"Genomic Deregulation during Metastasis of Renal Cell Carcinoma Implements a Myofibroblast-Like Program of Gene Expression."

Miguel A. López-Lago ¹, Venkata J. Thodima ¹, Asha Guttapalli ¹, Timothy Chan ², Adriana Heguy ²,  Ana M. Molina ⁴, Victor E. Reuter ³, Robert J. Motzer ⁴, and Raju S. K. Chaganti ^{1, 3}

¹ Cell Biology Program, ² Human Oncology and Pathogenesis Program, ³ Department of Pathology, and
⁴ Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York

Corresponding Author:
      Raju S. K. Chaganti, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021. Phone: 212-639-8121;   Fax: 212-717-3541;   E-mail: chagantr@mskcc.org

Received June 22, 2010. Revision received September 8, 2010. Revision requested November 4, 2010.
Accepted September 27, 2010.

Clear cell renal cell carcinoma (RCC) is the most common and invasive adult kidney cancer. The genetic and biological mechanisms that drive metastatic spread of RCC remain largely unknown. We have investigated the molecular signatures and underlying genomic aberrations associated with RCC metastasis, using an approach that combines a human xenograft model; expression profiling of RNA, DNA, and microRNA (miRNA); functional verification; and clinical validation. We show that increased metastatic activity is associated with acquisition of a myofibroblast-like signature in both tumor cell lines and in metastatic tumor biopsies. Our results also show that the mesenchymal trait did not provide an invasive advantage to the metastatic tumor cells. We further show that some of the constituents of the mesenchymal signature, including the expression of the well-characterized myofibroblastic marker S100A4, are functionally relevant. Epigenetic silencing and miRNA-induced expression changes accounted for the change in expression of a significant number of genes, including S100A4, in the myofibroblastic signature; however, DNA copy number variation did not affect the same set of genes. These findings provide evidence that widespread genetic and epigenetic alterations can lead directly to global deregulation of gene expression and contribute to the development or progression of RCC metastasis culminating in a highly malignant myofibroblast-like cell.

Supplementary data for this article are available at Cancer Research Online:
http://cancerres.aacrjournals.org/content/70/23/9682/suppl/DC1

Cancer involves multistep changes in the genome that lead to deregulation of gene profiles and disruption of molecular networks. A variety of genomic approaches have been used to identify the molecular profiles that contribute to and reflect cancer progression. Moreover, gene expression profiling has been useful in the prediction of clinical outcome, disease progression, and metastatic recurrence (1). During carcinogenesis, genetic and epigenetic alterations drive tumor evolution toward higher grades of malignancy; however, the extent to which these changes alter gene expression, and how these alterations influence each other, remains incompletely understood. Association between gene dosage and gene expression levels has been reported in a variety of tumors (2), and approximately 12% of gene expression variation can be explained by differences in DNA copy number (3). In addition, integrated analyses of genome copy number and gene expression have provided insights into the etiology and mechanisms by which DNA copy number changes contribute to tumor evolution (2). Genomic methylation patterns are clearly deranged in cancer cells (4); however, the impact of promoter methylation on genome-wide gene expression changes during the pathogenesis of individual cancers still is poorly defined. Recently, the impact of global cancer-related changes in DNA methylation, genomic imbalance, and gene expression has been analyzed using whole genomic profiling approaches (5, 6). Alternative approaches using recent advances in computational biology have successfully deciphered the functional impact of transcription factor networks on tumor progression (7).

Kidney cancer is the seventh most common cancer in the United States. Clear cell renal cell carcinoma (RCC) arises from the renal epithelium and accounts for approximately 85% adult kidney cancers (8). Hereditary type RCC is associated with von Hippel-Lindau disease (VHL), which arises as a result of mutations in the VHL gene; the latter is also inactivated by deletion, mutation, or gene silencing in most sporadic cases (9). In approximately 30% of patients, metastases are detected at the time of primary diagnosis, and an additional 30% to 50% of initially localized tumors progress to distant metastases (10). The genetic mechanisms of RCC progression, especially those which underlie metastatic conversion, are poorly understood.

The prevailing multistep progression model of tumorigenesis assumes that random mutations followed by selection eventually give rise to the metastatic phenotype in a rare subpopulation of tumor cells (11, 12). Early genomic studies suggested that the primary tumor may or may not carry a molecular signature indicative of high metastatic potential (13). More recently, Massague and colleagues extensively characterized molecular signatures that allow breast tumor cells to colonize specific secondary sites (14–16). It has also been proposed that metastasis could arise from early disseminated tumor cells based on studies that showed that disseminated tumor cells can be identified in the secondary site very early on, even before the primary tumor becomes clinically apparent (17, 18). Following up on Paget's “seed and soil” hypothesis (19), proposed more than a hundred years ago, it has been suggested that metastatic growth requires the existence of a “metastatic niche” (20).

