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. Author manuscript; available in PMC: 2021 Jan 1.
Published in final edited form as: Cancer Genet. 2019 Oct 21;240:5–14. doi: 10.1016/j.cancergen.2019.10.005

High-resolution copy number analysis of clear cell endometrial carcinoma

Andrea J O’Hara a, Matthieu Le Gallo a, Meghan L Rudd a, Daphne W Bell a
PMCID: PMC6911624  NIHMSID: NIHMS1058631  PMID: 31678638

Abstract

Uterine cancer is the 6th leading cause of cancer death amongst American women. Most uterine cancers are endometrial carcinomas (ECs), which are classified into histological subtypes including endometrioid, serous, and clear cell ECs. Somatic copy number alterations (SCNAs) are frequent in serous EC, infrequent in endometrioid ECs, and poorly defined in clear cell ECs. The purpose of this study was to evaluate the occurrence of SCNAs in clinically diagnosed clear cell ECs. Paired tumor-normal DNAs for 51 ECs were hybridized to Illumina Infinium HumanHap650Y or Human660W-Quad Beadchips. Copy number calls were made using the Hidden Markov Model based SNP-FASST2 segmentation algorithm within Nexus Copy Number software (v.6.1). High-level SCNAs were defined as gain of ≥5 copies or homozygous deletion, both <10Mb. GISTIC 1.0, in Nexus, was used to identify statistically significant SCNAs, corrected for multiple testing. One or more high-level SCNAs were detected in 50% of 6 clear cell ECs, 78.6% of 28 serous ECs, and 17.6% of 17 endometrioid ECs. A positive association was found between high-level SCNAs and TP53 mutation across ECs (two-tailed p value<0.0001). Classifying tumors according to POLE, MSI, and TP53 status yielded four molecular subgroups; copy number altered tumors were more frequent in the TP53-mutated subgroup (95.8%) than in the unspecified subgroup (22.2%), and absent from the POLE and MSI subgroups. In conclusion, our study provides evidence of inter-tumor heterogeneity in the extent to which SCNAs occur in clinically diagnosed clear cell EC, and across molecular subgroups of EC. The co-occurrence of high-level SCNAs and TP53 mutations in some clear cell ECs is consistent with the view that a subset of clinically diagnosed clear cell ECs have molecular similarities to serous ECs.

Keywords: Endometrial, uterine, cancer, clear cell, copy number

INTRODUCTION

Cancers of the uterine corpus are estimated to account for 11,350 deaths among American women in 2018 [1]. Endometrial carcinomas (ECs) make up the majority of uterine corpus tumors (reviewed in [2]) and are classified into a number of histopathologic subtypes including endometrioid, serous, and clear cell ECs. Endometrioid tumors constitute approximately 87–90% of newly diagnosed ECs, whereas serous and clear cell tumors, which by definition are high-grade tumors, account for approximately 3–10% and 1–3% of such cases [2, 3]. Compared with endometrioid ECs, serous and clear cell tumors have generally less favorable outcomes. For example, within the SEER registry (1988–2001), five-year relative survival rates were 91.2% for endometrioid, 44.7% for serous, and 64.8% for clear cell ECs [4], considering all stages and grades. For all histologies, increasing stage is associated with decreased outcomes [5, 6]. For endometrioid ECs, increasing grade is also associated with reduced outcomes [5, 6]. This fact is illustrated by a 5-year disease-specific survival rate of only 77% for grade 3-endometrioid ECs (all stages) within a study of cases in the SEER database [7], and by 5-year overall survival rates for grade 3 endometrioid EC ranging from 77.5% at stage I to 49.6% at stage III for cases within the National Cancer Database [6].

Endometrioid and serous ECs have been relatively well-studied at the molecular level (reviewed in [8, 9]), and were the focus of an integrated genomic analysis by The Cancer Genome Atlas (TCGA) [10]. They exhibit a number of differences in their molecular pathogenesis. The endometrioid subtype is typified by frequent aberrations in the PI3K-AKT-mTOR pathway, the WNT/β-catenin pathway, the RAS-RAF-MAPK-ERK pathway, and ARID1A, as well as frequent microsatellite instability (MSI) and relatively frequent somatic mutations affecting the exonuclease domain of POLE (reviewed in [11]). In contrast to endometrioid EC, serous EC frequently exhibits TP53 mutation and/or p53 stabilization, PPP2R1A mutation, ERBB2 amplification, and high-level somatic copy number alterations (SCNAs) (Reviewed in [12]).

The mutational profiles of some clear cell tumors resemble those of either serous or endometrioid ECs [13–21]. However, the extent to which genome-wide SCNAs occur in this subtype remains poorly understood. Very few clear cell ECs have been karyotyped or analyzed by chromosomal comparative genomic hybridization (cCGH) [22–24]. Moreover, high-resolution SNP array based genome-wide copy number analysis has, to our knowledge, only been reported for two primary clear cell ECs both of which were relatively copy number quiet [25].

Here, we used high-density, SNP array analysis to determine the incidence of SCNAs in six clinically diagnosed clear cell ECs, and, for comparison on the same platform, 28 serous ECs and 17 endometrioid ECs. Focal high-level SCNAs were detected in 50% of clear cell ECs, compared with 78.6% of serous, and 17.6% of endometrioid ECs. Stratifying tumors into “TCGA-like” molecular subgroups, agnostic of histology, revealed a significant association between copy number alterations and TP53 mutations.

