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Open Access Research article

MUC1-associated proliferation signature predicts outcomes in lung adenocarcinoma patients

Dhara M MacDermed1*, Nikolai N Khodarev2, Sean P Pitroda2, Darrin C Edwards3, Charles A Pelizzari2, Lei Huang4, Donald W Kufe4 and Ralph R Weichselbaum2

Author Affiliations

1 The Scripps Research Institute and Scripps Translational Science Institute, 3344 N. Torrey Pines Court Ste. 300, La Jolla, CA, 92037, USA

2 Department of Radiation and Cellular Oncology, The University of Chicago, Duchossois Center for Advanced Medicine, 5758 S. Maryland Avenue, MC9006, Chicago, IL 60637, USA

3 Department of Radiology, The University of Chicago Medical Center. 5841 S. Maryland Ave., Chicago, IL 60637, USA

4 Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney St., Dana 830, Boston, MA 02115, USA

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BMC Medical Genomics 2010, 3:16  doi:10.1186/1755-8794-3-16

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1755-8794/3/16


Received:16 October 2009
Accepted:6 May 2010
Published:6 May 2010

© 2010 MacDermed et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

MUC1 protein is highly expressed in lung cancer. The cytoplasmic domain of MUC1 (MUC1-CD) induces tumorigenesis and resistance to DNA-damaging agents. We characterized MUC1-CD-induced transcriptional changes and examined their significance in lung cancer patients.

Methods

Using DNA microarrays, we identified 254 genes that were differentially expressed in cell lines transformed by MUC1-CD compared to control cell lines. We then examined expression of these genes in 441 lung adenocarcinomas from a publicly available database. We employed statistical analyses independent of clinical outcomes, including hierarchical clustering, Student's t-tests and receiver operating characteristic (ROC) analysis, to select a seven-gene MUC1-associated proliferation signature (MAPS). We demonstrated the prognostic value of MAPS in this database using Kaplan-Meier survival analysis, log-rank tests and Cox models. The MAPS was further validated for prognostic significance in 84 lung adenocarcinoma patients from an independent database.

Results

MAPS genes were found to be associated with proliferation and cell cycle regulation and included CCNB1, CDC2, CDC20, CDKN3, MAD2L1, PRC1 and RRM2. MAPS expressors (MAPS+) had inferior survival compared to non-expressors (MAPS-). In the initial data set, 5-year survival was 65% (MAPS-) vs. 45% (MAPS+, p < 0.0001). Similarly, in the validation data set, 5-year survival was 57% (MAPS-) vs. 28% (MAPS+, p = 0.005).

Conclusions

The MAPS signature, comprised of MUC1-CD-dependent genes involved in the control of cell cycle and proliferation, is associated with poor outcomes in patients with adenocarcinoma of the lung. These data provide potential new prognostic biomarkers and treatment targets for lung adenocarcinoma.

Background

Lung cancer is the most common cancer worldwide and is the leading cause of cancer-related death in the United States. Approximately 213 000 new diagnoses and over 160 000 deaths from lung cancer occur annually in the United States [1]. About 85% of lung cancers are non-small cell histology (NSCLC), including lung adenocarcinoma, which is the most common lung cancer type [2]. Treatment of early and intermediate stage NSCLC usually involves surgery. Most patients with localized lung cancer are now treated with adjuvant platinum-based chemotherapy, which provides a survival advantage [3]. The utility of postoperative radiation is controversial and subsets of patients have been proposed to benefit, but clear clinical and/or molecular identification of patients who may benefit from postoperative radiation remains uncharacterized. In contrast, recently identified molecular classifiers based on statistically derived gene signatures may facilitate the selection of patients who will benefit from adjuvant chemotherapy [4,5]. Nonetheless, no prognostic or predictive signature for NSCLC is regularly used in a clinical setting.

Mucin 1 (MUC1) is a protein heterodimer that is overexpressed in lung cancers [6]. MUC1 consists of two subunits, an N-terminal extracellular subunit (MUC1-N) and a C-terminal transmembrane subunit (MUC1-C). Overexpression of MUC1 is sufficient for the induction of anchorage independent growth and tumorigenicity [7]. Other studies have shown that the MUC1-C cytoplasmic domain is responsible for the induction of the malignant phenotype and that MUC1-N is dispensable for transformation [8]. Overexpression of MUC1 also confers resistance to stress-induced cell death, conferred by exposure to certain genotoxic anticancer agents [9-11]. In this regard, targeting of the MUC1-CD subunit to the nucleus attenuates p53-mediated apoptosis in response to DNA damage [12]. Notably, MUC1 protein expression has been associated with poor prognosis in NSCLC [13,14]. Taken together, these data have provided a rationale for an in-depth analysis of transcriptional programs induced by the MUC1-C cytoplasmic domain (MUC1-CD).