We used an integrated genomic approach and performed a comprehensive analysis of clinical and xenograft models of RCC metastasis. For this, we used the RCC-derived and nonmetastatic SN12C cell line and a collection of derived cell variants capable of efficient spontaneous metastasis to the lung when injected orthotopically. Gene expression profiling of nonmetastatic and metastatic tumor cell lines revealed that metastatic activity was associated with the acquisition of a mesenchymal signature. Many of the genes in this signature are specifically enriched in the prototypical fibrotic cell. Notably, some of these genes are also know to control metastatic activity. By associating gene expression profiles and genetic and epigenetic alterations in the metastatic cells, we were able to gain new insights into the mechanisms by which the genome of RCC tumor cells is rearranged during metastasis, and to identify functionally relevant genes in this model.

Materials and Methods:

Full methods are described in supplemental methods.

Cell lines and the xenograft model

The SN12C cell line was established in culture from a primary RCC from a 43-year-old male patient and was described previously (21). Derivation of the lung-metastatic LM1 cells was also previously described (22). LM2 cells were isolated from lung metastasis of NOD/SCID mice injected with 106 LM1 cells in the kidney subcapsule and sacrificed 2 months later. Lung nodules from each mouse were dispersed into separate culture wells to generate individual cell lines. The cells were cultured in minimum essential medium supplemented with 10% fetal bovine serum. All animal studies were performed in accordance with a protocol approved by the Institutional Animal Care and Use Committee; 6- to 8-week-old NOD/SCID mice (NCI) were used for all xenograft studies.

Histology of xenograft tumors

Mouse lungs were harvested at necropsy. For hematoxylin and eosin staining, tissues were fixed overnight in 10% neutral buffered formalin, washed with PBS, and dehydrated in 70% ethanol prior to paraffin embedding (Histoserv Inc.).

Human tumor specimens

Fresh-frozen paired primary and metastatic tumor biopsies were obtained from 16 patients (32 samples) with RCC evaluated at the Memorial Sloan-Kettering Cancer Center following an Institutional Review Board–approved protocol.

RNA isolation, labeling, and microarray hybridization

RNA was isolated from cell lines and tumor tissues using an RNeasy kit (Qiagen). For expression profiling, the HG-U133 Plus 2.0 array (Affymetrix) was hybridized with cRNA from the samples using standard protocols. For microRNA (miRNA) analysis, total RNA was isolated using the Qiagen miRNeasy kit and hybridized to Agilent Human miRNA Microarray kit V2-G4470B (Agilent Technologies) following labeling and hybridization protocols recommended by the manufacturer.

Immunoblotting

Cells were lysed using sample buffer and proteins were separated by SDS-PAGE and blotted onto nitrocellulose membranes and hybridized with the anti-S100A4 antibody ab27957 (Abcom).

DNA extraction and array comparative genomic hybridization

Genomic DNA was isolated using the Qiagen DNA extraction kit. DNA digestion, labeling, and hybridization were performed following Agilent's protocol version 4.0 for Agilent Human Genome CGH 244A oligo microarrays.

5 aza 2' deoxycytidine treatment of cells

Cells were seeded (1×10⁶) in culture medium and maintained for 24 hours before treatment with 5 mmol/L 5 aza 2? deoxycytidine (5'-AZA; Sigma-Aldrich) for 3 days (23). Medium containing 5'-AZA was replaced every 24 hours during the treatment. Control cells were treated in the same way, but without the addition of 5'-AZA.

Generation of retrovirus and knockdown cells

For overexpression, S100A4 cDNA was subcloned into the pQCXIN retroviral-based vector (Clontech). For knockdown, the pLKO.1 plasmid (clone TRCN0000053608) encoding short hairpin RNA (shRNA)-targeting S100A4 was obtained from Open Biosystems. Viral supernatants were generated by transfecting 293-FT cells with the shRNA constructs in combination with the packaging vectors pVSVG and pDR2.

Results:

A xenograft mouse model for RCC metastasis

SN12C cells, when implanted in the renal subcapsule of NOD/SCID mice, showed limited lung metastatic activity (3–10 nodules). Lung-derived nodules, when expanded in culture and reinoculated into the kidney capsule showed higher lung metastatic activity (80 nodules), designated LM1 (22). Another round of selection in vivo yielded a second generation of highly metastatic cells (400 nodules), designated LM2 (Fig. 1B). Interestingly, the LM cells also showed an increased metastatic spread to other organs (ref. 22; our results). Of note, the growth rate of the highly metastatic cells at the primary site was similar to that of the parental SN12C cells (Fig. 1A). Histologic review of lung metastasis showed that the tumor cells, in addition to colonizing the lung parenchyma, associated in thrombus-like structures inside lymphatic and blood vessels (Fig. 1C).

Figure 1. A xenograft mouse model for RCC metastasis.

Figure 1. A xenograft mouse model for RCC metastasis.

NOD/SCID mice were injected with 10⁶ tumor cells as indicated in the kidney subcapsule.

A, primary tumor,

B, lung metastasis,

C, hematoxylin and eosin staining. Black arrow indicates thrombi inside the blood vessels; blue arrow indicates vascular thrombus invading the vessel wall.