MATERIALS AND METHODS

Clinical material

The NIH Office of Human Subjects Research Protection determined that the research using the specimens in this study was not “human subjects research” per the Common Rule (45 CFR 46) and was exempt from IRB review. Snap-frozen primary tumor tissues, matched non-tumor tissues, and corresponding hematoxylin and eosin (H&E)-stained tumor sections were acquired as de-identified material from the Cooperative Human Tissue Network (CHTN), which is funded by the National Cancer Institute. Matched tumor and normal DNAs for four cases were purchased from Oncomatrix Inc (San Marcos, CA). Tumor specimens were collected at surgical resection prior to treatment. A histological classification was rendered based upon the entire specimen at time of diagnosis; this classification is used herein. For fresh-frozen tumor specimens received from CHTN, a single gynecologic pathologist reviewed an H&E section of this tumor tissue to identify regions of high (>70%) neoplastic cellularity for macrodissection and DNA isolation. Matched normal samples were from uninvolved reproductive tissue (n=49), buffy coat (n=1), or whole blood (n=1).

DNA extraction

Genomic DNA was isolated from regions of macrodissected tissue comprised of >70% neoplastic cells, and from normal tissues, using the PUREGENE kit (Qiagen) as described elsewhere [16, 17]. DNA yield was quantified using the Qubit fluorometer (Invitrogen).

Identity testing

To confirm that tumor-normal pairs were derived from the same individual, paired DNAs were typed using the Coriell Identity Mapping kit (Coriell) as previously described [16]. Genotyping fragments were resolved on an ABI-3730xl DNA analyzer (Applied Biosystems) and scored using GeneMapper software.

SNP genotyping

Genomic DNA from each tumor and normal sample was hybridized either to an Illumina Infinium HumanHap650Y Genotyping BeadChip (660,919 probes) by the NHGRI Genomics Core, or to an Illumina Infinium Human660W-Quad BeadChip (657,367 probes) by the Johns Hopkins University SNP Center. Hybridizations, as well as quantification of signal intensities and allele calls, were performed according to standard procedures. SNP positions and downstream analyses were based on the hg18/NCBI Build 36 of the human genome.

SNP data were visualized and preprocessed using GenomeStudio (v2011.1) (Illumina Inc), including implementation of the updated cluster generation protocol for SNPs on the X chromosome. LogR intensity ratios and B allele frequencies for all probes were exported from GenomeStudio and imported into Nexus Copy Number™ software (v.6.1) (BioDiscovery). Within Nexus, a customized linear systematic probe correction was applied to correct for waviness in the data, and to exclude data from probes that were unique to either BeadChip; only data from the 549,242 probes present on both Beadchips was retained. Next, for each tumor-normal pair, the datasets were re-centered to user-defined regions of balanced heterozygosity that had similar Log R ratios across multiple chromosomes within the tumor sample. The copy number changes reported herein represent a derivation from this baseline of balanced heterozygosity, but it should not be assumed that the baseline represents a diploid state.

Copy number calls and allelic status calls were made using the Hidden Markov Model based SNP-FASST2 segmentation algorithm within Nexus Copy Number™ (v.6.1). For data generated on the HumanHap650Y Genotyping BeadChip, settings used in the SNP-FASST2 segmentation algorithm included a significance threshold of 1.0E-6, a maximum contiguous probe spacing of 1000kb, a minimum of 5 probes per segment, a homozygous frequency threshold of 0.85, a homozygous value threshold of 0.8, a heterozygous imbalance threshold of 0.4, a minimum LOH length of 500kb, a high gain of 0.6, a gain of 0.18, a loss of −0.18 and a big loss of −1.0. For data generated on the Human660W-Quad BeadChip, the settings used in the SNP-FASST2 segmentation algorithm were identical to those for the HumanHap650Y Genotyping BeadChip with the exception of the high gain and gain which were set at 0.38 and 0.1 respectively. We also used a setting of 3% for the removal of outliers in the Nexus calculation of probe-to-probe variance, or quality. Tumor-normal pairs for which the tumor and/or normal data had a Nexus quality score >0.1 after removing the top and bottom 1.5% of outlier probes, were considered to have poor quality data and were excluded from further analysis. Copy number alterations of the same type and at the same loci in tumor-normal pairs, were considered germline, and were manually removed from each tumor profile. Only somatic copy number alterations (SCNAs) were considered in subsequent analyses. We defined “focal high-level SCNAs” as gains of ≥5 copies or homozygous deletions, both <10Mb. Genomic co-ordinates of “high-level SCNAs” were converted from hg18 to hg19 using the UCSC LiftOver utility, and queried against “gold standard variants” in the Database of Genomic Variants (DGV) (URL:http://dgv.tcag.ca/dgv/app/home); regions that had at least 75% overlap with one or more “DGV Gold” variants were filtered out.

Regions of statistically significant copy number alteration (Q-bound value <= 0.25 and G-score cut-off ≥1.0) were identified within Nexus Copy Number (v.6.1) using GISTIC (Genomic Identification of Significant Targets in Cancer) 1.0 [26, 27]. Extended GISTIC regions of statistically significant SCNA and corresponding peak GISTIC regions were identified separately for tumor and normal datasets. Extended and peak regions present in both the combined tumor and combined normal datasets were manually excluded because they may represent germline CNVs. Extended and peak GISTIC regions that had at least 75% overlap with one or more “DGV Gold” variants were filtered out. Cancer genes in peak GISTIC regions were annotated by querying the Cancer Gene Census (COSMICv90) (URL: http://cancer.sanger.ac.uk/census/).

