expression levels of cyclin D1 compared with the control (p < 0.01; Figure 2B). No variability among the drug-treated groups was observed.

SC-560, Celecoxib and Taxol Inhibit Tumor Cell Proliferation

We assessed cell division in allografted tumors treated with SC-560, celecoxib, taxol or the combination by proliferation-associated nuclear antigen (Ki-67) staining. The population of Ki-67-positive cells in tumor sections was substantially lower when the mice were exposed to SC-560, celecoxib and the combination of the three drugs than the control ( Figure 3A). Data for the proliferation index of eight groups are shown in Figure 3B. In the SC-560, celecoxib or taxol groups, the proliferation index were 12.00% ± 5.22%, 9.83% ± 5.64% and 8.67% ± 5.68%, respectively while 24.67% ± 11.29% was observed in the control group; the independent groups or their combinations groups were all statistically significant compared with that of the control group (p < 0.01). No variability among the drug-treated groups was observed.  Celecoxib SC-560+Cel+Taxol

Correlation of Tumor Cell Proliferation with Cyclin D1 Expression

We assayed tumor cell proliferation index and cyclin D1-positive rates in allograft tumors. The results shown in Figure 4 show a positive correlation (r = 0.375, p < 0.01) between proliferation and cyclin D1 expression in the tumors.

COX Expression

The untreated tumors were analyzed for expression of both COX isoforms. The results of immunohistochemical analysis show that the levels of COX-1 proteins were substantially higher than the COX-2 levels in these tumor samples ( Figure 5).

Discussion

Treatment with novel drugs that selectively interfere with an important pathway controlling cancer cell proliferation, in combination with conventional anticancer treatments such as chemotherapy, has generated enormous clinical interest [22]. This study demonstrates that COX inhibitors in combination with taxol showed a better decreasing tendency in growth-inhibitory effect. This might be associated with reduced cyclin D1 expression.

Taxol is known as a front-line agent for ovarian cancer chemotherapy; however, long-term treatment often results in chemoresistance [4]. Studies have shown that involvement of COX-2 is associated with chemoresistance [5,23], and that taxol itself could induce COX-2 and prostaglandin biosynthesis, thus theoretically reducing its own cytotoxic effect. These findings suggest that COX-2 expression could play a role as an indicator of chemoresistance in ovarian cancer, and combination of a selective COX-2 inhibitor with taxol would overcome any decreases in efficacy related to the induction of COX-2 by taxanes [10]. Celecoxib has potent anti-tumor activity in a wide variety of human epithelial tumor types, and was most effective in decreasing tumor cell growth at the lowest dosages [24]. Furthermore, the combination of celecoxib and taxol has been used to treat carcinomas [10,25], and has previously been shown to be safe in a phase II study and has been tested with other agents with similarly good safety results [26]. SC-560 has been demonstrated to inhibit the overexpression of COX-1 in ovarian cancers [14]. However, studies on the combination of COX-1 selective inhibitor and taxol for cancer treatment have been rare. In our previous study [27], we found the combination of celecoxib and SC-560 lead to a greater efficacy than the individual agents administered alone. In this research, we also observed the three-drug combination showed a better decreasing tendency in growth-inhibitory effect. The result was better reflected in the effect of cyclin D1.

Induction of cyclin D1 is the rate-limiting step during the cell cycle, particularly in the transition from G1 to S phase. The G1/S checkpoint is frequently altered in many epithelial tumors and may confer growth advantage and enhanced tumorigenesis [28]. Cyclin D1 degradation is sufficient to induce G1 cell cycle arrest, and it might be a potential chemopreventive and chemotherapeutic target of ovarian cancer [29]. Substantial evidence for the involvement of cyclin D1 overexpression in cancers has been found [15], and research has demonstrated that an estimated 26% of sporadic epithelial ovarian cancers overexpress cyclin D1 [20]. Our previous trial presumed that celecoxib could inhibit the expression of cyclin D1 by a COX-2-dependent mechanism [21]. Moreover, not only celecoxib but also SC-560 could suppress the expression of cyclin D1 and inhibit proliferation in colon cancer cell lines [30]. Research has shown that diverse compounds, some with potential therapeutic application, lead to enhanced cyclin D1 degradation [31]. Therefore, cyclin D1 knockdown may provide a potential gene therapy approach [32]. In this research, we investigated whether treatment with SC-560/taxol and the combination of all three drugs significantly decreased the expression levels of cyclin D1. These findings suggested that the celecoxib-and SC-560-enhanced antitumor effect of taxol may in part be mediated through influencing the cell cycle. Shan J. et al. also showed promoting cyclin D1 degradation inhibited proliferation in cancer cells overexpressing cyclin D1 but not in normal fibroblasts, suggesting that targeting cyclin D1 degradation could be an effective, cancer-specific therapy [33].

