Gene expression profiling in adrenocortical neoplasia

Molecular and Cellular Endocrinology 351 (2012) 111–117

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Molecular and Cellular Endocrinology journal homepage: www.elsevier.com/locate/mce

Review

Gene expression profiling in adrenocortical neoplasia G. Assie a,b,c,d,⇑, T.J. Giordano g, J. Bertherat a,b,c,d,e,f a

INSERM U1016, Institut Cochin, Paris, France CNRS UMR8104, Paris, France c Université Paris Descartes, Sorbonne Paris Cité, Paris, France d Department of Endocrinology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France e Center for Rare Adrenal Diseases, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France f Rare Adrenal Cancer Network COMETE-INCA, Paris, France g Department of Pathology, University of Michigan Health System, Ann Arbor, MI, USA b

a r t i c l e

i n f o

Article history: Received 31 July 2011 Received in revised form 14 September 2011 Accepted 14 September 2011 Available online 25 October 2011

Keywords: Adrenocortical carcinoma Transcriptome Diagnosis Prognosis tumorigenesis

a b s t r a c t Transcriptome studies of adrenocortical tumors have shown clear differences between adenomas and carcinomas and identified two subgroups of carcinomas with different prognoses. This review focuses on how transcriptomes have enriched our knowledge about genes previously identified by classical candidate gene approaches, uncovered novel genes relevant to adrenocortical tumor biology, helped to identify and understand specific pathway alterations, and advanced the overall translational relevance of this field of research. Ó 2011 Elsevier Ireland Ltd. All rights reserved.

Contents 1. 2.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The candidate genes and the transcriptomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. The high expression of IG2 in ACCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. TP53 is often altered in ACCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Wnt-bcatenin is often activated in ACCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular classification of ACC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. The discrimination of ACCs and ACAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. The two types of ACCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. The three types of poor-prognosis ACCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clinical developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Diagnosing a malignant neoplasm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Prognosis of ACC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Should we perform today a pan-genomic transcriptome array in adrenal cancer management? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. The future place of transcriptome in the ‘‘omics’’ field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Moving from tumor groups to individual tumors characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Assess the clinical relevance of transcriptome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4. Impact of transcriptomes on treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction ⇑ Corresponding author. Address: Service d’endocrinologie, Hôpital Cochin, 27 rue du Fg Saint Jacques, 75014 Paris. Tel.: +33 158411820; fax: +33 158411805. E-mail address: [email protected] (G. Assie). 0303-7207/$ - see front matter Ó 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.mce.2011.09.044

Gene expression studies of cancers have been transformed by the advent of transcriptomes, transforming costly and time

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consuming studies of individual candidate genes into a standardized procedure that yields reproducible pan-genomic expression information. Beyond confirming the role of candidate genes, the derivation of transcriptomes have brought major advances in understanding tumorigenesis, in tumor classification schemes, and in patients management(Quackenbush, 2006). To date, about 10 primary studies have determined and reported the transcriptome of adrenocortical tumors (ACTs) (Giordano et al., 2003, 2009; de Fraipont et al., 2005; Velázquez-Fernández et al., 2005; Lombardi et al., 2006; Slater et al., 2006; West et al., 2007; Fernandez-Ranvier et al., 2008a, 2008b; Laurell et al., 2009; de Reyniès et al., 2009; Soon et al., 2009; Tömböl et al., 2009; Szabó et al., 2010). Consensual information has emerged, including a distinct difference in expression profiles of adenomas (ACAs) and carcinomas (ACCs). Genes driving this difference include an enrichment in steroidogenesis-related genes in ACAs and an enrichment in cell-cycle related genes in ACCs. Recent reviews and a meta-analysis have summarized these aspects (Assié et al., 2010; Szabó et al., 2010; Ragazzon et al., 2011). Tumor classifications based on the transcriptome also identified two groups of adult ACCs associated with different outcomes, as reported by three original studies (Giordano et al., 2009; Laurell et al., 2009; de Reyniès et al., 2009), and recently reviewed (Assié et al., 2010; Ragazzon et al., 2011). Of interest, the transcriptome-based prognosis remains significant after stratification by conventional pathological stage and grade. In children, one study compared the transcriptome of ACCs to ACAs and normal adrenal, evoking similarities between the childhood ACC transcriptome, and the foetal adrenal transcriptome, and between adulthood and childhood ACCs (West et al., 2007). In this review, we will discuss how transcriptomes of adrenocortical tumors have enriched our knowledge about genes previously identified by classical candidate gene approaches, uncovered novel genes relevant to adrenocortical tumor biology,