RESULTS

Somatic copy number status varies according to EC histotypes

We detected one or more SCNAs in 50.0% (3 of 6) of clear cell, 82.1% (23 of 28) serous, and 70.5% (12 of 17) of endometrioid ECs (Figure 1, Figure 2, Figure 3). Gains involving 3q, 5p, 8q, 20q, and 22q were the most frequent SCNAs amongst clear cell tumors (Figure 1, Supplementary Figure 1). In serous tumors, SCNAs occurred throughout the genome with gains often involving chromosomes 1p, 1q, 2p, 2q, 3q, 5p, 6p, 6q, 8q, 10p, 12p, 14q, 17q, 18p, 19q, 20p, and 20q, and losses affecting 4q, 7q, 8p, 9q, 15q, 16q, and 22q (Figure 2, Supplementary Figure 2). Gains involving chromosomes 1q, 3q, 7p, 8p, 8q, 10p, 10q, and losses involving 13q and 16q were the most common SCNAs among endometrioid ECs (Figure 3, Supplementary Figure 3). One or more focal high-level SCNAs were detected in 50% (3 of 6) of clear cell, 78.5% (22 of 28) of serous, and 17.6% (3 of 17) of endometrioid ECs (Supplementary Table 1, Supplementary Table 2, Supplementary Table 3, Supplementary Table 4). Consensus cancer genes residing within focal high-level SCNAs in clear cell EC included KAT6B (within a chr10q22.2-q22.3 gain in T28) and MECOM (within a chr3q26.1-q26.31 gain in T153) (Supplementary Table 2). Five genes (ERBB2, CDK12, FGFR1, INPP4A, and PRKAR1A) within focal high-level SCNAs in a clear cell EC (T153), have also been reported, via next generation sequencing, to be amplified among clear cell ECs [28].

Figure 1.

Figure 1.

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Distribution of SCNAs among six clear cell ECs. (A) Genome-wide frequency plots of SCNAs for all cases combined. The frequency plot shows the percentage of tumors with somatic copy number gains (dark blue) and losses (red) above and below baseline (0%). (B) Profiles of SCNAs within each individual tumor (T). Tumors with at least one SCNA are indicated by the dark green column (left); tumors with at least one focal high-level SCNA are indicated by the light green column (left). Chromosomes 1 through 22 and the sex chromosomes are displayed sequentially from left to right.

Figure 2.

Figure 2.

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Distribution of SCNAs among 28 serous ECs. (A) Genome-wide frequency plots of SCNAs for all cases combined. The frequency plot shows the percentage of tumors with somatic copy number gains (dark blue) and losses (red) above and below baseline (0%). (B) Profiles of SCNAs within each individual tumor (T). Tumors with at least one SCNA are indicated by the dark green column (left); tumors with at least one focal high-level SCNA are indicated by the light green column (left). Chromosomes 1 through 22 and the sex chromosomes are displayed sequentially from left to right.

Figure 3.

Figure 3.

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Distribution of SCNAs among 17 endometrioid ECs. (A) Genome-wide frequency plots of SCNAs for all cases combined. The frequency plot shows the percentage of tumors with somatic copy number gains (dark blue) and losses (red) above and below baseline (0%). (B) Profiles of SCNAs within each individual tumor (T). Tumors with at least one SCNA are indicated by the dark green column (left); tumors with at least one focal high-level SCNA are indicated by the light green column (left). Chromosomes 1 through 22 and the sex chromosomes are displayed sequentially from left to right.

Statistically significant SCNAs identified by GISTIC 1.0 analysis in histological subtypes of EC

GISTIC analysis identified one peak region of SCNA, involving chr4q34.3 loss, in clear cell EC (Supplementary Table 1); this region was devoid of protein-encoding genes but encompassed a long non-coding RNA gene (LINC02500) (Table 1). In contrast, there were 41 peak GISTIC regions of SCNA among serous tumors (Table 2). Genes in individual high level SCNAs in clear cell EC, and within GISTIC peaks in serous EC were KAT6B, MECOM-ACTRT3-MYNN-LRRC34-LRR1Q4, ZMAT4, LINC02548-LINC02545, RNU6–2; LINC02082 was located within an individual high level SCNA in clear cell EC and in an endometrioid EC GISTIC peak. Consensus cancer genes in peak regions of serous EC GISTIC gain were MECOM, and KAT6B. Consensus cancer genes in peak regions of GISTIC loss in serous EC were LRP1B, MAP2K4, ERBB4, and PTEN. Eighteen protein-encoding genes in peak GISTIC regions in our serous ECs were also located in peak GISTIC regions in serous ECs and/or copy number cluster 4 ECs in TCGA [10] (Table 1, Table 2). Genes (and chromosomal locations) identified by both studies in peak GISTIC gains were EVI1/MECOM/MDS1 and MYNN (both 3q26.2); TERC (3q26.2); NEDD9 (6p24.2); ZMAT4 (8p11.21); PSD4 (2q13); and KAT6B (10q22.2). Genes identified by both studies in regions of peak GISTIC loss were LRP1B (2q22.1-q22.2); WASF2 (1p36.11); PTEN (10q23.31); AHDC1 (1p36.11-p35.3); PDE4D (5q11.2-q12.1); DNAH9, MAP2K4, and ZNF18 (all 17p12); ARAP2 (4p14); TUSC1 (9p21.2); and LHX1 and AATF (both 17q12). Several of these genes are proposed driver genes in statistically significant recurring SCNAs in the TCGA Pan-Gyn cohort specifically: MECOM, NEDD9, and KAT6B in GISTIC gains; LRP1B, PDE4D, PTEN, and MAP2K4 in GISTIC losses [29].

Table 1.

Extended and peak GISTIC regions identified among clear cell ECs

SCNA Type Genomic Coordinates (hg18) of Extended GISTIC Region Chromosomal Location of Extended GISTIC Region Genomic Coordinates (hg18) of Peak GISTIC Region Q-bound for Peak Region G-Score for Peak Region Genes in Peak GISTIC Region
Loss chr4:97,886,832-191,273,063 chr4:182,415,084-182,433,256 0.24887827 1.17969014 LINC02500
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Table 2.