We previously reported a method for analysis of biologically derived data relevant to the identification of expressional signatures with prognostic and predictive value [15-17]. We used this approach to identify a MUC1-induced Tumorigenesis Signature (MTS) based on the profiling of MUC1-CD-transfected xenografts grown in nude mice [18]. The MTS was derived through comparison of MUC1-CD-transfected tumors in vivo and the corresponding cell lines grown in vitro. We hypothesized that such a comparison would detect the genes differentially expressed as a result of tumor-stromal interactions. Indeed, the major functional groups of genes represented in MTS were cell motility, metastasis and angiogenesis. In the current report, we focused on the in vitro profiling of 3Y1 cell lines transfected with MUC1-CD compared with mock-transfected cells to define "intrinsic" MUC1-CD-dependent transcriptional changes without stromal effects. Using this approach, we expected to identify MUC1-CD-dependent genes intrinsically associated with an aggressive tumor behavior. Here we report that a MUC1-Associated Proliferation Signature (MAPS) comprised of genes that mediate cell cycle control and mitotic spindle assembly has significant prognostic value in lung adenocarcinoma patients. Importantly, the MAPS is the first biologically derived gene signature comprised uniquely of MUC1-induced genes involved in the control of cell cycle and proliferation.

Methods

Cells and culture conditions

Rat 3Y1 embryonic fibroblasts were transfected by an empty vector (3Y1/Vector) and by the cytoplasmic domain of MUC1 (3Y1/MUC1-CD) as previously described [19]. Transfected 3Y1 cells were cultured in DMEM media with 10% heat-inactivated fetal bovine serum, 100 units/mL penicillin, 100 μg/mL streptomycin, and 2 mmol/L L-glutamine and maintained at 37°C in a humidified environment containing 5% CO2.

RNA extraction and purification

RNA was collected and purified from confluent 3Y1/Vector and 3Y1/MUC1-CD cell cultures using TRIzol reagent (Invitrogen Life Sciences, Carlsbad, CA, USA) according to the manufacturer's instructions. Further purification was performed using a combination of RNeasy spin columns and TRIzol reagent, as we previously described [20]. The quality of samples was assessed using gel electrophoresis in 1.8% agarose and spectrophotometry, and samples of high quality were transferred to the Functional Genomics Facility of The University of Chicago for labeling and hybridization with GeneChip® Rat Genome 230 2.0 Arrays (Affymetrix, Santa Clara, CA, USA).

DNA microarray data collection and analysis

The selection and analysis of genes differentially expressed in 3Y1/Vector cells compared to 3Y1/MUC1-CD cells in vitro was based on previously described methods. Briefly, each array was hybridized with a pooled sample normalized to total RNA and consisting of RNA obtained from 3 independent cell lines. After data retrieval and scaling using MAS 5.0 Microarray Suite software (Affymetrix, Santa Clara, CA, USA), data were rescaled using "global median normalization" across the entire dataset [21] and filtrated using a multi-step filtration method, which involves the application of Receiver Operating Characteristic analysis (ROC analysis) for the estimation of cut-off signal intensity values [22]. Subsequent analysis was based on pair-wise comparisons of duplicated arrays (3Y1/Vector in vitro vs. 3Y1/MUC1-CD in vitro) using Significance Analysis of Microarrays (SAM) version 3.0 (Stanford University Labs, http://www-stat.stanford.edu/~tibs/SAM/ webcite). Differentially expressed probe set IDs were selected using a 2.0-fold induction cut-off level with selection of delta values minimizing the False Discovery Ratio. Probe set IDs were gene annotated and functionally designated using Ingenuity Pathways Analysis (IPA, Ingenuity® Systems, http://www.ingenuity.com webcite).

Array data are deposited in GEO, accession # GSE14337 (MUC1-induced transcriptional alterations in rat 3Y1 embryonic fibroblasts [Rattus norvegicus]), http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14337 webcite.

Collection of publicly available cancer databases

Two publicly available cancer databases containing expressional data from adenocarcinoma of the lung were analyzed to determine whether the MUC1-CD-dependent genes have predictive value in determining the outcome for each patient sample. The first database was obtained from a multicenter consortium (University of Michigan Cancer Center, Moffitt Cancer Center, Memorial Sloan-Kettering Cancer Center, and the Dana-Farber Cancer Institute) consisting of surgically resected lung adenocarcinoma specimens from 442 patients [23]. These patients presented with stage I to III disease and were treated without pre-operative chemotherapy or radiation. A subset of patients (n = 108) had adjuvant treatment with radiation and/or chemotherapy. One patient was excluded from survival analysis because survival data was missing. An independent database was used for confirmation of our findings [24]. Analysis included 84 quality-controlled cases with at least 40% tumor cellularity, adenocarcinoma only (no mixed histology), and available survival data [25]. These patients were treated with primary surgical resection and data regarding adjuvant therapy were not available.

Statistical analyses

For initial analysis, the raw signal intensity for each probe set ID of interest for each patient was normalized to the median value of the probe set ID across the entire database and subsequently log2-transformed. For subgroup analyses, the raw data were divided into subgroups and normalization was performed across each subgroup. Multiple probe set IDs for a given gene were averaged for each patient sample to obtain a representative expression value for each gene.