Cyclin D1 has been proved to be associated with proliferation [16,17], and a recent study reported that cyclin D1 performs multiple functions as an oncoprotein through enhancement of proliferation and resistance to apoptosis, and may also contribute to chemoresistance in glioma [32]. High expression of Ki-67 has been found to indicate a poor prognosis in ovarian cancer [34], and it is positively correlated with tumor grade and may contribute to the identification of aggressive ovarian carcinomas [35]. A recent study reported that sustained proliferative signaling and activated invasion and metastasis are considered to be hallmarks of cancers [18]. In this study, we have shown that the tumor growth inhibition by celecoxib, SC-560 or taxol was accompanied by a decrease in proliferation index, and there was a positive correlation between proliferation and cyclin D1 expression in the tumors. The results suggested that the drugs may inhibit cell cycle progression through the G1-S transition in SKOV-3 cells by decreasing the expression of cyclin D1 as one of its potential antiproliferative mechanisms. The hypothesis to explain the relationship between the decrease of cyclin D1 and cell cycle arrest at G1 is that the loss of cyclin D1 results in recruitment of p21 to cyclin E2-cdk2 complexes, inhibiting cdk2 activity, which prevents pRb hyperphosphorylation, and that the E2F promoters remain repressed by the bound pRb complex, resulting in G1 arrest [29]. This antiproliferative effects could be attributed to the inhibition of cell survival signaling in ovarian cancer. Inhibition of cell proliferation is believed to be responsible for the chemo-preventative effects of COX inhibitors [14].

Materials and Methods

Human Ovarian Tumors in Nude Mice

The human ovarian carcinoma cell lines SKOV3 in our experiments was chosen for its ability to mimic the progression of ovarian carcinoma when injected into in vivo mouse models and it could be well used to observe the antitumor effect [11,36]. The SKOV-3 cells were purchased from China Type Culture Collection and grown in the recommended media under standard condition. SKOV-3 cells were implanted subcutaneously in the dorsal skin (2 × 10 6 cells) of female athymic nude mice (nu/nu, 7-8 weeks old). When the tumors became visible (7 days after inoculation), the mice were randomly separated into eight groups (6 mice in each group): control, celecoxib, SC-560, taxol, celecoxib/taxol, SC-560/celecoxib, SC-560/taxol and SC-560/celecoxib/taxol.  [37]. In a control group, mice were treated with physiological saline under similar conditions. Drugs or vehicle were administered for a period of 21 days, beginning on the day one week after the tumors became palpable.

Dose and Administration Time of Drugs

Measurement of Tumor Volume

The tumor dimensions were measured twice a week using a linear caliper, and tumor volume was calculated using the equation v (mm 3 ) = a × b2/2, where a is the largest diameter and b is the smallest diameter [38]. The animals were weighed weekly throughout the experiment. On day 28, all of the mice were sacrificed, and tumor tissue samples were collected and then fixed in 10% phosphate-buffered formalin solution for immunohistology or stored at −80 °C until analyzed. The tumor tissue samples were snap-frozen in liquid nitrogen before their storage at −80 °C.

Immunohistochemistry for Ki-67 and Cyclin D1

Proliferation index was evaluated by staining for Ki-67. At the same time, cyclin D1 protein was also detected by immunohistochemistry. Tumors were fixed in 10% neutral buffered formalin for 24-48 h prior to being embedded in paraffin. After deparaffinization, the tissue sections were heated at 121 °C for 15 min in 10 mM TrisHCl with 1 mM EDTA (pH 9.0). Endogenous peroxidase was blocked with 3% hydrogen peroxide in methanol for 10 min at room temperature. The samples were incubated with Ki-67 antibody (clone MIB-5 (M7248)) or cyclin D1 antibody (Santa Cruz Biotechnology, USA) for 90 min at room temperature. Then, the sections were incubated in EnVision reagent for 40 min and DAB/H2O2 for 8-12 min at room temperature. Proliferation was assessed by counting the number of Ki-67 positively staining nuclei and total number of cancer cells at 400× magnification in five representative regions of the tumor. Results are expressed as the proportion of positively staining cells over the total number of cells. For evaluation of the cyclin D1 staining, the tissues were scored for the protein by assessing the site of positive staining in the nucleus or cytoplasm. The status of nuclear expression of cyclin D1 was assessed by determining the percentage of positive cells stained in five fields of each tissue section at 400× magnification.

Immunohistochemistry for COX-1 and COX-2 Expression

In brief, formalin-fixed paraffin-embedded tumor sections (6 µm) were subjected to immunostaining using COX-1 or COX-2 (Santa Cruz Biotechnology, USA), as described above. Sections were deparaffinized and hydrated by sequential immersion in xylene and grade alcohol solutions. The section was then incubated with 3% hydrogen peroxide in methanol solution for 34 min to block endogenous preoxidase activity. For antigen retrieval, slides were pressured in the pressure cooker for 2 × 10 min for or COX-1 or COX-2 at maximum power in 0.01 M citrate buffer (pH 6.0). Immunohistochemical staining was performed using the streptavidin-biotin method.

Statistical Analyses

Statistical analysis was performed with SPSS software (SPSS Standard version 17.0, SPSS). Statistical significance among control and drug-treated groups on tumor growth were determined by Least Significant Difference (LSD)-t test. We used a Tukey's honest significance (Tukey HSD) test for evaluation of the inhibitory activity on tumor cell proliferation and cyclinD1 expression. All the experimental data were expressed as means values ± SE. Results were considered statistically significant when p value < 0.05.

