Abstract
Purpose: We aim to identify tumor-specific alternative splicing events having potential applications in the early detection, diagnosis, prognosis, and therapy for cancers.
Experimental Design: We analyzed RNA-seq data on 470 clear cell renal cell carcinomas (ccRCC) and 68 kidney tissues to identify tumor-specific alternative splicing events. We further focused on the fibroblast growth factor receptor 2 (FGFR2) isoform switch and characterized ccRCCs expressing different FGFR2 isoforms by integrated analyses using genomic data from multiple platforms and tumor types.
Results: We identified 113 top candidate alternatively spliced genes in ccRCC. Prominently, the FGFR2 gene transcript switched from the normal IIIb isoform (“epithelial”) to IIIc isoform (“mesenchymal”) in nearly 90% of ccRCCs. This switch is kidney specific as it was rarely observed in other cancers. The FGFR2-IIIb ccRCCs show a transcriptome and methylome resembling those from normal kidney, whereas FGFR2-IIIc ccRCCs possess elevated hypoxic and mesenchymal expression signatures. Clinically, FGFR2-IIIb ccRCCs are smaller in size, of lower tumor grade, and associated with longer patient survival. Gene set enrichment and DNA copy number analyses indicated that FGFR2-IIIb ccRCCs are closely associated with renal oncocytomas and chromophobe RCCs (chRCC). A reexamination of tumor histology by pathologists identified FGFR2-IIIb tumors as chRCCs and clear cell papillary RCCs (ccpRCC).
Conclusions: FGFR2 IIIb RCCs represent misdiagnosed ccRCC cases, suggesting FGFR2 isoform testing can be used in the diagnosis of RCC subtypes. The finding of a prevalent isoform switch of FGFR2 in a tissue-specific manner holds promise for the future development of FGFR2-IIIc as a distinct early detection biomarker and therapeutic target for ccRCC. Clin Cancer Res; 19(9); 2460–72. ©2013 AACR.
Translational Relevance
Aberration in alternative mRNA splicing is implicated in tumorigenesis and cancer progression. Through in silico analysis of The Cancer Genome Atlas (TCGA) RNA-seq data, we identified fibroblast growth factor receptor 2 (FGFR2) isoform switch from the normal epithelial “IIIb” isoform to the mesenchymal “IIIc” isoform in ∼ 90% of ccRCCs. This was experimentally validated by PCR sequencing using additional ccRCC cases. FGFR2-IIIb RCCs do not exhibit activated mesenchymal gene expression signature, are less hypoxic, and associated with longer patient survival. Through integrated cancer genomics analyses, we concluded that FGFR2-IIIb RCCs were misdiagnosed clear cell papillary RCC (ccpRCC) and chromophobe RCC (chRCC) cases. The FGFR2 isoform switch is ccRCC specific, as it is not observed in other cancers examined including those of breast, lung, colon, head and neck, endometrium, and bladder. Our study shows FGFR2-IIIc switch as a hallmark event in ccRCCs and suggests FGFR2-IIIc isoform as a promising candidate biomarker for early detection, diagnosis, and targeted therapy for ccRCC.
Introduction
Alternative splicing is a key mechanism that provides functional diversity for the eukaryotic genome. Aberrant splicing has been implicated in many human diseases including cancer (1). In breast cancer, an epithelial-to-mesenchymal transition (EMT)-driven alternative splicing program is thought to induce phenotypic changes during malignant transformation (2). In glioma and other cancers, the glycolysis enzyme pyruvate kinase is shifted from isoform PKM1 to an alternatively spliced isoform PKM2 to enhance aerobic glycolysis and facilitate tumor growth (3, 4). In colorectal cancer, unique expression of an alternative spliced SLC39A14 isoform is considered a specific marker for colon cancer and a surrogate for activation of the WNT signaling pathway (5). In myelodysplastic syndromes (MDS) and leukemia, mutations were found to strike multiple components of the RNA splicing machinery, with mutations in the splicing factor SF3B1 found in more than 80% of a subtype of MDS named refractory anemia with ring sideroblasts (6), although the molecular targets of these mutations remain unclear. A large body of evidence suggests that cancer-specific splicing events not only can serve as diagnostic markers but also as potential targets for therapeutic intervention. Hence, it is of great interest to detect cancer-specific alternative splicing events in the context of complex cancer genomes and understand their mechanisms in tumorigenesis and tumor progression.
Fibroblast growth factor receptor 2 (FGFR2) belongs to a family of transmembrane receptor tyrosine kinases (designated FGFR1-4), which are involved in the regulation of cell proliferation, differentiation, migration, wound healing, and angiogenesis during development and adult life (7). At least 18 FGF ligands (FGF 1–10 and 16–23) were identified to bind and activate FGFRs with different binding specificities (8). The extracellular domain of FGFR2, which is composed of 3 immunoglobulin (Ig)-like domains, mediates ligand binding as well as interaction with heparin sulfate proteoglycans. An amino acid segment within the third Ig-like domain is determined by an alternative splicing event at the mRNA level to produce either the FGFR2 IIIb or IIIc isoform. This is regulated in a tissue-specific manner with the IIIb isoform expressed in epithelial cells and IIIc in mesenchymal cells, and results in a change in ligand-binding specificity. FGFR2 IIIb binds specifically to FGF-1, 3, 7, 10, whereas IIIc binds to FGF-1, 2, and 9 (9, 10). Similar splicing events occur in FGFR1 and FGFR3, but not in FGFR4. Two DNA sequences (ISE-2 and ISAR) located in the intron between IIIb and IIIc exons are required for cell type-specific splicing of exon IIIb (11, 12). The RNA-binding proteins ESRP1, 2 are splicing factors known to modulate FGFR2 IIIb splicing through these cis-elements (11). Ectopic expression of ESRP1 or 2 is sufficient to induce the isoform switch from mesenchymal IIIc to epithelial IIIb (12).