Extended and peak GISTIC regions identified among serous ECs

SCNA Type Genomic Coordinates (hg18) of Extended GISTIC Region Chromosomal Location of Extended GISTIC Region Genomic Coordinates (hg18) of Peak GISTIC Region Q-bound for Peak Region G-Score for Peak Region Genes in Peak GISTIC Region
Gain chr1:176,942,142-190,068,868 1q25.2-q31.2 chr1:181,211,397-181,376,609 5.04E-3 4.568 LAMC1, LAMC1-AS1
Gain chr1:42,689,950-49,840,316 1p34.2-p33 chr1:44,358,961-44,455,683 7.26E-2 3.842 KLF17, DMAP1
Gain chr10:76,048,493-86,655,854 10q22.2-q23.1 chr10:76,285,006-76,292,848 8.97E-2 3.789 KAT6B (MYST4)^
Gain chr11:13,107,660-17,245,167 11p15.2-p15.1 chr11:13,807,145-13,855,429 1.72E-1 3.553 LINC02548, LINC02545
Gain chr12:24,710,025-25,457,711 12p12.1 chr12:24,983,158-25,022,328 6.31E-05 5.447 BCAT1
Gain chr13:91,908,665-92,569,702 13q31.3 chr13:92,353,870-92,391,097 9.63E-2 3.764 /
Gain chr14:42,169,974-85,498,415 14q21.2-31.3 chr14:45,216,232-45,273,870 9.97E-2 3.757 LINC02303
Gain chr16:29,245,504-32,045,466 16p11.2 chr16:30,279,779-30,400,867 1.20E-1 3.681 TBC1D10B, MYLPF, SEPTIN1, ZNF48, ZNF771, DCTPP1, SEPHS2, ITGAL, SNORA80C
Gain chr18:4,158,565-16,100,000 18p11.31-p11.1 chr18:10,911,569-11,090,060 1.49E-2 4.287 PIEZO2
Gain chr19:22,755,564-24,191,440 19p12 chr19:22,831,403-22,857,036 2.56E-3 4.717 ZNF723
Gain chr19:34,477,100-37,581,903 19q12-q13.11 chr19:34,839,864-34,892,756 5.73E-14 10.488 PLEKHF1, C19orf12
Gain chr2:94,793,151-118,939,207 2q11.1-q14.2 chr2:113,590,192-113,656,137 2.69E-2 4.124 IL1RN, PSD4^
Gain chr20:14,929,825-17,414,153 20p12.1 chr20:15,505,853-15,529,115 9.40E-3 4.408 MACROD2
Gain chr20:51,602,600-51,797,590 20q13.2 chr20:51,722,390-51,726,725 9.44E-08 6.587 /
Gain chr21:28,466,487-46,944,323 21q21.3-q22.3 chr21:43,661,758-43,750,095 1.40E-1 3.632 SIK1, SIK1B, LINC00319, LINC01669
Gain chr3:169,808,488-171,669,472 3q26.2 chr3:170,858,670-171,032,269 4.45E-11 7.895 MECOM^, ACTRT3, MYNN^, LRRC34, LRRIQ4, TERC **
Gain chr4:61,820,279-64,360,471 4q13.1 chr4:64,148,793-64,360,471 1.54E-2 4.275 /
Gain chr6:11,306,574-11,952,750 6p24.1 chr6:11,470,627-11,545,036 1.53E-3 4.823 NEDD9^
Gain chr7:37,375,580-57,890,326 7p14.2-p11.1 chr7:53,114,918-53,194,448 3.20E-2 4.070 /
Gain chr8:129,370,592-132,726,767 8q24.21-q24.22 chr8:129,601,251-129,636,803 1.49E-2 4.283 LINC00824
Gain chr8:40,154,687-43,910,848 8p11.21-p11.1 chr8:40,583,025-40,670,377 1.94E-2 4.214 ZMAT4**
Gain chrX:61,845,481-69,098,104 Xq11.1-q13.1 chrX:61,845,481-62,571,001 2.01E-1 3.495 SPIN4, LINC01278
Loss chr1:16,797,436-35,789,123 1p36.13-p34.3 chr1:27,616,247-27,756,094 6.82E-2 4.597 WASF2^, AHDC1^
Loss chr10:89,251,828-90,869,249 10q23.2-q23.31 chr10:89,620,956-89,745,318 9.04E-2 2.421 PTEN*
Loss chr16:33,744,011-34,319,481 16p11.2 chr16:33,744,011-34,311,321 6.82E-2 3.473 LOC112268173, MIR9901
Loss chr16:45,079,464-45,425,095 16q11.2 chr16:45,096,893-45,329,968 6.82E-2 3.188 SHCBP1, VPS35,ORC6,MYLK3
Loss chr17:10,300,648-16,935,194 17p13.1-p11.2 chr17:11,807,637-11,917,951 6.82E-2 3.290 DNAH9^, ZNF18^,MAP2K4^
Loss chr17:30,019,514-33,321,712 17q12 chr17:32,373,680-32,486,434 9.04E-2 2.426 LHX1^, AATF^, MIR2909
Loss chr19:5,166,212-8,072,557 19p13.3-p13.2 chr19:7,120,029-7,174,340 1.80E-1 2.069 INSR
Loss chr19:0-1,654,336 19p13-3 chr19:839,331-876,857 6.82E-2 5.197 MED16, R3HDM4, KISS1R, RNU6-2
Loss chr2:141,290,607-142,091,845 2q22.1 chr2:141,514,130-141,549,533 6.82E-2 3.629 LRP1B^
Loss chr2:212,586,754-213,027,807 2q34 chr2:212,761,893-212,772,982 8.48E-2 2.477 ERBB4
Loss chr22:14,884,399-20,930,009 22q11.1-q11.22 chr22:15,869,733-15,915,743 1.72E-1 2.089 CECR7
Loss chr22:45,341,436-49,691,432 22q13.31-q13.33 chr22:47,505,660-47,562,070 6.82E-2 3.612 TAFA5
Loss chr4:115,451,731-140,666,120 4q26-q31.1 chr4:129,663,522-129,691,216 6.95E-2 2.722 /
Loss chr4:181,771,427-182,320,436 4q34.3 chr4:182,084,359-182,181,085 6.82E-2 3.525 /
Loss chr4:35,651,979-48,132,950 4p14-p12 chr4:35,780,455-35,831,233 6.82E-2 3.158 ARAP2^
Loss chr5:56,081,575-63,823,550 5q11.2-q12.3 chr5:59,205,723-59,266,038 6.82E-2 3.602 PDE4D^
Loss chr9:74,060,683-90,190,867 9q21.13-q22.1 chr9:80,427,442-80,485,053 6.90E-2 2.785 /
Loss chrX:121,101,286-121,546,546 Xq25 chrX:121,162,142-121,262,032 6.82E-2 4.580 /
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Consensus cancer genes (* Tier-1 or ** Tier 2; COSMIC v90) are shown in bold

