Background

Cervical cancer is a preventable but the most common cancer among Indian women and second most common cancer among women worldwide [1, 2]. More than 500,000 women are expected to develop the disease every year worldwide. The tragedy is worsened by most of the women presenting in locally advanced disease as well. Human Papilloma virus has been identified as an important factor in the development of cervical cancer, but it alone is not sufficient and requires additional events [3]. Cervical cancer progresses through initial dysplastic or pre-malignant stages before becoming invasive. The evolution to an invasive cancer may take up to 15 years or more [4]. PAP smear testing has been found to aid in early detection of pre-malignant lesions, thereby preventing the morbidity and mortality of cervical cancer. However, organized screening is primarily available in the Western countries and most of the developing countries including India do not have an organized screening program.

The standard treatment currently recommended for stage IIB, IIIA, IIIB and IVA has been concurrent chemo-radiotherapy, usually with weekly cisplatin based chemotherapy [5]. This is based on the data from 5 randomized trials which had shown a 30 -50% benefit in reducing the risk of death compared to the radiotherapy only arm. While the addition of chemotherapy is clearly beneficial, not all patients may need it. Currently, there are no reliable ways of identifying the individuals who may respond to the radical radiotherapy from those who will not respond.

The use of molecular approaches, such as gene expression studies, has helped tailor treatment based on the molecular characteristics. The Oncotype Dx testing for breast cancer is an example of using the newer techniques to help tailor the treatment to individual patient [6]. Several potential markers for tumour response have been identified in the past, such as c-myc, Bcl2, Bax, HIF2α, Ki67 index, c-erbB2 [7–11]. However, most of these studies were on smaller number of patients and have not been used routinely to predict radiation response. More recently a few studies have used microarray based technology to identify gene signatures predictive of radiation response [12–14] however, none of them had validated their genes using further techniques such as Real time Polymerase Chain Reaction (PCR).

Avoiding morbidity and the cost of chemo-radiation in cervical cancer would be relevant particularly in a developing country. This study aims to develop a test which can help identify patients who may not need chemotherapy and who can be cured only with radical radiotherapy.

Methods

Archival total RNA extracted from punch biopsy samples collected in RNA later (Ambion, Austin, Tx; Cat no: AM7021) and stored in the tumour bank after an informed consent were used, after obtaining the Institutional Ethical committee's approval for the study. The RNA had been extracted from the biopsy samples using the RNeasy RNA extraction kit (Qiagen, Gmbh, Hilden; Cat no: 74106) as per the manufacturer's instructions.

Twenty five patients' samples were included in the study. The criteria for inclusion in the study were as follows: 1. good quality RNA as assessed by Bio-analyser (RIN 6 or above); 2. paired paraffin block having at least 70% tumor cells; 3. sufficient quantity of RNA be available; 4. patient should have completed prescribed radiotherapy and follow-up information till death/last disease free status be available.

All the patients had received radical radiotherapy, with 6 MV X-ray from Linear accelerator and brachytherapy with HDR-Ir192. The total dose given was between 64 - 66 Gy, over 56 - 60 days. The median and mean follow-up period among the NED group was 43 months and 42 months, respectively, with a minimum and maximum period of follow up of 38 and 45 months, respectively. All the Failed patients had been followed up till progression/relapse/death. Complete remission refers to complete resolution of measurable disease by clinical examination, hematologic, biochemical or radiological examination; Partial remission refers to 50% or more disease reduction lasting longer than one month; Progression refers to worsening or advancing disease.

HPV testing

The quality of the DNA was assessed by amplifying for β globin prior to HPV testing which was done using GP5+ and GP6+ primers [15]. HPV16 and 18 typing was done using Nested Multiplex Polymerase Chain Reaction (NMPCR) technique [16]. SiHa DNA for HPV16, HeLa DNA for HPV18 (positive controls) and C33A DNA (negative control) were included in all runs.

Microarray experiment

1 μg of total RNA from the tumour sample and universal RNA (Stratagene; Cat no: 740000-41) were reverse transcribed using Array script at 42°C for 2 hrs to obtain cDNA using the Amino Allyl MessageAmp II aRNA amplification kit (Ambion, Austin, Tx; Cat no: AM1797). The cDNA was amplified by in-vitro transcription in the presence of T7 RNA polymerase; aRNA thus obtained was purified and quantitated in NanoDrop (NanoDrop Technologies, Wilmington, DE, USA). 20 μg of tumour aRNA was labelled using NHS ester of Cy5 dye and control universal aRNA was labelled using NHS ester of Cy3 dye. The Cy3 and Cy5 labelled aRNA was used for hybridization onto the microarray chips from Stanford Functional Genomics Facility (SFGF, Stanford, CA) containing 44,544 spots, for 16 hrs in Lucidea SlidePro hybridization chamber (GE Health Care, Uppsala, Sweden) at 42°C. After hybridization, slides were washed in 0.1× SSC, 1× SSC followed by 0.1× SSC and dried.