Functional studies using mouse models suggest that FGFR2 IIIb plays a critical role in mesenchymal–epithelial signaling during early organogenesis. Studies based on expression of a soluble dominant negative FGFR2 IIIb mutant in mice have implicated FGFR2 in the development of many organs including the limb, lung, skin, kidney, and several glandular tissues (13–15). Results from studies on knockout mice corroborate these findings. FGFR2 IIIb-null mice are viable until birth, with severe defects of limbs, lung, anterior pituitary gland, and dysgenesis of many organs including the kidney, thymus, and pancreas (16–18).
Developmentally, the mammalian kidney is derived from intermediate mesoderm. For kidney formation, both EMT and the reciprocal mesenchymal–epithelial induction between the epithelial ureteric bud and the metanephric mesenchyme occur during embryogenesis (19). Hence, unlike other types of cancers, such as breast and colon cancer, where putative tumor-initiating cells originate from epithelial cells, the stem cell origin of renal cell carcinomas (RCC) is less clear due to the presence of both epithelial and mesenchymal cells during kidney development. Histologically, RCCs are divided into several major subtypes, including clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), and renal oncocytoma. Although ccRCC and pRCC have high expression of the mesenchymal marker vimentin, chRCC and renal oncocytoma express the epithelial marker E-cadherin. This suggests that different subtypes of RCC may develop from cells of different origins and/or experience EMT during tumor progression.
Alterations of the FGFR2 gene, especially FGFR2 IIIb, have been reported in many cancers, with evidence for both tumor promoting and suppressing roles within specific cancer contexts (20). For example, consistent with a tumor promoting role, FGFR2 IIIb is amplified in a subset of gastric cancers, which are poorly differentiated and do not have ERBB2 amplification (21, 22). Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene, with single-nucleotide polymorphism (SNP) located inside FGFR2 exon 2 correlated with increased risk of breast cancer (23, 24). Transfection of FGFR2 IIIb cDNA into NIH/3T3 fibroblasts induced foci formation (25). In addition, missense activating mutations of FGFR2 were found in endometrial, breast, lung, and ovarian cancer (26, 27). However, FGFR2 IIIb also exhibits cancer-specific properties suggestive of tumor suppressor functions. FGFR2 IIIb expression is downregulated in solid tumors, such as in bladder, prostate, and salivary cancer when compared with adjacent normal epithelial counterparts (28–30). High expression of FGFR2 IIIb is associated with a more differentiated phenotype and better prognosis in many cancers, such as liver, colon, and breast cancer (31–34). Forced expression of FGFR2 IIIb in prostate, bladder, and salivary adenocarcinoma cells reduces tumor growth through induction of differentiation and apoptosis (30, 31, 35). Thus, the role of FGFR2 in tumorigenesis seems to be largely tumor-specific.
In vitro studies have shown that isoform switch from FGFR2 IIIb to FGFR2 IIIc occurs in prostate cancer cell lines and xenografts, and is accompanied by increased malignancy (36, 37). However, such isoform switch has not been documented in patient samples from any type of tumors. In one study using 19 local and metastatic prostate tumors with normal controls, one single case of tumor was found to have increased FGFR2 IIIc expression, whereas the majority of tumors including the metastatic ones showed prominent expression of FGFR2 IIIb (38). In this study, through mining RNA-seq data on the exon expression level and exon junction reads, we identified specific expression of FGFR2 IIIc in nearly 90% of ccRCCs, as compared with predominant FGFR2 IIIb expression in normal kidneys. This isoform switch of FGFR2 is tissue-specific as it is rarely observed in paired normal and tumor tissues in breast, lung, colon, bladder, and head and neck cancer. Compared with FGFR2 IIIb ccRCCs, FGFR2 IIIc ccRCCs have higher expression of mesenchymal and hypoxic genes, are more malignant, and have worse clinical outcome.
Materials and Methods
Molecular profiling datasets and data preprocessing
Level 3 RNA-seq data (containing data on gene, exon, and junction levels), level 3 SNP array data, level 2 DNA methylation data (Infinium Human Methylation 450), mutation data, and clinical data for multiple cancers were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://tcga-data.nci.nih.gov/tcga/dataAccessMatrix.htm) by January 2012. For DNA methylation data, M values were calculated as log2 ratio of methylated intensity over unmethylated intensity (39). Normalized gene expression datasets of mouse kidney development and RCC subtypes (GSE1983 and GSE15641) were downloaded from NCBI GEO website (http://www.ncbi.nlm.nih.gov/geo). Probesets with matching human orthologs were kept by referencing University of California Santa Cruz (UCSC) blast tables (http://genome.ucsc.edu/cgi-bin/hgTables).