*

gene located in peak region in TCGA copy number cluster 4 [10]

**

gene located in peak region in TCGA serous EC [10]

^

genes located in peak region in TCGA copy number cluster 4 and serous EC [10]

To determine whether SCNA of genes in peak GISTIC regions in our serous EC cohort is associated with survival, we queried these genes in the TCGA serous EC (n=108) data [29], using the cBIOPortal [30, 31]. This analysis showed that LAMC1 gain was statistically significantly associated with decreased overall survival (OS) (median OS 14.7 months versus 106.9 months; Logrank Test P-value 5.659e-4) and shorter disease-free survival (DFS) (median disease-free interval 14.0 months versus NA; Logrank Test P-value 1.244e-4) for serous EC patients (Supplementary Figure 4). SPIN4 gain was statistically significantly associated with reduced OS (Logrank Test P-value 2.828e-3) (Supplementary Figure 4), although this finding is based on a single patient with amplification.

GISTIC analysis of endometrioid ECs identified six significant peak regions of gain and one significant peak region of loss (Table 3). The consensus cancer gene MRTFA/MKL1 was located within a peak region of GISTIC loss among endometrioid ECs. There was no overlap between the genes located in peak GISTIC regions in endometrioid ECs in this study and those in TCGA EC copy number cluster-2 or cluster-3, which were predominated by endometrioid ECs [10]. ZMAT4 (Zinc finger matrin type 4) was located within a peak region of gain in both serous and endometrioid ECs.

Table 3.

Extended and peak GISTIC regions identified among endometrioid ECs

SCNA Type Genomic Coordinates (hg18) of Extended GISTIC Region Chromosomal Location of Extended GISTIC Region Genomic Coordinates (hg18) of Peak GISTIC Region Q-bound for Peak Region G-Score for Peak Region Genes in Peak GISTIC Region
Gain chr1:147,305,744-247,249,719 1q21.1-q44 chr1:189,156,518-189,322,948 4.13E-03 1.669 /
Gain chr10:11,552,951-30,309,068 10p14-p11.23 chr10:23,244,490-23,432,842 4.75E-02 1.099 ARMC3
Gain chr10:41,756,307-45,536,421 10q11.1-q11.21 chr10:42,305,364-42,587,946 1.85E-02 1.194 ZNF33B, LINC008039, LINC01518,LOC283028,LOC105378269
Gain chr3:121,385,071-192,547,654 3q13.33-q28 chr3:170,041,278-170,109,492 9.99E-02 1.255 LINC02082
Gain chr7:109,318,773-109,646,296 7q31.1 chr7:109,318,773-109,412,379 7.37E-02 1.024 /
Gain chr8:37,345,764-43,910,848 8p12-p11.1 chr8:40,608,933-40,638,501 5.90E-03 1.319 ZMAT4
Loss chr22:14,884,399-49,691,432 22q11.1-q13.33 chr22:39,148,396-39,289,536 6.26E-02 1.875 MRTFA/MKL1
Loss chrX:61,845,481-148,937,305 Xq11.1-q28 chrX:93,562,756-93,703,848 6.26E-03 1.001 PLCXD1, GTPBP6, PPP2R3B, SHOX
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Consensus cancer genes (* Tier-1 or ** Tier 2; COSMIC v90) are shown in bold

Correlation of SCNA status with TP53 mutational status, MSI, and POLE mutational status

We previously reported the incidence of TP53 somatic mutations (all coding exons) and microsatellite instability (MSI) for the 51 tumors in this study [18, 19]. Here, we compared the SCNA status and TP53 status of tumors (Supplementary Table 5). We observed a statistically significant positive correlation between TP53 mutation and the presence of focal high-level SCNAs across ECs: 85.7% (24 of 28) of tumors with focal high-level SCNAs were TP53-mutated compared with 4.3% (1 of 23) of tumors without focal high-level SCNAs (two-tailed p value<0.0001). With respect to histologic subtype, all (3 of 3) clear cell tumors with focal high-level SCNAs were TP53-mutated versus 0% (0 of 3) of clear cell tumors without focal high-level SCNAs, a difference that was not statistically significant; 90.9% (20 of 22) of serous tumors with focal high-level SCNAs were TP53-mutated compared with 0% (0 of 6) of serous tumors without focal high-level SCNAs (two-tailed p value<0.0001); and 33.3% (1 of 3) of endometrioid tumors with focal high-level SCNAs were TP53-mutated versus 7.1% (1 of 14) of tumors without focal high-level SCNAs, a difference that was not statistically significant.