To evaluate genome wide changes in gene expression during metastatic conversion, we subjected the 3 cell types, SN12C, LM1, and LM2, to gene expression profiling using the Affymetrix U133 plus 2.0 arrays. Class comparison between SN12C and LM2 cells identified 349 unique genes that showed differential expression: 191 were upregulated and 158 were downregulated in the most aggressive metastatic cells. Analysis of the gene expression patterns revealed a mesenchymal signature as the dominant feature of the gene list that characterized the metastatic cells (Table 1; Supplementary Fig. S1). Epithelial–mesenchymal transition (EMT) has been proposed to be one of the initial steps during metastatic conversion that endows tumor cells with the ability to invade surrounding tissues (24). We therefore investigated whether the observed mesenchymal phenotype of LM cells correlated with an increase in invasiveness. Surprisingly, the ability of LM cells to invade through both an artificial basement membrane (Boyden chamber assay) and an artificial endothelium (human umbilical vein endothelial cell) was not significantly altered when compared with parental SN12C cells (Supplementary Fig. S2). This result suggested that the SN12C cells may already have undergone the initial stages of EMT. Consistent with this assumption, we found that epithelial markers such as E-cadherin were downregulated whereas mesenchymal markers such as N-cadherin and vimentin were already upregulated in the SN12C poorly metastatic cells (Supplementary Fig. S2C). Emerging evidence suggests that epithelial cells are also an important source of myofibroblasts in fibrosis and cancer (25). Therefore, we reasoned that a significant function of tumor EMT would be to provide tumor cells with stromal myofibroblastic properties, which would bypass the requirement of an activated stroma during metastatic colonization. Detailed analysis of mesenchymal genes upregulated in the LM cells revealed a high enrichment for expression of genes related to fibrosis and desmoplastic response (S100A4, COL11A1, COL1A1, COL3A1, CALD1, and TNNC1; refs. 26, 27). Conversely, the expression of some of the genes characteristic of the epithelial lineage (KRT34, DSC3, and KRT33A) was lost in the metastatic cells (Supplementary Fig. S3).

Table 1. A mesenchymal gene signature is upregulated during metastatic conversion of RCC cell lines

To validate the observations from SN12C and its metastatic derivatives in the clinical disease described earlier, we subjected a cohort of paired primary and metastatic biopsies from 16 RCC patients to gene expression profiling using the Affymetrix U133 plus 2.0 arrays. The metastatic components in these pairs were also highly enriched for genes typically associated with the myofibroblast phenotype (SFRP2, COL11A1, OGN, COL14A1, COL1A2, DCN, ASPN, and CALD1; refs. 26, 27; Table 2). Collectively, these results indicate that during RCC metastatic conversion, tumor cells acquire a myofibroblast-like phenotype.

Table 2. A mesenchymal gene signature is upregulated during metastatic conversion of RCC clinical samples

Components of the mesenchymal signature control metastatic activity

To formally demonstrate that the myofibroblast-like phenotype of the highly metastatic cells affects the metastatic behavior, we selected, from among a subset of genes biologically relevant to the mesenchymal phenotype, S100A4, a fibroblast marker for functional validation. We were particularly interested in S100A4 because this molecule could serve as a functional link between fibrosis and metastasis during kidney cancer progression, as strong evidence suggests S100A4 to be a crucial myofibroblast-expressed factor that regulates metastasis (28). To determine whether this gene plays a causal role in lung metastasis, we stably decreased its expression in LM2 cells using shRNA and found that S100A4 levels had a profound effect on metastatic activity (Fig. 2A). To investigate whether S100A4 was sufficient to induce metastatic spread, we engineered SN12C cells to overexpress S100A4 at levels comparable to those seen in LM2 cells. Injection of SN12C-S100A4 cells into the kidney subcapsule showed a very modest lung colonization advantage in comparison with control SN12C cells (Fig. 2B) suggesting that S100A4 is required but not sufficient for LM2 cells to metastasize, and that cooperation of other genes may be required for the attainment of full metastatic competence. Globally, these results support a model in which RCC cells co-opt a myofibroblastic gene expression program to colonize secondary sites during tumor progression.

Figure 2. S100A4 controls metastatic activity.

Figure 2. S100A4 controls metastatic activity.

A, LM2 cells were infected with lentiviruses encoding the indicated shRNAs. Equal amounts of total proteins were subjected to immunoblotting with antibodies to the indicated antigens. shSC, nontargeting shRNA; shS100A4, shRNAs targeting S100A4.

B, SN12C cells were infected with retroviruses bearing a vector encoding S100A4 or an empty vector. Total cellular lysis was subjected to immunoblotting using antibodies against the specified proteins. A and B, graphs, groups of 5 mice were injected in the kidney subcapsule with the indicated cell populations and numbers of lung metastatic nodules were scored after 2 months.