Identification of recurrent tumor-specific alternative splicing events
For each exon, its RPKM (Reads Per Kilobase per Million mapped reads, a measure of expression level by RNAseq) is normalized by dividing the sum RPKM of all exons from a gene. To identify recurrent ccRCC-specific alternative splicing events, we first limited exons to have a more than 1.25-fold difference of median normalized RPKM between normal kidney and ccRCCs. To exclude low-expressing exons, which might represent background or low frequency events, we also excluded exons having a median normal and tumor exon RPKM below 0.5. A total of 6,820 exons remain after this filtering step. The “limma” package in R was used to identify differentially expressed exons, which is essentially conducting Student t tests on normalized exon RPKMs between normal and tumor samples with adjusted output P values. We selected exons having more than 3-fold difference in expression and adjusted P value < 1e−5. Candidate exons from the above screen were further validated by junction reads using a similar strategy. For each junction read associated with a candidate exon, we determined whether there was a more than 3-fold difference in median normalized junction reads between normal and tumor. Multiple junctions may be associated with one single exon. We selected candidate exons with more than 50% junctions that passed this test. The frequency of altered splicing events in ccRCC was calculated as the percentage of tumors having exon RPKM values below or above 1.5 times the SD of normal exon RPKM reads. We selected exons with an altered frequency of more than 50%. Candidate exons were annotated using UCSC gene tables (http://genome.ucsc.edu/cgi-bin/hgTables) to designate exons as first (representing alternative transcriptional start), last (representing alternative transcriptional end), or internal within any known transcripts. R scripts for this study are available upon request.
Computational prediction of FGFR2 IIIb/c isoforms using RNA-seq data
To assign samples into FGFR2 IIIb/c groups, we calculated expression ratio of FGFR2 exon 8 (hg19:chr10:123,278,343-196) over exon 9 (hg19:chr10:123,276,986-728). Samples with more than 2-fold expression of exon 8, supported by 2-fold more reads from either junction of exon 7–8 over 7–9 or exon 8–10 over 9–10 were assigned to the FGFR2 IIIb group. Samples with more than 2-fold expression of exon 9, and supported by 2-fold more reads from either junction of exon 7–9 or 9–10 were assigned into the FGFR2 IIIc group. To deal with low expressers, for each sample, at least one junction raw reads involved must be equal or more than 3. Samples that met these criteria were called as either “IIIb” or “IIIc.” Otherwise, the sample was marked as no call.
Experimental validation of FGFR2 isoform switch in ccRCCs
A total RNA of 18 primary ccRCC tumor samples, composed of stage T1 to T3 tumors, were obtained by Dr. Ian Davis from the Victorian Cancer Biobank, which is supported by the Victorian Government, Australia, with appropriate ethics approval. Two ccRCC tumor cell lines (Caki-1 and 786-O) were purchased from American Type Culture Collection (ATCC) and grown according to the ATCC's recommendations. Total cellular RNA was harvested and subjected to real-time (RT)-PCR using primers specific for the exons upstream and downstream of the alternative exons IIIb and IIIc as shown in (Supplementary Fig. S2). cDNA was amplified with the OmniScript RT kit using 1 μg of total RNA. Primer sequences flanking exon8/9 are: forward-GGATCAAGCACGTGGAAAAG in exon7 and reverse-TCGGCACAGGATGACTGTTA in exon10. JumpStart REDTaq ReadyMix PCR Reaction Mix (Sigma) was used for PCR amplifications after the addition of 5 pmoles of each primer and one μL of the cDNA solution in 25 μL. The PCR conditions were 95°C for 3 minutes followed by 42 cycles at 95°C for 15 seconds and 60°C for 30 seconds and 72°C for 30 seconds, followed by a final 7-minute extension. PCR products were gel-purified and submitted to Sanger sequencing with the PCR primers to confirm the identity of the amplicon.
Clustering analysis and statistical testing
Exon level data were log transformed and clustered using the “hclust” function from R software (http://www.r-project.org). The options used were “canberra” for distance calculation, and “median” for clustering. We chose to use these conditions to better separate samples on both differential exon expression and overall FGFR2 expression. All other clustering analyses were done using the Cluster and TreeView software (http://rana.lbl.gov/EisenSoftware.htm). Fisher exact test, Wilcoxon test, Cox proportional hazard regression, and Kaplan–Meier log rank test were done using the R software.
Gene set enrichment analysis to identify RCC subtypes most related to FGFR2 IIIb RCCs
Differentially expressed genes between oncocytoma, chRCC, normal kidney, and ccRCC were identified by significance analysis of microarrays (http://www-stat.stanford.edu/~tibs/SAM) of published microarray data GSE15641. Genes with more than 3-fold differences in expression with a false discovery rate less than 0.01 were selected and were limited to top 250 genes with highest folds of difference. These RCC subtype-specific up- and downregulated gene signatures were combined with the MSigDB c4 computational gene sets (cancer-orient expression sets, available at http://www.broadinstitute.org/gsea/index.jsp) and subjected to gene set enrichment analysis (GSEA; ref. 40) using efault conditions on 22 FGFR2-IIIb tumors versus 55 randomly picked FGFR2-IIIc tumors.
Results
Identification of tumor-specific alternative splicing of FGFR2 mRNA in ccRCC
To search for recurrent tumor-specific alternative splicing events in ccRCC, which may play a critical role in tumorigenesis and tumor progression, we examined the RNA-seq data from the TCGA datasets on 470 ccRCCs and 68 normal kidney tissues (see Materials and Methods for algorithm details). This yielded a top candidate list of 234 exons from 113 genes (Supplementary Table S1). About one third of these cases involved 5′ alternative transcriptional start, and in 64% of cases, we observed differential exon usage (examples are shown in Supplementary Fig. S1).
