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Combined comparative genomic hybridization and transcriptomic analyses of ovarian granulosa cell tumors point to novel candidate driver genes
© Caburet et al.; licensee BioMed Central. 2015
Received: 21 August 2014
Accepted: 27 March 2015
Published: 10 April 2015
Ovarian granulosa cell tumors (GCTs) are the most frequent sex cord-stromal tumors. Several studies have shown that a somatic mutation leading to a C134W substitution in the transcription factor FOXL2 appears in more than 95% of adult-type GCTs. Its pervasive presence suggests that FOXL2 is the main cancer driver gene. However, other mutations and genomic changes might also contribute to tumor formation and/or progression.
We have performed a combined comparative genomic hybridization and transcriptomic analyses of 10 adult-type GCTs to obtain a picture of the genomic landscape of this cancer type and to identify new candidate co-driver genes.
Our results, along with a review of previous molecular studies, show the existence of highly recurrent chromosomal imbalances (especially, trisomy 14 and monosomy 22) and preferential co-occurrences (i.e. trisomy 14/monosomy 22 and trisomy 7/monosomy 16q). In-depth analyses showed the presence of recurrently broken, amplified/duplicated or deleted genes. Many of these genes, such as AKT1, RUNX1 and LIMA1, are known to be involved in cancer and related processes. Further genomic explorations suggest that they are functionally related.
Our combined analysis identifies potential candidate genes, whose alterations might contribute to adult-type GCT formation/progression together with the recurrent FOXL2 somatic mutation.
Ovarian granulosa cell tumors (GCTs) are the most frequent sex cord-stromal tumors, and account for more than 5% of ovarian cancers . Two different forms, juvenile and adult, have been described based on the age of onset and histopathological features . GCTs tend to be low-grade malignancies, but can recur up to 40 years after primary tumor resection . Various studies have revealed that a somatic mutation leading to the p.C134W substitution in the transcription factor FOXL2 appears in > 95% of adult-type GCTs .
Transactivation studies have suggested that the p.C134W mutation could perturb the functional interaction between FOXL2 with SMAD3  and FOXL2 activity in other systems . This variant is also deficient in its ability to promote apoptosis  and displays a mild loss-of-function on targets involved in cell cycle and DNA-damage repair .
We have recently performed a transcriptomic profiling of 10 human adult-GCTs and ethnically-matched GC controls. This study showed that GCTs display several typical hallmarks of cancer. For instance, among FOXL2 direct targets, we detected an up-regulation of genes associated with cell cycle control and a down-regulation of genes related with apoptosis . The pervasive somatic FOXL2 mutation is expected to be the main driver of GCTs. However, we hypothesize that it might engender or be accompanied by other mutations and genomic changes that might facilitate tumor formation and/or progression. Here, we have explored this possibility by performing a comparative genomic hybridization (CGH) analysis of the aforementioned tumor samples in correlation with their transcriptomes. This combined analysis is the first attempt to obtain a “bird’s eye” view of the genomic landscape of this cancer type and to identify new candidate (co-)driver genes (termed henceforth driver genes for simplicity).
This research involves human samples and has been performed with the approval of the Ethics Committee of the Helsinki University Central Hospital. Research was carried out in compliance with the Helsinki Declaration.
Comparative Genomic Hybridization (CGH)
The CGH was performed using genomic DNA from the tumor samples co-hybridized with an equimolar mix of 10 ethnically-matched (finnish) DNA samples on NimbleGen 12x135K CGH arrays, which 60-mer probes spaced every 13 kb on average. Sample processing, hybridization and data acquisition were performed at Nimblegen according to an in-house standard protocol. CGH microarray data are available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-2873. CGH data were analyzed as log2 values of the ratio between the fluorescences of tumor and reference genomic DNA samples, using MeV software (TM4 suite, http://www.tm4.org).
CGH and transcriptome correlations
For large-scale alterations, the CGH data were averaged for sliding windows of 130 kb over the relevant chromosomes. For the transcriptomic data, we used our previously published data from the 10 tumors, as described in  (NimbleGen Human Expression 12 × 135 K array set, accession E-MTAB-483 in the ArrayExpress database). The two independent transcriptomic hybridizations were averaged for each transcript, and then we computed the average expression levels for each gene.
To better measure the impact of large-scale genomic alterations on gene expression we divided the expression values for genes located within aneuploid regions by their mean expression in the tumors without the analyzed alteration. Expression ratios above/below 1 in the natural scale (or above/below 0 in log2 scale) in aneuploid regions are suggestive of a “correlation” between genomic duplications/deletions and gene expression. Finally, these ratios were averaged for 30 windows (of equal size) per chromosome. The CGH and transcriptomic profiles were displayed using MeV software.
Recurrently broken genes were identified by the existence, in at least 2 tumors, of one or several closely mapping breakpoints defined by amplifications/deletions upstream and downstream, within the relevant gene. We excluded genes for which the breakpoints mapped near or within frequent CNVs according to DGV. This step was necessary because the control DNA used for CGH was a pool of ethnically-matching DNA samples, and not the somatic DNA from the respective patients.
