Taylor & Francis Taylor & Francis Group
EXPERT REVIEW
ISSN: 1478-9450 (Print) 1744-8387 (Online) Journal homepage: http://www.tandfonline.com/loi/ieru20
Proteome analysis of adrenal cortical tumors
Hye Min Kim, Yu Kyung Lee & Ja Seung Koo
To cite this article: Hye Min Kim, Yu Kyung Lee & Ja Seung Koo (2016) Proteome analysis of adrenal cortical tumors, Expert Review of Proteomics, 13:8, 747-755, DOI: 10.1080/14789450.2016.1210008
To link to this article: http://dx.doi.org/10.1080/14789450.2016.1210008
Accepted author version posted online: 06 Jul 2016. Published online: 19 Jul 2016.
Submit your article to this journal
Article views: 34
Q
View related articles
View Crossmark data ☒ CrossMark
Taylor & Francis Taylor & Francis Group
REVIEW
Proteome analysis of adrenal cortical tumors
Hye Min Kim, Yu Kyung Lee and Ja Seung Koo
Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
ABSTRACT
Introduction: Adrenal tumor is a relatively common tumor. The discrimination between adrenal cortical adenoma (ACA) and adrenal cortical carcinoma (ACC) is crucial as these two diseases have distinct prognosis. ACA is a benign tumor curable by surgical excision, while the prognosis of ACC is extremely poor, with a 5-year mortality of 75-90%. Therefore, previous proteomic studies focused on markers allowing the differentiation between ACA and ACC.
Areas covered: Several proteomic approaches based on the analysis of various samples such as human tissues, urine, and cell lines. In this review, we focused on proteomic studies performed to improve adrenal tumor diagnosis and identify ACC therapeutic targets.
Expert commentary: The rapid development of cancer genomics provided a lot of information, which affects functional proteomics. In practice, differentially expressed proteins between ACA and ACC have been suggested in several proteomic studies and had a biologic implication in ACC.
ARTICLE HISTORY
Received 5 May 2016 Accepted 4 July 2016 Published online 20 July 2016
KEYWORDS
Adrenal cortical tumor; adrenal cortical adenoma; adrenal cortical carcinoma; diagnosis; mass spectrometry; proteomics
1. Adrenal cortical tumor overview
Adrenal cortical tumor (ACT) is classified into the benign tumor, adrenal cortical adenoma (ACA), and the malignant tumor, adre- nal cortical carcinoma (ACC). ACA is a relatively common tumor with an incidence of approximately 9% in the elderly, whereas ACC is an aggressive and rare tumor with an annual incidence of one to two cases per million people [1-4]. There are two impor- tant unmet needs with regard to ACT. One is the diagnostic decision between ACA and ACC. The other is the discovery of therapeutic targets for the treatment of ACC. In the case of ACT, the discrimination between ACA and ACC solely depending on the pathological findings is impracticable because the only defi- nitive criterion for malignancy is distant metastasis or local inva- sion. Even various diagnostic criteria have been suggested, established consensus regarding pathological finding is not cur- rently available. Although the differential diagnosis of ACA and ACC is often challenging in clinical settings, precise diagnosis is crucial as these two diseases clearly have distinct prognosis. ACA is curable only by surgical excision. Furthermore, in the case of nonfunctioning, small (<4 cm), and <10 Hounsfield unit on non- contrast computed tomography, it was suggested that ACA could be followed up in a regular period without a surgical intervention [5,6]. However, the average overall survival of ACC is 14.5 months and with a 5-year mortality rate of 75-90% [7,8], which necessitates the need for effective targeted therapies for ACC. Therefore, histological, genetic, molecular, and proteomic studies have been conducted to identify diagnostic and/or ther- apeutic targets [9-12]. In this review, we summarized the results of proteomic studies designed to identify potential molecular targets for the diagnosis of ACT and the treatment of ACC.
2. Clinical and pathological features of ACTs
Most adrenal gland tumors are nonfunctional tumors without symptom [13-16]. When a functional tumor occurs, symptoms caused by the abnormal hormonal secretion from the adrenal gland cortex appear [17]. Typical endocrine syndromes include primary hyperaldosteronism (including Conn’s syndrome), hypercortisolism (Cushing’s syndrome), and hypersecretion of sex steroids (adrenogenital syndrome, virilization of females, or feminization of males). They are almost always associated with adrenal gland tumor or hyperplasia [17]. However, both ACA and ACC can be either nonfunctional or functional tumors and can cause endocrine dysfunction or not. Thus, it is difficult to differentiate by laboratory findings alone [18,19].
Macroscopically, ACA is a well-circumscribed intra-adrenal tumor and most weighs less than 50 g and measures less than 5 cm [20]. Microscopically, ACA consists of clear cells and compact cells in various proportions. They form nest, cord, or trabecular shape. Most are lipid-laden cells with distinct cell borders and abundant foamy cytoplasm like zona fasciculata cells [21]. Some adenoma cells are slightly larger and show different cytoplasm and various nuclear sizes, but the features are not clearly distinguished. In addi- tion, different morphological finding is observed according to the hormone secreted by the tumor. In the case of aldos- terone-producing adenoma, large lipid-rich cell is most com- monly found, which appears as an yellow color. Furthermore, when treated with spironolactone, ‘spironolactone bodies’ can be present. In cortisol-producing adenoma, heteroge- nous admix of pale-staining lipid-rich cell and lipid-poor compact cytoplasm cell is observed [22].
Macroscopically, most ACCs weigh over 100 g and measure more than 5 cm [23,24]. Microscopically, ACC shows diverse growth patterns less ordered than ACA. Patternless sheets or nests of cells are most commonly observed, and the broad trabecular growth or nesting pattern can be detected. The nucleus of ACC cells is very pleomorphic from bland-looking to highly atypical with bizarre hyperchromasia. The only defi- nitive criteria for malignancy are distant metastasis or local invasion such as capsular or vascular invasion. However, such findings appear with a variable frequency [17].
Because ACC may look similar to the normal adrenal gland and there is no single microscopic finding confirming malig- nancy, several diagnostic criteria have been proposed for an accurate diagnosis. The Weiss criteria were first reported by Weiss et al. in 1989. The criteria include high nuclear grade, mitotic rate, atypical mitoses, clear-cell component, diffuse archi- tecture, necrosis, venous invasion, sinusoidal invasion, and cap- sular invasion. If any one feature is present, one point is given, and malignant potential is considered when the total score is greater than two points [25]. In 2002, Aubert et al. proposed the revisited Weiss criteria. The criteria excluded nuclear grade, dif- fuse architecture, and venous and sinusoidal invasion. Notably, a doubled score is given to mitotic rate and clear-cell component, with an identical cutoff value [26]. In addition, the System of Hough and the Van Slooten system were proposed in 1979 [27] and 1985 [28]. Although these criteria are referred in the 2004 World Health Organization classification of endocrine organs, the Weiss criteria are the most commonly used [5].
