Adrenocortical carcinoma survival rates correlated to genomic copy number variants
Elizabeth A. Stephan,1 Tae-Hoon Chung,2 Clive S. Grant,4 Seungchan Kim,2,5 Daniel D. Von Hoff,3 Jeffrey M. Trent,1 and Michael J. Demeure3,6
1Genetic Basis of Human Disease Division, 2Computational Biology Division, and 3Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona;
4Department of Surgery, Mayo Clinic, Rochester, Minnesota; 5School of Computing and Informatics, Ira Fulton School of Engineering, Arizona State University, Tempe, Arizona; and
6Department of Surgery, University of Arizona, Tucson, Arizona
Abstract
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy accounting for between 0.02% and 0.2% of all cancer deaths. Surgical removal offers the only current potential for cure. Unfortunately, ACC has undergone metastatic spread in 40% to 70% of patients at the time of diagnosis. Standard chemotherapy with mitotane is often ineffective with intolerable side effects. The modern molecular technology of comparative genomic hybridiza- tion allows the examination of DNA for chromosomal alterations, which can lend biological insight into cancer processes. Genomes of 25 ACC clinical samples were queried on the Agilent 44K Human Genome comparative genomic hybridization array detecting regions of chromo- somal gain and loss within the tumor population. Com- monly shared amplifications appearing in ≥50% of tumors at P ≤ 10-4 include regions within chromosomes 5, 7, 12, 16q, and 20. Deleted genomic regions within ACC include portions of chromosomes 1, 3p, 10q, 11, 14q, 15q, 17, and 22q. Genomic aberrations in regions associated with differential survival (P ≤ 0.05) and presence in ≥20% of tumors include amplifications of 6q, 7q, 12q, and 19p. Deletions within stratified survival groups include localized regions within 3, 8, 10p, 16q, 17q, and 19q. Statistical
Grant support: Advancing Treatments for Adrenocortical Carcinoma Fund.
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.
Note: Array CGH data have been deposited in Gene Expression Omnibus (accession no. GSE7482).
E.A. Stephan and T-H. Chung contributed equally to this work.
Requests for reprints: Elizabeth A. Stephan, Genetic Basis of Human Disease Division, Translational Genomics Research Institute, 445 North 5th Street, Phoenix, AZ 85004. Phone: 602-343-8817; Fax: 602-343-8840. E-mail: estephan@tgen.org
Copyright @ 2008 American Association for Cancer Research. doi:10.1158/1535-7163.MCT-07-0267
analysis of this genetic landscape reveals a set of chromo- somal aberrations strongly associated with survival in an accumulation-dependent fashion. These regions may hold prognostic indicators and offer therapeutic targets that can be used to develop novel treatments for aggressive tumors. [Mol Cancer Ther 2008;7(2):425 -31]
Introduction
Adrenocortical carcinoma (ACC) is one of the least commonly occurring but deadliest cancers. With an incidence of less than 0.77 in 1 million people, fewer than 250 new cases of ACC are seen within the United States each year (1). Carcinoma of the adrenal cortex accounts for between 0.02% and 0.2% of all cancer deaths (2, 3). The prognosis is poor with a 5-year survival rate of 20% to 45% due to typically late presentation and the limited effective- ness of broad-spectrum chemotherapy (4, 5). The standard chemotherapeutic treatment for this disease, since 1960, remains mitotane (Bristol-Myers Squibb), a derivative of the pesticide DDT (6). Response rates are poor (~22%), although survival for those who do respond is improved from 14 to 50 months (7). Due to the lack of effective treatments for ACC, increased knowledge of the cellular processes involved in oncogenesis is necessary for the development and implementation of targeted therapies.
Some have argued that the development and growth of ACC tumors is a multistep progression with accumulation of genetic alterations (8). Although ACC does occur in the context of inherited syndromes, such as the Li-Fraumeni and Beckwith-Wiedemann syndromes, most cases are of a sporadic nature. The Li-Fraumeni syndrome is traced to a germ-line mutation in the p53 tumor suppressor gene on chromosome 17p13 (9, 10). The genetic basis for the Beckwith-Wiedemann syndrome is an allelic loss at chromosome 11p15 (11). Characteristic mutations of both syndromes have been observed in some sporadic adreno- cortical cancers (12, 13).