DNA copy number variation–related gene expression changes

We performed high-resolution whole genome profiling of SN12C and derived metastatic LM1 and LM2 cells using the Agilent High-Density a-CGH Human 244A array to cross reference gene dosage and gene expression changes. The resultant data were analyzed with the Partek Genomic Suite. This analysis identified approximately 50 significant regions of chromosome copy number differences that distinguished the nonmetastatic and metastatic cells, spanning from 2 Kbp to 80 Mbp. These regional changes in chromosome copy number were scattered over 14 chromosomes with a certain degree of enrichment on chromosomes 1, 2, and 4. Of the 50 aberrations, 10 were gains and 40 were losses (Supplementary Fig. S4). Comparison of probe heat maps from the 3 different genomes showed complex differences (Fig. 3A). To identify genes belonging to regions of significant copy number change, the results of the segmentation algorithm of array comparative genomic hybridization (a-CGH) profiles were annotated with the Agilent human genome annotation library and further aligned with the list of genes differentially expressed between the 3 groups using the Partek Genomic Suite. This analysis yielded a list of 57 genes that mapped to chromosomal regions of DNA copy number changes (Fig. 3B). Only 1 gene mapped to a significant region of gain on chromosome 16, whereas 56 genes mapped to regions of significant loss on chromosomes 1, 2, 4, 10, 13, and 16 (Supplementary Fig. S5). Interestingly, 50 of the 57 genes mapped to chromosome 4. None of the genes defining the myofibroblast-like phenotype mapped to regions of DNA copy number variation, arguing against a role for genomic rearrangements during the acquisition of the myofibroblastic phenotype.

Figure 3. DNA copy variation.

Figure 3. DNA copy variation.

A, segmented copy numbers for each cell line were inferred with the GLAD (gain and loss analysis of DNA) algorithm and normalized to a median of 2 copies. Vertical dashed red lines represent the breakpoints detected with GLAD and the assigned statuses are indicated by a color code: green for loss, yellow for normal, and red for gain.

B, chromosomal mapping of regions of copy number alteration. Regions appearing increased in copy number are shown in red, and those decreased in copy number are shown in blue.

Impact of promoter methylation on the gene expression profile of metastatic cells

Because acquired changes in gene expression may be influenced by both genetic and epigenetic factors, we set out to study the contribution of promoter methylation in the establishment of the metastatic phenotype in our model of tumor progression. To identify epigenetically silenced genes, we used an epigenetic reactivation strategy that combined treatment of cancer cells in vitro with the DNA methyltransferase inhibitor 5'-AZA, followed by global gene expression analysis using microarrays. Treatment of SN12C cells resulted in the upregulation of more than 600 unique genes. To uncover genes upregulated by promoter demethylation during the SN12C-LM transition, we performed a Venn analysis using a dataset of genes upregulated in SN12C cells after 5'-AZA treatment and a dataset of genes spontaneously upregulated in LM cells (Fig. 4A). By this method we identified 21 genes, including S100A4, CALD1, COL9A3, CRIP1, CRIP2, COL1A1, and TNNC1, as hypomethylated in the LM cells, supporting a metastasis promotion role for those genes. Conversely, treatment of LM cells with the same inhibitor followed by analysis of reexpressed genes and Venn analysis of overlapping genes with the list of genes downregulated during SN12C-LM transition uncovered 31 hypermethylated candidate genes (Fig. 4B). Remarkably, some of the hypermethylated and silenced genes in the LM cells (KRT34 and KRT33A) have been known to be associated with an epithelial phenotype (29). Overall, 52 of 300 genes (~20%) deregulated during metastatic progression were upregulated after demethylation agent treatment, and therefore had their expression likely regulated by changes in promoter methylation. A total of 48 genes harbored CpG islands in their promoters. Moreover, 70 and 10 genes were downregulated by drug treatment of SN12C and LM cells, respectively, probably reflecting the reexpression of a repressor (data not shown). To validate the results from the 5'-AZA reexpression experiment, we selected 4 genes predicted to be demethylated during metastatic conversion (S100A4, COL1A1, CRIP1, and CRIP2) and subjected them to quantitation of CpG methylation using the EpiTYPER (Sequenom) mass-spectrometry analysis of bisulfite-converted DNA (Fig. 4D). Of these 4 genes, 3 showed decreased CpG methylation in the LM cells, as predicted. SN12C was also demethylated in a cohort of clinical samples as the tumor progressed to a metastatic stage (Supplementary Fig. S6).

Figure 4. Promoter methylation–related gene expression changes.

Figure 4. Promoter methylation–related gene expression changes.

A, Venn diagram of genes predicted to be hypermethylated in SN12C and spontaneously demethylated in LM2 cells.

B, Venn analysis of genes predicted to be hypomethylated in SN12C and spontaneously hypermethylated in LM2 cells.

C, genes predicted to be hypermethylated in LM2 cells and showing loss of copy number.

D, quantitation of DNA methylation in CpG dinucleotides of 4 genes from A by EpiTYPER analysis. CpG specific methylation is showed in a heat map.

Taken together, these results suggest that a number of genes undergo methylation-associated silencing or reexpression in the course of RCC tumorigenesis and metastasis. Even more interestingly, many of the methylated genes are associated with a myofibroblastic phenotype, suggesting that promoter methylation may be one of the pivotal events controlling metastatic conversion.