3. Immunohistochemistry
In ACT, immunohistochemistry (IHC) studies were conducted to identify the differential diagnostic markers between ACA and ACC and prognostic markers of ACC.
Several markers that help the differential diagnosis between ACA and ACC have been screened in numerous studies, and the most significant markers were given a number; however, the representative marker suggested in most studies is Ki-67 (Table 1). Ki-67 is a typical marker of cellular proliferation [29], and in malignant tumors, proliferation is more active than in benign tumors; thus, a higher expression of Ki-67 in malignant tumors can be expected. In most studies, the Ki-67 labeling index was significantly higher in ACC than in ACA [30-33]. However, there are a number of limitations. First, there is no consensus for the cutoff value to distinguish between ACA and ACC. Second, no standardized method for the interpretation of the results, including which parts of the tumor to select or how many areas to select for the analysis, was established. Thus, further evaluation is needed. Markers associated with the prog- nosis of ACC include survivin, anti-steroidogenic factor 1 (SF-1), B-catenin, Glut-1, and Snail. Patients with ACC showing the expression of these markers exhibit poor prognosis [34-39].
4. Genetics
Due to the development of molecular techniques, a variety of human diseases were studied using various molecular methods, including ACT. First, some studies used microarray gene expres- sion profiling. A previous study using Affymetrix HGU133plus2.0
GeneChip showed significantly higher expression of IGF2, MAD2L1, and CCNB1 and lower expression of ABLIM1, NAV3, SEPT4, and RPRM in ACC when compared to ACA [31]. Using Affymetrix HG_U95Av2 oligonucleotide arrays, more than a threefold change was observed in the expression of 91 genes between ACA and ACC. Among them, IGF2, SPP, and STK15 are representative genes [46]. Moreover, two gene clusters showed differential expression between ACA and ACC in a study using cDNA microarrays of 230 candidate genes. The insulin-like growth factor 2 (IGF-II) cluster consisting of eight genes (IGF-II, TGFß2, FGFR1, FGFR4, MST1R, TGFBR1, KCNQ1OT1, and GAPD) was highly expressed in ACC and low expressed in ACA. In contrast, the steroidogenesis cluster consisting of 14 genes (StAR, CYP11A, HSD3B1, CYP11B1, CYP21A2, CYP17, protein phos- phatase 1A, S100B, glypican 3, inhibin a-chain, cyclic adenosine monophosphate (cAMP) response element modulator, retino- blastoma 1, nonmetastatic protein 23, and TGFß type 3 recep- tor) was low expressed in ACC and highly expressed in ACA [47]. In a study using DNA chips containing 11,540 DNA spots, 21 genes showed more than a fourfold change between ACA and ACC. Genes highly expressed in ACC were IGF-22 and potential oncogenes, while genes highly expressed in ACA were potential tumor suppressor genes [48]. The representative genes upregulated in ACC were reported as two ubiquitin- related genes (USP4 and UFD1L) and several insulin-like growth factor-related genes (IGF2, IGF2R, IGFBP3, and IGFBP6) in a study using cDNA microarrays with 29,760 cDNA fragments. The genes downregulated in ACC were a cytokine gene (CXCL10), five cell metabolism-related genes (RARRES2, ALDH1A1, CYBRD1, and GSTA4), and the cadherin 2 gene (CDH2) [49].
DNA methylation analysis was also conducted in ACT. The DNA methylation levels of 27,578 CpG sites were examined using Infinium HumanMethylation 27 BeadChip in ACA, ACC, and normal tissues. A significant hypermethylation was detected in genes involved in the cell cycle regulation, apopto- sis, and transcriptional regulation associated with the develop- ment of ACT. Hypermethylation was detected in 212 CpG islands in ACC when compared with ACA. The representative genes were CDKN2A, GATA4, SCGB3A1, PYCARD, and DLEC1 [50]. Next-generation sequencing was performed on ACT specimens. Genomic alteration (GA) was investigated using high uniform coverage (Illumina HiSeq) in ACC. ACC showed a mean of 2.6 GA, affecting the following genes in order of frequency: TP53 (34%), NF1 (14%), CDKN2A (14%), MEN1 (14%), CTNNB1 (10%), ATM (10%), APC, CCND2, CDK4, DAXX, DNMT3A, KDM5C, LRP1B, MSH2, RB1 (7%), EGFR, ERBB4, KRAS, MDM2, NRAS, PDGFRB, PIK3CA, PTEN, and PTCH1 (3%). In 59% of ACC, at least one GA was reported to be associated with the available therapeutic or a mechanism-based clinical trial [12]. Exome sequencing and single-nucleotide polymorphism (SNP) array analysis using Illumina HiSeq in ACC indicated that the following genes showed recurrent alteration, CTNNB1, TP53, CDKN2A, RB1, MEN1, ZNRF3, DAXX, TERT, and MED12. Among them, ZNRF3 was the most common altered gene (21%) and was reported as a tumor suppressor gene associated with the ß-catenin path- way [51].
