Next Article in Journal
Promising Antigens for the New Frontier of Targeted Immunotherapy in Multiple Myeloma
Previous Article in Journal
Tumor Microenvironment Features and Chemoresistance in Pancreatic Ductal Adenocarcinoma: Insights into Targeting Physicochemical Barriers and Metabolism as Therapeutic Approaches
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

An Update on Silent Corticotroph Adenomas: Diagnosis, Mechanisms, Clinical Features, and Management

Pituitary Center, Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100730, China
*
Authors to whom correspondence should be addressed.
S.J. and X.C. contributed equally to the article.
Cancers 2021, 13(23), 6134; https://doi.org/10.3390/cancers13236134
Submission received: 13 October 2021 / Revised: 26 November 2021 / Accepted: 2 December 2021 / Published: 6 December 2021

Abstract

:

Simple Summary

The 2017 World Health Organization classification of endocrine tumors defines pituitary adenomas based on their cell lineages. T-PIT can serve as a complimentary tool for further identification of silent corticotroph adenomas (SCAs). Unlike functioning corticotroph adenomas in patients with Cushing’s disease, SCAs present no clinical and biochemical features of Cushing’s syndrome. SCAs have been shown to exhibit a more aggressive course characterized by a higher probability of recurrence and resistance to conventional treatment due to their intrinsic histological features. The aim of our review is to offer an update on the diagnosis, mechanisms, clinical features and management of SCAs. Studies of the molecular mechanisms of SCA pathogenesis will provide new directions for the diagnosis and management of SCAs.

Abstract

With the introduction of 2017 World Health Organization (WHO) classification of endocrine tumors, T-PIT can serve as a complementary tool for identification of silent corticotroph adenomas (SCAs) in some cases if the tumor is not classifiable by pituitary hormone expression in pathological tissue samples. An increase of the proportion of SCAs among the non-functioning pituitary adenomas (NFPAs) has been witnessed under the new rule with the detection of T-PIT-positive ACTH-negative SCAs. Studies of molecular mechanisms related to SCA pathogenesis will provide new directions for the diagnosis and management of SCAs. A precise pathological diagnosis can help clinicians better identify SCAs. Understanding clinical features in the context of the pathophysiology of SCAs is critical for optimal management. It could provide information on appropriate follow-up time and aid in early recognition and treatment of potentially aggressive forms. Management approaches include surgical, radiation, and/or medical therapies.

1. Introduction

Silent corticotroph adenomas (SCAs) are a subtype of non-functioning pituitary adenomas (NFPAs). Unlike functioning corticotroph adenomas in patients with Cushing’s disease (CD), SCAs present no clinical and biochemical features of Cushing’s syndrome.
Previous studies have shown SCAs account for 4.8–6.8% of all resected pituitary adenomas (PAs), 5–19% of all NFPAs, and up to 20% of all corticotroph adenomas [1,2,3,4,5,6].
The diagnosis of SCAs is made in a retrospective fashion with histopathological staining since clinical and endocrinological characteristics cannot distinguish them from other NFPAs. They are traditionally diagnosed by observation of positive immunoreactivity for adrenocorticotropic hormone (ACTH).
The 2017 World Health Organization (WHO) classification of endocrine tumors recently defined PAs based on their cell lineages [7]. It is generally acknowledged that cell lineage-specific pituitary transcription factors play a crucial role in the development of pituitary cells and corresponding PAs: T-box family member TBX19 (T-PIT) for corticotroph lineage; pituitary-specific POU-class homeodomain transcription factor (PIT-1) for somatotroph, lactotroph, and thyrotroph lineages; and steroidogenic factor 1 (SF1) for gonadotroph lineages [8]. Therefore, in light of the new WHO classification, the diagnosis of SCAs remains based on immunohistochemistry (IHC) for ACTH. However, if an adenoma, particularly for NFPA, is still not classifiable by pituitary hormone expression according to a cell lineage, assessment of transcription factors may serve as a second-step complimentary tool for further classification, e.g., T-PIT in the case of SCAs.
SCAs are reportedly highly proliferative and invasive [3,9,10,11,12]. They have been shown to exhibit a more aggressive course characterized by a higher probability of recurrence and resistance to conventional treatment and are therefore classified as “high-risk” tumors according to the 2017 WHO classification system [6,7,13].
This review provides an update on the SCA diagnostic approach; novel molecular mechanisms related to SCA pathogenesis, which are implicated in ACTH silence, growth, invasion, and aggressive behavior; and a summary of the clinical characteristics of SCAs and their management.