Expressional correlation, protein interactor sharing and transcriptomic neighbors sharing between candidate drivers
Hierarchical clustering of the expression levels of broken, amplified or deleted candidate drivers and FOXL2 was performed with the MeV software, using complete linkage and the Pearson correlation coefficient as a measure of similarity.
For each candidate driver and FOXL2, two sets of transcriptomic neighbors were defined by a statistically significant correlation of expression in all tumors (R > = 0.63 or R < = −0.63). These gene sets were analyzed using the Enrichr tool (http://amp.pharm.mssm.edu/Enrichr ). The extensive sharing of transcriptomic neighbors between the candidate drivers or FOXL2 was displayed using Cytoscape 3.0.1 software, keeping only the strongly correlated transcriptional neighbors (R > = 0,90) for clarity. The network was built using the “prefuse force directed” algorithm with EdgeBetweenness criteria, then manually edited for clarity.
Results and discussion
CGH of ovarian GCTs shows recurrent chromosomal imbalances
To identify DNA copy number changes in GCTs, we performed a CGH analysis of 10 tumor genomic DNA samples, using microarrays. All the tumors bear the FOXL2 somatic mutation C134W. Four tumors (H1, H8, H28 and H30) did not display any large-scale genome alterations. However, there was no obvious correlation between the absence of imbalances and tumor stage, size or age of occurrence. On the other extreme, the most altered tumor was H4, which is not surprising, owing to the fact that it is a recurrence (Additional file 1: Table S1a and S1b).
Concerning the FOXL2 locus, all tumors have kept the two alleles, although in two cases the DNA sequence displayed only the presence of the mutated version (data not shown). This can be due to either a second mutational hit or a gene conversion event that provides a selective advantage over heterozygous cells, as previously noted .
Large-scale genomic alterations and their transcriptomic translation
To further explore the influence of DNA copy number on gene expression, we compared the average expression of genes located in altered segments with that of genes located outside. For example, the copy number increased from 1.01 for the non-amplified segment of chromosome 1 in tumor H4 to 1.35 for the amplified region (Figure 2b). Consistently, the normalized gene expression averaged over the non-duplicated segment was 0.99 versus 1.21 for the duplicated region. A similar concordance was observed for other amplifications and deletions (Figure 2b and data not shown). Although there was a correlation between DNA amounts and mRNA levels, the degree of gene up- or down-regulation was always slightly lower. Although this effect might be due, in some cases, to contamination of tumor RNA with the transcriptome of neighboring normal cells, this explanation cannot apply to all samples. Thus, one is tempted to argue that some degree of expression compensation to chromosome dosage changes is taking place. Indeed, buffering of gene expression in response to genomic alterations have been reported in Drosophila harboring chromosomal imbalances [15-17], for human trisomy 21  and for genes included in Copy-Number Variants (CNVs) .
Identification of putative drivers: recurrently broken, amplified/duplicated or deleted genes
To further exploit our CGH and transcriptomic data, we focused on small-scale rearrangements that might help us pinpoint candidate genes whose duplication, deletion or breakage might be involved in tumorigenesis. First, we aimed at identifying amplified or deleted candidate drivers by combining GCH and transcriptomics. For this purpose, we generated a list of amplified or deleted CGH probes, whose log-ratio corresponded to at least 50% of the cells harboring a heterozygous duplication or deletion, in at least two tumors. Then, we computed the correlation coefficient (R) over all tumors between the CGH values and the mRNA expression values for the genes whose boundaries mapped at less than 25 kb from a copy-number-altered probe. This correlation filter was essential because a local genomic alteration does not necessarily imply a transcriptomic change. Thus, a meaningful driver, mapping to an amplified/deleted region, should display a reasonable correlation between copy number and mRNA expression. We set the threshold for statistical significance of Pearson’s correlation coefficient R to 0.63, which is the standard cut-off for ten samples. Genomic regions involved in large-scale imbalances such as trisomies or monosomies were analyzed separately by removing data from trisomic or monosomic tumors. For these regions, the threshold for R was adjusted to 0.67 or 0.71 in cases when 1 or 2 samples were removed. After excluding genes located within CNVs, we obtained a list of 48 candidates. After manual verification, we retained 13 amplified and 7 deleted genes fully located within the imbalances. Tumors harbored alterations ranging from 2 to 9/13 amplifications and from 1 to 7/7 deletions (Additional file 2: Table S2a).
Candidate driver genes identified as amplified or deleted in OGCTs, with correlated expression
# of tumors with
Function & Implication in cancer if known
Known oncogenic kinase, core of one of the most frequently activated survival pathways in human cance.
Ligand-sensitive transcription factor, regulates the expression of core clock proteins; required for survival and proliferation of breast cancers
catalyzes the final step for conversion of vitamin B(12) into adenosylcobalamin. Derivatives of the latter are used to image breast, lung, colon, thyroid, and sarcomatous malignancies.