Correlation between gene dosage and promoter methylation

Tumor-acquired promoter methylation often coincides with allele loss. To determine whether any of the 52 hypo- and hypermethylated candidate genes were also subject to DNA copy number changes, we cross-referenced this list of genes with the dataset of genes mapped to regions of significant DNA dosage changes, using the Venn diagram tool. We determined that only 3 of the 52 genes whose expression was regulated by methylation belonged to regions of significant copy number losses (Fig. 4C). However, we did not identify any candidate-methylated genes mapping to any of the regions of gene dosage gain. These results suggest a complex pattern in the genetic control of gene expression in which a subset of genes is synergistically impacted by both promoter methylation and DNA copy variation. Parenthetically, the approach utilized here provides a new way to sort and prioritize functionally relevant genes.

miRNA dosage–related gene expression changes

miRNAs are attractive candidates as upstream regulators of metastatic progression because they can regulate entire sets of genes. Therefore, we reasoned that miRNA deregulation may account for at least some of the gene expression changes that distinguish the highly metastatic LM cells from the poorly metastatic SN12C cells. We performed array-based miRNA profiling of SN12C, LM1, and LM2 cells. Hierarchical clustering based on the expression of the differentially expressed miRNAs correctly classified the cell populations into 2 groups (Fig. 5). Of 700 human miRNAs assayed, 38 were upregulated and 34 were downregulated in the highly metastatic cells, using a 1.5-fold threshold. On the assumption that the expression profiles of miRNA genes and their targets are inversely correlated, we identified their putative targets using TargetScan 4.2 and integrated the target genes with expression data from the same experiment. Using this methodology, we identified a network of putative functional miRNA-target regulatory relations involving 100 genes (Supplementary Fig. S7). We also validated the changes in expression of the 2 top miRNA, LM2 versus SN12C (Supplementary Fig. S8). A large proportion of genes included in the miRNAdown-mRNAup-predicted network have reported mesenchymal functions (CALD1, COL1A1, COL1A2, COL3A1, COL9A3, GREM1, and MYLK). Next, we sought to identify the underlying mechanisms that can explain the miRNA expression changes associated with the transition to a highly metastatic state in our model. miRNAs are frequently located in cancer-associated regions of the human genome (30). Thus, to determine whether genomic gains and losses might have impacted on miRNA expression, we correlated gains and losses of genomic regions, as determined by a-CGH on each cell line, with relative miRNA expression values. Notably, we found that only 2 miRNAs (hsa-miR-574-3p and hsa-miR-95) mapped to a region of gene-dosage change (Supplementary Fig. S9). In addition to genetic alterations, the expression of specific miRNAs could be dysregulated by epigenetic aberrations. To identify miRNAs with putative DNA methylation-related inactivation involved in metastasis, we treated SN12C and LM2 cell lines with 5'-AZA followed by hybridization to an expression microarray. By comparing the list of genes reexpressed by 5'-AZA and the list of genes differentially expressed in SN12C and LM2, we found that hsa-miR-224 and hsa-miR-34c-5p miRNAs undergo specific hypermethylation-associated silencing (Supplementary Fig. S10).

Figure 5. Identification of miRNAs altered in RCC cell derivatives.

Figure 5. Identification of miRNAs altered in RCC cell derivatives.

Clustering of normalized miRNA expression levels in SN12C and LM cell lines.

Discussion:

This study describes a comprehensive analysis of gene expression and genomic alterations in in vitro and xenograft models of RCC metastasis to elucidate the genetic mechanisms and associated biological functions that empower tumor cells with the ability to colonize distant sites. Molecular profiling of tumor cells in the xenograft model and RCC tumor biopsies uncovered a mesenchymal signature as the most significant feature linked to high metastatic activity. Unexpectedly, tumor cell invasion was not significantly increased in the highly metastatic populations. These results prompted us to search for alternative mechanisms by which a mesenchymal phenotype may be of advantage at the metastatic site. Detailed analysis of the gene functions associated with the mesenchymal signature revealed a substantial enrichment for profibrotic genes, suggesting that metastatic spread imposes a selective pressure for cells with a myofibroblastic makeup. We therefore suggest that an important function of tumor EMT is to generate cells with properties of stromal fibroblasts. It is logical to presume that a tumor cell endowed with such a phenotype would be highly effective at colonizing distant organs because it could bypass the requirement of an activated “compatible” stroma in the initial stages of metastatic establishment. Myofibroblasts produce and modify the extracellular matrix (ECM), secrete angiogenic and proinflammatory factors, and stimulate epithelial cell proliferation and invasion. Myofibroblasts were originally characterized by their role in wound healing (31) and its induction has been associated with diverse types of organ fibrosis(32). Myofibroblasts are also abundant in the reactive tumor stroma and are an established source of tumor promoting factors such as cell surface proteins, secreted growth factors, and ECM proteins (33, 34). A direct prometastatic effect of tumor-associated myofibroblasts was also recently implied (35). Consistent with our hypothesis, it has also been suggested that myofibroblasts are derived from malignant or normal epithelial cells undergoing EMT(36). Additional support for this hypothesis comes from the fact that increased levels of collagens and laminins have been associated with an increased likelihood of clinical metastasis of multiple human solid tumors (13). To substantiate the relevance of the mesenchymal signature, we selected S100A4 for functional validation. Enforced repression of this factor profoundly affected metastatic activity in agreement with previous studies that demonstrated that S100A4 is a crucial myofibroblast-expressed factor regulating metastasis (37). Furthermore, the expression of S100A4 in renal tubular epithelium undergoing EMT during fibrosis (38), together with the fact that proximal tubules arise from the differentiation of mesenchymal cells, suggest a model wherein RCC epithelial cells reverse this developmental process back to the original mesenchymal state, in order to metastasize. The plasticity of the renal epithelial cell has been further supported by studies of the role of the developmental gene, GREM1, which is reactivated in adult renal fibrotic disease (39). Remarkably, GREM1 is also a component of the myofibroblastic signature identified in our in vitro model of RCC progression.