Conclusions

This study suggests that the effects of COX selective inhibitors on the growth of tumors and decreased cell proliferation in a SKOV-3 cells mouse xenograft model were similar to taxol. The three-drug combination showed a better decreasing tendency in growth-inhibitory effect during the experiment, which may be explained by the suppression of cyclin D1 expression. However, intense research efforts are required to explore the effects on xenograft growth and additional possible mechanisms of the combinatorial strategy of COX-selective inhibitors and taxol in ovarian cancer therapies by other experiment models.

Figure 1 .
1Effects of SC-560, celecoxib or/and taxol on tumor growth in vivo.

Figure 2 .
2Effects of cyclooxygenase (COX) inhibitors and taxol on cyclin D1 activation in SKOV-3 xenograft tumors. (A) Representative pictures of cyclin D1 immunohistochemical staining of tumors. The expression of cyclin D1 in tumor sections was substantially lower when the mice were exposed to SC-560, celecoxib and the combination of the three drugs than the control. Magnification is ×400; (B) The results show that the positive rates of cyclin D1 in tumors of mice treated with SC-560 or/and celecoxib, and the combination of the three drugs were decreased. # p < 0.01 compared with control.

Figure 2 .
2Cont.

Figure 3 .
3Cell proliferation in xenograft tumors of nude mice treated with SC-560, celecoxib or/and taxol. (A) Immunostaining of cell proliferation (Ki-67) by immunohistochemistry. The population of Ki-67-positive cells in tumor sections was substantially lower when the mice were exposed to SC-560, celecoxib and the combination of the three drugs than the control. Magnification is ×400; (B) The proliferation index was determined by the percent of Ki-67-positive cells. Proliferation index illustrates the proliferation of SC-560, celecoxib and taxol on tumors. # p < 0.01 compared with control.

Figure 3 .
3Cont.

Figure 4 .
4Correlation of tumor cell proliferation index with cyclin D1 positive rates (r = 0.375, p < 0.01).

Figure 5 .
5Immunohistochemical analysis of COX-1 and COX-2 expression in untreated tumor samples.

COX inhibitors, SC-560 (Sigma Chemical Co. St. Louis, MO, USA), celecoxib (Pfizer Co. Groton, CT, USA) were administered by gavage and taxol (Bristol Myers Squibb SRL, Italy) was given by intraperitoneal (i.p.) in a 0.5 mL suspension of 0.5% methylcellulose (Sigma Chemical Co. St. Louis, MO, USA) and 0.025% Tween 20 (Sigma Chemical Co.) at a dose of 3 mg/kg (SC-560), 100 mg/kg (celecoxib) twice a day, 20 mg/kg (taxol) once a week. The doses of COX inhibitors were chosen for their specificity in inhibiting COX isotypes
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WEP: a high-performance analysis pipeline for whole-exome data
May 2012

Mattia D'antonio
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Paolo D'
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Onorio De Meo
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Daniele Paoletti
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Berardino Elmi
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Matteo Pallocca
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Nico Sanna
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Ernesto Picardi
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Graziano Pesole
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

Tiziana Castrignanò
From Ninth Annual Meeting of the Italian Society of Bioinformatics (BITS)
Sicily. 2-4Catania

WEP: a high-performance analysis pipeline for whole-exome data
May 2012R E S E A R C H Open Access
Background: The advent of massively parallel sequencing technologies (Next Generation Sequencing, NGS) profoundly modified the landscape of human genetics. In particular, Whole Exome Sequencing (WES) is the NGS branch that focuses on the exonic regions of the eukaryotic genomes; exomes are ideal to help us understanding high-penetrance allelic variation and its relationship to phenotype. A complete WES analysis involves several steps which need to be suitably designed and arranged into an efficient pipeline. Managing a NGS analysis pipeline and its huge amount of produced data requires non trivial IT skills and computational power.Results: Our web resource WEP (Whole-Exome sequencing Pipeline web tool) performs a complete WES pipeline and provides easy access through interface to intermediate and final results. The WEP pipeline is composed of several steps: 1) verification of input integrity and quality checks, read trimming and filtering; 2) gapped alignment; 3) BAM conversion, sorting and indexing; 4) duplicates removal; 5) alignment optimization around insertion/deletion (indel) positions; 6) recalibration of quality scores; 7) single nucleotide and deletion/insertion polymorphism (SNP and DIP) variant calling; 8) variant annotation; 9) result storage into custom databases to allow cross-linking and intersections, statistics and much more. In order to overcome the challenge of managing large amount of data and maximize the biological information extracted from them, our tool restricts the number of final results filtering data by customizable thresholds, facilitating the identification of functionally significant variants. Default threshold values are also provided at the analysis computation completion, tuned with the most common literature work published in recent years.Conclusions: Through our tool a user can perform the whole analysis without knowing the underlying hardware and software architecture, dealing with both paired and single end data. The interface provides an easy and intuitive access for data submission and a user-friendly web interface for annotated variant visualization. Non-IT mastered users can access through WEP to the most updated and tested WES algorithms, tuned to maximize the quality of called variants while minimizing artifacts and false positives.

