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Integrated genomic characterization of adrenocortical carcinoma

Abstract

Adrenocortical carcinomas (ACCs) are aggressive cancers originating in the cortex of the adrenal gland1. Despite overall poor prognosis, ACC outcome is heterogeneous2,3. We performed exome sequencing and SNP array analysis of 45 ACCs and identified recurrent alterations in known driver genes4,5 (CTNNB1, TP53, CDKN2A, RB1 and MEN1) and in genes not previously reported in ACC (ZNRF3, DAXX, TERT and MED12), which we validated in an independent cohort of 77 ACCs. ZNRF3, encoding a cell surface E3 ubiquitin ligase6, was the most frequently altered gene (21%) and is a potential new tumor suppressor gene related to the β-catenin pathway. Our integrated genomic analyses further identified two distinct molecular subgroups with opposite outcome. The C1A group of ACCs with poor outcome displayed numerous mutations and DNA methylation alterations, whereas the C1B group of ACCs with good prognosis displayed specific deregulation of two microRNA clusters. Thus, aggressive and indolent ACCs correspond to two distinct molecular entities driven by different oncogenic alterations.

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Figure 1: Pattern of somatic copy number alterations in ACC.
Figure 2: Mutational landscape of ACC.
Figure 3: Overview of somatic alterations in genes frequently altered in ACC.
Figure 4: miRNA expression patterns in ACC.
Figure 5: Coordinated analysis of genomic features in ACC subtypes.

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Acknowledgements

We thank F. Letourneur, R. Pelletier and S. Jacques from the Genomic platform of the Cochin Institute and P. Nietscke from the Paris Descartes University Bioinformatics platform for their technical support, J. Métral and J. Godet from the Ligue Nationale Contre le Cancer for organization of the Cartes d'Identité des Tumeurs (CIT) program, P.F. Plouin for organization of the COMETE network, the tumor bank of Cochin Hospital (B. Terris and M. Sibony) for help in sample collection, the Oncogenetic Department of Cochin Hospital (V. Duchossoy), A. Steel for management of the ENSAT database, the members of our laboratories and the COMETE and ENSAT networks for support and discussions, and all the staffs of the clinical and pathology departments who were involved in patient care. This study is part of the CIT Program from La Ligue Nationale Contre le Cancer. It was supported by funding from the Programme Hospitalier de Recherche Clinique to the COMETE network (grant AOM95201), the Seventh Framework Programme (FP7/2007-2013) under grant agreement 259735, Institut National du Cancer Recherche Translationelle 2009-RT-02, the Institut National du Cancer (to the Rare Adrenal Cancer Network COMETE), INSERM (G.A. receives a Contrat d'Interface) and the Conny-Maeva Charitable Foundation (to the laboratory of J.B.).

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Authors

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G.A., E.L., A.d.R. and J.B. conceived the study, designed the experiments, analyzed the data and wrote the manuscript. E.L., A.d.R., G.A. and W.L. performed the bioinformatics analyses. G.A., E.L., A.d.R., B.R. and J.B. provided analytical advice. A.d.R. and J.B. supervised the study. R.L., A.J., G.A., E.L., W.L. and N.E. managed the data. F.T. reviewed the histopathology. A.J. performed the Sanger sequencing experiments. K.P., F.R.-C., S.R., G.A., A.J., O.B., S.S., H.O. and B.R. performed the molecular analyses. E.C. performed the microsatellite instability experiments. M.F. coordinated the ENSAT centers. M.F., J.B., G.A., M.K., B.A., J.W., M.Q., M.M., F.M., T.P., R.D.K., A.T., V.K., E.B., B.D., L.G., L.A., X.B., F.B. and R.L. recruited the subjects. All authors discussed the results and implications and commented on the manuscript.

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Correspondence to Jérôme Bertherat.

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Integrated supplementary information

Supplementary Figure 1 SNP patterns frequently observed in ACC.

ACCs often display large LOH regions encompassing more than half of the genome. These profiles are either hypodiploid or polyploid. In hypodiploid tumors, LOH regions result from chromosome losses and have absolute copy number (CN) = 1, whereas other regions have normal CN = 2. In polyploid tumors, LOH regions have absolute CN = 2 and are thus copy-neutral LOH, whereas other regions are mostly gained (CN = 4). These characteristic patterns may be explained by an accumulation of chromosome losses (leading to hypodiploid tumors) followed by cell fusion, endoreplication or cytokinesis failure (leading to polyploid tumors) (Krajcovic et al. Cancer Res. 2012). (a) SNP array pattern of a hypodiploid ACC. The GAP pattern (left) is a sideview projection of segmented log R ratio (LRR, y axis) and B allele frequency (BAF, x axis) used to determine the absolute CN and genotype corresponding to each cluster of segments (Popova et al. Genome Biol. 2009). Clusters are designated by the ratio of CN to most abundant allele counts (e.g., 3/2 indicates that the segment has a total copy number of 3, with 2 copies of one allele and 1 of the other). SNP profiles are represented on the right by the absolute CN, LRR and BAF plots. Color codes in the copy number and LRR profiles are as follows: blue, homozygous deletion; green, loss; yellow, normal copy number; red, gain. Blue regions in the BAF plots correspond to regions of LOH. (b) SNP array pattern of a polyploid ACC. (c) Proposed mechanism explaining the existence of hypodiploid ACC with numerous losses and polyploid ACC with numerous copy-neutral LOH events.

Supplementary Figure 2 Coverage of exome sequencing.

(a) Mean depth (±s.d.) of exome sequences on each chromosome. (b) Proportion of bases in targeted exons sequenced at a depth of ≥1×, 4×, 10× or 25× for 45 ACCs and their normal counterparts.