In a study using multi-platform, genome-wide gene expres- sion, gene methylation, microRNA expression, and compara- tive genomic hybridization, ACA, ACC, and normal tissue were
| Authors | Included proteins | Included lesion | Important features | Reference |
|---|---|---|---|---|
| Soon et al. (2009) | IGF2 | 41 ACA | IGF2 negative in all normal and all ACA and positive in ACC (78%) | [31] |
| MAD2L1 | 23 ACC | MAD2L1 negative in all normal and ACA (95%) and positive in ACC (74%) | ||
| CCNB1 Ki-67 ACADVL ALOXI5B | 15 normal tissue | CCNB1 negative in all normal and all ACA and positive in ACC (43%) Ki-67 low proliferative index (<5%) in all normal and all ACA and high proliferative index (≥5%) in ACC (70%) ACADVL positive in all normal and all ACA and positive in ACC (65%) ALOXI5B positive in all normal and ACA (80%) and positive in ACC (22%) | ||
| Gupta et al. (2001) | Topo Il a | 15 ACA | p53 higher labeling index (>20%) in 73% ACC and 6.6% ACA | [32] |
| Ki-67 | 15 ACC | Ki-67 and RB significantly highly expressed in ACC (p = 0.001 and p = 0.004) | ||
| p53 E-cadherin RB | ||||
| HER-2 | ||||
| Iino et al. (1997) | Topo Il a | 28 ACA | Labeling index of Ki-67 0.48 ± 0.16 in normal, 0.64 ± 0.11 in ACA, and 5.84 ± 1.33 in ACC Labeling index of Topo Ila 0.44 ± 0.15 in normal, 0.72 ± 0.12 in ACA, and 6.13 ± 1.65 in ACC | [30] |
| Ki-67 | 17 ACC 6 normal tissue | |||
| Yang et al. (2013) | Calreticulin | 31 ACA | The H score of calreticulin, prohibitin, and HSP60 significantly higher in ACC than those in normal tissue The H score of calreticulin and prohibitin significantly higher in ACC than those in ACA | [40] |
| Prohibitin | 39 ACC | |||
| HSP60 | 39 normal tissue | |||
| Kjellin et al. (2014) | GRIM-19 | 3 ACA | Expression pattern of GRIM-19 is grain-like pattern in ACA and cytoplasmic pattern in ACC | [41] |
| STAT-3 | 3 ACC | |||
| Wang et al. (2014) | IGF2 | 25 ACA | IGF2 negative/low in ACA (72%) and elevated in ACC (64%) | [42] |
| SMAD4 | 25 ACC | SMAD4 negative/low in ACA (60%) and in ACC (92%) | ||
| Ye et al. (2012) | ATM | 19 ACA 18 ACC | ATM positive in ACA (95%) and negative in ACC (78%) | [43] |
| Szajerka et al. (2008) | MT | 48 ACA | Labeling index of MT 4.00 ± 1.48 in normal, 4.71 ± 2.16 in ACA, and 6.67 ± 3.61 in ACC Labeling index of Mcm-2 0.09 + 0.30 in normal, 0.88 ± 0.89 in ACA, and 2.17 ± 1.17 in ACC | [44] |
| Mcm-2 | 6 ACC | |||
| Ki-67 | 11 normal tissue | Labeling index of Ki-67 0.09 ± 0.30 in normal, 0.52 ± 0.54 in ACA, and 1.83 ± 1.47 in ACC | ||
| Kiiveri et al. (2005) | GATA-6 | 20 ACA | Expression of GATA-6 and SF-1 is most significantly reduced in normal, followed by ACA and ACC | [45] |
| SF-1 | 16 ACC | |||
| p21 | ||||
| Ki-67 | ||||
| Nakazumi et al. (1998) | p27 | 24 ACA | Labeling index of p27 61.7 ± 2.6 in normal, 59.4 ± 6.5 in ACA, and 48.9 ± 7.5 in ACC Labeling index of Ki-67 0.28 ± 0.08 in normal, 0.33 ± 0.11 in ACA, and 6.30 ± 6.21 in ACC | [33] |
| Ki-67 | 12 ACC | |||
| 6 normal tissue |
| ACC | ACA | |||||
|---|---|---|---|---|---|---|
| Poor prognosis | Good prognosis | |||||
| p53-pathway | ß-catenin- pathway | Unclassified | ||||
| IGF-2 | ||||||
| Cell cycle related genes | ||||||
| Transcription factor genes | ||||||
| p53 mutation | ||||||
| Wnt/B-catenin activation | ||||||
| Stereogenensis related genes | ||||||
| FGFR1 and FGFR4 | ||||||
| Retinoic acid signaling genes | ||||||
| Cell metabolism genes | ||||||
| Intracellular transport genes | ||||||
Figure 1. Transcriptome classification of adrenocortical tumors. Color in red indicates high expression. ACC, adrenocortical carcinoma, ACA, adrenocortical adenoma.
analyzed. There were 808 dysregulated genes between ACC and ACA and 1085 dysregulated genes between ACC and normal tissue, and reduced gene expression was prominent in ACC. Important pathway associated with dysregulated genes that is suggested include the oncostatin m signaling, which induces caspase 3-dependent apoptosis and inhibits cell proliferation [52]. Furthermore, in a study performed in ACC using exome sequencing, mRNA sequencing, miRNA sequencing, SNP arrays, DNA methylation arrays, and reverse-phase protein arrays, PRKAR1A, RPL22, TERF2, CCNE1, and NF1 were revealed as ACC driver genes. Whole-genome doubling was suggested as the hallmark of disease progres- sion, and three prognostic molecular subtypes have been reported to appear depending on the DNA methylation sig- nature [9]. Therefore, based on several research results, the transcriptome-based ACT classification can be proposed (Figure 1).
5. MicroRNA
Previous miRNA profiling of 368 miRNAs using a quantitative polymerase chain reaction (qPCR) low-density array in ACT was conducted. miR-184, miR-503, and miR-210 were highly expressed in the ACC [53]. The analysis of ACT by genome-wide miRNA expression profiling was performed. miR-195 and miR-335 expres- sion was low in ACC, while miR-483-5p was highly expressed in ACC [54]. Especially, high expression of miR-483-5p and low expression of miR-195 in ACC were associated with poor prog- nosis. Twenty-six cases of ACA and 10 cases of ACC were analyzed by genome-wide miRNA expression profiling. In ACC, miR-100, miR-125b, and miR-195 expression was low, while miR-483-5p expression was high. Therefore, it was reported that only miR- 483-5p expression could accurately differentiate between ACA and ACC [55]. miR-139-3p, miR-675, and miR-335 expression was remarkably low in ACC by miRNA profiling of 667 miRNAs using a qPCR low-density array using formalin-fixed, paraffin-embedded tissues of ACT [56]. A previous study using miRXplore microarrays with 728 human miRNAs in ACT reported that miR-195 and miR-335 expression was markedly low in ACC. In particular,
miR-139-5p expression was higher in recurrent ACC compared to nonrecurrent ACC, while miR-195 and miR-335 expression was lower in recurrent ACC when compared to nonrecurrent ACC [57].
6. Proteomics
Generally, the proteomic approach provides valuable informa- tion in terms of candidate biomarker identification as well as insights on the molecular characteristics, tumorigenesis, tumor progression, and aggressiveness of the tumor. Therefore, many proteomic studies have been conducted in a variety of tumors, and much progress has been made in proteomic methods. Here, we summarized the latest proteomic findings on ACT (Table 2).