Background

Next-generation DNA sequencing has dramatically accelerated biological and biomedical research, allowing the generation of large volumes of data at increasingly lower costs and in shorter times [1][2][3]. Today the widespread deployment of these technologies has enabled the analysis of genomes, transcriptomes and interactomes [4,5], developing a variety of genome-wide functional assays, such as ChIP-seq [6], RNA-seq [7] and many others.

A widely used application of NGS is Whole Exome Sequencing (WES), a new and efficient approach for studying the genetic basis of human phenotypes [8][9][10]. The WES technique consists in the selective capture and sequencing of the protein-coding portion of the genome. It has been successfully used to elucidate the genetic causes of many human diseases, starting from single gene disorders and moving on more complex genetic disorders, including complex traits and cancer [11][12][13][14][15][16][17].

Through the analysis of WES data it is possible to identify causative variations by comparing cases and controls, also with respect to the variation pattern observed in the normal population. A single experiment can identify several thousands of single nucleotide variants (SNVs) and small insertion or deletion (INDELs) which may contain specific mutations associated to genetic diseases [18,19].

While these achievements have demonstrated the usefulness of exome sequencing compared to whole genome sequencing, which still remains prohibitive for many applications, there are still many bioinformatics challenges that may limit their efficiency/application.

For example, the manipulation and interpretation of the millions of sequences produced by a typical experiment still present significant computational challenges. Analysis of NGS data is troublesome particularly given the shortread lengths and the huge volume of data [20].

Furthermore, sophisticated informatics tools are required [21], and technical skills such as the management and storage of NGS file formats is often impractical and cumbersome [22]. Finally there is no general solution at the present (at least in the author's knowledge) to apply due to the availability of a wide array of data formats, software and analytical tools.

Not less important is the fact that scientists need specific skills in computational biology to mine, analyze and interpret the NGS data.

The development of a streamlined, highly automated pipeline for WES data analysis is one of the possible solutions to address some of these issues. State of the art tools, including GATK, ANNOVAR and SAMTools, can be integrated into a custom pipeline for generating, annotating and analyzing sequence variants.

However, researchers of small biology labs with limited computational resources and experiences should avoid producing once again on their own such an analysis pipeline.

It would be much more efficient to access a web service capable to execute the complete analysis, with simplicity and without requiring any technical informatics skill. This solution thereby would allow the researchers to focus their work on downstream experiments.

In this paper we describe a free web resource, named WEP (Whole Exome sequencing Pipeline web tool): it aims to analyze WES data produced by Illumina platforms [23], which are the most developed and used platforms currently available for WES data production [1,24]. Our web tool automates the execution of an optimized WES pipeline. It is also capable to analyze a user-selected set of exome samples generating tables reporting variant information and their functional annotation.

Through WEP the user is able to quickly perform a WES analysis and identify the biologically significant sequence variations thanks to the use of a simple and intuitive interface. WEP execution does not require any specialized informatics expertise.

Although various analysis pipelines have already been published [25][26][27][28][29][30][31] and other solutions based on VirtualBox and Cloud computing are growing in number in recent years [32][33][34][35], the authors aren't aware of any web application comparable to WEP at the present moment. None of the other tools available perform a full WES pipeline analysis for free, without installation of codes and other programs on local computers.

Implementation and methods

WEP has been developed as an ensemble of modules. Its architecture allows each module to run independently, using data stored in MYSQL relational databases. WEP modules are handled by a program completely written in PHP Object Oriented, while mysql libraries are used for database interactions.

WEP has been designed to integrate several in-house developed scripts as well as open source analysis tools into one single pipeline. It handles all required management tasks needed for a cluster distribution of the computation required.

Raw sequence data can be uploaded and results can be viewed using an interactive, web-based graphical user interface (GUI) that has been tested on the main browsers.

The WEP web-based GUI has been written mainly in PHP: Hypertext Preprocessor (PHP) language using HyperText Markup Language (HTML) and JQuery, combined with HTML5 and CSS3 standards, to enable a better user-interaction.

The analysis pipeline includes 11 modules ( Figure 1) and performs quality statistics, filtering and trimming of sequence reads, alignment to a reference genome, post Figure 1 Bioinformatics analysis workflow of WEP pipeline. The WEP analysis pipeline consists of 11 major steps, some of which are further divided into sub-components. For each input file a quality control is performed. This step includes both the application of filters and trimmers (1) and the calculation of quality statistics on raw and processed sequences (2). In case of PE reads WEP processes both forward and reverse reads simultaneously and exports the filtered reads in a separate file, keeping the pairing information intact. Unpaired reads passing quality filters are also provided in a different output file. These filtered read are then aligned to their reference genome (3). The two paired files are mapped together (PE alignment), while the unpaired file is aligned individually (SE alignment); for each one is produced a SAM file. Afterwards, WEP executes a conversion step (4) where the resulting SAM files are converted in BAM format, sorted and merged together in a single file. Read groups are assigned and the file is indexed. In the variant preprocessing steps, the duplicates are removed (5), the reads are realigned around indels and the base quality score are recalibrated (6). Furthermore, WEP performs alignment statistics and enrichment target metrics (7). At this point, SNPs and indels are detected (8), several annotation are added to each variant (9) and the results are automatically parsed in optimized databases (10). At the end, WEP collects several information and statistics generated during the pipeline run and generates web pages and reports (11) useful to interpret the performed analysis. alignment analysis with the calculation of mapping rate, statistics and annotation of the detected variants.