No statistically significant associations were found between the presence of focal high-level SCNAs and MSI in any histotype. MSI was not detected in any clear cell or serous ECs in this study. For endometrioid ECs, 0% (0 of 3) of tumors with focal high-level SCNAs had MSI compared with 28.6% (4 of 14) of tumors without high-level SCNAs, a difference that was not statistically significant.

The POLE (exon 3–13) mutation status of 49 tumors in this study was reported elsewhere [32]. POLE hotspot mutations (Supplementary Table 5) were present in one endometrioid EC, and in two of eight (25%) TP53 wildtype serous tumors versus 0 of 20 (0%) TP53 mutant serous tumors (a difference that was not statistically significant) (Supplementary Table 5). Focal high-level SCNAs were not detected in either of the POLE-mutated tumors or in MSI tumors.

Distribution of SCNAs and GISTIC regions in “TCGA-like” molecular subtypes of EC

MSI status, the mutational status of the POLE exonuclease domain (ED)(exons 9–13), and the mutational status of TP53 can be used to approximate the TCGA molecular subtypes of EC [33]. We therefore stratified the 49 tumors previously typed for all three markers into the following molecular groups: POLE-ED-mutated (n=3), MSI (n=4), TP53-mutated (n=24), and unspecified (MSS/POLE-ED-wildtype/TP53-wildtype) (n=18) (Supplementary Table 5). Tumors with any level of SCNA were more common in the TP53-mutated group (100%, 24 of 24) than in the unspecified group (50%, 9 of 18) and were absent from the POLE-mutated (0 of 3) and MSI-high (0 of 4) groups. Tumors with focal high-level SCNAs (gains of ≥5 copies or homozygous deletions, both <10Mb) were more frequent in the TP53-mutated group (95.8%, 23 of 24) than in the unspecified group (22.2%, 4 of 18), a difference that was statistically significant (two-tailed p value<0.0001). No GISTIC regions of SCNA were detected in the POLE-ED-mutated or MSI groups whereas the TP53-mutated and unspecified groups had 41 and 8 GISTIC regions of SCNA respectively (Table 4 and Table 5). Among tumors within the TP53-mutated group, consensus cancer genes KAT6B, MECOM, ERBB2, PTEN and LRP1B were within peak GISTIC regions (Table 4). No consensus cancer genes were identified among tumors within the unspecified molecular subgroup (Table 5).

Table 4.

Extended and peak GISTIC regions identified among the TP53-mutated molecular subgroup of EC

SCNA Type Genomic Coordinates (hg18) of Extended GISTIC Region Location of Extended GISTIC Region Genomic Coordinates (hg18) of Peak GISTIC Region Q-bound for Peak Region G-Score for Peak Region Genes in Peak GISTIC Region
Gain chr1:114027311-121013322 1p13.2-p11.2 chr1:115921462-116005785 3.96E-2 4.374 VANGL1
Gain chr1:176942142-207275546 1q25.2-q32.2 chr1:181211397-181376609 2.00E-3 5.110 LAMC1, LAMC1-AS1
Gain chr10:76097779-76809750 10q22.2 chr10:76285006-76292848 3.32E-3 4.996 KAT6B
Gain chr12:24710025-25457711 12p12.1 chr12:24983158-25022328 1.57E-4 5.674 BCAT1
Gain chr13:91908665-92569702 13q31.3 chr13:92353870-92391097 2.25E-1 3.713 /
Gain chr14:73019559-85498415 14q24.3-q31.3 chr14:73276000-73364208 2.60E-2 4.491 ELMSAN1, MIR4505, LOC100506476, LINC02274
Gain chr17:34942209-35561769 17q12-q21.1 chr17:35024060-35199745 2.70E-2 4.482 PPP1R1B, STARD3, TCAP, PNMT, PGAP3, ERBB2, MIEN1, GRB7, IKZF3, MIR4728
Gain chr18:0-16100000 18p11.32-p11.1 chr18:10911569-11090060 1.37E-2 4.658 PIEZO2
Gain chr19:22755564-24191440 19p12 chr19:22831403-22857036 1.18E-2 4.692 ZNF723
Gain chr19:34477100-37895554 19q12-q13.11 chr19:34839864-34892756 7.50E-14 10.887 PLEKHF1, C19orf12
Gain chr2:94793151-118939207 2q11.1-q14.2 chr2:95017289-95050247 2.00E-2 4.561 /
Gain chr20:14929825-17414153 20p12.1 chr20:15505853-15529115 2.46E-3 5.069 MACROD2
Gain chr20:51602600-51797590 20q13.2 chr20:51722390-51726725 1.26E-8 7.356 /
Gain chr21:28466487-46944323 21q21.3-q22.3 chr21:43661758-43718330 1.27E-1 3.991 SIK1, SIK1B, LINC00319, LINC01669, LINC00313
Gain chr3:169808488-171669472 3q26.2 chr3:170858670-171032269 7.64E-14 8.985 MECOM, ACTRT3, MYNN, LRRC34, LRRIQ4, TERC
Gain chr4:61820279-64360471 4q13.1 chr4:64148793-64360471 4.12E-2 4.362 /
Gain chr6:11306574-11952750 6p24.1 chr6:11470627-11545036 7.64E-4 5.340 NEDD9
Gain chr6:71020557-74065543 6q13 chr6:72383552-72520206 1.88E-1 3.803 /
Gain chr7:37375580-57890326 7p14.2-p11.1 chr7:53114918-53194448 3.47E-2 4.410 /
Gain chr8:129370592-132726767 8q24.21-q24.22 chr8:129601251-129636803 7.64E-3 4.792 LINC00824
Gain chr8:40154687-43910848 8p11.21-p11.1 chr8:40608933-40638501 6.65E-4 5.365 ZMAT4
Gain chrX:61845481-67200492 Xq11.1-q12 chrX:61845481-62571001 2.22E-1 3.729 SPIN4, LINC01278
Loss chr1:16797436-35789123 1p36.13-p34.3 chr1:27616247-27756094 8.88E-2 4.704 WASF2, AHDC1
Loss chr10:89251828-90869249 10q23.2-q23.31 chr10:89620956-89745318 1.03E-1 2.504 PTEN
Loss chr16:33744011-34459363 16p11.2-p11.1 chr16:33744011-34319481 8.88E-2 2.860 MIR9901, LOC112268173
Loss chr16:45205840-66994888 16q11.2-q22.1 chr16:58316118-58346135 8.88E-2 3.042 /
Loss chr17:18083937-18771584 17p11.2 chr17:18558072-18591780 8.88E-2 3.474 TRIM16L, FBXW10
Loss chr19:5166212-8072557 19p13.3-p13.2 chr19:7120029-7174340 1.46E-1 2.318 INSR
Loss chr19:0-1654336 19p13.3 chr19:839331-876857 8.88E-2 4.636 MED16, R3HDM4, KISS1R, RNU6-2
Loss chr2:141290607-142091845 2q22.1 chr2:141512334-141549533 2.41E-1 2.074 LRP1B
Loss chr21:16104648-46944323 21q21.1-q22.3 chr21:25436494-25569678 2.04E-1 2.160 /
Loss chr22:45341436-49106708 22q13.31-q13.33 chr22:48742080-48975222 8.88E-2 3.386 PIM3, IL17REL, TTLL8, MLC1, MOV10L1, PANX2, TRABD, MIR6821*
Loss chr4:115451731-141479193 4q13.31-q13.33 chr4:129663522-129691216 1.01E-1 2.528 /
Loss chr4:181771427-182518809 4q34.3 chr4:182415084-182433256 8.88E-2 4.764 LINC02500
Loss chr4:35651979-40878942 4p14 chr4:35780455-35831233 8.88E-2 3.203 ARAP2
Loss chr5:59205723-59759773 5q12.1 chr5:59288399-59292832 8.88E-2 4.161 PDE4D
Loss chr7:109360477-116122766 7q31.1-q31.2 chr7:109663496-109845497 8.88E-2 2.990 /
Loss chr8:24314997-29040226 8p21.2-p21.1 chr8:26258130-26349606 1.19E-1 2.419 PPP2R2A, BNIP3L
Loss chr9:25609706-28594341 9p21.2-p21.1 chr9:25926637-26889573 8.88E-2 3.058 LOC100506422, CAAP1
Loss chr9:74060683-90190867 9q21.13-q22.1 chr9:78714346-78849837 8.88E-2 3.134 FOXB2
Loss chrX:121101286-121546546 Xq25 chrX:121162142-121262032 8.88E-2 4.675 /
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Consensus cancer genes (* Tier-1 COSMIC v90) are shown in bold