Although deregulation of gene expression during tumor progression is relatively well characterized, the associated genetic and molecular mechanisms are largely unknown. In addition, although the sequence of genetic events that drive the establishment of primary tumors has begun to emerge, the subsequent events that lead to metastasis have remained generally obscure. As a consequence, few mutations, genomic alterations, or allelic imbalances are currently known to distinctively endow tumor cells with metastatic functions (40). We reasoned that by studying the mechanisms by which RCC genomes are deranged during metastatic conversion, we would be able to improve the ability to pinpoint critical genes involved in this process. By analyzing mRNA expression levels and DNA copy number changes in parallel, we found that changes in gene dosage have a modest effect on gene expression. These results suggest that during the evolution toward a metastatic phenotype, these cells acquire a set of chromosomal losses and gains that are perhaps associated with retention or otherwise of specific cell clones within the cell population. Although, none of the genes mapped to regions of genomic imbalance related to mesenchymal functions, we cannot exclude that these changes do not indirectly impact the mesenchymal signature.

In addition to changes in DNA sequence, gene expression can be modulated by aberrations in the patterns of DNA methylation. However, the pattern of DNA methylation abnormalities in cancer cells seems paradoxical. Compared with normal cells, cancer cells are concomitantly hypermethylated at specific CpG island sequences but hypomethylated at CpGs found in most other sites, resulting in a net loss of genomic 5meC content(41). Understanding the relative timing of DNA hypermethylation and hypomethylation alterations in cancer is crucial to understanding the importance of these changes during tumor progression. We found that a number of mesenchymal-associated genes were reactivated on 5'-AZA treatment suggesting that they were hypermethylated in the poorly metastatic cells. S100A4showed complete methylation of the intronic CpG sites analyzed in the poorly metastatic cells, consistent with its epigenetic transcriptional silencing. Some of those genes, including S100A4, COL1A1, and GREM1, have previously been shown to be methylated in other tumor models and in RCC (42–44). The proposed impact of promoter methylation on EMT is not unprecedented in the literature, as altered methylation of some gene promoters have been reported to be one of the principal causes of EMT during tumor progression (45) or during epithelial to myofibroblast transition (46).

Several bioinformatic algorithms have been constructed to predict miRNA gene targets. These algorithms predict hundreds of potential gene targets, which cannot all be experimentally validated. Integration of putative targets with mRNA expression date provides a rational method to prioritize functionally relevant targets.

Interestingly, the most significant correlation in this study was found between Let-7 and COL1A1, COL1A2, COL3A1, COL9A3. In consonance with our results, Let-7  is widely viewed as a tumor suppressor miRNA (47, 48).

Our findings, revealing the lack of major genetic and epigenetic alterations accounting for the direct deregulation of miRNA expression, suggest that miRNAs alterations are likely to be induced indirectly as a consequence of the dysregulation of specific transcription factors. Moreover, this observation argues that miRNA might be downstream targets of pathways that are commonly dysregulated in cancer and not initiating events during tumor progression.

In summary, in this study, we have proposed and provided proof of principle for a new mechanism of tumor progression in RCC based on acquisition of a myofibroblastic trait by tumor cells. We also showed that this approach that cross-references multiple whole-genome datasets can identify targets and genetic mechanisms important for tumor progression.

Disclosure of Potential Conflicts of Interest:

No potential conflicts of interest were disclosed.

Acknowledgments:

We thank I. Fidler for providing the SN12C cell lines.

Grant Support This study was supported by grants from the V-Foundation, a Syms kidney cancer award, Pfizer, Inc., and the NIH (CA-121327).