WEP takes as input short-read data-sets produced by Illumina sequencing platforms and several standard file formats (FASTQ [36], SRA [37] or BAM [38]) can be accepted. The user can upload these files also in a compressed version to speed up the uploading process (zip, tar, gzip, bz or bz2 compression are admitted); WEP automatically detects the file format and chooses the necessary program to decompress it.

In the following paragraph we describe all the steps of a complete analysis pipeline executed by WEP after data submission (which is detailed in the next paragraph).

1) The first module performs an overall quality check (Quality Control, QC) of uploaded read data. Since this may affect downstream analysis results, an effective QC is critical for a reliable data analysis.

Briefly, for each uploaded FASTQ file WEP carries out a FastQC [39] run generating an HTML web page with detailed QC results in the form of tables and graphs. The user can obtain an overview of some relevant properties of raw reads (such as length, quality scores and base distribution) in order to assess data quality and to discard other low quality reads.

WEP includes also another free application for quality control: the NGS QC Toolkit (NQT) which can directly filter low quality reads after quality check and secondly also removes primer/adaptor sequences [40].

2) The arguably most crucial step of most NGS analysis pipeline is to map short-reads to their source genome. The NGS reads coming from the first step are then aligned to a reference genome. Alignment is usually the most time-consuming operation in a NGS pipeline.

There are many aligners and they differ a lot in performance/efficiency/accuracy [41]. Most alignment algorithms for NGS data are based on either 'hashing' or an effective data compression algorithm the 'Burrows-Wheeler transform' (BWT) [42].

Our pipeline uses BWA [43], based on BWT, a fast and memory-efficient read aligner. BWA is the most common choice for WES alignment [44][45][46]. It allows gapped alignment, using very little memory. It performs separated alignment on both strands of a paired-end lane, in multithreaded execution, unifying results in a single mapping file in the Sequence Alignment Map (SAM) format [38].

3) This step is an intermediate phase of the pipeline to pre-process alignment results before further steps.

a) The generated SAM file is converted to the binary Alignment/Map format (BAM), a much more suitable file type because it ensures efficient storing and access to alignment information. The BAM file is indexed to allow the access only to portions of the file without the need to load the whole file. b) SAMtools [38] are called to order the reads by chromosomal coordinates, to merge the paired and unpaired BAM files of the same sample. c) the Picard tool [47] is used to tag the read groups (the ID, the library, the sample, the sequence platform).

To enhance the quality of the alignments for more accurate variant detection, the pipeline carries out several "cleanup" procedures (see step 4, 5 and 6 below) before variant calling. These algorithms are also been adopted in the 1000 Genomes Project [48]. 4) Early during the rise of NGS, it became apparent that many of the reads from massively parallel sequencing instruments were identical -same sequence, start site and orientation -suggesting that they possibly were PCR artifacts [20]. These duplicates may introduce a bias in estimating variant allele frequencies, and thus it is advisable they are removed prior to the variant calling.

A solution is offered by the Picard suite, which not only applies optimal fragment-based duplicate identification, but marks duplicate reads using the FLAG field rather than removing them from a SAM file. This allows to minimize the experimental artifacts, reducing the number of false calls and improving the accuracy in the search of the variants.

5) The presence of INDELs within the sequences and the gapped alignment read-by-read often lead to false SNP identification towards the end of the aligned reads.

Current mapping algorithms are limited: they align each read independently, without seeing the full context in a region to guide placement of reads, whose first or last few bases overlap an indel tend to have those bases misaligned [48].

These artifacts can be resolved only by examining more sequences in their local context. WEP pipeline carries out a local realignment around INDEL and SNP clusters using the Genome Analysis ToolKit (GATK) realigner [49,50]. It allows to correct misalignments (due to indels) obtaining consensus indels. This module improves the indel calls and reduces false positives in the following step of SNPs calling.

6) The Phred-scaled quality scores indicates the probability of having called a base wrong, formula: Q = -10 log_10(P) [51]. Since it may not accurately reflect the true base-calling error rates [52], necessary for variant calling, we also use a further refinement to quality estimation calculated by the GATK recalibration tool. This application attempts to adjust the base quality scores to be more representative of the underlying data quality. It re-calibrates the base quality values using specific co-variates (as machine cycle and sequence context) and for each unknown base, a re-calibrated quality score is calculated in order to be used for variant calling.

Recalibrating these scores reflects more accurately the empirical probability of mismatches to the reference genome, and by doing so it provides not only more accurate quality scores but also more widely dispersed ones [50].