Knowledge of genomic gains, losses, and translocations associated with sporadic adrenocortical cancer has not yet revealed an ACC oncogenic pathway. Studies of the chromosomal and molecular abnormalities in ACC have relied on technologies, such as conventional comparative genomic hybridization (CGH) or fluorescence in situ hybridization, to identify genomic gains and losses.
Recent advances in modern array-based molecular genetic technologies offer the opportunity to study tumors with greater resolution and in a high-throughput fashion with the end goals of understanding oncogenesis, enabling the development of diagnostics and targeted therapies. Zhao et al. suggested that gains in DNA copy number in chromosome 17 or 17q represent the earliest genomic changes and are seen in benign adrenocortical adenomas
426 Adrenocortical Carcinoma CGH Linked to Survival
only (14). Additional studies have identified loss of heterozygosity at 17p13 and 11p15 along with increased expression of insulin-like growth factor 2 as molecular events associated with progression toward a malignant phenotype (15). As reported by Dohna et al., gains and high-level amplifications in chromosomes 7, 14, and 19 were seen only in ACC and not in benign adrenocortical neoplasms, suggesting that these events may be late genetic perturbations in tumorigenesis (16). Deletions have also been found within chromosomes 1p and 17p implicating potential losses of tumor suppressor genes in these regions, marking the progression of benign adrenal adenoma to ACC (17). Similar to other cancers, there is evidence that the number of genetic aberrations increases as a factor of tumor growth and this size progression has been correlated with malignancy (14, 18, 19).
Herein, we use high-density CGH array analysis to identify genomic aberrations associated with poor survival as potential prognostic markers and therapeutic targets in aggressive ACC.
Materials and Methods
Clinical Samples
The total set of 25 ACC tumors includes 21 flash-frozen tumor samples collected between 1987 and 2003 at the Mayo Clinic. The subsequent 4 ACC tumors were acquired through collaborations with the University Hospital Essen and the University of Calgary. Research consent was obtained at the respective institutes through accepted institutional review board protocols and accepted into our research program and tumor bank under the Western Institutional Review Board-approved protocol no. 20051769. Extensive clinical and demographic information was collected at time of diagnosis and surgery (Supple- mentary Data 1).7
Comparative Genomic Hybridization
DNA extraction from flash-frozen ACC tumor samples was done following the standard protocol for the Qiagen DNeasy Blood and Tissue Kit (Qiagen). Oligo-based CGH was done according to manufacturer’s instructions for the Agilent Human Genome Microarray Kit 44B (Agilent Technologies) starting with 800 ng DNA from clinical samples and a separate reference sample of 800 ng control male genomic DNA (Promega). Experimental samples labeled with Cy5 and reference samples labeled with Cy3 (GE Healthcare) were combined and hybridized to a single CGH array. Deviating from the Agilent Oligo aCGH Hybridization Kit protocol, samples were incubated with Cot1 (1 mg/mL), 10x blocking solution, and 2x hybrid- ization buffer at 99℃ for 3 min, 37℃ for 30 min, and centrifuged at high speed for 1 min. The samples were hybridized according to Agilent protocol. Arrays were washed, scanned, and data extracted using Agilent Feature Extraction 8.1. Data for arrays fulfilling standard Agilent
quality requirements were included in further statistical analyses.
Statistical and Survival Analyses
The log2 of Cy5 and Cy3 ratio (log-ratio) was used as the measure of copy number ratio between tumor and normal samples throughout the analysis. To detect the regions of copy number aberration, log-ratio values of a sliding window of 25 probes in length were compared using the Wilcoxon’s rank-sum test with the log-ratio values of an artificial reference chromosome constructed for each tumor (20). The reference chromosome was created by recursively accepting 25 randomly sampled log-ratio values from the autosomal regions with P value of Wilcoxon’s signed rank test (against 0) > 0.05 until the size reached 1,000. The P values and corresponding false discovery rates (or q values) for the copy number gain or loss (indicated as P gain and Qgain or Ploss and Qloss) were calculated independently for each sliding window and assigned to the center position of the window.