Clustering and survival analyses were performed using JMP 7.1 (SAS Institute Inc. Cary, NC, USA). Expression data were clustered using hierarchical clustering via Ward's method to visualize gene expression patterns across each database. Genes having uniform expression across the patient samples in the initial clustering of genes were eliminated and not used for further analyses (87 out of 254 genes were eliminated in this manner). Before any further reduction of the gene set, survival analysis was performed based on clustering of the biologically derived genes. To determine whether clustering based on the differential expression of the MUC1-CD-induced genes could identify patients with decreased survival, Kaplan-Meier survival analysis was performed on clusters defined by k-means clustering. The k-means clustering was performed using a predetermined number of clusters (k = 2) and a log-rank test was used to estimate significance of survival differences between the two clusters. Subsequently, a smaller number of genes was selected from this gene set for practical application. An F-test was further used to test the null hypothesis of no difference in variance for each gene between the two patient clusters. Results of the F-test were further entered into an unpaired 2-tailed Student's t-test to test the null hypothesis of no difference in the magnitude of gene expression of each gene between the two patient clusters. The alpha level for each t-test was 0.05. As a result, we obtained a set of probes comprising 42 unique genes. Once the final signature was selected using ROC analysis (see full details in subsequent ROC analysis section), the mean expression across genes in the signature was calculated as a relative expression score [17,18]. A positive value (> 0) designated the patient as MAPS+. Kaplan-Meier survival statistics were performed and log-rank tests were used to test the null hypothesis of no difference in survival functions between MAPS+ and MAPS- patients. We then performed univariate and multivariate analysis using a Cox proportional hazards model to evaluate prognostic factors, including MAPS. Clinical features such as tumor stage, lymph node involvement and histological grade were included in these analyses as binary variables: T1-T2 vs. T3-T4, nodes involved (N1-2) vs. uninvolved (N0), and intermediate or high grade vs. low grade. For each clinical variable, binary categories were selected based on maximum prognostic significance for overall survival in our dataset.

Receiver operating characteristic (ROC) analysis

To derive the final 7-gene signature, ROC analysis was used to assign an AUC value to each gene as a single feature. A ROC curve is a graphical plot of the sensitivity vs. (1 - specificity) for a binary classifier system as its discrimination threshold is varied. The area under the ROC curve (AUC) is a widely-used performance metric for the binary classifier system being evaluated. We used ROC analysis to assign area under the curve (AUC) scores to evaluate the ability of each individual gene in the set to classify patients into the two groups (expressors vs. non-expressors - see [26]). This analysis was performed independent of survival data. AUC scores were assigned to each gene by applying the PROPROC program [27]. We selected those genes with an AUC score above 0.95, representing a probability of error under 5%, which resulted in a subset of 7 genes. The algorithm by which we narrowed our gene set down to 7 genes is summarized in Additional File 1, Figure S1. Six of the seven genes had matching expressional data in the validation dataset (PRC1 was absent in this database due to the differences in array platforms).

Additional File 1. Figure S1. Algorithm used to select a short signature from our biologically derived set of genes correlated with MUC1 transfection.

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Results

1. MUC1 is associated with changes in the expression of genes that regulate cellular growth and proliferation

Differences in gene expression were identified by comparing MUC1-CD-transformed 3Y1 cells to those transfected with a control vector and grown in vitro. Gene expression analysis using Significance Analysis of Microarrays (SAM) yielded 254 differentially expressed genes in MUC1-CD-transfected cells, shown in Figure 1A and Additional File 2, Table S1. Functional gene analysis using Ingenuity Pathway Analysis (IPA) of the 254 genes identified cellular growth and proliferation as the most significantly represented function (91 molecules, Fisher's exact p = 1.26 × 10-8). Furthermore, IPA functional network analysis demonstrated that the most statistically significant networks representing the web of interactions among these genes indeed mediate cellular growth and proliferation (Fisher's exact p = 10-44) and cell cycle and cellular assembly (Fisher's exact p = 10-41) as shown in Figure 1B and Additional File 3, Table S2. Taken together, these data indicate that MUC1-CD-induced transformation is associated with distinct changes in gene expression associated with the control of cellular growth and proliferation.

thumbnailFigure 1. Differentially expressed genes in MUC1 transfected cells. A. Expressional clustering of genes expressed at least two-fold differently in (a) MUC1 transfected 3Y1 cells compared with (b) cells transfected with empty vector. B. A functional network involving many of these upregulated (pink) and downregulated (green) genes. Functions include cell cycle, cellular assembly and organization, DNA replication, recombination and repair.

Additional File 2. Table S1. 254 genes differentially expressed in MUC1 transfected cells.

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Additional File 3. Table S2. The top two functional networks represented by 254 genes with expressional changes associated with MUC1 transfection.

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2. Development of a MUC1-dependent, proliferation-associated molecular signature

To test the hypothesis that differential expression of the 254 genes regulated by MUC1-CD is linked to poor prognosis, a large, multi-institutional lung adenocarcinoma database of 442 cases [23] was utilized. K-means clustering based on expression of these 254 genes was used to divide patients into two groups (Figure 2A). Kaplan-Meier survival analysis of patient groups defined by k-means clustering demonstrated significant 5-year survival differences between groups (47.0% vs. 64.6%, log-rank p < 0.0001, Figure 2B). These data demonstrate that differential expression of MUC1-CD-associated genes is associated with poor outcome of lung adenocarcinoma patients.

thumbnailFigure 2. Expressional clustering based on 254 MUC1-CD-associated genes is associated with survival of lung adenocarcinoma patients. A. 3-dimensional representation of the centroids generated by K-means clustering. Each point forming a cloud surrounding the centroid represents a patient assigned to the cluster corresponding to the centroid. B. 5-year survival of patients assigned to each cluster.