Footnotes:

Supplementary data for this article are available at Cancer Research Online:

http://cancerres.aacrjournals.org/content/70/23/9682/suppl/DC1

This analytic study by Miguel López-Lago, Venkata Thodima, Asha Guttapalli, Timothy Chan, Adriana Heguy,  Ana Molina, Victor Reuter, Robert Motzer, and Raju Chaganti, examines the relationship between expressed genes and metastases in mouse and human renal cell carcinoma cell lines and human tumor samples in vitro and within xenografts into mice. Both primary and metastatic lesions were studied, gene transcription and DNA lesions were determined, and it was found that metastases displayed active epithelial-mesenchymal transitions (EMT) that correlated positively with the rates of formation of metastases. It was concluded that EMT activation of epithelial cell transformation into mesenchymal cells is the chief mechanism in the formation of metastases, and that gene or chromosome lesions were less important. Such EMT transformations display a reversion to earlier embryonic activity, and may provide the major embryonic gene network mechanism for formation of embryomas within both metastatic and primary neoplasms.

1. Roussos ET, Keckesova Z, Haley JD, Epstein DM, Weinberg RA, and Condeelis JS,
"AACR Special Conference on Epithelial-Mesenchymal Transition and Cancer Progression and Treatment".

2. Frenster JH, and Hovsepian JA,
"Reprogramming the human cancer cell nucleus".

Quantitative DNA methylation analysis

DNA methylation analysis was carried out using the Epityper system from Sequenom
(San Diego, CA). The EpiTYPER assay is a tool for the detection and quantitative
analysis of DNA methylation using base-specific cleavage of bisulfite-treated DNA and
Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDITOF
MS). Specific PCR primers for bisulfite-converted DNA were designed using the
EpiDesigner software (http://www.epidesigner.com), for the entire CpG island of the genes of
interest. T7-promoter tags are added to the reverse primer to obtain a product that can
be in vitro transcribed, and a 10-mer tag is added to the forward primer to balance the
PCR conditions. For primer sequences, target chromosomal sequence, and Epityper
specific tags, (see Fig S11). One µg of tumor DNA was subjected to bisulfite treatment
using the EZ-96 DNA methylation Kit, which results in the conversion of unmethylated
cytosines into uracil, following the manufacturer’s instructions (Zymo Research, Orange,
CA). PCR reactions were carried out in duplicate, for each of the 2 selected primer
pairs, for a total of 4 replicates per sample. For each replicate, 1 µl of bisulfite-treated
DNA was used as template for a 5 µl PCR reaction in a 384-well microtiter PCR plate,
using 0.2 units of Kapa2G Fast HotStart DNA polymerase (Kapa Biosystems, Cape
Town, South Africa), 200 µM dNTPs, and 400 nM of each primer. Cycling conditions
were: 94 ^oC for 15 minutes, 45 cycles of 94 ^oC for 20 seconds, 56 ^oC for 30 seconds, 72
^oC for 1 minute, and 1 final cycle at 72 ^oC for 3 minutes. Unincorporated dNTPs were
deactivated using 0.3 U of shrimp alkaline phosphatase (SAP) in 2 µl, at 37 ^oC for 20
minutes, followed by heat inactivation at 85 ^oC for 5 minutes. Two µl of SAP-treated
reaction were transferred into a fresh 384-well PCR plate, and in vitro transcription and
T cleavage were carried out in a single 5 µl reaction mix, using the MassCleave kit
(Sequenom) containing 1 X T7 polymerase buffer, 3 mM DTT, 0.24 µl of T Cleavage
mix, 22 units of T7 RNA and DNA polymerase, and 0.09 mg/ml of RNAseA. The
reaction was incubated at 37 ^oC for 3 h. After the addition of a cation exchange resin to
remove residual salt from the reactions, 10 µl of Epityper reaction product were loaded
onto a 384-element SpectroCHIP II array (Sequenom). SpectroCHIPs were analyzed
using a Bruker Biflex III matrix-assisted laser desorption/ionization–time of flight
(MALDI-TOF) mass spectrometer (SpectroREADER, Sequenom). Results were
analyzed using the Epityper Analyzer software, and manually inspected for spectra
quality and peak quantification.

Migration, invasion and transendothelial assay

A scratch-wound–closure assay was used to compare migration of SN12C and LM cells.
Monolayers were wounded by two perpendicular linear scratches across each well with
a 10-µL pipette tip, to produce 300-µm-wide strips. Wounds were photographed at the
indicated times.

For invasion assay cells (1 × 10⁵) were counted using Trypan blue reagent (Sigma-
Aldrich, St. Louis, MO), normalized for the number of viable cells, and placed in SFM on
Transwell inserts coated with 2 mg Matrigel. Following incubation in 24-well plates
containing complete medium for 6–8 hours, the noninvasive cells were removed with a
cotton swab. The cells that have migrated through the membrane and stuck to the lower
surface of the membrane were fixed with methanol and scored under a microscope.
For transendothelial assay, HUVECs were seeded into collagen-coated trans-well
inserts (1 mm pore size, BD Falcon) at 100,000 cells per well, and allowed to grow to
confluence for 4 days. Tumour cells were pulsed with 5 mM cell tracker green
(Invitrogen, Carlsbad, CA) for 30 min before being conditioned overnight in 0.2% FBS
ECM media without growth factors. The next day, 50,000 tumour cells were seeded into
trans-well inserts with or without a confluent endothelial monolayer, and the wells were
fixed in 4% paraformaldehyde after 10 h. Cells on the apical side of each insert were
scraped off and the trans-well membrane mounted onto slides. Migration to the
basolateral side of the membrane was visualized with a microscope.