7) This step of the analysis provides important statistical information in order to assess the quality of data before the search of variants.

SAMtools flagstat is used to generate alignment statistics, i.e. the calculation of the fraction of reads that successfully mapped to the reference, with number and percentages of the read mapped and unmapped, whereas NGSrich [53] provides detailed information about enrichment performance and target coverage, important quality criteria for the evaluation of the exome capture experiment.

8) After all these treatment phases of the read, the basic step of our pipeline is to identify the sites in the sample that statistically differ from the reference genomic sequence. SNPs and DIPs are detected where the reads collectively provide evidence of variation.

As with alignment tools, many algorithms have been developed to identify a high-quality set of variants in NGS projects. The most current variant callers implement base quality and posterior probability calculation to detect variant in NGS data [54].

To carry out this task we included in our pipeline the GATK algorithm, a state of the art program for reliable variant detection [50,55]. It uses a Bayesian statistical model for the calculation of the probability of the genotype, estimating the accuracy of the call with a score of Phred-like quality.

The pipeline is able to detect both SNPs and DIPs simultaneously and the results are reported in a standard Variant Call Format (VCF) file. 9) Before proceeding to the prioritization and filtering of the variants and to rank variations, it is necessary to add several annotation to each detected variant, as the genome position and the functional effect. The WEP pipeline includes the software ANNOVAR [56]. This tool takes advantage of information from different databases and external resources to annotate the variants. The gene annotation, variant function, prediction score of SIFT [57] and PolyPhen2 [58], conservation score of PhyloP [59] and GERP++ [60], the dbSNP [61] ID, the allele frequencies of 1000 Genomes Project [48] and NHLBI-ESP 5400 exomes [62] are only some of the possible annotations that we can retrieve through ANNO-VAR tool.

10) At the end of these steps, the pipeline transfers into specifically designed tables (the used DBMS is MySQL Enterprise 5.5) the resulting file, through the use of a specific PHP parser. Therefore the output contains annotated variants with both GATK and ANNOVAR information from previous phases. 11) Once the processed data is stored in the database, WEP generates reports that give end-users sample information to interpret raw data and analysis results.

WEP automatically creates output in one easy-to-navigate HTML page, which provides the project description, QC reports, target coverage and sequencing depth information, descriptions of the annotations, and links to the SNPs and DIPs reports. Each variant is hyperlinked to public databases for the visualization of read alignments and variant calling information at the variant position.

At this point the variants are ready to be analyzed and filtered according to user-defined criteria. A web interface has been developed to allow the user to perform further variant selections, according to the specific biological analysis.

WEP allows to navigate through the results of the experiment, interact with the variants, sort the annotation, export the results as CSV (comma-separatesvalues) file and much more. The web interface details are shown in the following paragraph.

Results

User interface overview

WEP provides an efficient and easy-to-use solution for WES analysis based on NGS data. To achieve this goal, we have implemented our WES tool with a user-friendly web interface. The interactive web application is used to analyze WES data creating specific projects, specifying project parameters, running the required algorithms, and viewing the output of computations by using our High Performance Computing cluster facilities. Through the web interface users can easily view, control and manipulate WES data with few mouse clicks. On the other hand, the interface is also used to deal with heavy computational tasks.

In order to grant security and private access to the user's uploaded data files and results, a login form has been implemented. Registration to the use of WEP is free of charge for academic/research purposes. Data submitted to the WEP interface is private for users, however a public link can be created to share each single result file.

We have developed WEP as modular and extensible as possible, allowing a seamless integration of new data analysis algorithms and visualization features.

The web interface is composed by three main layers: data submission, analysis monitoring and results visualization ( Figure 2).

Submitting the short read sequences

The submission interface workflow is shown on the left part of Figure 2. As described, two submission modules are available: Quick and Complete Submission.

Because of the huge amount of WES data the development of Complete Submission module was needed to introduce a metadata assignment feature. Through this procedure the user can better manage and interpret more in deep WES results.

In the Complete analysis an experiment can be submitted uploading the short reads files and specifying metadata information according to the EBI/ENA data format standards (http://www.ebi.ac.uk/ena/about/ sra_submissions).

Taking the SRA data model as our main guideline, we have implemented metadata structure as follows:

Study: information about the a single sequencing project (more analysis can be run in a single study); Sample: information about the sequenced samples and libraries;

(Actually only human samples can be processed by our analysis architecture.)

Run: information about the platform; associated with study and sample(s);

(Our analysis architecture only admits Illumina runs.) Analysis: contains the analysis data files, associated with study, sample and run objects.

In the "Complete Submission" panel (as shown in Figure 2), the user can define a new study providing the name and description (Study Creation). After the study creation a form for uploading short reads files is available (Input Files Upload). Then the single or paired end assignment is needed to prepare the analysis (Input Files Tagging). The next phase regards the metadata association to the input files (Metadata Assignment). At this stage the completion of submission is performed by providing the name and description of the analysis (Analysis Creation), by selecting the proper parameters (Parameters Selection) and eventually by launching the analysis (Analysis Start).