Commonly shared genomic aberrations in ACC tumors were identified by gathering overlapping sliding windows that were gained or lost by ≥50% of all ACC patients. A sliding window was called gained if Pgain < 10-5 and Qgain < 10-4 and lost if Ploss < 10-5 and Qloss < 10-4. The statistical significance of an identified region was evaluated by testing the distribution of copy number changed tumor fractions of sliding windows contained in the region against the ratio cutoff 0.5 using Wilcoxon’s signed rank test.
The clinical implication of the observed genomic aberra- tions was examined in detail by identifying chromosomal regions whose gains or losses were associated with the overall survival difference using the log-rank test for all sliding windows across the genome. Sliding windows whose gains or losses were linked to poor prognosis (P ≤ 0.05) and occurred in ≥20% of all patients were called survival relevant and retained for further analysis.
Unsupervised clustering of a summary matrix was done to detect the coappearance of survival relevant genomic aberrations from different chromosomal regions. For each chromosome that contained survival relevant sliding windows, the survival relevant region was identified first by collecting all survival relevant sliding windows across all patients within it. Then a summary indicator was scored for the chromosome of a patient as +1 (gain), 0 (no change), or -1 (loss) if >50% of sliding windows that fell within the survival relevant region of the patient harbored corresponding genomic aberrations. In the clustering, the distance between two vectors was measured by counting the number of elements that were not the same and the complete linkage was used to determine the distance between two intermediate clusters.
Results
Common Genomic Aberrations
Genomic aberrations are visualized as the histogram of copy number change frequencies over all ACC tumors
7 Supplementary material for this article is available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).
(Fig. 1A) and the heat map of chromosomal gain and loss for individual patients (Fig. 1B). The copy number aberration frequency plot (Fig. 1A) represents the fraction of patients with P ≤ 10-5 and q ≤ 10-4 for each sliding window. The results clearly indicate that ACC tumors have an extensive level of genomic aberrations as has been reported previously (Table 1). In most of the chromosomes, common aberrations (gains or losses) encompassing large fractions of the samples are observed, indicating high occurrence of genomic change. The most distinctive aberrant regions include gains of chromosomes 5, 6q, 7, 8q, 12, 16q, and 20 and losses of chromosomes 1, 2q, 3, 6p, 7p, 8p, 9, 10, 11, 13q, 14q, 15q, 16, 17, 19q, and 22q (visualized in Fig. 1 and detailed in Supplementary Data 2).
Survival Relevant Genomic Aberrations
Chromosomal regions were further analyzed to deter- mine if the occurrence of genomic aberrations was associated with survival difference. Figure 2 shows the distribution of log-rank test P values across the genome. The P values above and below the X axis represent the association between gains and losses (respectively) and survival difference. Specific genomic aberrations associated with worse survival rates are detailed in Table 2 with gains in 6q, 7q, 12q, and 19p and losses in 3, 8, 10p, 16q, 17q, and 19q. Survival curves created for each aberration linked to poor survival can be seen in Supplementary Data 3.