We used a combination of parametric statistics and receiver operating characteristic (ROC) analysis to reduce the size of this gene set and identify those genes whose expression was most closely linked to the prognostic clusters identified using the entire 254 gene set. Forty-two genes were initially identified using parametric statistics (see Table 1). Network analysis using IPA showed that these genes form a network representing DNA replication/cell cycle, recombination and repair (e.g. CCNB1, CDC2, CDC20, CDKN3, MAD2L1 and MCM7). For more details on this network, see Additional File 4, Figure S2. Similar data were obtained by IPA-based analysis of the most significant functions of these 42 genes, which represented the same pathways (p = 1.5 × 10-6; Fisher's exact test). Seven genes, including CCNB1, CDC20, CDKN3, CDC2, MAD2L1, PRC1, and RRM2, were identified through ROC analysis (see Table 2). Representative ROC curves for the top three genes (CCNB1, CDC20 and CDKN3) are shown in Figure 3. Given that IPA analysis of these genes demonstrated significant functions in the regulation of cell proliferation, cell cycle and chromosome segregation, we designated this 7-gene set as the MUC1-Associated Proliferation Signature (MAPS).

Additional File 4. Figure S2. The top functional network represented by 42 selected genes with expressional changes associated with MUC1 transfection and prognostic significance in lung adenocarcinoma patients.

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Table 1. 42 genes selected based on their differential expression between prognostic groups.

Table 2. Top scoring genes from ROC analysis.

thumbnailFigure 3. ROC curves analyzing the use of expression levels of individual genes, CCNB1, CDC20 and CDKN3, to accurately assign patients to prognostic groups.

3. MAPS is associated with poor prognosis in lung adenocarcinoma

Induction of the MAPS by MUC1-CD and its association with cell proliferation suggested that expression of this signature might identify an aggressive tumor phenotype. To test this hypothesis, we investigated two independent expressional databases of lung adenocarcinomas (see Materials and Methods). Expression of the MAPS in 442 lung adenocarcinomas [23] demonstrated distinct differences in expression across patients (Figure 4). Importantly, patients expressing the MAPS (MAPS+) had a significantly worse prognosis (5-year survival 45% vs. 65%, log-rank p < 0.0001, 5-year disease free survival 41% vs. 49%, log-rank p = 0.003, Figure 4) compared to non-expressors (MAPS-).

thumbnailFigure 4. Expression of genes in the MUC1-Associated Proliferation Signature (MAPS). Survival and disease-free survival for expressors of the signature (MAPS+, n = 212) compared with non-expressors (MAPS-, n = 229).

To evaluate the prognostic value of the MAPS, a multivariate Cox proportional analysis, including tumor stage, tumor grade, lymph node involvement and MAPS expression, was performed as described in the Materials and Methods. Distribution of clinical characteristics in the study population is shown in Table 3. The data presented in Table 4 demonstrate that expression of the MAPS is an independent prognostic factor for overall survival (HR = 1.6, p = 0.024). Lymph node involvement was the most significant prognostic factor for overall survival based on univariate and multivariate analysis (multivariate HR 2.6, p = 2.29 × 10-11). Furthermore, expression of the MAPS enhanced the prognostic ability of lymph node status. In this regard, the 5-year survival for MAPS-/node-negative was significantly better than MAPS+/node-negative (71% vs. 59%, p = 0.0135) and survival for MAPS-/node-positive was significantly better than MAPS+/node-positive (46% vs. 22%, p = 0.0003) (Figure 5). Using an independent database of 84 patients [25] for validation of these results, the data show that patients stratified by expression of these genes also showed significant differences in overall survival (5-year survival 57% in MAPS- vs. 28% in MAPS+, log-rank p = 0.005, Figure 6). These data thus support that expression of the genes comprising the MAPS has clinical relevance in the identification of lung adenocarcinoma patients with poor prognosis.

Table 3. Clinical variables in 441 lung adenocarcinoma patients included in survival analysis.

Table 4. Univariate and multivariate analysis of clinical variables affecting 5-year survival of lung cancer patients, including our classifier, with hazard ratios (HR).

thumbnailFigure 5. Survival in all patients in the database in whom lymph node status is known. Survival is inferior in node-positive patients and in patients who are expressors (MAPS+). MAPS adds significantly to prognostication in both node-negative and node-positive patients.

thumbnailFigure 6. Expressional clustering of genes in the MAPS signature in a second lung adenocarcinoma database. Survival for these groups is shown in a Kaplan-Meier curve with expressors (MAPS+, n = 52) and non-expressors (MAPS-, n = 32).