We focused our study on the FGFR2 gene, because this is the only gene where we observed mutually exclusive exon usage, and because the known important role of FGFR2 in tumor growth, invasion, and stem cell maintenance. As shown in Fig. 1A, the median normalized expression of FGFR2 exon 8 (encoding FGFR2 IIIb) is statistically significantly higher in normal kidney than in ccRCC, and this situation is reversed in exon 9 (encoding FGFR2 IIIc). The finding is confirmed by the unsupervised clustering of exon expression reads of both normal kidney and ccRCCs (Fig. 1B middle), where exon 8 expression is clearly associated to normal tissues and exon 9 to ccRCCs. Notably, a small fraction of ccRCCs cluster with normal kidney and also express exon 8. These results are further corroborated by the exon junction sequence reads (Fig. 1B bottom), where normal tissues have more reads of exon 7–8 and exon 8–10, whereas the majority of ccRCCs predominantly have reads of exon 7–9 and exon 9–10. Clearly, most ccRCCs express FGFR2 having exons 7-9-10 (FGFR2 IIIc) and normal kidney expresses FGFR2 with exons 7-8-10 (FGFR2 IIIb). Another tumor-specific change in FGFR2 exon19 (Fig. 1A) could not be validated by the junction data. Using exon and junction sequencing reads, we were able to predict the major FGFR2 isoform present in both normal kidney and ccRCCs (see Materials and Methods). This predicted 47 (out of 68) normal tissues and 22 ccRCCs as IIIb type, and 398 ccRCCs as IIIc type (Supplementary Table S2). None of the normal tissues were predicted as major IIIc type, although there were 21 normal (31% of 68) and 50 ccRCCs (11% of 470) whose major FGFR2 isoform type could not be determined, presumably due to low gene expression or comparable expression of both FGFR2 IIIc and IIIb isoforms. To validate our findings of FGFR2 IIIc as the major isoform expressed in ccRCCs, we amplified the mRNA coding region spanning FGFR2 exon8 and exon9 in 18 primary ccRCC tumors and 2 ccRCC cell lines using RT-PCR followed by Sanger sequencing of the PCR products (See Materials and Methods). This showed that exon9 (IIIc) is the predominant form expressed in all 20 ccRCC samples tested (example result shown in Supplementary Fig. S2).
Alternative splicing of the FGFR2 mRNA in normal kidney and ccRCC. A, comparison of median normalized RNA-seq RPKM on each FGFR2 exon between normal kidney (blue) and ccRCC (pink). Exons with statistically significant changes were marked (***, adjusted P value < 1e−5; see Materials and Methods for details). Schematic of FGFR2 splicing options involving exons 8 and 9 are shown on the top. B, unsupervised clustering of RNA-seq exon reads from 68 normal kidney samples and 470 ccRCCs (red, high expression; green, low expression). FGFR2 gene expression levels are aligned above the cluster heatmap (blue, normal kidney; pink, ccRCC). FGFR2 isoform calls are aligned on the top (green, IIIb isoform; pink, IIIc isoform; gray, not determined). Exon junction reads involving exon 7–10 are plotted at the bottom.
Isoform IIIc switch of FGFR2 is tissue specific and correlates with reduced expression of ESRP1 and GRHL2
To examine whether the FGFR2 IIIc switch is a more general event occurring in additional cancer types besides ccRCC, we used TCGA RNA-seq exon and junction level data to predict major FGFR2 isoforms present in normal and tumor tissues from a panel of cancer types including breast, lung, colon, head and neck, uterus, kidney, and bladder. This revealed that FGFR2 mainly exists as the IIIb isoform in all cancer types examined except ccRCC (Supplementary Table S3). Closer examination of paired normal and ccRCC tumor samples obtained from the same patient showed prevalent FGFR2 isoform switching from normal IIIb to tumor IIIc. This occurs only in ccRCCs, except for a single case of breast cancer with a clear IIIc switch (Fig. 2A). Thus, FGFR2 IIIc isoform switching in cancer has a strict tissue specificity.
Tissue-specific FGFR2 IIIc isoform switch correlates with reduced expression of ESRP1 in ccRCC. A, log2 values of FGFR2 exon 9/exon 8 ratios in paired normal (blue) and tumor (red) tissues from the same patient in multiple cancer types were drawn. Upward bars indicate existence of more FGFR2 IIIc isoforms. Downward bars indicate more IIIb isoforms. B, scatter plot of FGFR2 expression versus ESRP1/GRHL2 expression in ccRCC and normal kidney. Each sample is colored to show FGFR2 isoform and tissue type (dark blue, normal IIIb; green, tumor IIIb; red, tumor IIIc; gray, not determined). The yellow vertical lines mark the point where FGFR2 isoforms could be separated by ESRP1/GRHL2 expression, with Wilcoxon test P values (see Materials and Methods). C, ESRP1 gene expression in multiple normal and tumor tissues corresponds to FGFR2 isoform IIIb expression. Bottom, samples (blue, normal; red, tumor) are ordered by ESRP1 expression levels. For lung cancer, adenocarcinomas and matching normal controls are on the left and squamous carcinomas and matching normal controls are on the right. Top, corresponding FGFR2 exon 8/exon 9 ratios indicating presence of major isoform of IIIb (facing up) or IIIc (facing down).