Table 5.

Extended and peak GISTIC regions identified among the “unspecified” molecular subgroup of EC

SCNA Type Genomic Coordinates (hg18) of Extended GISTIC Region Location of Extended GISTIC Region Genomic Coordinates (hg18) of Peak GISTIC Region Q-bound for Peak Region G-Score for Peak Region Genes in Peak GISTIC Region
Gain chr1:176889044-247249719 1q25.2-q44 chr1:226520130-226684198 1.201E-2 1.625 OBSCN, TRIM11, TRIM17, HIST3H3, MIR6742
Gain chr11:0-2278975 11p15.5 chr11:2262382-2277501 1.302E-2 1.174 C11orf21
Gain chr16:25589827-32045466 16p12.1-p11.2 chr16:30005992-30178440 2.767E-2 1.076 TBX6,YPEL3,GDPD3,MAPK3,CORO1A, BOLA2B,BOLA2, SLX1A, SLX1B, SULT1A3, SULT1A4, NPIPB13, NPIPB12, SLX1B-SULTA4, SLX1BA-SULT1A3
Gain chr17:46022175-71330793 17q21.33-q25.1 chr17:49051857-49099570 2.574E-2 1.083 /
Gain chr19:32615675-37581903 19q12-q13.11 chr19:33868381-33998333 1.265E-2 1.180 /
Loss chr16:34584567-35106851 16p11.1 chr16:34911034-35106851 1.563E-1 1.356 /
Loss chr5:70715382-98608326 5q13.2-21.1 chr5:98552049-98608326 1.563E-1 1.100 /
Loss chrX:13889477-58363397 Xp22.2-p11.1 chrX:29494339-29543664 1.563E-1 1.092 IL1RAPL1, MIR4666B
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DISCUSSION

Clear cell EC is a rare histological subtype of uterine cancer that is associated with relatively poor clinical outcomes [4, 34] and to date is lacking the genomic characterization to which other EC histologic subtypes have been subjected. Here we provide high-resolution copy number analysis of 6 clinically diagnosed clear cell ECs and show that half of these tumors have acquired one or more SCNAs; the most frequent SCNAs involved gains of 3q, 5p, 8q, 20q, and 22q. Our findings are consistent with a comparative genomic hybridization (CGH) study by Micci et al., which reported genomic imbalances in 75% (3 of 4) of clear cell ECs, with gains involving 3q and 8q occurring in all chromosomally unstable tumors [23]. Compared to gains, we found that genomic losses in copy number altered clear cell ECs were relatively rare, which is also consistent with previous observations [23]. Although we observed no statistically significant association between TP53 mutation status and copy number status in clear cell EC, it is noteworthy that TP53 mutations were restricted to tumors with focal high-level SCNAs.