helped to understand specific pathway alterations, and advanced the overall translational relevance of this research. 2. The candidate genes and the transcriptomes Several genes have been identified for their relevance in adrenal carcinogenesis prior to the transcriptome era (Libé and Bertherat, 2005). Transcriptome studies have confirmed the differential expression of many of these genes and also identified their downstream targets. Beyond a confirmatory role, transcriptome studies have also expanded our knowledge of these genes and provided an assessment of their relative importance to the biology of adrenocortical neoplasia. 2.1. The high expression of IG2 in ACCs ACCs occur in patients with Beckwith–Wiedmann syndrome (OMIM #130650). This syndrome is related to complex alterations at the 11p15 locus, resulting in the alteration of the expression of several genes in this region (see (Weksberg et al., 2003) for review) including IGF2 and H19. These two genes are regulated by a parental imprint in a reciprocal manner. Beckwith–Wiedmann seems related to a loss of the maternal IGF2 allele and expression of the paternal allele, resulting in an increased expression of IGF2 and reduced expression of H19. Other genes, also at the 11p15 locus, are implicated in the Beckwith–Wiedmann syndrome, which include p57kip2, and KCNQ1. These two genes are also regulated by a parental imprint, but which is not related to the IGF2/H19 imprint. The interaction between these two imprinted regions is complex and incompletely understood. Given the occurrence of ACCs in the Beckwith–Wiedmann syndrome, expression of these genes at the 11p15 region was analyzed in sporadic ACCs well before genomic approaches were taken. High expression of IGF2 was found in approximately 90% of ACCs com-

Fig. 1. IGF2 expression in ACCs. IGF2 is among the top overexpressed genes in ACCs compared to ACAs (from (Giordano et al., 2003)). The IGF2 locus in 11p15 is part of an imprinted region that is commonly altered in sporadic ACCs, with duplication of the paternal allele, and loss of the maternal allele. The imprinted genes from this regions show expression profiles in ACCs in agreement with this genome alteration, with high expression of maternally imprinted genes IGF2 and KCNQ1OT1, and low expression of paternally imprinted genes H19, CDKN1C AND KCNQ1 (SLC22A1L AND TSSC3 do not show any obvious difference) (from de Reyniès et al., 2009).

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Fig. 2. Transcriptome-based classification of adrenocortical tumors (adapted from Giordano et al., 2009; Laurell et al., 2009; de Reyniès et al., 2009; Ragazzon et al., 2010). The boxes represent the groups of tumors generated by unsupervised hierarchical clustering. The color code (black, white, gray) characterizes the presence (black), absence (white) or intermediate state (gray) of the molecular event mentioned for each line. In the IGF2 overexpression line, the white vertical bars represent individual ACCs with no IGF2 expression, which correspond to approximately 10% of ACCs. These IGF2-negative ACCs are found in all of the the subgroups of carcinomas identified by the transcriptome-based classification.

Fig. 3. Recurrence prediction in non-metastatic tumors (from de Reyniès et al., 2009).

pared to ACAs (Gicquel et al., 1994). The first ACC transcriptome study and all subsequent ones have confirmed this result (Fig. 1). In children ACTs, IGF2 is also overexpressed in ACCs (West et al., 2007). It is therefore fair to ask, ‘‘What new knowledge has arisen from transcriptome analyses?’’ First, as these studies have included almost all of expressed genes, it was easily demonstrated that altered IGF2 expression

was one of the most dominant trasncritpional events in ACCs, further underlying its importance in these tumors (Fig. 1). Transcriptome data also provided a global picture of all the 11p15 genes expression in sporadic ACCs. A majority of tumors with a high IGF2 expression showed low H19 expression, which is in favor of the expression of the paternal allele and the loss of the maternal allele. Other imprinted genes in the 11p15 show