To search for possible regulators controlling this isoform switch, we carried out the Wilcoxon test to find association of gene expression to FGFR2 isoforms in ccRCC, endometrial, and bladder cancer. The top 100 genes with the least P values from all 3 cancer types were collected and intersected (data not shown). This identified 2 genes in common, ESRP1 and GRHL2, among which ESRP1 is a splicing factor previously reported to control alternative splicing of FGFR2 (12). As shown in Fig. 2B, there is a good correlation of high ESRP1 or GRHL2 expression with the prediction of FGFR2 isoform being IIIb, where expression above the yellow lines correlated perfectly well with the presence of FGFR2 IIIb but not IIIc isoform. Because ESRP1 is a known FGFR2 splicing regulator, this result serves well as an indirect validation of our FGFR2 isoform prediction algorithm. When we looked at ESRP1 expression in all cancer types, we observed universally equivalent high-level expression of ESRP1 in most tumor tissues, as contrasted by a substantial reduction of ESRP1 expression in ccRCCs. High levels of ESRP1 were correlated with FGFR2 IIIb expression (Fig. 2C). Because FGFR1 and FGFR3 have exon structures similar to those of FGFR2, we also examined whether these receptors were undergoing similar isoform switches as in FGFR2. No isoform IIIc switch was found in FGFR1 and FGFR3 in ccRCCs (Supplementary Fig. S3). Thus, despite similar organization of exons, alternative splicing of FGFR1 and FGFR3 in ccRCC is either not controlled by ESRP1 alone or requires additional regulators. Taken together, these results suggest that FGFR2 IIIc switch occurs in a tissue-specific manner as well as in a FGFR2-specific manner within the FGFR receptor family.
FGFR2 IIIb ccRCC has gene expression and FGFR2 promoter methylation patterns resembling that of normal kidney but not IIIc ccRCC
To study the difference at the molecular level between FGFR2 IIIb and IIIc ccRCC, we carried out an unsupervised clustering of the top 3,600 variably expressed genes in 470 ccRCCs and 68 normal kidney tissues (Fig. 3A, similar results can be obtained using 2,500–5,000 genes). All normal tissues were clustered together as expected. The majority of ccRCCs were clustered into 2 major groups with one cluster of tumors being more malignant when assessed by tumor grade, stage, and size, although VHL mutation was observed to occur at similar frequency between the 2 subtypes. Interestingly, all FGFR2 IIIb ccRCCs were outside of these 2 major ccRCC clusters, clustered next to normal tissues, and possessed a global gene expression pattern very similar to that of normal samples, indicating these tumors have a different transcription program from FGFR2 IIIc ccRCCs. Notably, with FGFR2-IIIb ccRCCs, tumors can further be clustered into 2 subgroups based on their transcriptome differences (designated FGFR2-IIIb-1 and 2, Fig. 3A). We further carried out an unsupervised cluster analysis of promoter DNA methylation in 227 ccRCCs and 21 normal kidney tissues which have both RNA-seq and methylation data. This was done on 82 methylation probes within a region encompassing 20kbp of the FGFR2 gene. Again, normal tissues and most IIIb ccRCCs were clustered together (Fig. 3B). These results argue that FGFR2 IIIb ccRCC represents a small subset of renal clear cell carcinomas (22/470, ∼5%) constituting a distinct molecular subtype. In addition to isoform switch, we also observed a significant reduction of FGFR2 gene expression in ccRCC compared with that in normal kidney. This downregulation can be at least partially attributed to the hypermethylation of exon1 and intron1 region of the FGFR2 gene (Supplementary Fig. S4).
FGFR2 IIIb ccRCC exhibit gene expression and methylation patterns similar to those of normal kidney tissues. A, unsupervised clustering of top 3,600 variedly expressed genes in normal kidney and ccRCCs (red, high expression; green, low expression). White lines mark 3 major clusters. Sample characteristics are plotted at the bottom. Normal samples are marked in blue and tumors in pink above the zoomed heatmap. FGFR2 isoforms IIIb, IIIc, and not determined were marked as green, pink, and gray, respectively. Tumor grades are marked in blue, cyan, pink, and red for G1, G2, G3, and G4, respectively. Tumor stages are indicated in cyan, pink, and red for stage I/II, stage III, and stage IV, respectively. Tumor sizes are marked in cyan, pink, and red for stage T1, stage T2, and stage T3/4, respectively. VHL mutation status is marked in cyan, red, and white for wild-type, mutated, and not examined, respectively. Subgroups of FGFR2-IIIb RCCs were marked as FGFR2-IIIb-1, and -2. B, unsupervised clustering of DNA methylation status of probes inside the FGFR2 gene and 20kbp flanking area (red, hypermethylated; green, hypomethylated), with a zoomed in region where normal is hypomethylated. FGFR2 gene expression levels are aligned above the cluster heatmap (blue, normal kidney; pink, ccRCC). FGFR2 isoform calls are aligned on the top (green, IIIb isoform; pink, IIIc isoform; gray, not determined).