2. Identification of SCAs

With the implementation and wider use of T-PIT IHC, the proportion of SCAs among all NFPAs is expected to increase if T-PIT-positive ACTH-negative SCAs can be identified. Nishioka et al. studied 119 previously diagnosed hormone-negative tumors by evaluating the expression of transcription factors. The authors were able to reclassify 95% of them as silent pituitary tumors, of which 26.9% (32/119) were reclassified as SCAs as they displayed differentiation towards the corticotroph lineage with T-PIT immunopositivity. The IHC studies of transcription factors have drastically reduced the percentage of null-cell tumors (from 23% to 1%) and further increased the percentage of SCAs (from 10% to 16%) from the surgically resected NFPAs [14]. It is noteworthy that ACTH remains the first line for detection of SCAs. Nevertheless, for laboratories using less sensitive ACTH antibodies, or using automatic platforms without antigen retrieval protocols IHC, T-PIT may serve as a second-step complimentary tool for identification of SCAs.
Some studies on SCAs have been conducted following the 2017 WHO classification guidelines [9,15,16,17,18,19]. All have seen an increase of the proportion of SCAs among the NFPAs with the detection of T-PIT-positive ACTH-negative SCAs as T-PIT immunostaining reduces the number of false-negatives and false-positives. However, the implementation of T-PIT IHC in clinical practice is not without its pitfalls. Though anti-T-PIT antibodies (HPA072686) that are both sensitive and specific have been developed [20], it is not available in all institutions, and in addition, the unavailability of well-trained pathologists for IHC techniques restricts the interpretation of transcription factors to a few laboratories and platforms [21]. Even though some institutions have performed the T-PIT IHC, variability in the results among different departments has been observed and their reliability may be questioned [22]. Furthermore, the sensitivity of IHC in identifying the variant subtypes of tumors may be decreased as the protein expression of a specific gene is sometimes aborted.
To overcome the limitations of IHC and to complement it at the same time, Torregrosa-Quesada et al. [23] from Alicante General University Hospital-Institute for Health and Biomedical Research (ISABIAL), Spain, targeted the quantification of transcription factor gene expression by RT-qPCR to identify the cellular origin of pituitary tumors. In all tumor samples, they analyzed the gene expression of the main transcription factors associated with three cell lineages. Based on the clinically functioning pituitary adenomas, the authors established the interquartile range (p25–p75) of relative expression for all genes studied in each subtype. Among silent adenomas, when there is only the dominant gene of the corticotroph cell lineage, i.e., only a fold change (by RT-qPCR) of T-PIT above p25, the tumor is diagnosed as SCAs, the silent counterparts of functional corticotroph adenomas. They also studied the protein expression of transcription factors in a subset of 56 tumors to calculate the concordance between gene and protein expression. The concordance (Cohen’s kappa coefficient is 0.913) proved high between T-PIT immunopositivity and T-PIT expression.
We also validated the transcription factors at the RNA level in a subset of 61 NFPAs (25 of them were SCAs) after they were reclassified according to transcription factor IHC. We demonstrated good concordance between the molecular and IHC identification of the NFPAs subtypes [18]. According to these results, the molecular characterization of T-PIT complements the IHC results, and whenever IHC study of T-PIT is not available, RT-qPCR of T-PIT could help pathologists, neurosurgeons, and endocrinologists for better identification of SCAs. Further classification of PAs may be dependent on RT-qPCR of transcription factors that is precise, with little bias and unified standardization among different pathology departments.

3. Pathogenesis

3.1. Why Silent?

There are several hypotheses on the silencing mechanism of SCAs. Kovacs et al. [24] observed that SCA cells have a high number of lysosomes in the cytoplasm and show fusion of these lysosomes with secretory granules as evidenced by electron microscopy, leading to the hypothesis that ACTH is destroyed before it can be released. The possibility that a defective POMC gene was responsible for the silent nature of SCAs was denied by ribonuclease mapping analysis as no mutation was detected of POMC [25]. There is also speculation that SCA cells may not derive from anterior pituitary lobe ACTH-expressing cells, which give rise to CD cells, and rather they may arise from the pars intermedia POMC-expressing cells. Therefore, SCAs may have distinct characteristics from CD adenomas [26]. We will discuss in the next section the origin of SCAs. Others suggest that SCAs secrete predominantly biologically inactive high-molecular-weight ATCH to compete with normal ACTH (1–39) at the ACTH receptor, thus silencing corticotroph adenomas [27].
Recent advances have focused on the transcription, post-transcriptional regulation of POMC, and post-translational regulation of ACTH to explain the silencing mechanism of SCAs.
Araki et al. [28] observed that an additional regulatory region near the POMC gene, functioning as a second promoter of POMC, is highly methylated in SCAs. In contrast, it is highly demethylated in pituitary ACTH-secreting tumors, suggesting that methylation of the second promoter might play a role in transcription of the POMC gene, possibly silencing POMC in SCAs.
Given the fact that SCAs and CDs in most studies expressed similar POMC [11,29,30], a most likely and feasible hypothesis has been established that the function or expression of prohormone convertase (PC) 1/3, a POMC-processing enzyme, may be disturbed in SCAs. PC1/3 has been extensively studied in the literature [11,29,30,31,32,33,34]. PC1/3, encoded by the PCSK1 gene, is involved in the processing of POMC into mature and biologically active ACTH [34]. Concerning POMC post-transcriptional regulation, Tateno et al. [31] and Jahangiri et al. [11] observed that the expression of PC1/3 was present at 15-fold and 30-fold higher levels in CDs than SCAs, respectively, indicating defects in the conversion of POMC to ACTH in SCAs. ISABIAL’s lab [29] also observed lower PCSK1 and PC1/3 expression in SCAs compared with CDs, reinforcing the hypothesis that defective PC1/3 is implicated in the impaired POMC post-transcriptional regulation.
MicroRNAs(miRNAs) have also been proposed to play a potential role in the silencing mechanism of SCAs. miRNome analysis revealed differences between SCAs and CDs as they were arranged in two clusters with a different miRNAs signature, suggesting a potential part played by the miRNAs in the pathophysiology of different corticotroph adenomas [35]. Comparing 23 SCAs and 24 CDs, García-Martínez [1] observed significantly higher levels of miR-200a and miR-103 in SCAs than in CDs (indeed, both miRNAs appeared normo-regulated in SCAs while downregulated in CDs), and suggested these miRNAs may serve as potential diagnostic markers to distinguish SCAs from CDs. In SCAs, miR-200a and miR-103 could participate in the regulation of POMC expression. POMC transcription is associated with the PKA-MAP3K8-MEK-ERK1/2-NGFIB pathway [36], while miR-200a and miR-103 negatively regulated this pathway by inhibiting the MAP3K8 expression. Additionally, miR-103 correlated negatively with the expression of MEK. Therefore, miR-200a and miR-103 are involved in the post-transcriptional regulation of POMC expression in SCAs by inhibiting its upstream pathway [1]. Moreover, the authors demonstrated other miRNAs, such as miR-375, miR-488, and miR-383, could participate in the regulation of POMC transcription, probably through their association with different pathways and relevant transcription factors.
Post-translational regulation of ACTH has also been studied [11,29,31,37]. The proconvertase PC2 is proposed to contribute to the silencing mechanism of SCAs. Encoded by PCSK2, PC2 is an endoproteolytic enzyme responsible for the processing of biologically active ACTH into α-melanocyte-stimulating hormone and corticotropin-like intermediate lobe peptide in the intermediate lobe [38]. Most studies reported a decreased degradation of ACTH in corticotroph adenomas (SCAs and CDs) compared with other subtypes of NFPAs because PC2 mRNA levels in SCAs and CDs were less than those in other subtypes of NFPAs, without differences between SCAs and CDs [11,31]. Further study by García-Martínez et al., however, found a stronger correlation between PCSK2 and PC2 expression in SCAs than in CDs. Moreover, the authors found that SCAs exhibited higher PCSK2 expression than CDs (though not statistically significant). These results are consistent with a more efficient and accelerated degradation of ACTH in SCAs than in CDs, thereby partly accounting for silent manifestations of Cushing’s syndrome in SCAs [29]. In addition, more peptidylglycine α-amidating monooxygenase (PAM) and carboxypeptidase E (CPE) expression in SCAs than in CDs, both involved in the post-translational regulation of ACTH, might lead to the inability to secrete ACTH in these SCAs.
Another interesting phenomenon is that SCAs can transform into active CDs after years of silence. The phenotypic transition has been related to three mechanisms: first, an increase in PCSK1 and PC1/3 expression has been reported in three cases of SCAs that evolved during follow-up to functioning ones, thus underscoring the role of PC1/3 as one of the most feasible mechanism for silencing corticotroph adenomas [39]. Second, though decreased in SCAs, the accumulative effect of PC1/3 could still process POMC into biologically active ACTH if time allows. Little by little, ACTH could gradually and eventually rise to a higher level to cause clinical manifestations of Cushing’s syndrome. Indeed, the transformation took a median of 30 months, with the longest interval being 10 years summarized by Zheng et al. [40]. Additionally, a most recent mechanism proposed by Araki et al. [28] suggested that the sequential hypomethylation of the second promoter might activate POMC and promote ACTH secretion in the phenotypic transition.