To clarify possible coappearances of survival relevant genomic aberrations, unsupervised hierarchical clustering was done with sliding windows in regions relevant to
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| No. ACCs | Genetic analysis | Regions gained | Regions lost | Reference |
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| 12 | Conventional | CGH 5q12-q13, 5q22-qter, 9q32-qter, 12q13-q14, 12q24, 20q, Xq13-q21 | 1p21-p31, 1q23-q41, 2p21-pter, 2q, 3p, 3q, 69, 9p, 11q14-qter, 18q | (14) |
| 14 | Conventional CGH | 1p34.3-pter, 1q22-q25, 3p24-pter, 3q29, 5, 7, 7p11.2-p14, 8, 9q, 9q34, 11q, 11q12-q13, 12q, 12q13, 12q24.3, 13q34, 14q, 14q11.2-q12, 14q32, 16, 16p, 17q, 17q24-q25, 19, 19p13.3, 19q13.4, 20, 22q, 22q11.2-q12 | 9p | (16) |
| 13 | Conventional CGH | 4, 5, 12, 12q14-q21, 19 | 1p, 1p34-pter, 2q, 2q34-qter, 11q, 11q24-qter, 17p, 17p13-pter, 22 | (17) |
| 8 | Conventional CGH | 4q, 4q31, 5, 12, 12cent-q24, 15q, 15q21-qter, 16q, 19p | 2, 2p23-cen-q21, 3p21-cent, 6q, 8p, 9p, 11p, 11q, 11q22-qter, 17p, 17q, 18q, 22q | (18) |
| 12 | Conventional CGH and oncogene-specific microarray | 1q, 4p15-pter, 5p, 5p15, 5q, 5q13, 5q32-qter, 7p, 7q, 8q, 8q24, 9p, 9q, 12q13-q15, 13q, 16q, 17p, 17q, 20p, 20q | (19) |
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| Chromosome | Cytogenic band | Segment start | Segment end | Segment length | |
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| Gain | 6 | 6q15-6q16.1 | 91152410 | 95641352 | 4488942 |
| 7 | 7q36.1 | 150275058 | 151470304 | 1195246 | |
| 12 | 12q13.2 | 54496959 | 54887816 | 390857 | |
| 19 | 19p13.12-19p13.11 | 15352798 | 16118051 | 765253 | |
| Loss | 3 | 3p21.31 | 46534671 | 47516603 | 981932 |
| 3 | 3p21.31 | 49687490 | 50219610 | 532120 | |
| 3 | 3p21.1 | 52432656 | 53876467 | 1443811 | |
| 3 | 3q23 | 142987765 | 144031265 | 1043500 | |
| 8 | 8p21.3 | 21408650 | 22539377 | 1130727 | |
| 8 | 8p21.1-8p12 | 29147511 | 31607545 | 2460034 | |
| 8 | 8p12-8p11.23 | 36481039 | 39128088 | 2647049 | |
| 8 | 8q24.3 | 144753570 | 146201712 | 1448142 | |
| 10 | 10p14-10p13 | 11285212 | 16864223 | 5579011 | |
| 10 | 10p12.1 | 26030462 | 27568983 | 1538521 | |
| 16 | 16q23.2-16q23.3 | 79195214 | 80590034 | 1394820 | |
| 16 | 16q24.1 | 82774218 | 84680399 | 1906181 | |
| 16 | 16q24.1-16q24.2 | 85171711 | 86599633 | 1427922 | |
| 16 | 16q24.3 | 87455812 | 88254372 | 798560 | |
| 17 | 17q12 | 33805634 | 34663603 | 857969 | |
| 19 | 19q13.2 | 45108448 | 45718366 | 609918 | |
| 19 | 19q13.31-19q13.32 | 49537470 | 50323207 | 785737 | |
| 19 | 19q13.32 | 50358143 | 53057498 | 2699355 | |
| 19 | 19q13.32-19q13.33 | 53210832 | 53917176 | 706344 |
NOTE: Segment lengths are measured in kilobases according to the National Center for Biotechnology Information Build 36 of Human Genome Map.
survival difference (see Materials and Methods for details). The results shown in Fig. 3A indicate that genomic aber- rations relevant to survival difference in unique chromo- somes do not cosegregate. Furthermore, the clustering of samples results in three sample groups, each of which is associated with varying presence of selected aberrations. The groups are differentially associated with survival trends given by a Kaplan-Meier analysis P value of 7.4 x 10-6 (Fig. 3B). Additional tumor characteristics (Fig. 3A) are not associated with the described survival difference, indicating that the survival clusters are dependent on accu- mulation of genomic aberrations alone.