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differential expression, also suggesting the loss of the maternal allele and/or imprinting (Fig. 1). Transcriptomes studies have also shown that IGF2 is so uniformaly and highly expressed that its expression was not responsible for the ACC subclassification subsequently derived by unsupervised clustering (see below). This is ascertained by the clustering of the IGF2-negative ACCs with the IGF2-positives (Fig. 2). From a clinical perspective, it is reasonable to ask what role can IGF2 play as a marker of a malignant neoplasm? Due to the occurrence of around 10% of ACCs that lack high IGF2 expression, sensitivity of this marker is reduced. Without IGF2, the transcriptome performs better as a classification tool. By looking for a reduced number of genes deduced from the transcriptome, DLG7-PINK1 was found to perform better as a discrimination tool (Fig. 3). Finally, IGF2 overexpression has led to new therapies designed to target this pathway. Transcriptome analyses could be used to assess the expression level of the IGF2, its receptors and binding proteins. Interestingly, IGF1-R, which mediates the main cellular effects of IGF2 in vivo (Gicquel and Le Bouc, 2006), is not downregulated in ACCs overexpressing IGF2. Two preclinical studies have confirmed the implication of this pathway in ACC tumorigenesis (Almeida et al., 2008; Barlaskar et al., 2008), and an international clinical trial targeting the IGF1-R is ongoing (Clinical Trial.gov #NCT00924989).

identified by immunohistochemistry, often related to activating bcatenin mutations in almost all ACCs, and in one third of adenomas. The role Wnt-bcatenin activation was be further documented by the description of adrenocortical tumors in patients carrying APC mutations(Seki et al., 1992; Gaujoux et al., 2010). Transcriptome studies have also provided important information about Wnt-bcatenin. Indeed several Wnt-bcatenin target genes are overexpressed in ACC, including baculoviral IAP repeatcontaining 5 (BIRC5), ectodermal-neural cortex 1 (ENC1), pituitary tumor-transforming 1 (PTTG1), and twist homolog 1 (TWIST1) (de Fraipont et al., 2005; Velázquez-Fernández et al., 2005; Slater et al., 2006; Giordano et al., 2009; de Reyniès et al., 2009). Moreover, gene set enrichment analyses showed enrichment in Wntbcatenin genes in ACC compared to ACA (de Reyniès et al., 2009). In addition, Wnt-bcatenin alterations accumulate in a subgroup of aggressive ACCs also identified by transcriptome-based classification (Fig. 2). Positive transcriptional targets such as claudin 1 (CLDN1), axin 2 (AXIN2) and Leucine-rich repeat (LRR)-containing G-protein coupled receptor 5 (LGR5) were also found overexpressed in this subgroup of ACCs (Ragazzon et al., 2010). These data suggest that the Wnt-bcatenin activation is a driver molecular alteration in a subgroup of ACCs, occurring late in tumorigenesis, similar to p53 mutation. Interestingly, p53 and b-catenin mutations in aggressive ACCs seem mutually exclusive, thus defining two distinct subgroups of high-grade ACCs (Fig. 2).

2.2. TP53 is often altered in ACCs

3. Molecular classification of ACC

TP53 is a major player in cancer. Attention was brought on TP53 in sporadic ACCs due to the occurrence of ACCs in Li-Fraumeni patients, a syndrome often related to germline TP53 mutations (OMIM #151623). In adult sporadic ACCs, about one quarter of tumors harbor somatic TP53 mutations (Reincke et al., 1994; Barzon et al., 2001; Libè et al., 2007), and more than a half harbor loss of heterozygosity at the TP53 locus (Gicquel et al., 2001; Soon et al., 2008). Immunohistological alterations of p53 protein staining have also been identified. An unexpected high prevalence of unique exon 10 TP53 mutation in south Brazil was identified in childhood ACCs (Ribeiro et al., 2001). Transcriptome studies have led to further understanding of the role of p53 in sporadic ACCs. Indeed, TP53 mutated tumors are enriched in a subgroup of ACCs identified by unsupervised clustering of the tumors (Ragazzon et al., 2010). That means that specific transcriptome features are associated with this subgroup. According to the transcriptome based classification, this subgroup lies within the group of aggressive ACCs (Fig. 2). Finally, genes positively regulated by p53 such as RRM2B, TP53INP1 and MDM2, were found underexpressed in this subgroup. These data suggest that p53 is a driver of tumorigenesis in a specific subtype of high-grade ACCs, occurring relatively late in the tumor development.