Discussion

Previously we found that the cytoplasmic domain of MUC1 (MUC1-CD) induces multiple, stable transcriptional changes in transfected cells. We investigated these changes in in vivo models and identified two unique MUC1-CD-dependent expressional signatures. One, which we denoted as the MTS (MUC1 Tumorigenesis Signature), was identified in vivo and reflected the interactions of tumor cells with the host microenvironment, as was evidenced by the activation of genes involved in angiogenesis and extracellular matrix signaling [18]. A second expressional signature representing lipid and cholesterol metabolism was also identified in the context of tumor-stromal interactions [17]. Therefore, in the current report, we focused on the detection of MUC1-CD-dependent transcriptional changes unique to oncogenic cells. We hypothesized that these "intrinsic" changes would be important for MUC1-CD signaling leading to oncogenesis and may be connected with the fundamental mechanisms of the malignant transformation. In this regard, we profiled MUC1-CD-transformed cells grown in vitro, without any influences from the host stroma.

In the current report, we describe a MUC1-Associated Proliferation Signature (MAPS) that provides independent prognostic information, adding to standard pathological evaluation and clinical staging of lung adenocarcinoma. This signature was derived from the set of genes initially detected in an experimental system as upregulated by MUC1-CD in vitro and potentially involved in a highly oncogenic phenotype [18]. The MAPS is distinct from our previously reported MUC1-related signature (MTS, [18]) and was identified using a contrasting experimental approach. The major functional groups of differentially expressed genes in vitro represented cellular growth, proliferation and cell cycle control. In comparison, the MTS was derived from genes highly enriched by functional groups representing cell motility, metastasis and angiogenesis. We believe that these results demonstrate important differences between the intrinsic properties of tumor cells and the properties that are determined by tumor-stromal interactions. Interestingly, only two genes (CDC20 and RRM2) were common between both signatures, perhaps indicating that, at least in our experimental system, expression of these two genes is independent of the host microenvironment.

All the genes that comprise the MAPS are related to cell cycle control and proliferation. For instance, CDC20 (homolog of S. cerevisiae cell division cycle 20 protein) directly binds to and activates anaphase-promoting complex (APC), which leads to ubiquitination and degradation of cyclin B (CCNB1) and therefore promotes the onset of anaphase and mitotic exit [28]. The APC/CDC20 complex is under negative control of MAD2L1 (human homolog of S. Cerevisiae MAD2) and BUB1 (see Table 1). Also, PRC1 (protein regulating cytokinesis 1) is a human homolog of S. cerevisiae Ase1, which is involved in spindle formation and also promotes anaphase and mitotic exit [29]. CDC2 (cell division cycle 2), or CDK1 (cyclin-dependent kinase 1), is a catalytic subunit of a protein kinase complex, called the M-phase promoting factor, formed with cyclin B1 (CCNB1) that induces entry into mitosis [30]. CDC2 phosphorylates securin, which is another target of APC/CDC20 and is an inhibitor of separase-protease, responsible for the cleavage of sister chromatid cohesions. CDC2-dependent phosphorylation of securin protects it from APC/CDC20-induced ubiquitination and degradation [31]. CDKN3 (cyclin-dependent kinase inhibitor 3) is a dual-specificity protein phosphatase that interacts with CDC2 and CDK2 and inhibits their activity [32]. These data show that six of the seven genes comprising MAPS not only belong to a cell cycle-related functional group but represent a specific pathway of interacting proteins responsible for anaphase control, chromosome segregation and mitotic entrance/exit (see also Figure 1B). RRM2 (ribonucleotide reductase, M2 subunit) encodes the small subunit (R2) of ribonucleotide reductase, the heterodimeric enzyme that catalyzes the rate-limiting step in deoxyribonucleotide synthesis. Using siRNA screening, Kittler et al. [33] identified 37 genes required for cell division, one of which was RRM2.

There is substantial literature indicating that the genes in MAPS are co-expressed and are involved in tumorigenesis and cancer progression. Five of seven MAPS genes are upregulated in immortalized breast cancer cell lines compared to primary breast tumor cell cultures (CDC2, CDC20, CDKN3, MAD2L1 and RRM2) [34] and all seven MAPS genes are upregulated in response to infection of HPV-18, a virus associated with cervical cancer, in keratinocytes [35]. All seven were also found to be co-expressed with E2F, which is expressed in breast cancer compared with normal breast tissue and is elevated during the G2/M transition [36]. This suggests a possible role for E2F inhibitors in treating poor-prognosis cancers that express MAPS. All seven MAPS genes are downregulated in response to Brd4 transfection in a mouse mammary cell line and are included in a 141-gene prognostic signature based on differential expression in this cell line. Expression of this signature correlated with prognosis in five separate human breast cancer cohorts [37]. This is one of many published results from tumor expression profiling experiments which have linked increased expression of genes from common pathways involved in cell growth and proliferation to poor outcomes in cancer patients [38]. A meta-signature was identified consisting of sixty-nine genes expressed more in high-grade compared to low-grade tumors in eight separate microarray analyses spanning seven types of cancer including lung adenocarcinoma [39]. These included many genes associated with cell proliferation, including five of the seven genes in our abbreviated MAPS signature: CCNB1, CDC2, CDC20, CDKN3 and MAD2L1. Thus, MAPS reflects a pattern of gene expression associated with high-grade cancers, but having greater prognostic significance than histological grade in our results.