6.1. Proteomics on tissue samples
The comparative proteomic approach is an efficient method to identify novel biomarkers in cancer. Thus, this proteomic approach has also been applied to analyze ACC [10,40,41]. Twelve fresh primary ACC and their paired adjacent normal adrenocortical tissue samples were studied using two-dimensional electrophor- esis (2-DE) and tandem mass spectrometry (MS) three times, con- secutively. A differential expression of more than twofold was detected in 29 spots. Among these, 22 proteins (secretogranin-1, retinal dehydrogenase 1, prelamin-A/C, transitional endoplasmic reticulum ATPase, keratin, type I cytoskeletal 10, prohibitin, trans- gelin-2, selenium-binding protein 1, actin, cytoplasmic 1, glu- tathione S-transferase P, peroxiredoxin-2, Rho guanosine diphosphate-dissociation inhibitor 1, elongation factor 1-beta, peroxiredoxin-1, 14-3-3 protein epsilon, 60-kDa heat-shock pro- tein, ATP synthase subunit d, mitochondrial, retinal dehydrogen- ase 1, calreticulin, and aflatoxin B1 aldehyde reductase member 3) showed higher expression in ACCs than in adjacent normal adre- nocortical tissues, while 9 proteins (T-complex protein 1 subunit beta, elongation factor Tu, mitochondrial, protein disulfide-iso- merase, vimentin, serum albumin, proxiredoxin-2, tropomyosin alpha-4 chain, proteasome subunit beta type-2, and fibrinogen gamma chain) were low expressed in ACC [40]. Gene ontology
| Authors | Sample type | Included group | Analysis method | Important features | Reference |
|---|---|---|---|---|---|
| Yang et al. (2013) | Fresh tissue | Paired 12 ACC and normal adrenal tissue | 2-DGE/MS | 20 proteins showed higher expression in ACC than normal tissue, while nine proteins were low expressed in ACC The proteins highly expressed in ACC were associated with protein binding and oxidoreductase activity The validation study was conducted by IHC staining (calretinin, prohibitin, and HSP60) | [40] |
| Kjellin et al. (2014) | Fresh tissue | 6 ACA 8 ACC | Liquid chromatography/MS | The expression of IGF2 and aldolase A was increased in ACC and NDUFA13 (or GRIM-19) was low expressed in ACC The validation study was conducted by IHC staining (GRIM-19 and STAT3) | [41] |
| Poli et al. (2015) Minowada et al. (1985) | Fresh tissue Urine | 10 ACC 8 normal adrenal tissue 5 ACA 3 ACC | 2-DGE/MS Gas chromatographic analysis | 22 proteins showed higher expression in ACC than normal tissue and mainly associated with metabolism, cell redox homeostasis, and production of energy The proteins were differentially expressed at intracellular and mitochondrial level The validation study was conducted by Western blot and IHC staining (aldehyde-dehydrogenase-6-A1, transferrin, fascin-1, lamin A/C, adenylate-cyclase-associated-protein-1, and ferredoxin reductase) Excessive 3 alpha, 17 alpha, 21-trihydroxy-5 beta-pregnan-20-one (tetrahydro-11-deoxycortisol, THS) and delta 5-pregnene-3 beta, 11 alpha, 20 alpha-triol (delta 5-pregnenetriol) values were observed in all cases of ACC | [10] [58] |
| Arlt et al. (2011) | Urine | 102 ACA 45 ACC | Gas chromatography/MS | 32 distinct adrenal-derived ste roids were analyzed and immature, early-stage steroidogenesis was shown in ACC THS, 5-PT, and 5-PD were most effective in differentiation between ACA and ACC | [59] |
| Tiu et al. (2009) | Urine | 76 ACA 5 ACC 172 healthy control | Gas chromatography/MS | (1) Hypersecretion in multiple steroid axes (2) Excretion of unusual metabolites, such as 5-pregnene-3alpha, 16alpha, 20alpha-triol, 5-pregnene-3beta, 16alpha,20alpha-triol, and neonatal steroid metabolites in the postneonatal period (3) Increase of tetrahydro-11-deoxycortisol relative to total cortisol metabolites | [11] |
| Kotlowska et al. (2011) | Urine | 28 ACT 30 healthy control | Gas chromatography/MS | a-cortol, tetrahydrocorticosterone, tetrahydrocortisol, allo-tetrahydrocortisol, and etiocholanolone were associated with pathological adrenal function | [60] |
| Kotlowska et al. (2009) | Urine | 20 ACT 25 healthy control | Gas chromatography/MS | The secretion of total urinary cortisol metabolites was higher in adrenal incidentaloma The shift towards tetrahydrocorticosterone, cortisol, and etiocholanolone production was observed | [61] |
| Kerkhofs et al. (2015) | Urine | 102 ACA 45 ACC | Gas chromatography/MS | The concentration of 18 steroid metabolites was higher in ACC and the concentration of allo-tetrahydrocorticosterone was lower in ACC THS at a cutoff value of 2.35 pmol/24 h differentiated ACC from benign adrenal mass with 100% sensitivity and 99% specificity | [62] |
| Stigliano et al. (2008) | Cell line | H295R ACC cell line | 2-DGE/MALDI-TOF MS | In total cell extracts, the different expression of nine proteins was observed after mitotane treatment In mitochondrial fraction, the expression of six proteins was decreased after mitotane treatment, whereas the expression of three proteins was increased | [70] |
| Tremoen (2014) | Cell line | H295R ACC cell line | 2-DGE | The estradiol and cortisol secretion was increased after treatment of PCB 118 The estradiol secretion was increased after treatment of PCB 153 | [63] |
| After treatment of PCB 126, the markedly increased secretion of estradiol, cortisol, and progesterone was shown | |||||
| Sbiera (2015) | Cell line | NCI-H295 ACC cell line | Gas chromatography/MS Electrospray ionization tandem MS | After mitotane treatment, free cholesterol, oxysterols, and fatty acids were increased Sterol-O-acyltransferase 1 was inhibited after mitotane treatment, leading to accumulation of these toxic lipids, so it caused adrenal-specific cytotoxicity | [64] |
ACC: adrenal cortical carcinoma; 2-DGE: two-dimensional gel electrophoresis; MS: mass spectrometry; IHC: immunohistochemical; HSP60: heat-shock protein 60; IGF-2: insulin-like growth factor 2; THS: tetrahydro-11- deoxycortisol; 5-PT: pregnenetriol; 5-PD: pregnenediol; MALDI-TOF: matrix-assisted laser desorption/ionization time-of-flight; PCB: polychlorinated biphenyls.
analysis performed to understand the function of the proteins highly expressed in ACC indicated that seven proteins (secreto- granin-1, prelamin A/C, keratin 10, actin cytoplasmic 1, glutathione S-transferase P, elongation factor 1-beta, and peroxiredoxin-1) were associated with protein binding and four proteins (retinal dehydrogenase 1, peroxiredoxin-1, peroxiredoxin-2, and aflatoxin B1 aldehyde reductase member 3) were associated with oxidor- eductase activity. Since then, a validation study using ACA, ACC, and paired normal tissue samples was conducted. IHC staining of calretinin, prohibitin, and heat-shock protein 60 (HSP60) was per- formed. Calretinin and prohibitin expression was higher in ACC than in ACA, supporting the previous proteomic study results [40].