The submission process can be also simplified by using the "Quick Submission" module; the first module depicted in Figure 2. In the quick analysis metadata assignment and parameter selection are not required. Study creation and parameter selection are automatically managed by the system. The user needs only to create the analysis, upload input files, tag them and launch the run. This procedure can be useful in case of single input file analysis, very small datasets or lack of metadata information.

Short read datasets have to be uploaded in the standard FASTQ, BAM or SRA format. Also compressed format, such as zip, tar, gzip, bz or bz2 files are allowed to speed up the uploading process. By clicking on the upload button three files at once are loaded and the interface shows several time bars advising the users about the remaining time.

Analysis execution and monitoring

After submission, a monitoring page providing the list of pipeline steps and the access to the associated output files is available. For each step the running status, the used algorithm and its version, are shown. The running status switches from queued to running, to completed. In case of execution fault, the running status is marked as error. At a completion of each step of the pipeline, all the generated output files are listed and displayed in the monitoring page. In this way the user can visualize or download all the produced raw output files in each step of the pipeline in addition to check the whole analysis progress. For example, when the quality control of the short reads is completed two links to graphical and tabular statistics of qualities are created for both QC tools described in the previous paragraph.

It's also possible to display additional information regarding every single step of user's analysis, e.g. the command line and the estimated running time.

Gathering output results

After the full analysis completion all raw results are parsed and stored into a dedicated and optimized database. An e-mail containing the link to the available results will be sent from the system to the user email address provided at registration time.

Summary section

The main results web page shows the summary of the statistics about the whole analysis: the input files, the number of short-reads, total and filtered (after the first step), mapped reads, number of reads after removing duplicates, exome coverage and the number of total variants detected.

Besides, there are additional reports for QC, alignment, enrichment and coverage statistics, and a summary of the resulting variants. The summary includes links to the output files generated by some programs during the execution of the pipeline. This avoids the user to return to the monitoring page of the pipeline to extract such information and have a complete, appealing overview useful for the control of the experiment.

Quality Checks section

The "Quality checks" section refers to the results of quality control using FastQC [39] and NGS QC Toolkit [40]. The first table shows a summary of the tests executed by FastQC, and gives a quick evaluation of distribution of base quality per read position, deviation from the expected GC content, distribution of fragment sizes, degree of duplication. This section aims to detect possible problems that may be present in the data.

Moreover, NGS QC Toolkit link shows the results of QC displayed both in the form table (number of reads and bases, total and filtered) and in graphs (various QC statistics, as average quality score for each base position, GC content distribution, average Phred quality score distribution).

Statistics section

The "Statistics" section lead to the alignment and coverage statistics, useful for evaluating data quality and alignment results.

The "mapping statistics" are produced by SAMtools [38]. This section provides a quick summary of the performed alignment, including total number of reads, number of mapped and unmapped reads and the fraction of reads that was successfully paired (only for paired reads).

The "Enrichment performance" section reports the output produced by the NGSrich software [53] for target enrichment evaluation. For each sample a pie chart is provided, giving the fractions of the target regions covered to a particular average depth. Moreover bar plots per chromosome are shown for a quick overview of the coverage across the genome. Further displayed information include the number of genes that are highly or poorly covered (depending on the specific thresholds selected by the user as parameters of the analysis). Eventually a summary table with target-specific coverage information is shown: information about number of reads, mean coverage, fraction of the target region with a particular depth.

Variants, SNPs and DIPs sections

The "Variants" section shows summary information about detected variants. These are collected in tables containing total variants, SNPs and DIPs. For each of them several information are reported: the total and filtered number of variants, the known variants (such as the variants included in the databases dbSNP and 1000 genomes project).

The default values of the variant filters are: Coverage > 10, ambiguously mapped reads per variant < 5, Phredscaled consensus quality > 50, variant confidence/consensus quality > 1.5, strand bias Fisher exact test < 60. These filters, in our experience, are suitable to discriminate among true variants and sequencing errors or false positive. However, these values can be changed (with an appropriate form) in function of user's requirements.

By selecting one of the link buttons, contained in the tables, a web page will appear showing the list of genetic variants associated.

This page ( Figure 3) provides a simple and convenient interface to browse the results. The output tables can be dynamically sorted, filtered and exported as textual/excel files for offline downstream analyses. A series of annotations mined from the main public databases related to each variant is shown. These information include genomic information (the chromosome and genome position), allelic variation (reference base and identified alternative base), homozygous/heterozygous variant in the sample, type of variation (SNP or DIP), the number and type of bases mapping, variant classifications (synonymous, missense, non-sense, frameshift, etc.), gene name and region (exonic, intronic, intergenic, UTR, etc.), some scores from GATK, dbSNP ID, MAF (minor allele frequency) from 1000 Genomes Project and the SIFT-score.

In this table, some items are also directly linked to main databases: UCSC Genome Browser, Entrez Gene (NCBI), dbSNP (NCBI), 1000 Genomes Project, SIFT (JCVI).

WEP allows to easily filter and remove from the list already known SNPs using information from dbSNP or the 1000 genome project.

