- Open Access
Expression ratio of CCND1 to CDKN2A mRNA predicts RB1 status of cultured cancer cell lines and clinical tumor samples
© Mizuarai et al; licensee BioMed Central Ltd. 2011
Received: 12 June 2009
Accepted: 29 March 2011
Published: 29 March 2011
The retinoblastoma product (RB1) is frequently deregulated in various types of tumors by mutation, deletion, or inactivation through association with viral oncoproteins. The functional loss of RB1 is recognized to be one of the hallmarks that differentiate cancer cells from normal cells. Many researchers are attempting to develop anti-tumor agents that are preferentially effective against RB1-negative tumors. However, to identify patients with RB1-negative cancers, it is imperative to develop predictive biomarkers to classify RB1-positive and -negative tumors.
Expression profiling of 30 cancer cell lines composed of 16 RB1-positive and 14 RB1-negative cancers was performed to find genes that are differentially expressed between the two groups, resulting in the identification of an RB1 signature with 194 genes. Among them, critical RB1 pathway components CDKN2A and CCND1 were included. We found that microarray data of the expression ratio of CCND1 and CDKN2A clearly distinguished the RB1 status of 30 cells lines. Measurement of the CCND1/CDKN2A mRNA expression ratio in additional cell lines by RT-PCR accurately predicted RB1 status (12/12 cells lines). The expression of CCND1/CDKN2A also correlated with RB1 status in xenograft tumors in vivo. Lastly, a CCND1/CDKN2A assay with clinical samples showed that uterine cervical and small cell lung cancers known to have a high prevalence of RB1-decifiency were predicted to be 100% RB1-negative, while uterine endometrial or gastric cancers were predicted to be 5-22% negative. All clinically normal tissues were 100% RB1-positive.
We report here that the CCND1/CDKN2A mRNA expression ratio predicts the RB1 status of cell lines in vitro and xenograft tumors and clinical tumor samples in vivo. Given the high predictive accuracy and quantitative nature of the CCND1/CDKN2A expression assay, the assay could be utilized to stratify patients for anti-tumor agents with preferential effects on either RB1-positive or -negative tumors.
Retinoblastoma protein (RB1) is a crucial regulator of appropriate cell cycle progression, including G1 to S and G2 to M phase transitions [1, 2]. The activity of RB1 is mainly regulated by the upstream CDKN2A/CCND1 pathway [3–5]. Anti-proliferative stresses including DNA damage, therapeutic agents, and anti-mitogens increase the expression of CDKN2A followed by the dissociation of CDK4 or CDK6/CCND1 complexes. The inactivation of CDKs/CCND1 leads to the hypophosphorylation of RB1, resulting in the transcriptional repression of E2F/DP regulatory genes that contributes to the progression of cell cycles including mini-chromosome maintenance (MCM) genes , A/D/E-type Cyclins [7–9], and CDC6 . In accordance with the important role of RB1 as a cell cycle regulator, RB1 deregulation is frequently observed in multiple types of cancers . Functional loss of RB1 causes accelerated E2F1 mediated transactivation, followed by uncontrolled cell cycle progression. In small cell lung cancer (SCLC), for example, the prevalence of RB1 loss is more than 90% [12, 13], while RB1 function in cervical cancer is suppressed by directly associating with HPV-E7 oncoprotein at a frequency of at least 90% [14, 15]. A moderate frequency of RB1 dysfunction has been reported in several other types of cancers such as bladder, prostate cancers, and melanoma [16–18]. The RB1 loss is caused by several molecular events such as point mutations, deletion, or inactivation through associating viral proteins . In addition, genetic alteration or aberrant expression in upstream molecules also results in dysfunction of RB1. Accumulating evidence has shown that hypermethylation in the promoter region of CDKN2A or overexpression of CCND1, which results in RB1 dysfunction, frequently occurs in various types of cancers . Therefore, deregulation in the RB1 pathway is recognized to be a hallmark of tumorigenesis.