Microsomal proteins from fresh tissues from ACA and ACC were analyzed using narrow-range isoelectric focusing (IEF) and reversed-phase liquid chromatography and orbitrap tandem MS. The expression of IGF2 (6.5-fold) and aldolase A (2.6-fold) was increased in ACC, while NDUFA13 (or GRIM-19), one of the mitochondrial respiratory chain complex I proteins, was low expressed in ACC (0.5-fold) [41].
Twenty-seven differentially expressed two-dimensional spots were identified by using 2-DE and MS on fresh samples from ACC and normal adrenal tissues. Among these, 22 proteins (transferrin, ezrin, phosphoglucomutase 2, radixin, mitochondrial phosphoe- nolpyruvate carboxykinase 2 isoform 1 precursor, lamin A/C iso- form 2, cytochrome P450, family 11, subfamily A, polypeptide 1 isoform a precursor, prolyl 4-hydroxylase, beta subunit precursor, glucose phosphate isomerase, keratin 10, glutamate dehydrogen- ase 1, fascin 1, dihydrolipoamide dehydrogenase precursor, glu- tathione reductase, aldehyde dehydrogenase 6A1 precursor, ATP synthase, H+ transporting, mitochondrial F1 complex, alpha sub- unit precursor, ferredoxin reductase isoform 1 precursor, adenylyl cyclase-associated protein, uridine diphosphate-glucose pyropho- sphorylase 2 isoform, cAMP-dependent protein kinase, regulatory subunit alpha 2, and thiosulfate sulfurtransferase) were highly expressed in ACC [10]. The biological network analysis of the differentially expressed proteins indicated that 15 biological pro- cesses were enriched in ACC. They were mainly associated with metabolism, cell redox homeostasis, and production of energy. The proteins were differentially expressed at the intracellular and mitochondrial levels in cellular compartment analysis. The valida- tion study of six proteins (aldehyde-dehydrogenase-6-A1, trans- ferrin, fascin-1, lamin A/C, adenylate-cyclase-associated-protein-1, and ferredoxin-reductase) was performed by using Western blot and IHC. All proteins showed higher expression in ACC than in normal adrenal tissues, confirming the proteomic study results [10].
6.2. Proteomics on urine samples
One way to differentiate ACA and ACC is the urinary steroid profiling method because the urinary steroid metabolome is often deregulated in patients with ACC. In a previous study, the urine of eight patients with ACT was analyzed using gas chroma- tography. In ACT patients with Cushing’s syndrome, 5 beta- and 11 beta-hydroxy steroid metabolites were dominant. Excessive values of 3 alpha, 17 alpha, 21-trihydroxy-5 beta-pregnan-20-one
(tetrahydro-11-deoxycortisol [THS]) and delta 5-pregnene-3 beta, 11 alpha, 20 alpha-triol (delta 5-pregnenetriol) were observed in all cases of ACC. Therefore, they were presented as possible urinary markers [58]. Thirty-two distinct adrenal-derived steroids were analyzed in 24-h urine samples from ACT by gas chromatogra- phy/MS. Predominantly immature, early-stage steroidogenesis was observed in ACC, and generalized matrix learning vector quantization analysis indicated that the subsets of three steroids (THS, pregnenetriol (5-PT), and pregnenediol (5-PD)) and nine steroids (THS, 5-PT, 5-PD, PT, THDOC, 5aTHA Etio, 5aTHF, and PD) allowed the efficient differentiation between ACA and ACC [59]. In a previous analysis of steroid profiles of urine samples from ACA, ACC, and controls, the urine of ACC patients presented the following characteristics [11]: (1) hypersecretion in multiple steroid axes; (2) excretion of unusual metabolites such as 5-pregnene- 3alpha, 16alpha, 20alpha-triol, 5-pregnene-3beta, 16alpha, 20alpha-triol, and neonatal steroid metabolites in the postneona- tal period; and (3) increase of tetrahydro-11-deoxycortisol relative to total cortisol metabolites. The urine of nonfunctioning adrenal incidentaloma cases and healthy controls was analyzed using gas chromatography-MS. a-Cortol, tetrahydrocorticosterone, tetrahy- drocortisol, allo-tetrahydrocortisol, and etiocholanolone were associated with pathological adrenal function [60]. In a previous study, gas chromatography was used to analyze the urine of nonfunctioning adrenal incidentaloma cases and controls. The secretion of total urinary cortisol metabolites was higher in adrenal incidentalomas, and the shift towards tetrahydrocorticosterone, cortisol, and etiocholanolone production was observed [61].
The concentration of 18 steroid metabolites (androsterone, etiocholanolone, dehydroepiandrosterone, 11-keto-etiochola- nolone, 11-hydroxy-androsterone, 11-hydroxy-etiocholano- lone, tetrahydrocortisone, tetrahydrocortisol, alpha-cortolon, beta-cortolon, alpha-cortol, allo-pregnanediol, pregnanediol, pregnanetriol, epi-pregnanolone, tetrahydro-11-deoxycortisol, pregnanediolone, and pregnantriolone) was higher in ACC, and the concentration of allo-tetrahydrocorticosterone was lower in ACC according to the analysis of 24-h urine samples from ACA and ACC using gas chromatography/MS [62]. Among them, THS at a cutoff value of 2.35 umol/24 h was reported to differentiate ACC from benign adrenal mass with 100% sensitivity and 99% specificity.
6.3. Proteomics on cell lines
Proteomic studies were conducted on ACC cell lines. Mainly, changes after chemical agent treatment of ACC cell lines in vitro were analyzed by proteomic methods. Total cell extracts and mitochondria-enriched fractions were studied by two-dimensional gel electrophoresis (2-DGE)/matrix-assisted laser desorption/ioni- zation time-of-flight MS before and after treatment with mitotane [1,1-dichloro-2-(o-chlorophenyl)-2-(p-chloro-phenyl) ethane (o, p’-DDD)] in H295R ACC cell line. In total cell extracts, the different expression of triose phosphate isomerase, alpha-enolase, D-3-phosphoglycerate dehydrogenase, peroxiredoxin II, peroxire- doxin VI, heat-shock protein 27, prohibitin, histidine triad nucleo- tide-binding protein, and profilin-1 were observed. In
mitochondrial fractions, the expression of aldolase A, peroxire- doxin I, heterogeneous nuclear ribonucleoprotein A2/B1, tubu- lin-beta isoform II, heat-shock cognate 71 kDa protein, and nucleotide diphosphate kinase decreased after treatment, whereas the expression of adrenodoxin reductase, cathepsin D, and heat-shock 70 kDa protein 1A was increased after treatment.