Intersections section

Finally WEP provides an "intersections" section, allowing the user to search for variants shared between samples. The variants are classified as homozygous or heterozygous by the GATK algorithm within each analyzed individual. A boolean search allows to include or exclude each lane. This feature is particularly useful to quickly detect those loci that are either recessive or dominant. For example, in a trio sequencing, with unaffected parents and affected child, it allows to identify the list of loci harboring alleles consistent with the disease inheritance model.

A summary table provides a complete list of genes containing the searched variants. Once again the final table can be downloaded in the same formats as above.

Data archive

All created studies by each user are listed in the Archive section. From this section it is possible to visualize uploaded files and the analyses executed on them. It is also possible to add new files and run new analyses integrating them to previously created studies. This feature is particularly useful in case of lanes obtained by different experiments and performed at different time or to substitute low qualities lanes. The opportunity to perform several analyses related to the same project allows researchers to easy compare different experimental results.

This section enables the user to increase biological insights on a specific project.

Conclusions

WEP is a web tool which allows any user to freely submit and perform a complete WES optimized pipeline for Illumina sequenced exome samples. It performs a complete analysis starting from quality controls of submitted short reads produced in single or paired end to SNP/DIP identification and variant annotation.

The GUI, a user-friendly graphic interface, drives the user in each step allowing to browse, search, check, classify and download the identified variants. Furthermore, WEP allows researchers without high-performance computing facilities to build a new study/project and run several analyses. Further samples derived from new experiments can be added to a previously created study in order to increase biological insights.

Output files, status and information about each module of a running analysis are provided. Results can be filtered and managed through the GUI and can be downloaded as CSV standard formats as well. Codes and algorithms are regularly updated and tested.

This web tool performs a complete analysis of wholeexome data speeding up the gathering of results from big data experiments and facilitating the use of WES technologies by laboratories without a specialized bioinformatics staff. Reports generated by our tool are accurate and easy to understand and to use also for non-bioinformatician researchers.

The pipeline executed by WEP is implemented and optimized on a HPC infrastructure to speed up data analysis. This layer is completely hidden to the user, so that the execution of each WES analysis is straightforward and easy.

In order to facilitate the use of the WEP tool, a user help has been developed and is available on the WEP web site, along with a selected FAQ section. The user help includes an application example step-by-step and a video tutorial demonstrating how to use the various options of the application.

Further improvements will be released in future versions of the toolbox.

The WEP tool should be cited in all papers using the results as part of the analysis.  The filter form on the right shows all the possible filters that can be chosen to select some variants according to the user's requests. The resulting table on the left, instead, shows the list of genetic variants with all the annotation mined from main public databases (some annotation are also hyperlinked).

Figure 2
2Workflow of the WEP user interface. The WEP interface is composed by three main layers: The web submission (on the left) shows the procedures to correctly submit the read input files. Two submission modules are available. The Complete module allows the user to store several information and metadata for each experiment, while in the Quick module the system automatically generates the minimum set of metadata to execute an analysis. The web monitoring (in the center) provides a web page which displays the status of running of WEP pipeline and where the user can visualize and/or download intermediate output results. The web results (on the right) contains all the web pages which show the user all the results obtained from the analysis. These are collected in different sections allowing for an easier viewing. The variant result tables are also exportable in CSV file formats.

List of abbreviations used NGS: Next Generation Sequencing; WES: Whole Exome Sequencing; ChIPseq: Chromatin Immunoprecipitation sequencing; RNA-seq: RNA sequencing; PCR: Polymerase Chain Reaction; WEP: Whole Exome sequencing Pipeline tool; SAM/BAM: Sequence/Binary Alignment Map; VCF: Variant Call Format; CSV: Comma Separated Values; SRA: Sequence Read Archive; SNV: Single Nucleotide Variants; SNP: Single Nucleotide Polymorphism; DIP/InDel: Deletion Insertion Polymorphism; IT: Information Technology; GUI: Graphical User Interface; QC: Quality Control.

Figure 3
3Figure 3 The filter form on the right shows all the possible filters that can be chosen to select some variants according to the user's requests. The resulting table on the left, instead, shows the list of genetic variants with all the annotation mined from main public databases (some annotation are also hyperlinked).
D'Antonio et al. BMC Bioinformatics 2013, 14(Suppl 7):S11 http://www.biomedcentral.com/1471-2105/14/S7/S11
Authors' contributions MD, PDDM, DP, BE, MP developed the web interface along with the whole analysis pipeline. TC, GP supervised the project development. NS acted as technological supervisor. GP, EP acted as scientific supervisors. TC, MD, PDDM, BE, MP wrote the manuscript. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Submit your next manuscript to BioMed
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. 10.1186/1471-2105-14-S7-S11doi:10.1186/1471-2105-14-S7-S11

WEP: a high-performance analysis pipeline for whole-exome data. D' , Antonio , BMC Bioinformatics. 711Cite this article as. SupplCite this article as: D'Antonio et al.: WEP: a high-performance analysis pipeline for whole-exome data. BMC Bioinformatics 2013 14(Suppl 7):S11.