3.1. The discrimination of ACCs and ACAs

2.3. Wnt-bcatenin is often activated in ACCs Wnt-bcatenin is an important player in the adrenal development, starting early with the development of the urogenital ridge, further acting until the development of the normal cortex after birth (Keegan and Hammer, 2002). The pathway is activated by an accumulation of the bcatenin protein in the cytoplasm, which translocates in the nucleus where it induces expression of several target genes. Aberrant Wnt-bcatenin pathway activation plays a key role in the malignant transformation in several tissues, including the gut and liver. Aberrant activation of the Wnt-bcatenin pathway was identified in sporadic adrenocortical tumors (Tissier et al., 2005; Huang and He, 2008). Accumulation of bcatenin in adrenocortical cells was

The clear discrimination of ACCs from ACAs is the most universal finding that emerged from adrenocortical tumors transcriptome studies. Separation of ACA from ACC in unsupervised transcriptome-based classifications is very robust (Fig. 2), with strong agreement with conventional histopathology. Considering disease-free survival, agreement is also strong with the transcriptome-based classification, with almost no recurrence nor metastases occurring in the group corresponding to benign tumors (de Fraipont et al., 2005; Giordano et al., 2009; de Reyniès et al., 2009). Interestingly, in the subgroup of tumors corresponding to malignant tumors, not all tumors recur, raising two hypotheses: either these tumors do not possess malignant potential (i.e. are not malignant), or these tumors are malignant, but were effectively treated by surgery. The pathological features of malignancy (Weiss scores of 3 and more), and the high number of chromosomal alterations in CGH in these tumors (Assié et al., 2009) (personal data) strongly support the second hypothesis. Discrimination between ACCs and ACAs in childhood ACTs, in contrast to adult ACCs, is often difficult and unreliable with standard histopathologic approaches. The unsupervised clustering analysis performed by West et al. showed no clear difference between ACCs and ACAs either, probably reflecting a biological continuum between benign and malignant tumors in children (West et al., 2007). These pediatric tumors would potentially greatly benefit from additional integrative genomic study. 3.2. The two types of ACCs One of the major findings from unsupervised transcriptomebased tumor classification, reported by three studies (Giordano et al., 2009; Laurell et al., 2009; de Reyniès et al., 2009), is the existence of two distinct groups of ACCs (Fig. 2). Considering overall survival associated with these tumors, a major difference between the two subgroups could be demonstrated. These studies described increased malignancy-related pathological features in the sub-