2-DGE assays were conducted on the H295R ACC cell line after 48 h of treatment with polychlorinated biphenyls (PCB) 118, PCB 126, and PCB 153. Consequentially, the estradiol and cortisol secretion increased after PCB 118 treatment, while the secretion of estradiol increased after PCB 153 treatment. After PCB 126 treatment, the secretion of estradiol, cortisol, and progesterone was markedly increased [63]. In a previous study using gas chromatography/MS and electrospray ioniza- tion tandem MS analysis of lipids in the NCI-H295 ACC cell line after mitotane treatment, free cholesterol, oxysterols, and fatty acids increased. It was suggested that mitotane treatment inhibits sterol-O-acyltransferase 1, leading to the accumulation of these toxic lipids, causing adrenal-specific cytotoxicity [64].
6.4. Drug target based on the proteomic findings
Based on the proteomic findings, several target-based drug therapies might be possible. First, inhibition of highly expressed IGF2 in ACA could hamper tumor growth. Suggested inhibitory agents of IGF2 are monoclonal anti- bodies against IGF (DX-2647) [65] and IGF-binding protein- 6, which reduces ligand bioactivity [66]. Second, the expression of AKR1B1 is high in ACC, and as AKR1B1 is involved in diabetic complications, various AKR1B1 inhibi- tor developed can be applicable [67]. Third, as fascin-1 expression is high in ACC and the inhibition of fascin-1 was reported to inhibit tumor invasion and metastatic colonization [68], fascin-1 inhibitor (FASCIN-G2) can be a therapeutic option in ACC.
7. Expert commentary
Studies on ACT, including proteomic studies, primarily focused on the differentiation between ACA and ACC and the discovery of an effective target molecule in ACC. The rapid development of cancer genomics provided a lot of information, which affects functional proteomics. In prac- tice, differentially expressed proteins between ACA and ACC have been suggested in several proteomic studies and had a biologic implication in ACC. However, there are several limitations. First, the meaningful proteins were extremely different in each study. Second, the studied specimens varied between studies, including urine, tissues, and cell lines. Third, differences were observed in the analysis methods used. Proteomic analyses of ACC were conducted with narrow-range IEF with reversed-phase liquid chromatography, conventional 2-DE, and 2D-differ- ence gel electrophoresis. Among the analytic methods, the powerful 2-DE, a traditional method, is currently replaced with mono- or bi-dimensional liquid chromatography
linked with MS as it allows the simultaneous identification and quantitative analysis of many proteins. Various mar- kers mentioned in this review were evaluated only in research levels [69] and is not routinely applicable in clin- ical practice.
8. Five-year view
The clinical manifestation of ACT is difficult to predict by histologic findings alone, and there is no effective targeted therapy for the treatment of ACC. Therefore, to date, this has been the focus of genomic, transcriptomic, and proteomic studies. Although many studies were conducted, studies including large numbers of cases are insufficient. Many of which are considered as being too small to be directly applied to clinical practice. In other words, proteomic studies of ACT have been performed with a limited number of samples (especially ACC), and the validation tests are difficult to standardize because they were conducted in small cohorts. Thus, the need and importance of proteomic studies should increase. As the amount of genomic and transcriptomic information about tumors increases, the understanding of cancer proteomics increases, and proteomic analysis methods are improved due to the development of instruments and analysis programs. Especially, the urine steroid profiling analysis based on the differ- ential steroid metabolism in ACT is a noninvasive method. Therefore, the development of proteomic tools to supports its use in the clinic is expected. Furthermore, proteomic research on tissue samples from patients with ACC should be pursued to identify an effective therapeutic target for the treatment of ACC. However, in order to identify a biomarker with clinical implications, studies using multiple, complementary, and quantitative proteo- mic platforms on large numbers of matched tissue sample pairs are needed. In conclusion, approaches such as multicenter or international consortium studies rather than studies including a single institution seem useful.
Key issues
. ACT is a relatively common tumor. However, it is difficult to histologically differentiate between ACA and ACC and there is no effective targeted treatment for ACC.
· Studies using genetic, transcriptomic, and proteomic meth- ods were conducted to develop a differential diagnosis between ACA and ACC and to identify significant biomar- kers of ACC and many significant molecules were identified and described.
. In ACT, proteomic studies were conducted with human tissues, urine, and cell lines using liquid chromatography, conventional 2-DE, 2D-DIGE, gas chromatography, and MS.
. In order to identify a meaningful biomarker of ACT, proteomic studies using multiple, complementary, and quantitative pro- teomic platforms as well as very large numbers of matched human samples are needed. Therefore, approaches such as multi-center or international consortium studies rather than studies including a single institution seem useful.
Funding
This study was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (1420080). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2015R1A1A1A05001209).
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
References
Papers of special note have been highlighted as either of interest (.) or of considerable interest ( .. ) to readers.
1. Else T, Kim AC, Sabolch A, et al. Adrenocortical carcinoma. Endocr Rev. 2014;35(2):282-326.
2. Lehmann T, Wrzesinski T. The molecular basis of adrenocortical cancer. Cancer Genet. 2012;205(4):131-137.
3. Libe R, Fratticci A, Bertherat J. Adrenocortical cancer: pathophysiology and clinical management. Endocr Relat Cancer. 2007;14(1):13-28.
4. Stigliano A, Cerquetti L, Sampaoli C, et al. Current and emerging therapeutic options in adrenocortical cancer treatment. J Oncol. 2012;408131:2012.
5. Maehana T, Tanaka T, Itoh N, et al. Clinical outcomes of surgical treatment and longitudinal non-surgical observation of patients with subclinical Cushing’s syndrome and nonfunctioning adreno- cortical adenoma. Indian J Urol. 2012;28(2):179-183.
6. Zeiger MA, Thompson GB, Duh QY, et al. The American Association of Clinical Endocrinologists and American Association of Endocrine Surgeons medical guidelines for the management of adrenal inci- dentalomas. Endocr Pract. 2009;15(Suppl 1):1-20.
7. Veytsman I, Nieman L, Fojo T. Management of endocrine manifes- tations and the use of mitotane as a chemotherapeutic agent for adrenocortical carcinoma. J Clin Oncol. 2009;27(27):4619-4629.
8. Balasubramaniam S, Fojo T. Practical considerations in the evalua- tion and management of adrenocortical cancer. Semin Oncol. 2010;37(6):619-626.
9. Zheng S, Cherniack AD, Dewal N, et al. Comprehensive pan-geno- mic characterization of adrenocortical carcinoma. Cancer Cell. 2016;29(5):723-736.
10. Poli G, Ceni E, Armignacco R, et al. 2D-DIGE proteomic analysis identifies new potential therapeutic targets for adrenocortical car- cinoma. Oncotarget. 2015;6(8):5695-5706.