3.2. SCAs Could Represent Silent Cortico-Gonadotroph Adenomas?

Cooper et al. [41] proposed a pathologic and clinically distinct typification of SCAs as silent cortico-gonadotroph adenomas as they exhibit features consistent with both the corticotroph and the gonadotroph cell lines.
In this study, SCAs have been shown to be immunopositive for corticotroph markers, ACTH, NeuroD1, as well the as gonadotroph markers, SF-1 and DAX-1. Some cells co-expressed ACTH with either SF-1 or LH. In contrast, their secreting counterparts are immunonegative for DAX-1. It is noteworthy that only 44% of SCAs are immunoreactive for T-PIT, which could be attributed to T-PIT antibodies that were not readily available and reliable when the research was conducted. Under electron microscopy, SCAs demonstrated ultrastructural features consistent with both corticotroph and gonadotroph cells. It is thus hypothesized that SCAs derive from an intermediary cell that shares both gonadotroph and corticotroph characteristics.
Intriguingly, further investigation reported that 20.45% (9/44) of the corticotroph adenomas expressed GATA2, a gene of the transcription factor involved in the development of gonadotrophs [23], also considered a marker of gonadotroph adenomas [42]. Eight of the nine cases that expressed GATA2 are SCAs. The expression of GATA2 in SCAs suggests that this subtype could have a component of gonadotrophic features [23].
Coincidentally, by using a canonical transcriptome signature for each pituitary cell type, Neou et al. observed that 88% (7/8) of SCAs displayed both corticotroph and gonadotroph signatures [35]. It was later confirmed by co-expression of GATA3 (a genomic paralogy of GATA2) to be characteristic of pituitary gonadotroph adenomas [43], ACTH, and T-PIT IHC in these tumors. The result highlights that SCAs manifest both corticotroph and gonadotroph cytologic features, supporting the arguments by some authors [23,41]. Further research by Ricklefs et al. [44] analyzing genome-wide DNA methylation profiles also demonstrated that SCAs show a spatial relationship with gonadotroph adenomas. These findings suggest that SCAs might arise from a specific cell lineage distinct from other corticotroph adenomas and that they could represent a new pituitary tumor subtype named cortico-gonadotroph adenomas, but it still requires further investigations in a larger cohort of SCAs.