Since most current standard-of-care anti-tumor drugs are cytotoxic agents, they do not possess selective killing effects on tumors compared with normal proliferating cells. Development of drugs which preferentially exert anti-tumor effects in tumors harboring specific deregulation in tumor-related genes is eagerly anticipated . Given the different status of RB1 between tumor and normal cells, several studies have attempted to develop anti-tumor agents which selectively inhibit growth of tumors with dysfunctional RB1 [20–24]. The silencing of ECT2, which is a GTP exchanging factor involved in cytokinesis, is effective in RB1-negative cells when examined with RB1-positive and -negative isogenic cell lines in the presence of DNA damage . It has also been shown that inhibition of Aurora B can induce increased apoptosis in RB1-deficient cells . The response to standard chemotherapeutics is also affected by the status of RB1. RB1-negative cancers are more sensitive to some DNA-damaging agents such as 5-Fluorouracil , doxorubicin , and ionizing radiation , although the detailed mechanism for the RB1-negative selective effect remains unresolved. Tamoxifen is more effective against RB-positive breast cancers than -negative ones in vitro . This knowledge might be integrated in future clinical trials to develop RB1-status dependent anti-tumor agents, and potentially increase the therapeutic window of those agents.
In order to utilize anti-tumor agents whose effect is different between RB1-positive and -negative cancers, the development of a predictive biomarker that discriminates the status of RB1 in cells is essential. Conventionally, the protein expression level of RB1 as measured by immunohistochemistry (IHC) has been used to estimate the prevalence of RB1 deficiency in various tumor types. Direct measurement of RB1 by IHC has generated vast amounts of information on RB1 deficiency in many types of cancers; however, it has several drawbacks as a biomarker to predict RB1 status. First, IHC for RB1 detects limited types of RB1 dysfunction, although RB1 loss can be caused by mutations, deletion, or association with viral proteins . For instance, IHC for RB1 would be positive in tumors infected with HPV-virus expressing E7 oncoprotein, although RB1 is functionally lost . Second, IHC is semi-quantitative, and comparisons of RB1 loss frequency among different studies are somewhat difficult . This also makes it difficult to establish a threshold to distinguish RB-positive and -negative tumors that would guide investigators to determine whether the tumors be treated with anti-tumor agents whose effects are dependent on RB1 status . Lastly, IHC methods mandate a relatively large amount of biopsy samples. When a predictive marker is used clinically, a minimally invasive biopsy is highly preferable from the patient's point of view. Therefore, the development of a novel RB1 predictive biomarker that overcomes these drawbacks would be useful for patient stratification by complementing or replacing the current direct RB1 protein measurement.
In the present study, we compared expression profiling of RB1-positive and -negative cell lines among 30 cancer cell lines, and identified a gene signature that was differentially expressed between the two groups. In the gene signature, CDKN2A and CCND1, which are the important nodal proteins in the RB1 pathway, were included. We found that the expression ratio of CCND1 to CDKN2A clearly classified RB1-positive and -negative cancer lines, the predictive accuracy of which was superior to that of individual CDKN2A or CCND1, respectively. The CCND1/CDKN2A ratio could also classify the RB1 status of xenograft tumors in vivo. Finally, CCND1/CDKN2A expressions were measured in formalin fixed paraffin embedded (FFPE) samples of clinical tumor and normal tissues. We found that uterine cervical and small cell lung cancers were predicted to be RB-negative at high prevalence, while most uterine endometrial and stomach cancers were predicted to be RB1-positive by the CCND1/CDKN2A assay.