.. This article represents proteomic data of adrenocortical carci- noma and comments a possible therapeutic target.
11. Tiu SC, Chan AO, Taylor NF, et al. Use of urinary steroid profiling for diagnosing and monitoring adrenocortical tumours. Hong Kong Med J. 2009;15(6):463-470.
12. Ross JS, Wang K, Rand JV, et al. Next-generation sequencing of adrenocortical carcinoma reveals new routes to targeted therapies. J Clin Pathol. 2014;67(11):968-973.
. This article shows update genetic information about adreno- cortical carcinoma by next-generation sequencing.
13. Ng L, Libertino JM. Adrenocortical carcinoma: diagnosis, evaluation and treatment. J Urol. 2003;169(1):5-11.
14. Zografos GC, Driscoll DL, Karakousis CP, et al. Adrenal adenocarci- noma: a review of 53 cases. J Surg Oncol. 1994;55(3):160-164.
15. Kloos RT, Gross MD, Francis IR, et al. Incidentally discovered adrenal masses. Endocr Rev. 1995;16(4):460-484.
16. Lerario AM, Moraitis A, Hammer GD. Genetics and epigenetics of adrenocortical tumors. Mol Cell Endocrinol. 2014;386(1-2):67-84.
17. McNicol AM. Diagnostic and molecular aspects of adrenal cortical tumors. Semin Diagn Pathol. 2013;30(3):197-206.
18. Young WF Jr. Clinical practice. The incidentally discovered adrenal mass. N Engl J Med. 2007;356(6):601-610.
19. Arnold DT, Reed JB, Burt K. Evaluation and management of the incidental adrenal mass. Proc (Bayl Univ Med Cent). 2003;16(1):7-12.
20. Lloyd RV. Adrenal cortical tumors, pheochromocytomas and para- gangliomas. Mod Pathol. 2011;24(Suppl 2):S58-S65.
21. Jia AH, Du HQ, Fan MH, et al. Clinical and pathological analysis of 116 cases of adult adrenal cortical adenoma and literature review. Onco Targets Ther. 2015;8:1251-1257.
22. Pinto A, Barletta JA. Adrenal tumors in adults. Surg Pathol Clin. 2015;8(4):725-749.
23. Gicquel C, Baudin E, Lebouc Y, et al. Adrenocortical carcinoma. Ann Oncology. 1997;8(5):423-427.
24. Ayala-Ramirez M, Jasim S, Feng L, et al. Adrenocortical carcinoma: clinical outcomes and prognosis of 330 patients at a tertiary care center. Eur J Endocrinol. 2013;169(6):891-899.
25. Weiss LM, Medeiros LJ, Vickery AL Jr. Pathologic features of prog- nostic significance in adrenocortical carcinoma. Am J Surg Pathol. 1989;13(3):202-206.
. This article highlights pathologic parameters associated with prognosis of adrenocortical carcinoma.
26. Aubert S, Wacrenier A, Leroy X, et al. Weiss system revisited: a clinicopathologic and immunohistochemical study of 49 adreno- cortical tumors. Am J Surg Pathol. 2002;26(12):1612-1619.
27. Hough AJ, Hollifield JW, Page DL, et al. Prognostic factors in adrenal cortical tumors. A mathematical analysis of clinical and morphologic data. Am J Clin Pathol. 1979;72(3):390-399.
28. Van Slooten H, Schaberg A, Smeenk D, et al. Morphologic charac- teristics of benign and malignant adrenocortical tumors. Cancer. 1985;55(4):766-773.
29. Scholzen T, Gerdes J. The Ki-67 protein: from the known and the unknown. J Cell Physiol. 2000;182(3):311-322.
30. Iino K, Sasano H, Yabuki N, et al. DNA topoisomerase II alpha and Ki-67 in human adrenocortical neoplasms: a possible marker of differentiation between adenomas and carcinomas. Mod Pathol. 1997;10(9):901-907.
31. Soon PS, Gill AJ, Benn DE, et al. Microarray gene expression and immunohistochemistry analyses of adrenocortical tumors identify IGF2 and Ki-67 as useful in differentiating carcinomas from adeno- mas. Endocr Relat Cancer. 2009;16(2):573-583.
32. Gupta D, Shidham V, Holden J, et al. Value of topoisomerase II alpha, MIB-1, p53, E-cadherin, retinoblastoma gene protein pro- duct, and HER-2/neu immunohistochemical expression for the pre- diction of biologic behavior in adrenocortical neoplasms. Appl Immunohistochem Mol Morphol. 2001;9(3):215-221.
33. Nakazumi H, Sasano H, Iino K, et al. Expression of cell cycle inhibitor p27 and Ki-67 in human adrenocortical neoplasms. Mod Pathol. 1998;11(12):1165-1170.
34. Sbiera S, Kroiss M, Thamm T, et al. Survivin in adrenocortical tumors - pathophysiological implications and therapeutic potential. Horm Metab Res. 2013;45(2):137-146.
35. Sbiera S, Schmull S, Assie G, et al. High diagnostic and prognostic value of steroidogenic factor-1 expression in adrenal tumors. J Clin Endocrinol Metab. 2010;95(10):E161-E171.
36. Duregon E, Volante M, Giorcelli J, et al. Diagnostic and prognostic role of steroidogenic factor 1 in adrenocortical carcinoma: a valida- tion study focusing on clinical and pathologic correlates. Hum Pathol. 2013;44(5):822-828.
37. Gaujoux S, Grabar S, Fassnacht M, et al. Beta-catenin activation is associated with specific clinical and pathologic characteristics and a poor outcome in adrenocortical carcinoma. Clin Cancer Res. 2011;17(2):328-336.
38. Fenske W, Volker HU, Adam P, et al. Glucose transporter GLUT1 expres- sion is an stage-independent predictor of clinical outcome in adrenocortical carcinoma. Endocr Relat Cancer. 2009;16(3):919-928.
39. Waldmann J, Feldmann G, Slater EP, et al. Expression of the zinc-finger transcription factor Snail in adrenocortical carcinoma is associated with decreased survival. Br J Cancer. 2008;99(11):1900-1907.
40. Yang MS, Wang HS, Wang BS, et al. A comparative proteomic study identified calreticulin and prohibitin up-regulated in adrenocortical carcinomas. Diagn Pathol. 2013;8:58.
.. This article represents proteomic analysis and validates possi- ble protein markers by immunohistochemistry in adrenocorti- cal carcinoma.
41. Kjellin H, Johansson H, Hoog A, et al. Differentially expressed proteins in malignant and benign adrenocortical tumors. Plos One. 2014;9(2):e87951.