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group of poor prognosis. One of the studies showed a significantly higher number of mitoses in this subgroup (so called high-grade tumors) (Giordano et al., 2009). However, in none of these studies was histopathology alone able to fully discriminate between poor and good prognosis ACCs with the same accuracy as the transcriptome. One question remains: do the poor prognosis ACCs evolve from the good prognosis ACCs over time? Or do these two groups correspond to two independent types of ACCs? Data to separate these two possible mechanistic outcomes are limited. Two lines of evidences are in favor of the ‘‘two-types’’ of ACCs hypothesis. Indeed, if the poor prognosis ACCs evolved from the good prognosis, one would have expected the genomic alterations to accumulate, and therefore to be more abundant in tumors of the poor prognosis subgroup, compared to tumors of the good prognosis. In fact the number of chromosomal alterations is not increased in the subgroup of poor prognosis defined by the transcriptome (personal data). In addition, the transcriptome-based survival prediction remains significant after stratification on tumor extension, which means that the transcriptome-based subgroups do not only reflect different tumor stages. On the other hand, the increasing observation of high-grade clones within otherwise low-grade ACC supports ‘‘one-type’’ hypothesis (personal communication, TJG). However more work still need to be done to settle this issue and it may be that both mechanistic pathways play a role in the development of poor prognosis ACCs. 3.3. The three types of poor-prognosis ACCs A recent study characterized in the subgroup of ACCs of poor prognosis, a third level of classification (Ragazzon et al., 2010). Unsupervised clustering identified three subgroups of ACCs. Interestingly, one was enriched in p53 mutations, the second in bcatenin cytonuclear alterations and/or mutations, and the third presented neither of these two alterations (Fig. 2), illustrating that the full mutational spectrum in ACC is not known. 4. Clinical developments 4.1. Diagnosing a malignant neoplasm The ACT transcriptome contains a strong signature of malignant behavior. This signature can obviously be translated to clinical practice. In one study, the pan-genomic transcriptome information was summarized to a minimal number of genes, actually two (DLG7 and PINK1) (de Reyniès et al., 2009). Assessment of their expression level by retrotranscriptase quantitative PCR was demonstrated to have a powerful diagnostic value in adrenocortical tumors. Of note, this tool was designed on disease-free survival, a stringent definition of malignancy. Indeed this definition could overcome the limitations of histopathology. From a statistical point of view, considering disease-free survival prediction was the only way to compare the predictor to pathology. In addition, diseasefree survival prediction permitted to prove that the molecular prediction contained some information that was independent from the pathology. One drawback of this strategy is the classification as ‘‘at low risk of recurrence’’ of obviously malignant tumors, but that are completely resectable by surgery, as discussed in the previous paragraph. Is there a need for molecular malignancy tools? Certainly not for a majority of situations in which tumor classification into benign and malignant categories is straightforward using conventional histopathology. However two points deserve consideration. First the malignancy status of a tumor can be uncertain or undetermined. In the Weiss score system, these tumors correspond to

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scores of 2 and 3 (Weiss, 1984; Weiss et al., 1989). The DLG7-PINK1 gene predictor of recurrence seems to perform better than the Weiss score, whatever the cut-off (Fig. 3B). Second, due to the rarity of adrenal cancer, not all pathologists are expert in adrenocortical pathology. Transcriptome studies are published by expert teams with highly skilled pathologists, who might not reflect the true level of all pathologists facing an adrenal specimen. Therefore, the performance of pathology for assessing malignancy might be overestimated in the published transcriptome studies. In childhood ACTs, a set of genes differentially expressed between ACCs and ACTs could be identified in one study (West et al., 2007). Further validation of the diagnostic value is expected. 4.2. Prognosis of ACC The ACC transcriptome contains some strong prognostic information, as discussed in the previous paragraph. As for the prediction of recurrence, a prediction of the specific survival was proposed, based on summarized transcriptome information. This approach led to the identification of a marker based on the expression level analysis of two genes. These two genes (BUB1B-PINK1) provide a strong prognostic assessment that remains significant after stratification on the tumor stage (de Reyniès et al., 2009). Prognostic assessment of ACCs is a critical issue in clinical practice. Tumor stage provides the strongest prognostic prediction. However, among each tumor stage, survival varies substantially. For metastatic disease, variable survival has been described, and prognostic parameters have been identified, reflecting the tumor grade (mitotic count in the primary) and the tumor extension (number of metastatic organs) (Assié et al., 2007). The progression slope provides a strong prognostic marker, but that can only be determined retrospectively. Determination of the prognosis might direct the treatments in the metastatic setting. In situations of good prognosis, especially in case of limited tumor extension, locoregional approaches (mainly repeated surgery) might be proposed and be effective. In situations of poor prognosis, systemic treatments would be prioritized. For ACCs limited to the adrenal gland and in apparent complete remission after an appropriate surgery, prognostic determination is even more critical. The tumor grade, based on mitotic count, seems an important prognostic factor (Weiss et al., 1989). However most experienced centers at present consider prognostic prediction in this case to be difficult. A molecular prediction in this setting would be of great potential. Finally, future studies on adjuvant therapy would probably benefit from appropriate tumor stratification. 4.3. Should we perform today a pan-genomic transcriptome array in adrenal cancer management? Beyond scientific benefits, several lines of evidence would support immediate clinical benefits of prospectively determining the transcriptome of each tumor that is not a straightforward adenoma. Indeed, diagnostic information could be harvested, including the malignancy status and a confirmation of adrenocortical origin (especially in non-secreting tumors). Further, valuable prognostic information would also be obtained. In addition, costs has come down and the technology is widely available at academic centers. Finally in some cancer types, such as breast carcinoma (Kaufmann and Pusztai, 2011), transcriptome-based molecular typing has been proposed and is currently available. Then why do not we do it? Several obstacles remain: merging transcriptome information from different experiments, even when derived from the same platform, is challenging (Xu and Wong, 2010). The best transcriptomic data is derived from unfixed, frozen