Identification of a gene signature to classify RB1 status by microarray analysis
RB1 status of 30 cancer cell lines used for gene expression
RB1 genetic status
RB1 functional status
Δ21 2107-2A > G
repressed by E7
frame shift: G203fs*8
repressed by E7
deletion: 4aa in exon2
frame shift: R661fs*1
frame shift: R418fs*2
frame shift: R661fs*1
Expression ratio of CCND1 to CDKN2A predicts RB1 status
Validation of the CDKN2A and CCND1 ratio as an RB1 classifier
Validation of CCND1/CDKN2A expression in clinical samples
RB1-negative frequency in clinical samples determined by the CCND1/CDKN2A expression ratio
Deregulation of RB1 has been reported in multiple types of tumors, including retinoblastoma, cervical cancer, and SCLC. Development of anti-tumor agents which selectively kill RB1-negative tumors is expected to increase the therapeutic window for treatment of tumors with an RB1-negative background. Hence, various researchers are trying to develop novel anti-tumor agents with selective anti-tumor effects on RB1-deficient tumors [20, 22, 24], or find RB1 context specificity for current standard-of-care medicines [23, 24]. Examples include the studies of topoisomerase inhibitors or ECT2 inhibition which are more effective in RB1-negative tumors compared with RB1-positive counterparts [20, 21]. Since topoisomerases or ECT2 are regulated by E2F transcription factors as downstream effectors of RB1, it is reasonable that their inhibitions are sensitive to RB1-negative tumors. In order to apply the information of the RB1 context specificity to clinical trials, it is necessary to develop a classifier of RB1 status of clinical samples. The CCND1/CDKN2A assay as a predictive biomarker for RB1 demonstrated in the present study has several advantages. First, the assay showed 100% prediction accuracy in the original 30 cancer cell lines determined by microarray, and 12 cultured cell lines and four xenograft tumors determined by quantitative qPCR. The prevalence of RB1 deficiency in clinical tumor and normal samples were also consistent with previous reports using IHC for RB1. Second, the assay provides quantitative and reproducible data when determined by RT-PCR with defined primer and probe sequences. Therefore, the cut-off value to discriminate RB1-positive and -negative cell lines by discriminant analysis could be established, while criteria to determine RB1 prevalence by conventional IHC-based assays is relatively arbitrary. Third, although RB1 protein can be expressed as an inactive form when tumor cells harbour missense mutation in RB1 gene or HPV-mediated RB1 inactivation which makes it difficult for IHC to classify RB1 functional status, the CCND1/CDKN2A ratio expression assay could detect various types of RB1 functional inactivation as shown in Figure 1 and 2. Finally, the RT-PCR assay requires a smaller amount of biopsy specimen. Recent advances in RNA/cDNA amplification might enable investigators to conduct the assay with less than 10 cells, in contrast to IHC which mandates several slices of FFPET with good morphological integrity.
An inverse correlation of CDKN2A and RB1 expressions has been reported in several types of cancers [12, 13]. In cervical cancers infected with HPV, the function of RB1 is lost due to association with the E7 oncoprotein. Cervical cancers express high levels of CDKN2A probably by a feedback mechanism . An anti-correlation of RB1 and CDKN2A expression was also reported in SCLC, gastric cancer, oral cavity squamous carcinoma or urinary bladder cancer [13, 29–31]. In our experiments, the expression levels of both CDKN2A and CCND1 were negatively and positively correlated with RB1 status in accordance with previous reports; however, neither accurately classified the status of RB1 as demonstrated in Figure 2A. By combining the expressions of CCND1 and CDKN2A, the prediction accuracy was significantly increased to 100% in all 30 samples in the microarray, and in an additional study with 12 cell lines in RT-PCR analysis.