Bioinformatics

Affymetrix CEL files were normalized using the RMA preprocessing algorithm
and significantly differentially expressed genes were identified using the Significance
Analysis of Microarrays (SAM) algorithm [24]. SAM analysis was performed on the
normalized RMA data with a two-class unpaired t-test option and 500 iterations. In
accordance with Minimum Information About a Microarray Experiment guidelines, raw
data files for all cell lines and clinical specimens have been deposited in the Gene
Expression Omnibus public repository (GEO accession no. GSE 23631). Genomic copy
number data and their integration with gene expression data were performed using the
Partek® software, version 6.5 (Partek Inc., St. Louis, Missouri). The Partek
segmentation algorithm was used to identify significant copy number change regions
following quantile normalization of the raw data. The smoothing parameters for this
segmentation algorithm were set to 'minimum genomic markers' as 7, 'the segmentation
p-value threshold' as 0.001, and the signal-to-noise at 0.3. The reporting parameters of
a region called to be aberrant were set as 'expected range' at 0.3 with a P-value of
0.01. Regions of significant gain or loss were filtered based on the category attribute,
here LM versus the parental. Aberrant regions were annotated to the corresponding
genes and correlated with the expression data obtained from SAM analysis. Data from
the Agilent miRNA profiles were quantile-normalized using the Partek Genome Suite
and the significantly differentiated miRNAs were identified by SAM analysis. The GLAD
(Gain and Loss Analysis of DNA) algorithm aims at identifying the chromosomal regions
with identical DNA copy number, which is delimited by breakpoints. More details about
the GLAD algorithm can be found in Hupé P, et al. Analysis of array CGH data: from
signal ratio to gain and loss of DNA regions. Bioinformatics (2004) 20:3413–3422.
The GLAD algorithm segments the genomic profile, defining regions of homogeneous
DNA copy number. This function allows the detection of breakpoints in genomic profiles
obtained by array CGH technology, for each of these regions; it provides a smoothing
value and a status (gain, normal or loss).

Analyisis of mRNA expression

Four-hundred nanograms of total purified RNA was subjected to a reverse transcriptase
reaction according to the manufacturer's protocol (invitrogen). cDNA corresponding to
approximately 4 ng of starting RNA was used in three replicates for quantitative PCR.
Indicated Taqman gene expression assays (Applied Biosystems) and the Taqman
universal PCR master mix (Applied Biosystems) were used to quantify expression.
Quantitative expression data were acquired and analyzed using an ABI Prism 7900HT
Sequence Detection System (Applied Biosystems).

1. Each cell retains all of its embryonic genes for a lifetime.

2. Controls for embryonic genes are often absent in adults.

3. Uncontrolled embryonic genes can replicate wildly.

4.  Replicating genes participate in  intra-cellular competition.

5.  The basis for gene competition is selective transcription.

6.  MicroRNAs can reprogram embryomic transcription.

7.  Gene reprogramming can produce normal phenotypes.

8.  Normal phenotypes can by-pass chromosomal lesions.

9.  MicroRNA therapy may need to be permanent.

10. Transplantation of microRNAs could be preferred.

http://www.embryomas.net/

1. Pathways within cell genomes involve a flow of information.

2. Information can flow by direct contact or by third parties.

3. Direct contact within whole genomes is difficult to regulate.

4. DNA-DNA direct contects are influenced by agents.

5. Nuclear agents include hydrophilic ionic and hydrophobic conforming ligands.

6. Third parties within genomes involve RNAs and proteins.

7.  RNAs and proteins are easy to regulate or reverse.

8.  Information can be shared, lost, or transformed.

9. System information can be hidden during system isolation.

10.  Local information can be permanently lost during system entropy.

http://www.cancerbiophysics.net/

Links to Current Research in Euchromatin:
Links to Euchromatin Activator RNA Reviews:
Links to Euchromatin Activator RNA Research:
Links to Ultrastructural Probes of DNase I-Sensitive Sites:
Links to RNA as a Therapeutic Agent:
Links to Hodgkin Lymphoma Immuno-Pathology:
Links to Activated T-Lymphocyte Immunotherapy:
Links to Medical Systems Biology:
Links to Selective Gene Transcription:
Links to RNA-Induced Epigenetics:
Links to RNA-Induced Embryogenesis:
Links to RNA and Biological Causality:
Links to Reprogramming and Neoplasia:

A Brief History of Activator RNA:

"Ultrastructural Probes of Active DNA Sites, and the RNA Activators of DNA".
(PowerPoint Presentation).

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For Further Information and Feedback:

Jeannette A. Hovsepian, M.D.
E-mail: frensasc@ix.netcom.com
Phone:  +1 650 367 6483