3.3. Progression and Growth

SCAs are among the five types of “high-risk” tumors proposed by the 2017 WHO classification system that demonstrate aggressive behavior. The concept of aggressiveness in SCAs is a composite of high invasion, proliferation, progression, and frequent recurrences.
Over the past years, advanced techniques have allowed the exploration of either mono-omic or multi-omics, such as genomic, transcriptomic, epigenomic, metabolomic, proteomic, and lipidomic, to study the characteristics of NFPAs [45,46,47,48,49,50,51]. Though attempts to explore the underlying mechanism of invasiveness and aggressiveness of SCAs are still limited and inconclusive, we have already witnessed some advances, especially those using various -omics approaches to provide novel insights into the invasive and aggressive behavior of SCAs.
Various factors have been implicated in the development of SCA progression and growth including cyclin-dependent kinases inhibitor 2A, galectin-3, kallikrein 10, osteopontin, and O6-methylguanine-DNA methyltransferase. They have been reviewed by Ben-Shlomo et al. [4], but they are merely associative and remain to be further elucidated. Of note, as is pointed out in the 2017 WHO edition [7], galectin-3 has been reported to be weak or absent in SCAs compared with CDs and thus can be used as an important marker to distinguish between the two forms [52]. However, the direct effect of the absence of galectin-3 on SCAs’ behavior remains further elucidated. Since NFPAs are best studied in the emerging multi-omics studies, here, we summarize insights from the recent developments in SCA progression and growth.
Recently, Wang et al. investigated the invasiveness-associated lipid alterations in SCAs through ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based lipidomic analysis [16]. Twenty-eight differential lipids were identified in 54 SCAs (34 invasive/20 noninvasive) and found to be associated with active proliferation of SCAs. Transcriptome analysis was also conducted in another cohort of 42 NFPAs (23 invasive/19 noninvasive). Then, a multiomic functionally connected network was established with two lipids, 17 differentially expressed genes, and four molecular pathways to explore their potential molecular mechanism in the invasiveness of SCAs. These lipids and genes were found enriched in the energy metabolism-related pathway, leading to the invasiveness of SCAs since rapid proliferation and invasion of tumors require the metabolism of a large amount of lipids. Finally, by using multiple machine learning methods, the authors identified the four most critical lipids contributing to the invasive phenotype of SCAs, including three membrane-constituting lipids and one signal-transducing lipid, and thereby constructed a four-lipid risk model, which demonstrated excellent predictive ability to discriminate the invasive SCAs from non-invasive ones.
Neou et al. [35] generated a pangenomic classification of PAs. In their comprehensive research, transcriptomic analysis revealed low epithelial-mesenchymal transition (EMT) in SCAs along with their functioning USP8-mutated counterparts, while increased EMT was seen in their functioning USP8 wild-type counterparts showing increased invasiveness. As EMT is associated pathologically with tumor invasion and progression, the result might suggest that MET is not the main process that drives for the invasiveness of SCAs, and factors or processes other than MET may play a more crucial role in the proliferation and invasiveness in SCAs.
Epigenetic mechanisms, such as DNA methylation, have been shown to well distinguish between SCAs and CDs through DNA methylomic analysis [35], which has also been suggested by Ricklefs et al. [44]. DNA methylation of specific genes is related to the growth and invasiveness of PAs [53,54,55]. However, further research is needed to elucidate which key genes are involved in invasiveness-related aberrant epigenetic deregulation that lead to the invasiveness and aggressiveness of SCAs.
By high-throughput sequencing, weighted gene co-expression network analysis, and functional annotation, we observed gene signatures correlated with the invasiveness of SCAs [18]. Twenty differentially expressed genes were identified enriched in the “Pathway in cancers” and “MAPK pathway” and contributed to alterations of related factors. For instance, several receptor tyrosine kinases (EGFR and NTRK1) and their downstream signal transducers (RRAS and JAK1) showed enhanced expression in invasive SCAs. These components jointly regulate the tumorigenesis of SCAs. We next analyzed three invasion-related molecular markers (INSM1, HSPA2, and CDK6) in three subtypes of NFPAs (SCAs, SGAs, and null-cell adenomas) and found that INSM1 and HSPA2 expressions were higher in SCAs than other subtypes and that CDK6 showed a tendency of positive correlation with invasive SCAs. These results further supported the strong invasiveness of SCAs [18].
Uraki et al. studied the mismatch repair genes mutS homologs 6/2 (MSH6/2) and programmed cell death 1 ligand 1 (PD-L1), which are involved in tumor growth and tumor immunity, respectively, in a cohort of 73 NFPAs, 23 of them SCAs [56]. Results showed that reduced expressions of MSH6/2 and PD-L1 mRNA in SCAs partially account for the molecular mechanism causing proliferative and invasive characteristics as the reduction of MSH6/2 could decrease the rate of apoptosis and promote cell-cycle progression, and the reduction of PD-L1 could provide a relatively functional tumor immunity. However, their detailed mechanism warrants further in-depth studies.
In corticotroph adenomas, pituitary-specific hormone gene expression of arginine vasopressin receptor 1B (AVPR1B) is observed to express higher in SCAs than in CDs. Since arginine vasopressin is capable of activating pathways associated with cell proliferation, higher AVPR1B mRNA expression could be related to the growth and aggressiveness of SCAs [29]. However, another study by ISABIAL’s lab found comparable AVPR1B mRNA between SCAs and CDs, inconsistent with their previous finding [57].

4. Clinical Features

4.1. Clinical Manifestations

SCAs commonly manifest as NFPAs, presenting with symptoms of mass effects. Patients may present headache, visual symptoms, and hypopituitarism, without the clinical features of Cushing’s syndrome [58]. However, the clinical course of SCAs is different from its name, ‘silent’; rather, they show aggressiveness. Some studies show that SCAs may have a female predominance [10,59,60], while others do not [3,11,61]. Regarding the age of onset, some studies have observed SCAs may present at younger ages than other subtypes of NFPAs [10,12,17,60].