FGFR2 IIIb and IIIc ccRCCs show distinct gene expression signatures and signaling pathways
Although EMT is thought to be a critical event implicated in cancer progression and metastasis, the prevalence of mesenchymal FGFR2 IIIc ccRCCs among all ccRCCs studied (∼85%), together with results from recent studies on RCC stem cells, raises the possibility that FGFR2 IIIc RCCs may have a mesenchymal stem cell origin (41). To this end, we compared the gene expression differences between FGFR2 IIIb and IIIc ccRCCs with previously published data on mouse embryonic kidney development (42), in which gene expression of ureteric bud and metanephric mesenchyme, 2 integral components for kidney development with epithelial and mesenchymal origins, respectively, were profiled. This confirmed that FGFR2 IIIb ccRCCs displayed an epithelial phenotype, with elevated expression of epithelial markers E-cadherin (CDH1), keratin 7 (KRT7) and claudins (CLDN4, 7, 8), and reduced expression of mesenchymal markers N-cadherin (CDH2), collagen 23 A1 (COL23A1), and vimentin (VIM; ref.Fig. 4A).
FGFR2 IIIb and IIIc ccRCCs have distinct gene expression patterns and signaling pathways. A, samples are grouped into normal kidney, FGFR2 IIIb-1 and -2 RCCs, and IIIc ccRCCs. Gene expression data of EMT markers, FGF signaling components, HOX proteins, and cell surface markers are drawn as a heatmap (red, high expression; green, low expression). RNAseq RPKMs for each gene were logged, median removed, and normalized by gene using the Cluster software. Corresponding gene expression data of embryonic mouse kidney components (UB t, ureteric bud tip; UB s, ureteric bud stalk; MM, metanephric mesenchyme) are drawn on the right. B, samples are ordered in the same order as in A, and expression data of a 15-gene hypoxia gene expression signature are plotted as a heatmap. Expression levels of VHL, PTEN, and p53 genes are shown at the bottom, along with their copy number status (blue, copy loss; red, copy gain; white, copy neutral).
In addition, we observed other gene expression changes in signaling pathways between FGFR2 IIIb and IIIc ccRCCs, which are similar to those between mouse ureteric bud and metanephric mesenchyme tissues. For example, both FGFR2 IIIb RCCs and ureteric bud tips had elevated FGF9 gene expression. The HOXB genes are overexpressed and HOXA4 downregulated in FGFR IIIb RCCs as well as ureteric bud tips as compared with IIIc RCCs and metanephric mesenchyme. This is also true for a panel of cell surface markers (e.g., CD82). Because HOX gene expression perfectly matches the timing and route of development, combination of these results suggested that FGFR2 IIIc and IIIb ccRCC have originated from stem cells of mesenchymal and epithelial origins, respectively, during tumorigenesis.
Downregulation of VHL and hypoxia are hallmarks of renal cell carcinomas. When we examined the hypoxia response in FGFR2 IIIb and IIIc ccRCCs using a 15-gene hypoxic gene expression signature (43), we observed elevated expression of the hypoxic signature in IIIc ccRCCs (Fig. 4B) and FGFR2-IIIb-1 tumors. There is no activation of the 15-gene signature in normal kidney tissues, as expected. However, activation of this hypoxic signature in FGFR2-IIIb-2 ccRCCs is much lower compared with IIIc ccRCCs, suggesting they are less hypoxic (Fig. 4B). This result can be further confirmed using a different set of 36-gene hypoxia-inducible factor alpha target signature (data not shown). When VHL gene copy number was examined, we observed prevalent copy number loss in FGFR2-IIIc ccRCCs, as expected. However, VHL copy numbers are largely intact in IIIb-1 tumors despite they also exhibit elevated hypoxia activation, and VHL copy numbers are gained in FGFR2-IIIb-2 tumors. In contrast, FGFR2-IIIb-2 tumors exhibit loss of p53 and PTEN copy numbers, which is correlated with reduced expression of these tumor suppressors in these tumors (Fig. 4B). Taken together, these findings argue that FGFR2 IIIb and IIIc ccRCCs are two or more distinct molecular subtypes, and might represent different disease entities.
FGFR2 IIIb ccRCCs are closely related to clear cell papillary RCC and chromophobe RCC and have better clinical outcomes
The fact that FGFR2 IIIb ccRCCs possess gene expression and methylation patterns resembling normal kidney tissues, express epithelial markers, and do not carry VHL loss of heterozygosity (LOH) events, suggests that these tumors may be of a more differentiated state and less malignant in nature. We tested this hypothesis by examining the clinical parameters from 22 FGFR2 IIIb ccRCCs and 398 IIIc ccRCCs (available on the TCGA website). Remarkably, all 22 FGFR2 IIIb ccRCCs do not show distant metastasis (Table 1, Fisher exact test 2-sided P value = 0.034) and for those tumors having lymph node status, none of the IIIb ccRCCs had spread of tumor into regional lymph nodes. Compared with FGFR2 IIIc ccRCCs, IIIb tumors are statistically significantly smaller in tumor size and of lower tumor grade and stage (Table 1). Specifically, 18 of 22 FGFR IIIb tumors were diagnosed as stage T1 tumors. Only one out of 22 FGFR2 IIIb tumors was diagnosed as a stage T3 tumor (i.e., tumors extending into major veins or perinephric tissue and not beyond Gerota's fascia). This frequency (∼5%) is much lower than the overall T3/T4 frequency found in ccRCCs (42% for the TCGA dataset).