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tumor tissue, which is often not obtained at the time of surgery. In addition, bioinformatics analyses are complex and require comparison to a large data set, ideally generated locally. Finally considering common cancers, despite some available molecular tools, their use is currently limited, and no clear recommendation is available for their widespread use (Kaufmann and Pusztai, 2011).

6. Conclusion

5. Perspectives

Acknowledgements

5.1. The future place of transcriptome in the ‘‘omics’’ field

The authors would like to acknowledge the ‘‘Ligue Contre le Cancer’’ for supporting our transcriptome studies through the ‘‘Carte d’Identité des Tumeurs’’ project, especially Dr. Aurélien de Reynies and Dr. Jacqueline Godet. Our genomic studies are supported in part by the Plan Hospitalier de Recherche Clinique (AOM06179) to the COMETE Network, the Recherche Translationnelle DHOS/INCA 2009 (RTD09024) and the FP7 (ENSAT-Cancer) program.

The field of ‘‘omics’’ is at a turn now, with the current rapid acceptance of high-throughput deep sequencing. However, the transcriptome should remain central, because it is an intermediate phenotype of biological features. Genome-related techniques do not have such a close and direct relationship to biology. Transcriptomes in ACTs should improve: exon specific arrays already exist, and deep sequencing should also be apply to transcriptome studies, with techniques like RNA sequencing, enabling extensive deciphering of alternative transcription (Hallegger et al., 2010). 5.2. Moving from tumor groups to individual tumors characterization Transcriptome studies were first designed to characterize ACC as a group of tumors with common features. Unsupervised tumor classification identified subgroups that no other technique had described. The granulation of this tumor classification has moved forward with the input of additional information, either molecular (TP53 and Wnt-bcatenin pathway analyses) or pathological (tumor grade). The full molecular explanation of individual tumors is a goal to achieve. Integration of different genomic approaches has begun, combining transcriptome and tumor genome alterations (Szabó et al., 2010), or transcriptome and miRnome (Tömböl et al., 2009). Deep sequencing studies have now been launched in ACC, and resulting mutation information should yield important advances in this field. Finally efforts should be pursued to integrate this information with other clinical features, such as pathology, hormone secretion, or any other relevant clinicopathologic information. 5.3. Assess the clinical relevance of transcriptome Several challenges remain before the transcriptome and its derived tools can be fully employed in a routine laboratory setting. First, further validation is required. Prospective and retrospective validation of the molecular prognosis/predictive biomarkers are ongoing. Second, RNA handling is challenging in clinical practice, and robustness of the methods and/or technological improvements are needed. Finally ACC is a rare disease. Large cohorts can only be obtained in multicenter studies. International research networks such as the European Network For The Study of Adrenal Tumors (ENSAT) should continue and expand. 5.4. Impact of transcriptomes on treatments Transcriptome studies should impact treatment of ACC patients at different levels: stratification of trials into prognostic subgroups, pharmacogenomics (prediction of response to a given chemotherapy), and identification of new molecular targets through a better understanding of the molecular mechanisms leading to tumorigenesis in ACC. These topics remain to be approached by genomic studies in ACC.

Gene expression profiles of ACTs reflect tumor biology, pathology and clinical behavior. Further developments in basic science and ongoing translation of ACC molecular biology into novel molecular diagnostics are expected in the coming years.

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