4.2. Preoperative Diagnosis

It is difficult to distinguish SCAs from other NFPAs preoperatively. Researchers have attempted to find clinical evidence to detect SCAs before surgery [15,62]. Kim et al. identified that female, tumor hemorrhage, cavernous sinus invasion, and weaken ACTH response in the combined pituitary function test (CPFT) may be associated with SCAs [62]. Meanwhile, Zhang et al. summarized that female, cystic degeneration, and high blood ACTH levels (for ACTH positive SCAs) may serve as reliable predictors for SCAs [15]. Recent studies suggested that lipidomic signature [16] and serum-exosomal INSM1 mRNA [18] may serve as promising diagnostic techniques for the invasive SCA.

4.3. Radiologic Characteristics

SCAs usually present as macroadenomas, typical of NFPAs characteristics [4,58]. However, it is not uncommon to find marked cavernous sinus invasion in SCAs than in other NFPAs [11,14,17,60,63], while this finding was not demonstrated in other studies [12,64]. Jahangiri et al. argued that differences may come from relying exclusively on the radiologist’s diagnosis of the preoperative MRI [11]. They addressed that it was indispensable to incorporate a surgeon’s impression on the cavernous sinus invasion. Otherwise, wrong conclusions may be drawn [11]. In addition, Kim et al. revealed that surgical findings should play a decision role in the diagnosis of cavernous sinus invasion when invasiveness could not be confirmed before surgery [65]. Some have observed that the occurrence of multiple microcysts [17,63,64,66] and intertumoral hemorrhage [63,64,66] are more prevalent in SCAs than those in NFPAs, while others not [11,67]. In the group of SCAs and SGAs, the sensitivity and specificity of multiple microcyst for predicting SCAs are 76% and 95%, respectively [68]. However, this study only assessed between SCAs and SGAs, without other types of PAs. Kasuki et al.’s research made up for this shortcoming [69]. They analyzed the frequency of these special microcysts in all subtypes of pituitary adenomas. They concluded that microcystic patterns are more common in SCAs than in other PAs. The sensitivity, specificity, and accuracy to define an SCA are 58%, 93%, and 90%, respectively [69]. The high specificity of multiple microcysts to predict an SCA has also been confirmed by other reports [17].

5. Postoperative Course

5.1. Postoperative Hypopituitarism

A retrospective series concluded that more than half of the patients newly developed postoperative adrenal insufficiency [3,41]. Ben-Shlomo et al. put forward a hypothesis in their article that development of postoperative hypopituitarism may result from the suppression of normal ACTH [4]. They proved that SCAs may release some tumoral ACTH locally, leading to the insufficiency of ACTH after the resection of tumor [4].

5.2. Recurrence

A number of studies have investigated whether SCAs have a more aggressive behavior (Table 1). Some studies have demonstrated that SCAs may have higher rates of recurrence than other NFPAs [3,11,17,41,63], while others described there were no significant differences between SCAs and other NFPAs in the time or rates of recurrence [10,12,61,62,64,66,70,71]. These differences may result from the time of follow-up, sample size, and the use of adjuvant radiotherapy [4]. A recent meta-analysis, which included 244 SCAs and 1622 other NFPAs, did not verify that SCAs have higher recurrence rates than NFPAs [70]. Although the recurrence or progression rates between SCAs and other NFPAs may have no statistical significance, SCAs still present a more aggressive clinical course than other NFPAs judging from the mean time to disease progression [10]. Drummond et al. summarized that SCAs may present aggressive behavior and higher recurrences rates than other NFPAs after a follow-up time of more than 3 years [58]. Bradley et al. noted that SCAs may show a more aggressive course when they do regrow [71]. Cho et al. reviewed the medical records of 162 cases diagnosed with NFPAs (28 SCAs and 134 ACTH-immunonegative NFPAs) [63]. They found that the overall recurrence rate was not significant between SCAs and non-SCAs at the beginning, even without the interference of adjuvant radiotherapy. As time goes by, the difference became apparent. After a follow-up time of more than 5 years, the recurrence rates of SCA and non-SCA were 57.1% and 13.9% (p = 0.04), respectively. Additionally, their research also suggested multiple recurrences and aggressive tumor behavior may be associated with younger age [63]. They highlighted the importance of careful long-term monitoring for patients who are diagnosed at a young age [63]. However, other reports did not come to a unanimous conclusion [12,17]. One study performed a separate analysis in SCA patients who underwent multiple surgeries [12]. In this group of SCAs, patients were not found to be at a young age nor did they exhibit other distinct clinical features.

5.3. Recurrence Prediction

One recent retrospective series confirmed that SCAs were 2.9 times more likely to have a tumor residual than ACTH-immunonegative NFPAs [72]. Meanwhile, SCAs have a lower event-free survival [17,72] and a lower progression-free survival [10] compared with other NFPAs. It is worth mentioning that one study observed SCAs have a median recurrence at 3 vs. 8 years for other NFPAs (p < 0.0001) [41].
Reports have not figured out significant differences in the Ki-67, p53, and matrix metallo-proteinase-9 expression between PAs with and without cavernous sinus invasion [60]. Although, cavernous sinus invasion existed in 85% cases of SCAs in Yamada et al.’s research [60]. However, recent studies have confirmed that the Ki-67 index can be a valuable predictor when surrounding structures are invaded [73]. Additionally, the combination of mitotic count, Ki-67, and p53 was confirmed to be prognostic, especially when combined with invasive tumor growth [74,75,76]. Several cell-cycle regulators, such as p16, cyclin D1, and pRb, were reported to be associated with NFPAs progression [77]. Other reports have suggested that male gender, MIB index ≥ 3%, and SCA tumor pathology serve as significant factors predicting recurrence [17].