Fisher exact tests of clinical parameters in FGFR2 IIIb and IIIc ccRCCs
Because microarray gene expression profiles of different RCC subtypes are available (44; GSE15641), we further compared differential expression between FGFR2 IIIb and IIIc ccRCCs with known changes in gene expression between ccRCCs and other RCC subtypes, using GSEA analysis (see Material and Methods). This analysis indicated that FGFR2 IIIb ccRCC is most related to renal oncocytoma, which is benign in nature, and to chRCC (Fig. 5A). This result was further supported by examining the gene copy number alterations in FGFR2-IIIb and -IIIc tumors (Fig. 5B). On the basis of SNP array-derived copy number data, FGFR2-IIIb-1 tumors had minimum copy number alterations, whereas FGFR2-IIIb-2 tumors had copy number gains and losses on almost every autosome chromosome. Copy number changes in FGFR2-IIIb-2 tumors are recurrent at an extremely high frequency (∼ 90%) and this differentiates them from FGFR2-IIIc tumors, which had consistent VHL LOH on chromosome 3 (Fig. 5B, Supplementary Fig. S5). The cytogenetic abnormalities observed in FGFR2-IIIb-2 RCCs matched perfectly well with those reported for chRCCs (45), where LOH on chromosomes 1, 2, 6, 10, 13, 17, and 21 were reported. This strongly suggests FGFR2-IIIb-2 RCCs are in fact misdiagnosed chRCC cases, which is confirmed by pathologic reexamination of tumor histology. With respect to FGFR2-IIIb-1 tumors, although these tumors resemble renal oncocytomas on the transcriptome level, pathologically they were rediagnosed as clear cell papillary RCCs (ccpRCC), a unique RCC subtype known not to metastasize and which lacks known copy number alterations found in other RCC subtypes (46). Consistent with all these findings, Kaplan–Meier analysis on patient survival showed a trend that patients with FGFR2 IIIb RCCs had a better clinical outcome (Fig. 5C, Cox proportional hazard regression likelihood ratio test P = 0.047).
FGFR2 IIIb ccRCCs are related to renal oncocytoma and chRCC. A, GSEA enrichment score plots showing oncocytoma and chRCC-specific genes are highly enriched in FGFR2-IIIb tumors, whereas ccRCC-specific genes are negatively correlated. B, average gene copy number alterations across the genome in FGFR2-IIIb-1/2 and FGFR2-IIIc ccRCCs. Average gene copy number alterations were plotted for 9 FGFR2-IIIb-1, 9 FGFR2-IIIb-2, and 395 FGFR2-IIIc tumors across the genome. Red and blue represent copy number gain and loss regions. The y-axis ranges from −0.5 to 0.5 of log2 SNP array segment values. C, Kaplan–Meier plot of ccRCCs stratified by FGFR2 isoforms.
Discussion
In this study, we carried out an in silico analysis of the ccRCC transcriptome to identify recurring tumor-specific alternative splicing events. Two major types of tumor-specific alternative splicing events identified were alternative transcriptional start sites and exon skipping. For one gene (FGFR2), we identified a mutually exclusive usage of exons. Although previous studies suggested that pyruvate kinase isoform switch from PKM1 to PKM2 in cancers is important for cancer metabolism and tumor growth (3), we did not observe such a switch in paired normal kidney and ccRCCs (data not shown). However, this is consistent with a recent study reporting that such isoform shift does not occur in kidney, lung, liver, and thyroid cancers (47). In another interesting case, we observed disrupted expression of the AP1M2 gene at the 5′ end exons in ccRCC but not in normal kidney (Supplementary Fig. S1). The basis of this phenomenon is unclear but suggests possible gene translocation, alternative transcription start, or deletion of part of the AP1M2 gene.
Through integrated analysis using data from 3 types of cancers (ccRCC, endometrial, and bladder cancer), we identified ESRP1 and GRHL2 as putative regulators of the FGFR2 IIIc isoform switch (Fig. 2B and C). Identification of ESRP1 is proof-of-principle of our approach as previous in vitro studies have established a role for ESRP1 in controlling the fate of FGFR2 splicing in epithelial cells (12). Our analysis suggests that GRHL2 is also a likely regulator of FGFR2. This gene encodes a transcription factor with an emerging role as a master regulator in maintaining the epithelial phenotype and suppression of EMT (48, 49). In addition, our data revealed a good correlation between ESRP1/GRHL2 expression level with FGFR2 expression level in normal kidney (Pearson's r of 0.78 and 0.80, respectively), which is lost in ccRCCs (Pearson r of −0.013 and 0.017, respectively) in both IIIc and IIIb tumors (Supplementary Fig. S3). One possible explanation for this phenomenon is that in tumors an additional mechanism(s) such as promoter methylation contributes to FGFR2 transcriptional regulation. Indeed, ccRCCs are subjected to increased DNA methylation proximal to FGFR2 intron 1, which correlates with downregulation of FGFR2 expression (Supplementary Fig. S6).
EMT is a process that potentiates cell migration and invasion, and is thought to be a critical step in tumor metastasis. Consistent with this notion, we found FGFR2 IIIb ccRCCs, which retain an epithelial phenotype, do not have distant metastasis and lymph node spread (Fig. 4, Table 1). Surprisingly, while EMT is a known phenomenon in cancer, the FGFR2 IIIc switch, which is linked to the mesenchymal phenotype and EMT, was rarely detected in cancers except in ccRCC in our analysis of paired normal and tumor samples (Fig. 2A). Possible explanations are either FGFR2 IIIc expression per se is not important for EMT or EMT and FGFR2 IIIc switch occurs transiently or in a small percentage of tumor cells and thus cannot be detected by our analysis, which used bulk tumor tissues.