Discussion
With few therapeutic options and a poor understanding of ACC oncogenesis, emerging high-density genomic scan- ning technologies enable identification of genomic events linked to disease. Table 1 summarizes previous genomic analyses of ACC tumors presented in five publications. A new, more precise high-resolution technology is used in the present study to identify genomic aberrations in ACC with >44,000 60-mer oligonucleotide probes spaced across the entire genome. Compared to the conventional CGH or interphase fluorescence in situ hybridization, which offer a resolution of ≤10 Mb, new Agilent chip-based array CGH offers a much improved resolution with probes on average every 35 kb (21). The previous studies of chromosomal amplifications and deletions used sample sets between
8 and 14 adrenocortical tumors. In the present study, we used high-resolution CGH to analyze 25 ACC samples for genetic gains and losses within the tumor population.
Array-based CGH analysis identifying chromosomal aberrations shared among a high percentage of ACC tumors gives a superior evaluation of areas characteristi- cally linked to ACC oncogenesis. Genomic aberrations within the ACC genome (Fig. 1A) illustrate the overall gains within chromosomes 5, 6q, 7, 8q, 12, 16q, and 20 and losses within chromosome 1, 2q, 3, 6p, 7p, 8p, 9, 10, 11, 13q, 14q, 15q, 16, 17, 19q, and 22q (Supplementary Data 2). These high-resolution results recapitulate genomic abnor- malities presented in past research (see Table 1). Of note, the analysis identified unique losses in 10q, 14q, and 15q, giving a more comprehensive characterization of the ACC genomic landscape. Genes within these regions can give insight into the pathways of oncogenesis active within ACC. Among other deleted regions, literature suggests that the loss of chromosomes 1p and 17p are linked to progression from benign to malignant ACC (17). Recent studies have identified a chromodomain helicase DNA- binding protein 5 within 1p36.31 as a tumor suppressor gene (22, 23). The absence of this protein allows uncon- trolled cell growth and immortalization through the p19Arf /p53 pathway (22). With mutations of p53 observed in Li-Fraumeni syndrome (with associated development of ACC), the loss of chromodomain helicase DNA-binding protein 5 may act as an additional hit within this pathway, increasing malignant growth, proliferation, and a lack of
430 Adrenocortical Carcinoma CGH Linked to Survival
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apoptosis. It is the elucidation and validation of these proposed pathways that will lead to an understanding of ACC oncogenesis.
Linking genomic events to clinical phenotype further increases the characterization of cancerous processes. Analysis of Kaplan-Meier survival curves for common aberrations identified a set of genomic gains and losses occurring specifically within populations with shortened survival rates. As illustrated in Fig. 2, genomic aberrations in red show selective presence in ≥20% of patients with short postsurgical survival. The regions include gain of 6q, 7q, 12q, and 19p and loss of 3, 8, 10p, 16q, 17q, and 19q (Table 2). These regions further cluster by accumulation of aberrations (Fig. 3A) and survival analysis identified an
association between level of accumulation and survival rate (Fig. 3B). It is hypothesized that an increase in total aberrations, and not a combination of specific aberrations, links to shortened survival. This situation would indicate increasing genomic aberration with progressing disease. Multistep cancer progression cannot be illustrated in this data set due to the absence of analysis on benign adrenal tumors. Addition of benign and early-stage ACCs will allow development of a cancer progression model.
Disease variables, such as tumor size, tumor weight, tumor grade, functionality, and localization, were not shown to be significantly linked to the described survival differences (Fig. 3A). It is not clear whether these clinical variables truly have no effect on survival or whether there is insufficient statistical power (due to small sample size) to identify the underlying clinical stratifications.
Herein, high-resolution CGH has been used to character- ize genomic differences between a group of 25 ACC tumors and normal DNA. These areas of gain and loss give insight into the mechanisms of disease and identify genomic regions containing potential targets for therapeutic intervention. Accumulation of gains and losses in specific regions can also be linked with survival difference, creating a genetic footprint for ACC survival and progression of disease.
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