Membrane protein, regulates T cell proliferative responses. Tetraspanins are implicated in various steps of tumorigenesis.
cysteine proteinase up-regulated in Large granular lymphocyte leukemia
converts DAG into PA, a second messenger activating multiple signaling pathways implicated in tumorigenesis (i.e. mTOR signaling)
Soluble component of the nuclear pore complex. Oncogenic overexpression of eIF4E induces overexpression of RANBP1
cell-cycle regulated protein, one of the 5 immunohistochemical markers in the Mammostrat test used to stratify breast cancers
Promotes PI3K/Akt signaling, KD = > decreased proliferation. High expression associated with aggressive hepatocellular carcinoma
transmembrane protein involved in mechanotransduction. Mediates integrin activation by recruiting R-Ras to the ER, modulating cell adhesion
SPRY domain containing 3. Not studied
Not studied - now named PRR34, proline rich 34
postulated to function as brain-specific chemokines or neurokines, acting as regulators of immune and nervous cells.
AAA-ATPase, hTERT binding, essential for telomerase assembly. A nucleolar isoform is a component of pre-ribosomal particles
not studied. Identified as significantly binding to oligomeric β-amyloid
Unknown function. Down-regulated by microRNA124 during neurogenesis. Identified as a target of the E3 ubiquitin-ligase FANCA.
Inhibits actin depolymerization and cross-links filaments in bundles. Putative suppressor of epithelial-mesenchymal transition and metastasis
transcriptional activator adaptor, in the PCAF and ATAC histone acetylase complexes, mediates DNA damage-induced apoptosis and G1/S arrest
Heat shock chaperone of the HSP110 family. Regulates cell proliferation and G1/S progression by releasing transcription factor ZONAB from tight junction sequestration
part of the Paf1/RNA polymerase II complex, key regulator of transcription-related processes and cell-cycle progression
Among the recurrently deleted genes, HSPA4, deleted in 3 of the tumors, encodes a chaperone of the HSP110 family, predominantly expressed in the ovary . Interestingly, HSPA4 is known to regulate cell migration, both positively and negatively [32,33]. The second gene deleted in 3/10 tumors is RTF1, encodes a member of the Paf1 complex, which is a key regulator of RNA polymerase II transcriptional activity and of cell-cycle progression. RTF1 is critical for histone and chromatin modifications and telomeric silencing [34,35]. Another link with telomere maintenance is NVL, also found deleted in 3 tumors. NVL encodes an AAA-ATPase essential for hTERT binding and telomerase assembly . In addition, a nucleolar isoform of NVL participates in ribosome biosynthesis . LIMA1 (a.k.a. EPLIN, Epithelial protein lost in neoplasm), deleted in 2/10 OGCTs, encodes a metastasis suppressor, frequently lost in cancer cells [38,39]. Consistently, it acts as a negative regulator of epithelial-mesenchymal transition and invasiveness  and its expression is inversely correlated with the aggressiveness of breast cancer . Another interesting deleted gene, TADA2A, encodes an adaptor subunit of the PCAF and ATAC histone acetylase complexes. TADA2A-containing PCAF complex is essential for DNA-damage-induced acetylation of p53, necessary to promote cell cycle arrest and cell survival after DNA damage [42,43]. Moreover, TADA2A overexpression is pro-apoptotic in response to DNA damage . Thus, its deletion in GCTs should provide resistance to apoptosis . FOXO factors are known to be acetylated by PCAF upon stress to promote cycle arrest and DNA damage repair, or apoptosis. We have previously shown that FOXL2 is acetylated  and that it upregulates stress-response genes and induces cell-cycle slow-down . A hyperlinked gene list with more complete information is provided in Additional file 2: Table S2a and S2b.
Genes identified as broken in OGCTs
Function & Implication in cancer if known
cAMP-dependent protein kinase regulator. Associated with irinotecan-related toxicities in patients with non-small-cell lung cancer.
CELF/BRUNOL protein, alternative splicing factor. When lost, independent prognostic indicator in colorectal cancer.
Basic helix-loop-helix and PAS domain-containing transcription factor, tumor suppressor in astrocytomas
Potential transmembrane protein phosphorylated upon DNA damage. Mutated in recessive hereditary spastic paraplegia.
CBF transcription factor subunit. Tumor suppressor, with oncogenic fusions in leukemias and mutations in breast cancers.
The candidate drivers are expressionally clustered and share transcriptomic neighbors
In conclusion, our analysis identifies candidate co-driver genes, whose various alterations could contribute to GCT pathogenesis besides the FOXL2 somatic mutation. This is strengthened by their high degree of expressional interconnection, which suggests the existence of functional interactions among them, and by their known or suggested implication in cancer and related processes. However, we are aware that, given the small sample size for which CGH and transcriptomic data were available, this genomic exploration only provides leads for functional analyses to formally demonstrate the implication of the candidate drivers in GC tumorigenesis.
We gratefully acknowledge financial support from the Centre National de la Recherche Scientifique, La Ligue Nationale contre le Cancer (Comité de Paris), l’Université Paris Diderot-Paris7, l’Institut Universitaire de France, the Academy of Finland, and Helsinki University Central Hospital Research Funds. We thank A-E. Lehesjoki for providing the Finnish control DNA pool, and M. Heikinheimo for support on the GCT research program in Helsinki.
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