5.4. Transformation into CD

Several studies have demonstrated that SCAs may transform into CD [11,40], which usually occur in the later course of SCAs [11]. Although the number of cases with transformation is very small, it necessitates attention because it indicates malignant tumor behavior [78]. A retrospective series reported 3.9% of the corticotroph cell adenomas have the ability to bidirectionally transform the phenotype from SCA to CD [39]. Reports have identified that transformation may be more prevalent in Type 1 SCAs than in Type 2 [11]. Zheng et al. retrospectively collected and analyzed 16 patients with SCAs that converted to typical Cushing’s disease [40]. Their research unveiled SCAs that are aggressive and recurrence many times despite receiving multiple treatments tend to transform into CD during follow-up. Results also suggest that all patients that transformed to CD were macroadenomas [40]. Rotman et al. reported the first case of CD transforming into silent corticotroph pituitary carcinoma [79]. Nine SCAs cases who transformed into pituitary carcinoma were reported in a recent systematic review [78].

6. Management and Prognosis

Kim et al. reported that recurrence rates between SCAs and hormonally negative adenomas (HNAs) were not statistically different when gross total resection (GTR) had been achieved [65]. However, the new WHO classification was not used in this retrospective series, which questions the reliability of the conclusion. The utility of 3T-MRI in endoscopic transsphenoidal surgery may help to achieve GTR of pituitary macroadenomas [80]. However, the aggressive course of SCAs underscores a need for complementary treatments in the postoperative period. Studies have confirmed that radiotherapy, targeted therapies, and cytotoxic chemotherapy, etc. may be useful in the management of SCAs [4,81].

6.1. Radiotherapy

Some reports prove that there is no need for prophylactic radiotherapy following GTR in SCAs [10,65]. For those SCAs that did not achieve GTR, early postoperative adjuvant radiosurgery is highly recommend for residual tumor [10]. Prendergast et al. reported a 13-year-old boy diagnosed with SCAs received proton therapy after subtotal gross resection [82]. At the 12-month follow-up post-radiotherapy, MRI showed minimal residual disease, which was stable for 24 months post-radiotherapy [82]. In a study of 104 NFPAs patients treated with stereotactic radiosurgery, 62% of the 34 patients with SCAs achieved tumor control [83]. Progression-free survival (PFS) rates were 73% at 3 years and 31% at 8 years in SCA patients compared with 94% at 3 years and 87% at 8 years in NFPAs patients. Improved PFS was observed at doses >17 Gy [83].

6.2. Targeted Therapies

In view of the molecular characteristics of SCAs, targeted therapies may be a promising choice [4,81]. The higher expression of membrane SSTR1 and SSTR2 compared to CD by immunohistochemical analysis [58], suggesting somatostatin receptor ligands (SRLs), like PAsireotide, a second-generation SRL, may serve as a promising drug for the treatment of SCAs, particularly in refractory SCAs. However, the efficiency of SRLs for SCAs has not yet been proved. The phase II randomized clinical trial (PASSILCORT; PAsireotide LAR therapy of SCA; NCT02749227) for residual or recurrent SCAs has been terminated and no conclusion has been reached.

6.3. Chemotherapy

Several studies have discovered the low expression of O6-methylguanine-DNA methyltransferase (MGMT) in SCAs [66,81,84,85]. Salehi et al. investigated the expression of MGMT in a range of corticotroph adenoma subtypes [84]. In their research, seven SCAs cases showed only 10% MGMT immunohistochemical staining, indicating SCAs, particularly in refractory cases, may be appropriate candidates for temozolomide therapy [84]. Some studies have reported cases of SCAs treated with temozolomide [40,86,87]. In Zheng’s study, four patients with refractory functional SCA received temozolomide [40]. Serum cortisol levels returned to normal in all four cases, and the tumor volume decreased in three of them. Decaroli et al. reported a case of a 78-year-old female who was initially diagnosed with SCA and transformed to CD during the later course [86]. This patient had already received surgery, cabergoline, PAsireotide, and external radiation (a total dose of 54 Gy) before application of temozolomide. However, all these treatments showed an unsatisfactory effect. After being treated with temozolomide for a total of 47 cycles, the tumor volume remarkably decreased, and the serum cortisol levels progressively decreased. Therefore, when therapies like radiotherapy or surgical attempts may be unsuccessful, temozolomide should be considered [87].

7. Conclusions

SCAs commonly manifest as NFPAs. Unlike their functioning counterparts, CD, SCAs present no symptoms of hypercortisolism. The diagnosis of SCAs is dependent on a retrospective fashion with histopathological staining of transcription factors and pituitary hormones. SCAs are among the five types of “high-risk” tumors proposed by the 2017 WHO classification system. Studies of the molecular mechanisms related to SCA pathogenesis will provide new directions for the diagnosis and management of SCAs.