Numerous studies with expression profiles have reported gene signatures which are differentially expressed between RB1-positive and -negative cells [32–35]. Markey et al. found that more than 200 genes are decreased by the induction of a mutated RB1 that is refractory to CDK-mediated phosphorylation . The function of the gene signature is related to cell cycle control, DNA repair, or chromatin control, consistent with the well-established role of RB1. Semizarov et al. reported another set of genes that are commonly deregulated by several different sequences of siRNA for RB1 . Pathway analysis of the gene set by GeneMapp and MappFinder software packages found that cellular processes involved in DNA replication, mitotic cell cycle, and chromosome maintenance were statistically condensed in the gene set. These pioneering works analyzing global expression profiling of genetically engineered RB1 loss highlighted the downstream effectors, which are responsible for the function of RB1 to control cell cycle or tumorigenesis. We tested whether the previously reported RB1 regulated gene signatures could classify the RB1-positve and -negative cell lines used in the present study (Table 1); however, the signatures did not distinguish the RB1 status of the cell lines (data not shown). The present study provides an RB1 classifier that distinguishes the status of RB1 in both cell lines and clinical samples. Since expression profiling of naturally occurring RB1-positive and -negative cell lines was used to develop the signature followed by the narrowing-down of genes with biological information, this may have resulted in higher accuracy for predicting RB1 status in additional cell lines and xenograft tumors.
The prevalence of RB1 (-) in clinical tumors and normal samples was reasonable compared with previous reports using IHC. While several reports have shown that loss of RB1 is found in more than 90% of clinical SCLCs , 100% (5/5) of SCLC samples examined here were RB1-negative when classified by the CCND1/CDKN2A expression assay. The reported RB1 loss frequency of uterine cervical cancers and uterine endometrial cancers is more than 90% [14, 15] and 3-6% [36, 37], respectively. Our assay showed 100% (7/7) and 22% (2/9) for cervical and endometrial cancers, respectively. The prevalence of RB1 (-) in previous studies of gastric cancers using IHC for RB1 is relatively variable, ranging from 0-40% [27, 28, 38, 39] depending on the different criteria used among the studies. The CCND1/CDKN2A assay predicted 5% (1/20) as RB1 (-) cancers. In general, the frequency of RB1 loss is highly correlated between previous reports and the present study, and the data imply that the CCND1/CDKN2A assay could be utilized to predict RB1 status clinically as a quantitative assay.
Development of predictive biomarkers is of great significance for anti-tumor agents, since the response rate is relatively lower compared with other diseases. In order to develop a clinically available gene signature as a biomarker, it is preferable to develop a biomarker composed of a minimal number of genes . Although microarray technology allows us to measure genome-wide expression profiles without limiting the number of genes, it would be difficult to use the platform to stratify patients in a clinical setting from the viewpoints of cost performance and assay turn-over. A quantitative RT-PCR-based method with two genes would be feasible for conventional clinical trials. Additionally, a PCR-based method to measure the status of RB1 as a biomarker might be developed as an in vitro diagnostic (IVD), since some IVDs utilize PCR to detect biomarkers such as Ras, EGFR mutation, or HIV infection [41, 42].
The present study identified that the expression ratio of CCND1/CDKN2A predicts the status of RB1 in both cultured cancer cell lines and clinical tumor samples. Since many studies have been attempting to find currently available or novel anti-tumor agents which are selectively effective for tumors with dysfunctional RB1, the CCND1/CDKN2A ratio expression assay might contribute to patient stratification as a highly accurate, quantitative, and less invasive biomarker.
Cell lines and RB1 functional assay
Cell lines used to detect RB1 status listed in Table 1 were obtained from the American Type Culture Collection (ATCC), and were cultured according to the supplier's instructions. The RB1 functional status of each cell line was determined by measuring the activity of the E2F-regulatory reporter gene in response to CDKN2A induction . Cells in 6-well plates were co-transfected with 100 ng of CDKN2A expression plasmid , 100 ng of luciferase reporter plasmid (pGL3-SV40, Promega, Madison, WI) for normalization of transfection efficiency, and 100 ng of SEAP reporter plasmid containing the E2F-responsive element, pSEAP2-CDC6  by a lipofection method using FuGENE6 (Roche Diagnostics, Mannheim, Germany). At 48 hr post transfection, SEAP activity was measured with a Reporter Assay Kit-SEAP (Tokyo, Osaka, Japan). The SEAP activity was normalized to luciferase activity. RB1 mutation information of cell lines were obtained from the literature (Table 1) and from the COSMIC database at Sanger Center (URL: http://www.sanger.ac.uk/genetics/CGP/cosmic/).