Current data of Whitfield et al. [38] indicate that proliferation-associated genes can be considered not only as common prognostic/predictive markers in different cancers, but also as promising targets for anti-cancer therapy. Among the genes comprising MAPS, at least two are targets of known drugs. These are hydroxyurea for RRM2 and flavopiridol and staurosporin (UCN-01) for CDK1 (CDC2). In addition, taxol, which affects microtubule formation and blocks mitosis at the G2/M transition, may have interactions with 6 of 7 gene products included in MAPS. RRM2 is also a target that may be used to potentiate chemotherapy. Kittler et al. [33] demonstrated that silencing of RRM1 and RRM2, which encode the large and small subunits of the human ribonucleotide reductase (RNR) complex, respectively, markedly enhanced the cytotoxicity of the topoisomerase I (Top1) inhibitor camptothecin (CPT). Silencing of RRM2 was also found to enhance DNA damage as measured by γ-H2AX. Upregulation in RRM2 expression levels suggests an active role for RNR in the cellular response to DNA damage that could potentially be exploited as strategy for enhancing the efficacy of Top1 inhibitors [40]. The MUC1-CD is also involved in the control of DNA damage response [41]. The data presented in the current report suggest that this control may be associated with a set of genes regulating G2/M transition and exit from mitosis through the network of reactions connected with spindle formation and chromosome segregation.

Many existing biomarkers that have been identified for non-small-cell lung cancer indicate the presence of disease, as in screening or recurrence. The genes in the MAPS are, however, potential biomarkers of prognosis and could help guide treatment in patients with a new diagnosis of primary lung cancer. There are cytokeratin biomarkers that have been studied which show evidence of prognostic significance in lung adenocarcinoma, including CYFRA 21-1, TPA and TPS [42]. These biomarkers are detected on the protein levels in relatively high concentrations from freshly prepared tissues. Our signature has the potential to be measured by PCR at picogram levels both in frozen tissues and paraffin-embedded archival samples. Further prospective investigations are needed to compare potential protein and RNA-based biomarkers, which might be complementary to each other.

Conclusions

These data indicate new insights into the mechanisms through which MUC1-CD performs its DNA damage-response and tumorigenic functions. They also suggest targets that can be accessed for tumor suppression/sensitization to genotoxic treatments in a MUC1-CD-dependent pathway. Therefore, the MUC1-Associated Proliferation Signature (MAPS) described in the current report not only serves as a new classifier, but also sheds light on the mechanisms of MUC1-CD-associated tumorigenesis and suggests potential gene products and drugs for targeted cancer therapy.

Competing interests

D. Kufe holds equity in Genus Oncology and is a consultant to the company.

Authors' contributions

DMM and NNK designed the study, carried out data analysis and drafted the manuscript. SPP participated in data analysis and helped draft the manuscript. DCE carried out ROC analysis and generated ROC curves. CAP participated in design and coordination of the ROC analysis. LH helped with data collection and analysis. DWK and RRW coordinated the entire study, designed the concept and helped draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank Masha Kocherginsky for her assistance with statistical analysis. Research support: NIH Research Project Grant (R01) CA111423 and Lung Cancer Research Foundation (RRW). NIH Research Project Grant (R01) CA97098 (DWK).

References

  1. Jemal A, Tiwari RC, Murray T, Ghafoor A, Samuels A, Ward E, Feuer EJ, Thun MJ: Cancer statistics, 2004.

    CA Cancer J Clin 2004, 54(1):8-29. PubMed Abstract | Publisher Full Text OpenURL

  2. Govindan R, Page N, Morgensztern D, Read W, Tierney R, Vlahiotis A, Spitznagel EL, Piccirillo J: Changing epidemiology of small-cell lung cancer in the United States over the last 30 years: analysis of the surveillance, epidemiologic, and end results database.

    J Clin Oncol 2006, 24(28):4539-4544. PubMed Abstract | Publisher Full Text OpenURL

  3. Auperin A, Le Pechoux C, Pignon JP, Koning C, Jeremic B, Clamon G, Einhorn L, Ball D, Trovo MG, Groen HJ, et al.: Concomitant radio-chemotherapy based on platin compounds in patients with locally advanced non-small cell lung cancer (NSCLC): a meta-analysis of individual data from 1764 patients.

    Ann Oncol 2006, 17(3):473-483. PubMed Abstract | Publisher Full Text OpenURL

  4. Tsao MS, Zhu C, Ding K, Strumpf D, Pintilie M, Meyerson M, Seymour L, Jurisica I, Shepherd FA: A 15-gene expression signature prognostic for survival and predictive for adjuvant chemotherapy benefit in JBR.10 patients.

    J Clin Oncol 2008., 26

    (May 20 suppl; abstr 7510)

    OpenURL

  5. Chen HY, Yu SL, Chen CH, Chang GC, Chen CY, Yuan A, Cheng CL, Wang CH, Terng HJ, Kao SF, et al.: A five-gene signature and clinical outcome in non-small-cell lung cancer.

    N Engl J Med 2007, 356(1):11-20. PubMed Abstract | Publisher Full Text OpenURL

  6. Seregni E, Botti C, Lombardo C, Cantoni A, Bogni A, Cataldo I, Bombardieri E: Pattern of mucin gene expression in normal and neoplastic lung tissues.