Author Contributions

Conceptualization, S.J.; writing—original draft preparation, S.J. and X.C.; writing—review and editing, Y.W.; supervision, R.W. and X.B.; funding acquisition, R.W. and X.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82170799, 82171475), the National Key Research and Development Program of China (2018YFA0108602, 2021YFE0114300) and the CAMS Initiative for Innovative Medicine (2021-1-I2M-019).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. García-Martínez, A.; Fuentes-Fayos, A.C.; Fajardo, C.; Lamas, C.; Cámara, R.; López-Muñoz, B.; Aranda, I.; Luque, R.M.; Picó, A. Differential Expression of MicroRNAs in Silent and Functioning Corticotroph Tumors. J. Clin. Med. 2020, 9, 1838. [Google Scholar] [CrossRef] [PubMed]
  2. Mete, O.; Cintosun, A.; Pressman, I.; Asa, S.L. Epidemiology and biomarker profile of pituitary adenohypophysial tumors. Mod. Pathol. 2018, 31, 900–909. [Google Scholar] [CrossRef] [Green Version]
  3. Langlois, F.; Lim, D.S.T.; Yedinak, C.G.; Cetas, I.; McCartney, S.; Cetas, J.; Dogan, A.; Fleseriu, M. Predictors of silent corticotroph adenoma recurrence; a large retrospective single center study and systematic literature review. Pituitary 2018, 21, 32–40. [Google Scholar] [CrossRef]
  4. Ben-Shlomo, A.; Cooper, O. Silent corticotroph adenomas. Pituitary 2018, 21, 183–193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Hinojosa-Amaya, J.M.; Lam-Chung, C.E.; Cuevas-Ramos, D. Recent Understanding and Future Directions of Recurrent Corticotroph Tumors. Front. Endocrinol. 2021, 12, 657382. [Google Scholar] [CrossRef] [PubMed]
  6. Lopes, M.B.S. The 2017 World Health Organization classification of tumors of the pituitary gland: A summary. Acta Neuropathol. 2017, 134, 521–535. [Google Scholar] [CrossRef]
  7. WHO. WHO Classification of Tumours of Endocrine Organs, 4th ed.; Lloyd, R.V., Osamura, R.Y., Klöppel, G., Rosai, J., Eds.; IARC Press: Lyon, France, 2017. [Google Scholar]
  8. Melmed, S. Pituitary-Tumor Endocrinopathies. N. Engl. J. Med. 2020, 382, 937–950. [Google Scholar] [CrossRef] [PubMed]
  9. Jiang, S.; Zhu, J.; Feng, M.; Yao, Y.; Deng, K.; Xing, B.; Lian, W.; Wang, R.; Bao, X. Clinical profiles of silent corticotroph adenomas compared with silent gonadotroph adenomas after adopting the 2017 WHO pituitary classification system. Pituitary 2021, 24, 564–573. [Google Scholar] [CrossRef] [PubMed]
  10. Strickland, B.A.; Shahrestani, S.; Briggs, R.G.; Jackanich, A.; Tavakol, S.; Hurth, K.; Shiroishi, M.S.; Liu, C.J.; Carmichael, J.D.; Weiss, M.; et al. Silent corticotroph pituitary adenomas: Clinical characteristics, long-term outcomes, and management of disease recurrence. J. Neurosurg. 2021, 1, 3236. [Google Scholar] [CrossRef]
  11. Jahangiri, A.; Wagner, J.R.; Pekmezci, M.; Hiniker, A.; Chang, E.F.; Kunwar, S.; Blevins, L.; Aghi, M.K. A comprehensive long-term retrospective analysis of silent corticotrophic adenomas vs hormone-negative adenomas. Neurosurgery 2013, 73, 8–17, discussion 17–18. [Google Scholar] [CrossRef]
  12. Ioachimescu, A.G.; Eiland, L.; Chhabra, V.S.; Mastrogianakis, G.M.; Schniederjan, M.J.; Brat, D.; Pileggi, A.V.; Oyesiku, N.M. Silent corticotroph adenomas: Emory University cohort and comparison with ACTH-negative nonfunctioning pituitary adenomas. Neurosurgery 2012, 71, 296–303, discussion 304. [Google Scholar] [CrossRef] [PubMed]
  13. Nishioka, H.; Inoshita, N. New WHO classification of pituitary adenomas (4th edition): Assessment of pituitary transcription factors and the prognostic histological factors. Brain Tumor Pathol. 2018, 35, 57–61. [Google Scholar] [CrossRef] [PubMed]
  14. Nishioka, H.; Inoshita, N.; Mete, O.; Asa, S.L.; Hayashi, K.; Takeshita, A.; Fukuhara, N.; Yamaguchi-Okada, M.; Takeuchi, Y.; Yamada, S. The Complementary Role of Transcription Factors in the Accurate Diagnosis of Clinically Nonfunctioning Pituitary Adenomas. Endocr. Pathol. 2015, 26, 349–355. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, K.; Shou, X.; Chen, H.; Qiao, N.; He, W.; Chen, Z.; Shen, M.; Li, S.; Zhao, Y.; Zhang, Z.; et al. Clinical Parameters of Silent Corticotroph Adenomas With Positive and Negative Adrenocorticotropic Hormone Immunostaining: A Large Retrospective Single-Center Study of 105 Cases. Front. Endocrinol. 2020, 11, 608691. [Google Scholar] [CrossRef] [PubMed]