The slides were scanned in ProScanArray (PerkinElmer, Shelton, CT, USA). Griding was done using Scan array Express software package (version -4). The integrated or mean intensity of signal within the spot was calculated. The files were saved as GPR files.

All the raw data files have been submitted to GEO with an assigned GEO accession number - GSE14404.

Microarray data analysis

The Foreground Median intensity for Cy3 and Cy5, Background Median intensity for Cy3 and Cy5, spot size data were imported into BRB-ArrayTools software [17] using the Import wizard function. Background correction was not done. Global normalization was used to median centre the log-ratios on each array in order to adjust for differences in labelling intensities of the Cy3 and Cy5 dyes. The data was analysed using the Class comparison module in the BRB-Array Tools software. The normalized Log ratios were also imported into Significance Analysis of Microarray (SAM) [18] software and analysed.

Class Comparison in BRB-Array Tools

We identified genes that were differentially expressed among the two classes (NED VS Failed) using a random-variance t-test. The random-variance t-test is an improvement over the standard separate t-test as it permits sharing information among genes about within-class variation without assuming that all genes have the same variance [19]. Genes were considered statistically significant if their p value was < 0.0015. In addition a two fold difference was required between the two classes.

SAM Analysis

The normalized log ratios of all the samples were imported into SAM software and analysed. First a two class unpaired analysis with 100 permutations was done. A delta value of 0.96 and a fold difference of 2 was used to identify the genes differentially expressed. In addition, Survival analysis for significant genes was done using 100 permutations and with a delta of 0.85.

Quantitative Real time PCR

High Capacity Reverse Transcription kit (Applied Biosystems, Foster City, CA; Cat no: 4368814) was used to reverse transcribe 2 μg of total RNA from the 23 samples in a 20 μl reaction volume. In two samples, due to the limiting amount of RNA, 0.75 μg was used for the cDNA synthesis.

These cDNA samples were used for real time PCR amplification assays using TaqMan® arrays formerly TaqMan® Low density arrays (TLDA) (Applied Biosystems, Foster City, CA; Cat no: 4342261). The fluorogenic, FAM labelled probes and the sequence specific primers for the list of genes with endogenous control 18S rRNA were obtained as inventoried assays and incorporated into the TaqMan® array format. Quadruplicate (n = 23) and duplicate (n = 2; with limiting amount of RNA for cDNA synthesis) cDNA template samples were amplified and analysed on the ABI Prism 7900HT sequence detection system (Applied Biosystems, Foster City, CA).

The reaction set up, briefly, consisted of 1.44 μg of cDNA template made up to 400 μl with deionised water and equal amounts of TaqMan® Universal PCR Master Mix (Applied Biosystems, Foster City, CA; Cat no: 4304437). 100 μl was loaded into each of the 8 ports of the array (2 ports comprise of one sample replicate on the array). Thus, the samples run as duplicates were only loaded into 4 ports of the array. Thermal cycling conditions included a 50°C step for 2 minutes, denaturation for 10 min at 94°C followed by 40 cycles consisting of 2 steps: 97°C for 30 seconds and 59.7°C for 1 minute for annealing and extension.

The raw data from the Prism 7900HT sequence detection system was imported into the Real-Time StatMiner™ software for statistical analysis of the data. Among the endogenous reference genes included on the array (18S ribosomal gene; UBC, β2 microglobulin), UBC was chosen after visualizing the global Ct value distribution, for normalizing the data (see Additional file 1). The TLDA assays were run at LabIndia Instruments Pvt Ltd laboratories at Gurgaon, New Delhi.

Development of the scoring method

Relative quantification is based on the relative expression of a target gene versus a reference gene. The UBC normalized Delta Ct values were imported into the Excel spread sheet for obtaining the RQ values for the 24 samples (one sample which had not worked in RQ-RT-PCR was excluded), using each NED sample once as a calibrator. The genes were provided a score of 2, if the RQ value was >2. This analysis was done initially on all the selected genes taken up for validation and then on genes which had greater than two fold difference between the two classes, with an attempt to obtain a group of genes which can help distinguish between the two classes. The Mean score, the standard error (SE), the mean score ± 1SE and mean score ± 2SE were calculated and the Mean ± 2SE was used for categorizing as either High risk or Low risk. The cut-off was set at 40% (5.6) of the potential total score possible.