42. Wang C, Sun Y, Wu H, et al. Distinguishing adrenal cortical carci- nomas and adenomas: a study of clinicopathological features and biomarkers. Histopathology. 2014;64(4):567-576.
43. Ye J, Qi Y, Wang W, et al. Lower expression of ATM and gene deletion is more frequent in adrenocortical carcinomas than adre- nocortical adenomas. Endocrine. 2012;41(3):479-486.
44. Szajerka A, Dziegiel P, Szajerka T, et al. Immunohistochemical evalua- tion of metallothionein, Mcm-2 and Ki-67 antigen expression in tumors of the adrenal cortex. Anticancer Res. 2008;28(5B):2959-2965.
45. Kiiveri S, Liu J, Arola J, et al. Transcription factors GATA-6, SF-1, and cell proliferation in human adrenocortical tumors. Mol Cell Endocrinol. 2005;233(1-2):47-56.
46. Giordano TJ, Thomas DG, Kuick R, et al. Distinct transcriptional profiles of adrenocortical tumors uncovered by DNA microarray analysis. Am J Pathol. 2003;162(2):521-531.
47. de Fraipont F, El Atifi M, Cherradi N, et al. Gene expression profiling of human adrenocortical tumors using complementary deoxyribo- nucleic acid microarrays identifies several candidate genes as mar- kers of malignancy. J Clin Endocrinol Metab. 2005;90(3):1819-1829.
48. Slater EP, Diehl SM, Langer P, et al. Analysis by cDNA microarrays of gene expression patterns of human adrenocortical tumors. Eur J Endocrinol. 2006;154(4):587-598.
49. Velazquez-Fernandez D, Laurell C, Geli J, et al. Expression profiling of adrenocortical neoplasms suggests a molecular signature of malignancy. Surgery. 2005;138(6):1087-1094.
50. Fonseca AL, Kugelberg J, Starker LF, et al. Comprehensive DNA methylation analysis of benign and malignant adrenocortical tumors. Genes Chromosomes Cancer. 2012;51(10):949-960.
51. Assie G, Letouze E, Fassnacht M, et al. Integrated genomic char- acterization of adrenocortical carcinoma. Nat Genet. 2014;46 (6):607-612.
52. Gara SK, Wang Y, Patel D, et al. Integrated genome-wide analysis of genomic changes and gene regulation in human adrenocortical tissue samples. Nucleic Acids Res. 2015;43(19):9327-9339.
. This article gives integrated and comprehensive genomic information in adrenocortical carcinoma.
53. Tombol Z, Szabo PM, Molnar V, et al. Integrative molecular bioin- formatics study of human adrenocortical tumors: microRNA, tissue- specific target prediction, and pathway analysis. Endocr Relat Cancer. 2009;16(3):895-906.
54. Soon PS, Tacon LJ, Gill AJ, et al. miR-195 and miR-483-5p identified as predictors of poor prognosis in adrenocortical cancer. Clin Cancer Res. 2009;15(24):7684-7692.
55. Patterson EE, Holloway AK, Weng J, et al. MicroRNA profiling of adrenocortical tumors reveals miR-483 as a marker of malignancy. Cancer. 2011;117(8):1630-1639.
56. Schmitz KJ, Helwig J, Bertram S, et al. Differential expression of microRNA-675, microRNA-139-3p and microRNA-335 in benign and malignant adrenocortical tumours. J Clin Pathol. 2011;64 (6):529-535.
57. Singh P, Soon PS, Feige JJ, et al. Dysregulation of microRNAs in adrenocortical tumors. Mol Cell Endocrinol. 2012;351(1):118-128.
58. Minowada S, Kinoshita K, Hara M, et al. Measurement of urinary steroid profile in patients with adrenal tumor as a screening method for carcinoma. Endocrinol Jpn. 1985;32(1):29-37.
59. Arlt W, Biehl M, Taylor AE, et al. Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors. J Clin Endocrinol Metab. 2011;96(12):3775-3784.
60. Kotlowska A, Sworczak K, Stepnowski P. Urine metabolomics ana- lysis for adrenal incidentaloma activity detection and biomarker discovery. J Chromatogr B Analyt Technol Biomed Life Sci. 2011;879(5-6):359-363.
61. Kotlowska A, Malinski E, Sworczak K, et al. The urinary steroid profile in patients diagnosed with adrenal incidentaloma. Clin Biochem. 2009;42(6):448-454.
62. Kerkhofs TM, Kerstens MN, Kema IP, et al. Diagnostic value of urinary steroid profiling in the evaluation of adrenal tumors. Horm Cancer. 2015;6(4):168-175.
. This article shows summarized information about urine steroid profiling in adrenocortical neoplasm.
63. Tremoen NH, Fowler PA, Ropstad E, et al. Exposure to the three structurally different PCB congeners (PCB 118, 153, and 126) results in decreased protein expression and altered steroidogenesis in the human adrenocortical carcinoma cell line H295R. J Toxicol Environ Health A. 2014;77(9-11):516-534.
64. Sbiera S, Leich E, Liebisch G, et al. Mitotane inhibits sterol-O-acyl transferase 1 triggering lipid-mediated endoplasmic reticulum stress and apoptosis in adrenocortical carcinoma cells. Endocrinology. 2015;156(11):3895-3908.
65. Dransfield DT, Cohen EH, Chang Q, et al. A human monoclonal antibody against insulin-like growth factor-II blocks the growth of human hepatocellular carcinoma cell lines in vitro and in vivo. Mol Cancer Ther. 2010;9(6):1809-1819.
66. Gallicchio MA, van Sinderen M, Bach LA. Insulin-like growth factor binding protein-6 and CCI-779, an ester analogue of rapamycin, additively inhibit rhabdomyosarcoma growth. Horm Metab Res. 2003;35(11-12):822-827.
67. Zhang L, Zhang H, Zhao Y, et al. Inhibitor selectivity between aldo- keto reductase superfamily members AKR1B10 and AKR1B1: role of Trp112 (Trp111). FEBS Lett. 2013;587(22):3681-3686.
68. Huang FK, Han S, Xing B, et al. Targeted inhibition of fascin func- tion blocks tumour invasion and metastatic colonization. Nat Commun. 2015;6:7465.
69. Abiven G, Coste J, Groussin L, et al. Clinical and biological features in the prognosis of adrenocortical cancer: poor outcome of corti- sol-secreting tumors in a series of 202 consecutive patients. J Clin Endocrinol Metab. 2006;91(7):2650-2655.
70. Stigliano A, Cerquetti L, Borro M, et al. Modulation of proteomic profile in H295R adrenocortical cell line induced by mitotane. Endocr Relat Cancer. 2008;15(1):1-10.