    Anticancer Res 1996, 16(4B):2209-2213. PubMed Abstract OpenURL

  7. Li Y, Liu D, Chen D, Kharbanda S, Kufe D: Human DF3/MUC1 carcinoma-associated protein functions as an oncogene.

    Oncogene 2003, 22(38):6107-6110. PubMed Abstract | Publisher Full Text OpenURL

  8. Huang L, Ren J, Chen D, Li Y, Kharbanda S, Kufe D: MUC1 cytoplasmic domain coactivates Wnt target gene transcription and confers transformation.

    Cancer Biol Ther 2003, 2(6):702-706. PubMed Abstract | Publisher Full Text OpenURL

  9. Yin L, Li Y, Ren J, Kuwahara H, Kufe D: Human MUC1 carcinoma antigen regulates intracellular oxidant levels and the apoptotic response to oxidative stress.

    J Biol Chem 2003, 278(37):35458-35464. PubMed Abstract | Publisher Full Text OpenURL

  10. Ren J, Agata N, Chen D, Li Y, Yu WH, Huang L, Raina D, Chen W, Kharbanda S, Kufe D: Human MUC1 carcinoma-associated protein confers resistance to genotoxic anticancer agents.

    Cancer Cell 2004, 5(2):163-175. PubMed Abstract | Publisher Full Text OpenURL

  11. Raina D, Kharbanda S, Kufe D: The MUC1 oncoprotein activates the anti-apoptotic phosphoinositide 3-kinase/Akt and Bcl-xL pathways in rat 3Y1 fibroblasts.

    J Biol Chem 2004, 279(20):20607-20612. PubMed Abstract | Publisher Full Text OpenURL

  12. Wei X, Xu H, Kufe D: Human MUC1 oncoprotein regulates p53-responsive gene transcription in the genotoxic stress response.

    Cancer Cell 2005, 7(2):167-178. PubMed Abstract | Publisher Full Text OpenURL

  13. Ohgami A, Tsuda T, Osaki T, Mitsudomi T, Morimoto Y, Higashi T, Yasumoto K: MUC1 mucin mRNA expression in stage I lung adenocarcinoma and its association with early recurrence.

    Ann Thorac Surg 1999, 67(3):810-814. PubMed Abstract | Publisher Full Text OpenURL

  14. Guddo F, Giatromanolaki A, Koukourakis MI, Reina C, Vignola AM, Chlouverakis G, Hilkens J, Gatter KC, Harris AL, Bonsignore G: MUC1 (episialin) expression in non-small cell lung cancer is independent of EGFR and c-erbB-2 expression and correlates with poor survival in node positive patients.

    J Clin Pathol 1998, 51(9):667-671. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  15. Khodarev NN, Kataoka Y, Murley JS, Weichselbaum RR, Grdina DJ: Interaction of amifostine and ionizing radiation on transcriptional patterns of apoptotic genes expressed in human microvascular endothelial cells (HMEC).

    Int J Radiat Oncol Biol Phys 2004, 60(2):553-563. PubMed Abstract | Publisher Full Text OpenURL

  16. Weichselbaum RR, Ishwaran H, Yoon T, Nuyten DS, Baker SW, Khodarev N, Su AW, Shaikh AY, Roach P, Kreike B, et al.: An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer.

    Proc Natl Acad Sci USA 2008, 105(47):18490-18495. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  17. Pitroda SP, Khodarev NN, Beckett MA, Kufe DW, Weichselbaum RR: MUC1-induced alterations in a lipid metabolic gene network predict response of human breast cancers to tamoxifen treatment.

    Proc Natl Acad Sci USA 2009, 106(14):5837-5841. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  18. Khodarev NN, Pitroda SP, Beckett MA, MacDermed DM, Huang L, Kufe DW, Weichselbaum RR: MUC1-induced transcriptional programs associated with tumorigenesis predict outcome in breast and lung cancer.

    Cancer Res 2009, 69(7):2833-2837. PubMed Abstract | Publisher Full Text OpenURL

  19. Huang L, Chen D, Liu D, Yin L, Kharbanda S, Kufe D: MUC1 oncoprotein blocks glycogen synthase kinase 3beta-mediated phosphorylation and degradation of beta-catenin.

    Cancer Res 2005, 65(22):10413-10422. PubMed Abstract | Publisher Full Text OpenURL

  20. Khodarev NN, Yu J, Nodzenski E, Murley JS, Kataoka Y, Brown CK, Grdina DJ, Weichselbaum RR: Method of RNA purification from endothelial cells for DNA array experiments.

    Biotechniques 2002., 32(2)

    316, 318, 320

    PubMed Abstract OpenURL

  21. Kimchi ET, Posner MC, Park JO, Darga TE, Kocherginsky M, Karrison T, Hart J, Smith KD, Mezhir JJ, Weichselbaum RR, et al.: Progression of Barrett's metaplasia to adenocarcinoma is associated with the suppression of the transcriptional programs of epidermal differentiation.

    Cancer Res 2005, 65(8):3146-3154. PubMed Abstract | Publisher Full Text OpenURL

  22. Khodarev NN, Park J, Kataoka Y, Nodzenski E, Hellman S, Roizman B, Weichselbaum RR, Pelizzari CA: Receiver operating characteristic analysis: a general tool for DNA array data filtration and performance estimation.