Supplementary Figure 3 Mutation rates and types of somatic substitutions in 45 ACCs.

(a) Number of somatic mutations and indels per Mb across the cohort of tumors. The mean mutation rate (0.60 mutations per Mb) is indicated by the blue dashed line. Two samples (ACC33 and ACC39, in red) display unusually high mutation rates (>10 mutations per Mb). (b) Relative proportions of the six possible base-pair substitutions in each ACC, as indicated in the legend on the right.

Supplementary Figure 4 Location of mutations in key genes recurrently mutated in ACC.

Nonsynonymous somatic substitutions (black), frameshift indels (blue) and nonsense mutations (red) identified in the entire cohort of 122 ACCs are represented. TAD, transcription activation domain; PRD, proline-rich domain; DBD, DNA-binding domain; NLS, nuclear localization signal; OD, oligomerization domain; CTD, C-terminal regulatory domain; SP, signal peptide; TM, transmembrane domain; RING, Really Interesting New Gene finger domain; GTP, GTPase consensus motif; LZ, leucine zipper–like motif; PAH, paired amphipatic helix; CC, coiled-coil domain; SPT, S/P/T-rich domain; L, leucine-rich domain; LS, leucine- and serine-rich domain; PQL, proline- glutamine- and leucine-rich domain; OPA, opposite paired domain.

Supplementary Figure 5 Activation of β-catenin target genes in tumors harboring ZNRF3-inactivating alterations.

We downloaded the curated list of Wnt/β-catenin target genes from the Wnt homepage (http://www.stanford.edu/group/nusselab/cgi-bin/wnt/target_genes) and selected as reporters the four genes most significantly overexpressed in CTNNB1-mutated ACCs compared to normal controls (P < 1 ×10–2, log2 (fold change) > 1). The RMA-normalized expression levels of these four genes are presented for non-tumor adrenal samples (NT), ACC without alterations in CTNNB1 or ZNRF3 (ACC), ACC with ZNRF3 alterations (ZNRF3) and ACC with CTNNB1-activating mutations (CTNNB1). The fifth panel represents the mean expression of these four β-catenin targets in each group. A t test was used to assess the statistical significance of differences between each group: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

Supplementary Figure 6 DNA methylation–based classification of ACC.

(a) Heat-map representation of DNA methylation profiles classified using the recursively partitioned mixture model (RPMM). The degree of DNA methylation (β value) is presented with a color scale (dark blue, non-methylated; yellow, methylated). The status of key genes (ZNRF3, CTNNB1 and TP53) is indicated above the heat map, together with 5-year overall survival (missing values are in white). The bar on the left indicates whether each CpG site is located within (black) or outside (white) a CpG island (CGI). (b) Scatter plots comparing methylation levels between tumor subgroups and adrenocortical adenomas are shown for probes located within (top) or outside (bottom) CpG islands. Probes significantly hyper- or hypomethylated in ACC (q value < 0.01, Wilcoxon rank-sum test) are indicated in red and green, respectively. (c) Proportion of hypermethylated (red) and hypomethylated (green) CpG sites in each tumor cluster within (top) and outside (bottom) CpG islands.

Supplementary Figure 7 mRNA expression–based classification of ACC.

(a) Cumulative distribution functions (CDF) of the consensus matrix for each number k of clusters (k = 2, 3,..., 8, top) and a delta area plot showing the relative change in area under the CDF curves (bottom). The shape and area under CDF curves allow one to select the appropriate number of clusters (Monti et al. Machine Learning 2003). Here partitions in two to four clusters seem appropriate. (b) Consensus matrices (left) for k = 2 and k = 4 clusters. Consensus values range from 0 (never clustered together, white) to 1 (always clustered together, dark blue). Samples are ordered on the x and y axes by consensus clustering, which is depicted as a dendogram atop the heat map. Kaplan-Meier curves for overall survival are represented on the right for each partition. (c) Heat-map representation of mRNA profiles. The expression level is represented with a color scale (red, high expression; green, low expression). Tumors are ordered by transcriptome cluster. Probes are arranged by similarity, as assessed by hierarchical cluster analysis. Transcriptome and DNA methylation clusters are indicated above the heat map, together with the status of key genes (ZNRF3, CTNNB1 and TP53) and 5-year overall survival (missing values are in white).

Supplementary Figure 8 Volcano plot analysis of differentially expressed miRNAs in each tumor cluster.

The expression difference in miRNA expression between ACCs and normal adrenal samples is plotted on the x axis, and false discovery rate (FDR)-adjusted significance is plotted on the y axis (–log10 scale). Upregulated and downregulated miRNAs in each ACC subgroup are indicated in red and green, respectively.

Supplementary Figure 9 Expression of MEG3 across 65 normal human tissues.

The median expression level of MEG3 in 65 normal human tissues was derived from published data (Roth et al. 2006; GEO accession GSE3526). The highest expression of MEG3 is observed in adrenal gland cortex, ovary and pituitary gland, suggesting an important role for the MEG3 locus in endocrine tissues. Note that in a genome-wide transcriptome analysis, MEG3 is the 38th gene most strongly overexpressed in adrenal cortex compared to other normal tissues (data not shown).

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Supplementary Figures 1–9 and Supplementary Tables 1–3 and 5–12 (PDF 3618 kb)

Supplementary Table 4

List of somatic nonsynonymous mutations identified by exome sequencing of 45 ACCs. (XLSX 182 kb)

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Assié, G., Letouzé, E., Fassnacht, M. et al. Integrated genomic characterization of adrenocortical carcinoma. Nat Genet 46, 607–612 (2014). https://doi.org/10.1038/ng.2953

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