About 20% of FGFR2 IIIc ccRCCs from the TCGA dataset are recorded to have distant metastasis, a percentage much lower than IIIc isoform switch observed in these tumors, which is approximately 90% (Supplementary Table S3). In the context of embryonic kidney development during which both epithelial and mesenchymal stem cells are involved, we suggest that the prevalence of FGFR2 IIIc isoform switch points to a mesenchymal stem cell origin for this subtype of ccRCC. This is partially supported by data in Fig. 4A, and provides a plausible explanation for why the FGFR2 IIIc isoform switch is kidney-specific. Studies on tumor-initiating population of renal carcinomas also support this idea (41). Three types of EMT have been described (50). Type 1 EMT occurs during embryonic development and is associated with processes, such as implantation and gastrulation. Type 2 EMT is involved in response to inflammation, wound healing, and tissue regeneration. Type 3 EMT occurs in neoplastic cells and is attributed to tumor metastasis. Although we could not exclude the possibility that type 3 EMT occurs in ccRCC during tumor dissemination, we propose that FGFR2 IIIc expression in most ccRCCs may be linked to a type 1 EMT in the early developmental stage, when ccRCC first originated in cells having a mesenchymal phenotype.
This notion is further supported by our integrated GSEA and copy number analysis to suggest that FGFR2-IIIb RCCs are likely to be a mixture of ccpRCCs and chRCCs. Because the cancer genome of FGFR2-IIIb-2 RCCs possesses distinct marker LOH events on multiple chromosomes (1, 2, 6, 10, 13, 17, and 21) found in chRCCs, this suggests that conversion between chRCC and ccRCC tumors would not happen. This further indicates that FGFR2 isoform switch is an early event during RCC tumorigenesis, and may play an important role in regulation of tumor growth and invasion. Recently, TCGA has initiated construction of a chRCC dataset. We have checked the existing RNAseq data from this new dataset and confirmed that both normal and tumor tissues dominantly express the FGFR2 IIIb isoform (data not shown).
Although GSEA analysis suggested that FGFR2-IIIb-1 tumors possess a transcriptome resembling that of renal oncocytoma, a reexamination of the tumor histology revealed that these were actually ccpRCCs. This raises the possibility that ccpRCCs might develop from oncocytomas. Future study on gene expression profiling of ccpRCCs will answer the question as whether these tumors are similar to each other on the transcriptional level. Our study is based on significant changes in tumors as a whole, and would likely miss some splicing alterations occurring in only a subset of tumors. For example, an examination of CD44 splicing in ccRCC revealed that loss of expression of CD44 variant exons was enriched in a subset of tumors with better prognosis (corresponding to the tumors clustered on the right side in Fig. 3A, data not shown). Such tumor subtype-specific alterations would be missed in our current study.
Our identification of FGFR2 isoform switching in ccRCC has several potential clinical implications. First, detection of FGFR2 IIIb in RCC not only suggests the presence of ccpRCC or chRCC but also predicts a better prognosis. Second, given the high specificity of FGFR2 IIIc isoform switch, it is important to explore its potential use as an early detection biomarker for ccRCC in patient urine and serum samples. Third, FGFR2 IIIc and IIIb have different ligand specificity. Although FGFR2 IIIb binds specifically to FGF-1, 3, 7, 10, FGFR2 IIIc binds to FGF-1, 2, and 9. Therefore, FGFR2 IIIc or its specific ligands (FGF-2 and -9) potentially represent specific targets for ccRCC intervention. Our results show the power of integrated analyses of cancer genomics data toward improved understanding of potential molecular drivers of specific cancers, and suggest new approaches for targeted intervention of human cancers.
Disclosure of Potential Conflicts of Interest
W.K. Alfred Yung has a commercial research grant from Daiichi, has honoraria from speakers' bureau from Novartis and Merck, and is a consultant/advisory board member of Novartis, Merck, and Actelion. No potential conflicts of interest were disclosed by the other authors.
Authors' Contributions
Conception and design: Q. Zhao, W.K. Alfred Yung, R.L. Strausberg, J. Yao
Development of methodology: Q. Zhao, O.L. Caballero, E. Jonasch, J. Yao
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): O.L. Caballero, I.D. Davis, J. Yao
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Q. Zhao, E. Jonasch, P. Tamboli, W.K. Alfred Yung, J. Yao
Writing, review, and/or revision of the manuscript: Q. Zhao, I.D. Davis, E. Jonasch, P. Tamboli, W.K. Alfred Yung, J.N. Weinstein, R.L. Strausberg, J. Yao
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.N. Weinstein, K. Shaw
Study supervision: J. Yao
Grant Support
This study is supported in part by the TCGA grant U24CA143883 from NCI/NIH, and funds from Ludwig Institute for Cancer Research. I.D. Davis was supported by a Victorian Cancer Agency Clinician Researcher Fellowship.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Acknowledgments
The authors thank Drs. Ralf Krahe and Hongwu Zheng for critical reading of the manuscript and insightful discussions.
Footnotes
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Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
- Received December 2, 2012.
- Revision received January 25, 2013.
- Accepted February 4, 2013.
- ©2013 American Association for Cancer Research.
References
Volume 19, Issue 9
