Adrenocortical carcinoma (ACC), compared to most other carcinomas, is an extremely rare disease and accordingly challenging to study. The unavailability of large series of cases precludes many types of analyses, especially those designed to identify clinicopathologic parameters related to treatment and survival, although progress related to the efficacy of mitotane therapy has been recently reported (40
). The need for such studies is great and will continue to grow as novel targeted therapies are used to treat patients with ACC.
Our DNA microarray analysis clearly demonstrates the power of molecular profiling as a tool for the diagnosis of adrenocortical tumors. Microarray-based assessment can accurately separate ACAs from ACCs and may actually do so with slightly higher accuracy than morphology given that one tumor diagnosed as an ACC may be a large ACA. Use of DNA microarrays as an adjunctive diagnostic tool may be useful, especially at centers with limited experience with these rare tumors. Additionally, we provide a rich source of potential diagnostic markers that could be developed into useful immunohistochemical tools, to be used singly or in small panels. These genes, some of which are shown in , includes many cell cycle and proliferation genes (e.g. CCNB2, ASPM, RRM2, TOP2A, and CDKN3), as well as genes known to play a role in tumor invasion in other carcinoma types (e.g. SPP1).
The main significance of this study lies in the integration of a large cohort of normal tissues and benign and malignant tumors with associated clinicopathologic and genome-wide transcriptional profiles. Our previous work in this area (21
) was limited by the smaller number of samples and lack of outcome data and, thus, several important questions were not addressed. For example, it could not be determined whether ACC can be divided into clinically-relevant subtypes based on expression profiles and whether gene expression information provides useful information beyond that provided by standard clinicopathologic analysis. Other DNA microarray work utilized a similar series of tumors, but with a limited DNA microarray (22
). The present study overcomes these shortcomings.
Our results, together with what is known about the significance of mitotic rate grading (3
), strongly confirm the important role of cell growth and proliferation as a prognostic factor for ACC. However, the most novel aspect of our study is the finding that gene expression data contains independent prognostic information even when mitotic rate and stage data are included in the multivariate analysis. Thus, our study indicates that it should be possible to provide a more refined prognostic evaluation of ACCs based on gene expression. Future efforts will be directed at distilling this result into a manageable assay that can be employed using routinely-fixed ACC tissues. Our results using a 10-gene panel of prognostic genes suggest that this is feasible. In the meantime, pathologists should consider routinely reporting actual mitotic rate counts rather than simple low- or high-grade assessment.
It is possible using other array data (with stage and mitotic rate estimates) to obtain each tumor’s value for our principal component 1, which is just a linear combination of the values from the array, and so to obtain the estimate of the relative risk for each patient using our Cox model. However, our small sample size and the lack of an independent data set on which to test the predictions of our fitted Cox model dictate caution in recommending such a procedure. Data on more patient samples will likely lead to improved risk prediction, or better indicate which particular pathway alterations or genetic mutations are primarily responsible for the predictive ability of the gene expression values.
The high degree of overlap in gene expression between ACC Cluster 1 and a set of genes associated with chromosomal instability and tumor aneuploidy (35
) is entirely consistent with the idea that adrenal cancer follows the common cancer paradigm in which genomic instability leads to gross chromosomal changes and aneuploidy. The available cytogenetic data on adrenal tumors (32
) is also consistent with this model.
Enrichment analysis of the expression data predicted possible gains of 12q and 5q and possible loss of 11q, 1p and 17p in ACC. A similar analysis of the ACC clusters predicted possible gains of 1q, 22q, 6q, 10p and 6p in Cluster 1 ACCs. These findings suggest that these regions represent deletions of tumor suppressor genes or amplifications of oncogenes. Comparison with the available CGH data (32
) shows a high degree of agreement for some changes, suggesting that expression data could be combined with array CGH data to pinpoint the specific causative amplifications and deletions within these large chromosomal regions.
expression was identified in this study as one of the most dominant transcriptional changes specifically present in ACC relative to ACA and NC, as it was in our prior microarray study (21
). This finding is consistent with a large body of published literature on perturbation of the IGF2
locus in ACC (for reviews, see (36
)). While the molecular basis for the 2-fold elevated level of IGF2
transcription in familial ACC associated with BWS is pathologic imprinting of the IGF2/H19
locus and paternal isodisomy, the markedly elevated IGF2
expression (with concomitant downregulation of H19
) in sporadic ACC is likely to involve additional mechanisms of transcriptional regulation (43
). Regardless of the precise mechanisms leading to increased expression, IGF2 has a mitogenic effect and is directly involved in the proliferation of the adrenal cancer cell line NCI H295R via an IGF1R-dependent mechanism (46
). This autocrine stimulatory loop, together with the IGF2
expression pattern in adrenocortical tumors (90% of ACCs and rare in ACA), makes targeting the IGF system an attractive therapeutic approach for ACC (47
). Accordingly, multi-institutional trials with an anti-IGF1R monoclonal antibody are being developed.
Enrichment analysis of the ACC genes identified a significant number of genes containing the binding domain for the E2F transcription factor. This is consistent with a bioinformatic study that revealed upregulation of E2F-regulated genes as a common event across a broad range of tumor types, including ACC (50
We fully expect that our diagnostic and prognostic results will be broadly applicable to adult ACC. While our ACC cohort does include 2 pediatric cases that were typical of the other ACCs, this number is too small to make a valid assessment about differences between pediatric and adult ACCs. We also fully expect that our data can be used to classify individual adrenocortical tumors, either by performing DNA microarray analysis or by a multiplex Q-RT-PCR approach using a selected set of informative genes combined with a simple statistical classifier such as a nearest neighbor classifier. Furthermore, it should also be possible to use our data to develop novel IHC markers into useful diagnostic and prognostic markers. Future efforts will focus on translating our results into clinically useful tools for the pathologic evaluation of these tumors.
In summary, DNA microarray analysis of a large group of adrenocortical tumors accurately classified benign and malignant tumors, confirmed the diagnostic and prognostic importance of cell growth and proliferation in ACC, and divided the malignant tumors into 2 groups that possessed prognostic significance. In addition, gene expression profiles provided prognostic information independent of tumor mitotic rate and stage. Looking forward, if effective targeted therapies for ACC are developed in the not too distant future, then the results of this study strongly suggest that it will be possible and desirable to use DNA microarray analysis, or a defined panel of genes, to simultaneously confirm the diagnosis of ACC, determine a more precise prognosis, and assist in the selection of appropriate therapy. While expensive and technically challenging, a microarray-based assay that delivers such relevant information would significantly advance the care of ACC patients and represent a large step towards the realization of personalized genomic medicine for these patients.