Sensitivity is the probability for a NED sample to be correctly predicted as Low risk; Specificity is the probability for a Failed sample to be correctly predicted as High risk; PPV is the probability that a sample predicted as Low risk actually belongs to class NED; NPV is the probability that a sample predicted as High risk actually does not belong to NED [17].

Statistical Analysis

Kaplan Meier survival analysis was used to study the disease free survival (DFS) in the two classes, using the Stata 10 software. This was done initially based on the stage of the disease and then based on the gene signature risk stratification (Score ≤ 5.60 and score >5.60). Log rank test was used to assess the survival difference.

Results

The patients' clinico-pathological status and their response to treatment are given in Table 1. Twenty four of the tumours were Squamous cell carcinomas (16 Large cell non-keratinizing, 4 large cell keratinizing and 4 unspecified) and one was a poorly differentiated carcinoma. Fourteen were HPV16 positive, 7 were HPV18 positive and 4 were HPV16 and 18 subtype negative (but HPV positive).

Table 1 Clinico-pathological and disease free status of the patients
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Using different methods, as described above, genes that were found to be differentially expressed between the two classes (NED and Failed) were obtained. The List of genes significant by different methods of analysis are given as a Additional file 2.

Twenty genes were selected for further validation by RQ-PCR using the Taqman Low Density Array card (TLDA) format (Table 2). These 20 genes formed part of the 95 genes selected for analysis using the TLDA format. Most of the additional genes were those which had been found to be differentially expressed between cervical cancer and normal cervix; cervical cancer and CIN1; CIN3 and CIN1; CIN3 and Normal cervix. Apart from the mandatory endogenous 18S rRNA included in the TLDA cards, based on the microarray data, UBC and β2 microglobulin, were included as additional endogenous reference genes.

Table 2 List of genes selected for RQ-RT-PCR validation
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One of the sample CXL19-hov160 which had worked in microarray did not amplify in the RQ-TLDA assay and had to be removed from further analysis. In addition, OPTC and C11orf9 genes did not amplify in any of the samples.

The RQ values after calibrating with the NED samples (Mean) for all the 95 genes showed two additional genes, UBE2C and IGF2BP2, to be overexpressed in the Failed samples compared to the NED samples. These two genes had initially been chosen for validation of the differentially expressed genes between cancer and normal cervix. After excluding the genes which did not amplify, we now had 20 genes for further analysis (see Additional file 3).

There were 10 genes (including UBE2C and IGF2BP2) with more than 2 fold difference between the two classes. In addition, 4 genes had a fold change between 1 and 2. There was only 44% (8/18) concordance between the microarray data and the Relative quantitation RT-PCR data, when a 2 fold difference was used as the criteria. The best concordant rates were with the SAM survival analysis, wherein of the 11 genes taken up for validation, 8 were concordant (73%).

We then tried to analyse the RQ data using each of the NED sample once as a calibrator. The focus was on genes which had an over-expression of at least two fold between the two classes. A scoring system was then developed as described in the Methods section. Using this we found that the seven gene score (UBE2C, MMP3, DCUN1D5, SDCCAG8, IGF2BP2, CCL18 and FST) can help distinguish between the Failed and the NED samples. Table 3 gives the mean score of the 24 experiments, the standard deviation, the standard error (SE), and score ± 2SE (see Additional file 4 for the details). Using a cut-off value of > 5.60, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) are 0.64, 1.0, 1.0, 0.67, respectively, for the Mean ± 2SE. At this cut-off point, 9 of the 14 (64) NED samples were classified as low risk, while all the failed samples were classified as high risk.

Table 3 Scores based on RQ-RT-PCR values
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The Kaplan Meier survival curves are given in Figures 1 and 2, the former based on the stage of the disease and the latter based on the gene expression signature.

Figure 1
figure 1

Based on stage of disease.

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Figure 2
figure 2

(1) Kaplan Meier disease free survival analysis based on risk stratification (Low versus High risk) (2).

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Discussion

Cervical cancer although a preventable disease, still kills a significant number of women. Most of the tertiary centres in developing countries still see patients in advanced stages. Radiotherapy has been the main stay of treatment and the addition of concurrent cisplatin based chemotherapy has been found to improve cure rates. However, not all patients will need concurrent chemotherapy, which has its own cost implications and morbidity, particularly in a developing country. With the emphasis shifting to tailoring therapy to individuals based on the patient and tumour characteristics, it is essential to develop newer tests which can help identify individuals who can be cured only with radiotherapy. This paper provides one such approach, wherein a seven gene score was found to help distinguish those who are likely to be cured with radiotherapy alone from those who may require additional forms of treatment.