    Genomics 2003, 81(2):202-209. PubMed Abstract | Publisher Full Text OpenURL

  23. Shedden K, Taylor JM, Enkemann SA, Tsao MS, Yeatman TJ, Gerald WL, Eschrich S, Jurisica I, Giordano TJ, Misek DE, et al.: Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

    Nat Med 2008, 14(8):822-827. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  24. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, et al.: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.

    Proc Natl Acad Sci USA 2001, 98(24):13790-13795. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  25. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG, et al.: Gene-expression profiles predict survival of patients with lung adenocarcinoma.

    Nat Med 2002, 8(8):816-824. PubMed Abstract | Publisher Full Text OpenURL

  26. Metz CE: Basic principles of ROC analysis.

    Seminars in Nuclear Medicine 1978, VIII(4):283-298. Publisher Full Text OpenURL

  27. Metz CE, Pan X: "Proper" Binormal ROC Curves: Theory and Maximum-Likelihood Estimation.

    J Math Psychol 1999, 43(1):1-33. PubMed Abstract | Publisher Full Text OpenURL

  28. Yu H: Cdc20: a WD40 activator for a cell cycle degradation machine.

    Mol Cell 2007, 27(1):3-16. PubMed Abstract | Publisher Full Text OpenURL

  29. Kurasawa Y, Earnshaw WC, Mochizuki Y, Dohmae N, Todokoro K: Essential roles of KIF4 and its binding partner PRC1 in organized central spindle midzone formation.

    EMBO J 2004, 23(16):3237-3248. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  30. Paris J, Leplatois P, Nurse P: Study of the higher eukaryotic gene function CDK2 using fission yeast.

    J Cell Sci 1994, 107(Pt 3):615-623. PubMed Abstract | Publisher Full Text OpenURL

  31. Holt LJ, Krutchinsky AN, Morgan DO: Positive feedback sharpens the anaphase switch.

    Nature 2008, 454(7202):353-357. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  32. Patterson KI, Brummer T, O'Brien PM, Daly RJ: Dual-specificity phosphatases: critical regulators with diverse cellular targets.

    Biochem J 2009, 418(3):475-489. PubMed Abstract | Publisher Full Text OpenURL

  33. Kittler R, Putz G, Pelletier L, Poser I, Heninger AK, Drechsel D, Fischer S, Konstantinova I, Habermann B, Grabner H, et al.: An endoribonuclease-prepared siRNA screen in human cells identifies genes essential for cell division.

    Nature 2004, 432(7020):1036-1040. PubMed Abstract | Publisher Full Text OpenURL

  34. Dairkee SH, Ji Y, Ben Y, Moore DH, Meng Z, Jeffrey SS: A molecular 'signature' of primary breast cancer cultures; patterns resembling tumor tissue.

    BMC Genomics 2004, 5(1):47. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  35. Karstensen B, Poppelreuther S, Bonin M, Walter M, Iftner T, Stubenrauch F: Gene expression profiles reveal an upregulation of E2F and downregulation of interferon targets by HPV18 but no changes between keratinocytes with integrated or episomal viral genomes.

    Virology 2006, 353(1):200-209. PubMed Abstract | Publisher Full Text OpenURL

  36. Tedesco D, Zhang J, Trinh L, Lalehzadeh G, Meisner R, Yamaguchi KD, Ruderman DL, Dinter H, Zajchowski DA: The ubiquitin-conjugating enzyme E2-EPF is overexpressed in primary breast cancer and modulates sensitivity to topoisomerase II inhibition.

    Neoplasia 2007, 9(7):601-613. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  37. Crawford NP, Alsarraj J, Lukes L, Walker RC, Officewala JS, Yang HH, Lee MP, Ozato K, Hunter KW: Bromodomain 4 activation predicts breast cancer survival.

    Proc Natl Acad Sci USA 2008, 105(17):6380-6385. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  38. Whitfield ML, George LK, Grant GD, Perou CM: Common markers of proliferation.

    Nat Rev Cancer 2006, 6(2):99-106. PubMed Abstract | Publisher Full Text OpenURL

  39. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

    Proc Natl Acad Sci USA 2004, 101(25):9309-9314. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  40. Zhang YW, Jones TL, Martin SE, Caplen NJ, Pommier Y: Implication of checkpoint kinase-dependent up-regulation of ribonucleotide reductase R2 in DNA damage response.

    J Biol Chem 2009, 284(27):18085-18095. PubMed Abstract | Publisher Full Text OpenURL

  41. Raina D, Ahmad R, Joshi MD, Yin L, Wu Z, Kawano T, Vasir B, Avigan D, Kharbanda S, Kufe D: Direct Targeting of the Mucin 1 Oncoprotein Blocks Survival and Tumorigenicity of Human Breast Carcinoma Cells.

    Cancer Res 2009, 5133-5141. PubMed Abstract | Publisher Full Text OpenURL

  42. Cho WC: Potentially useful biomarkers for the diagnosis, treatment and prognosis of lung cancer.

    Biomed Pharmacother 2007, 61(9):515-519. PubMed Abstract | Publisher Full Text OpenURL

Pre-publication history

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http://www.biomedcentral.com/1755-8794/3/16/prepub