By comparison to clear cell ECs, 82.1% of serous and 70.5% of endometrioid ECs in our study had at least one SCNA. The fraction of tumors with focal high-level SCNAs was greater for serous than endometrioid ECs (78.6% versus 17.6% respectively). Our observations that SCNAs in serous tumors were widely distributed throughout the genome and consisted of gains and losses, whereas SCNAs in endometrioid tumors often involved chromosomes 1, 3, 7, 8 and 10 and tended to be gains, is in keeping with the distribution of SCNAs observed by TCGA in these histotypes [10] and in an early CGH study by Pere et al [35].

We noted a positive correlation between TP53 mutation and the presence of focal high-level SCNA in ECs (two-tailed p value<0.0001). Although the overall frequency of TP53 mutations in our serous EC cohort (71%) is relatively low, it is within the range (59%-93%, mean 81.7%) reported by others [10, 28, 36–40]. Inter-study variability in TP53 mutation rate may reflect differences in the extent of pathological review of tumors; studies based on clinical diagnoses, such as ours, will inevitably reflect the challenges in reliably determining the histology of some high-grade endometrial tumors [41]. Because of the well-known challenges associated with reproducible histopathologic diagnoses of a subset of ECs [42], we stratified our entire tumor cohort into “TCGA-like” molecular subgroups based on POLE (exonuclease domain) mutation status, MSI status, and TP53 mutation status. Clear cell ECs were equally distributed among the TP53 mutated and unspecified groups. Copy number altered tumors were more common in the TP53-mutated group than in the unspecified group and were absent from the POLE-mutated or MSI-high groups. The most statistically significant peak GISTIC regions in the TP53-mutated group were gains involving 3q26.2 (MECOM, ACTRT3, MYNN, LRRC34, LRRIQ4, TERC), 19q12-q13.11 (PLEKHF1, C19orf12), and 20q13.2. Although the peak GISTIC region on 20q13.2 did not encompass any gene, the corresponding extended GISTIC region included ZNF217, a putative oncogene that is amplified and/or overexpressed in a variety of tumor types [43]. Of note, MECOM, TERC, and ZNF217 are also in significantly recurring regions of amplification in TCGA copy number cluster 4 tumors. Other genes in peak GISTIC regions in the TP53-mutated group and in significantly recurring regions of SCNA in TCGA copy number cluster 4 tumors are NEDD9, ERBB2, and LRP1B.

While our study provides insights into the occurrence of SCNAs in clear cell ECs, it has several limitations. Our analysis was restricted to a small number of clinically diagnosed clear cell ECs. Because clear cell ECs and a subset of serous ECs are recognized as being difficult to reproducibly classify, even by multiple gynecologic pathologists [41, 44–48], our findings should be viewed in the context of clinically diagnosed cases, not consensus (pure) cases. As such, our study was not designed to reliably evaluate certain genotype-phenotype correlations such as the issue of whether TP53-mutant clear cell ECs are really serous carcinomas with clear cell-like morphology. Studies to address such questions are important future directions for the field. Finally, in the absence of orthogonal validation, the copy number alterations reported herein should be regarded as putative alterations.

In conclusion, using high resolution SNP-based array analysis, we show that some clinically diagnosed clear cell ECs have high-level copy number alterations and are TP53-mutated whereas others are relatively copy number quiet and TP53-nonmutated, a finding that corroborates previous reports of genomic heterogeneity in clear cell ECs with respect to mutations [13–21]. Our observations that SCNAs are more frequent in serous EC than in low- and intermediate-grade endometrioid ECs also confirms previous observations [10]. To our knowledge this is only the second series of primary clear cell ECs analyzed genome-wide for somatic copy number alterations using high-resolution SNP arrays. Our findings add to the current understanding of the molecular etiology of this rare but often clinically aggressive histological subtype of EC and provide impetus for future studies.

Supplementary Material

Highlights
Supplemental Tables 1_5
Supplementary Figure 1
Supplementary Figure 2
Supplementary Figure 3
Supplementary Figure 4

ACKNOWLEDGEMENTS

Our sincere thanks to Dr. Mary Ellen Urick for critical and insightful comments on the manuscript. We thank Settara Chandrasekharappa, Ph.D., MaryPat Sussex Jones, M.S., and Ursula Harper M.S., of the NHGRI Genomics Core, and Kim Doheny, Ph.D., Roxann Ashworth, M.H.S., and Michelle Zilka, M.S., at the Center for Inherited Disease Research (CIDR) at Johns Hopkins University for technical assistance. Data have been deposited into dbGaP (phs001690.v1.p1) with controlled-access.

FUNDING

This work was supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health.

Footnotes

Declaration of interest: Dr. Daphne W. Bell receives royalty income from the licensing of US patent No. 7,294,468 “Method to determine responsiveness of cancer to epidermal growth factor receptor targeting treatments”

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Highlights
NIHMS1058631-supplement-Highlights.docx (13.9KB, docx)
Supplemental Tables 1_5
NIHMS1058631-supplement-Supplemental_Tables_1_5.xlsx (76.4KB, xlsx)
Supplementary Figure 1
NIHMS1058631-supplement-Supplementary_Figure_1.pdf (235.2KB, pdf)
Supplementary Figure 2
NIHMS1058631-supplement-Supplementary_Figure_2.pdf (494.6KB, pdf)
Supplementary Figure 3
NIHMS1058631-supplement-Supplementary_Figure_3.pdf (350.3KB, pdf)
Supplementary Figure 4
NIHMS1058631-supplement-Supplementary_Figure_4.pdf (197.4KB, pdf)

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