Expression profiling of cell lines
Total RNAs were extracted from cells growing in log phase using an RNeasy Kit (QIAGEN, Valentia, CA), and were subjected to mRNA profiling using custom Human 3.0 A1 arrays (Agilent Technologies, Santa Clara, California) as directed by the manufacturer's protocol. As a common reference sample for all cell lines, Human Universal Reference RNA (Stratagene, La Jolla, CA) was used. Scanned data were processed by Resolver (Rosetta Biosoftware, Seattle, WA) to calculate the ratios and p-values based on an error-weighted model for each probe. For statistical analysis, R (GNU freeware) and MATLAB (MathWorks Inc., Natick, MA) were used. To identify genes whose expressions were correlated with RB1 status, we first filtered out genes whose standard deviation of log ratio values were less than 0.1 to exclude genes with poor expression changes among cell lines, and selected 194 genes whose expression levels showed a p-value of 0.001 or less for Pearson correlation coefficient with RB1 status (1 for RB1 positive, 0 for RB1 negative).
Quantitative RT-PCR analysis
For prediction of RB1 status, the log10 (CCND1/CDKN2A) data of the test cell lines which include 7 RB1-positive and 5 RB1-negative lines were obtained by the quantitative RT-PCR. Then, the following linear discriminant function was derived: Y = 0.54 × log10 (CCND1/CDKN2A)-2.051094.
Then the functional RB1 status was determined as follows:
Y > 0 (log10 (CCND1/CDKN2A) > 0.404), positive functional RB1 status; and
Y < 0 (log10(CCND1/CDKN2A) < 0.404) negative functional RB1 status.
Xenograft tumor samples
All animal studies were carried out in accordance with good animal practices as defined by the Institutional Animal Care and Use Committee (IACUC). Tumor cells (NCI-H353, NCI-H441, HCT116, HeLaS3, and NCI-H1048) were inoculated in the hind flank of immunodeficient nude rats (F344/NJcl-rnu, CLEA Japan) as a suspension in Matrigel (BD Bioscience, San Jose, CA). After 1-2 weeks, xenograft tumors were collected and stored as FFPETs. Extraction of RNA from normal and tumor FFPET samples was carried out using FFPE-RNeasy kit (QIAGEN). The purified RNA was subjected to CCND1/CDKN2A expression analysis.
Clinical human tumors and normal tissues
Cervical, endometrial, gastric, small cell lung cancers and corresponding normal tissue samples with informed consent were obtained from Kanagawa Cancer Research and Information Association (Kanagawa, Japan). Individual institutional ethical committees approved the use of all clinical materials. FFPET of the tumors were used for RNA purification. First, normal tissues were dissected and collected from a section of FFPET if the section contained enough normal tissue. The tumor samples contained small amount of normal samples by the macro-dissection. Extraction of RNA from the normal and tumor FFPET samples was carried out using a FFPE-RNeasy kit (QIAGEN). The purified RNA was subjected to CCND1/CDKN2A expression analysis.
Microarray analysis of public data
Expression profiling data for mouse osteoblast cells derived from both RB1 wild-type and RB1 deficient mice were retrieved from the NCBI Gene Expression Omnibus (GEO: GSE19299). Expression profiling data for RB1-expressing and RB1-deficient SAOS-2 paired cell lines were also retrieved from the NCBI GEO (GSE9690). Expression profiling data of SCLC and normal lung samples were retrieved from Broad Institute website http://www.broadinstitute.org/mpr/lung/. The signal intensities of the probe sets were normalized using the RMA method, and log ratio values to the average of all of the samples were calculated. The log10(CCDN1/CDKN2A) value was then calculated for each data.
We would like to express our cordial gratitude for Kanagawa Cancer Research and Information Association for providing clinical samples with informed consent. We would like to thank Kazunori Yamanaka, Hiroki Irie and Aki Kawagishi for technical assistance and helpful discussion and suggestions.
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