The concordance between the microarray and the RQ-PCR data on genes differentially expressed with more than 2 fold was 44% (8/18). SAM Survival analysis had identified 13 genes (of the 14 genes, one was a duplicate -YAP1) as being differentially expressed and 11 of these genes were included in our validation. One gene did not have a suitable assay for the TLDA format. Of the 11 genes tested, 8 were found to be concordant in the RQ-RT-PCR validation (73% concordance rate). The SAM Survival analysis had correctly identified 5 of the 7 genes which we had used for the prediction.

One group had addressed the issue of concordance, when they compared Affymetrix data with Real time quantitative data for 48 genes. Their concordance rate was around 69% and they have reported that this may be usually due to differences in the target transcripts being identified by the two platforms (microarray versus Real time PCR) [20].

Klopp et al [14] had used microarray analysis on samples obtained prior to treatment and 48 hours after start of chemoradiation in 12 patients and found that a 58 gene signature can help predict recurrence. Others have developed either a Lymph node prediction model using 156 gene signatures, which had a prediction accuracy of 77% in predicting lymph node metastasis [21] or thermo-radio-response prediction model using a 35 gene signature [22]. Wong et al [13] used supervised clustering analysis to classify radiosensitive and radioresistant tumours in 13 patients.

Six of the seven genes, identified in our study, are known to be involved in cancers, with some of them having a role in inducing therapeutic resistance.

Insulin like growth factor 2 mRNA binding protein 2/IGF2BP2/IMP2

This oncofetal protein functions by binding to the 5'UTR region of IGF2 and thereby regulating its translation. Yisraeli (2005) [23] had suggested that the VICKZ family, to which IGF2BP2/IMP2 belongs, have a role to play in cell polarity and migration, cell proliferation and cancer. Autoantibodies to IGF2BP2 has been detected in different cancers including hepatocellular carcinoma [24, 25]. IGF2BP2 was also found to be overexpressed in epithelial ovarian cancers [26].

Ubiquitin-conjugating enzyme E2C/UBE2C/UBCH10

The gene is a member of the E2 ubiquitin-conjugating enzyme family, playing key roles in regulation of cell cycle. The gene was found to be overexpressed in cervical cancers [27] and was reported to contribute to chemotherapy resistance in breast cancer [28]. Donata et al [29] have reported over-expression in low grade astrocytomas and in glioblastomas, wherein they could contribute to therapeutic resistance.

Follistatin/FST

This protein inhibits follicle stimulating hormone release. FST has been shown to be associated with Basal cell carcinomas [30]. In addition, integrin alpha6beta4, which is involved in apoptosis resistance, invasion, metastasis, can induce the expression of several genes, including FST, which could help mediate these processes [31].

Matrix metallopeptidase 3 (stromelysin1; progelatinase)/MMP3

MMP3 has been reported to be expressed in lymph node metastasis and in recurrent cervical cancers [32]. MMP3 has been suggested to have a role in modulating chemotherapeutic response in head and neck cancers [33] and has been shown to play a role in breast tumour carcinogenesis [34].

Chemokine (C-C motif) ligand 18/CCL18/MIP4/AMAC/DCCK1/PARC

CCL18/PARC has been found to be overexpressed in the serum of acute lymphoblastic leukaemia patients [35] and in the ascitic fluid of patients with ovarian carcinoma [36].

Serologically defined colon cancer antigen 8/SDCCAG8/Centrosomal colon cancer autoantigen protein/CCCAP

Autologous antibody responses to SDCCAG8 has been reported in colon and ovarian cancers [37, 38]. It is a protein integral to the centrosomes [39].

Defective in cullin neddylation 1, domain containing 5/DCUN1D5

Little is known about the function of this gene, particularly in cancer.

Conclusion

Locally advanced cancer of the cervix is now being treated with chemo-radiotherapy. However, nearly 60% of stage IIB and 40% of stage IIIB can be cured with radiotherapy only. Currently there are no reliable ways of identifying those patients who can be cured with radiotherapy alone.

Using microarray technique followed by Relative Quantitation Real Time PCR (RQ RT-PCR), we have identified a 7 gene signature with differential expression between cervical cancers with and without recurrence/progression after radiation treatment. This finding needs to be validated in an independent larger patient population.

Conflict of interests

The authors declare that they have no competing interests.