  16. Wang, Z.; Guo, X.; Wang, W.; Gao, L.; Bao, X.; Feng, M.; Lian, W.; Zhu, H.; Xing, B. UPLC-MS/MS-based lipidomic profiles revealed aberrant lipids associated with invasiveness of silent corticotroph adenoma. J. Clin. Endocrinol. Metab. 2020, 106, e273–e287. [Google Scholar] [CrossRef] [PubMed]
  17. Goyal-Honavar, A.; Sarkar, S.; Asha, H.S.; Kapoor, N.; Balakrishnan, R.; Vanjare, H.; Chacko, G.; Chacko, A.G. A clinicoradiological analysis of silent corticotroph adenomas after the introduction of pituitary-specific transcription factors. Acta Neurochir. 2021, 163, 3143–3154. [Google Scholar] [CrossRef] [PubMed]
  18. Bao, X.; Wang, G.; Yu, S.; Sun, J.; He, L.; Zhao, H.; Ma, Y.; Wang, F.; Wang, X.; Wang, R.; et al. Transcriptomic analysis identifies a tumor subtype mRNA classifier for invasive non-functioning pituitary neuroendocrine tumor diagnostics. Theranostics 2021, 11, 132–146. [Google Scholar] [CrossRef]
  19. McDonald, W.C.; McDonald, K.N.; Helmer, J.A.; Ho, B.; Wang, A.; Banerji, N. The Role of T-box Transcription Factor in a Pituitary Adenoma Diagnostic Algorithm. Arch. Pathol. Lab. Med. 2021, 145, 592–598. [Google Scholar] [CrossRef]
  20. Sjöstedt, E.; Bollerslev, J.; Mulder, J.; Lindskog, C.; Pontén, F.; Casar-Borota, O. A specific antibody to detect transcription factor T-Pit: A reliable marker of corticotroph cell differentiation and a tool to improve the classification of pituitary neuroendocrine tumours. Acta Neuropathol. 2017, 134, 675–677. [Google Scholar] [CrossRef]
  21. Turchini, J.; Sioson, L.; Clarkson, A.; Sheen, A.; Gill, A.J. Utility of GATA-3 Expression in the Analysis of Pituitary Neuroendocrine Tumour (PitNET) Transcription Factors. Endocr. Pathol. 2020, 31, 150–155. [Google Scholar] [CrossRef]
  22. Sanchez-Tejada, L.; Sanchez-Ortiga, R.; Lamas, C.; Camara, R.; Riesgo, P.; Fajardo, C.; Aranda, F.I.; Pico, A. Contribution of molecular analysis to the typification of the non-functioning pituitary adenomas. PLoS ONE 2017, 12, e0180039. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Torregrosa-Quesada, M.E.; García-Martínez, A.; Silva-Ortega, S.; Martínez-López, S.; Cámara, R.; Fajardo, C.; Lamas, C.; Aranda, I.; Picó, A. How Valuable Is the RT-qPCR of Pituitary-Specific Transcription Factors for Identifying Pituitary Neuroendocrine Tumor Subtypes According to the New WHO 2017 Criteria? Cancers 2019, 11, 1990. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Kovacs, K.; Horvath, E.; Bayley, T.A.; Hassaram, S.T.; Ezrin, C. Silent corticotroph cell adenoma with lysosomal accumulation and crinophagy. A distinct clinicopathologic entity. Am. J. Med. 1978, 64, 492–499. [Google Scholar] [CrossRef]
  25. Nagaya, T.; Seo, H.; Kuwayama, A.; Sakurai, T.; Tsukamoto, N.; Nakane, T.; Sugita, K.; Matsui, N. Pro-opiomelanocortin gene expression in silent corticotroph-cell adenoma and Cushing’s disease. J. Neurosurg. 1990, 72, 262–267. [Google Scholar] [CrossRef] [PubMed]
  26. Horvath, E.; Kovacs, K.; Lloyd, R.V. Pars intermedia of the human pituitary revisited: Morphologic aspects and frequency of hyperplasia of POMC-peptide immunoreactive cells. Endocr. Pathol. 1999, 10, 55–64. [Google Scholar] [CrossRef]
  27. Matsuno, A.; Okazaki, R.; Oki, Y.; Nagashima, T. Secretion of high-molecular-weight adrenocorticotropic hormone from a pituitary adenoma in a patient without Cushing stigmata. Case report. J. Neurosurg. 2004, 101, 874–877. [Google Scholar] [CrossRef]
  28. Araki, T.; Tone, Y.; Yamamoto, M.; Kameda, H.; Ben-Shlomo, A.; Yamada, S.; Takeshita, A.; Yamamoto, M.; Kawakami, Y.; Tone, M.; et al. Two Distinctive POMC Promoters Modify Gene Expression in Cushing’s Disease. J. Clin. Endocrinol. Metab. 2021, 106, e3346–e3363. [Google Scholar] [CrossRef] [PubMed]
  29. García-Martínez, A.; Cano, D.A.; Flores-Martínez, A.; Gil, J.; Puig-Domingo, M.; Webb, S.M.; Soto-Moreno, A.; Picó, A. Why don’t corticotroph tumors always produce Cushing’s disease? Eur. J. Endocrinol. 2019, 181, 351–361. [Google Scholar] [CrossRef] [PubMed]
  30. Raverot, G.; Wierinckx, A.; Jouanneau, E.; Auger, C.; Borson-Chazot, F.; Lachuer, J.; Pugeat, M.; Trouillas, J. Clinical, hormonal and molecular characterization of pituitary ACTH adenomas without (silent corticotroph adenomas) and with Cushing’s disease. Eur. J. Endocrinol. 2010, 163, 35–43. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Tateno, T.; Izumiyama, H.; Doi, M.; Yoshimoto, T.; Shichiri, M.; Inoshita, N.; Oyama, K.; Yamada, S.; Hirata, Y. Differential gene expression in ACTH -secreting and non-functioning pituitary tumors. Eur. J. Endocrinol. 2007, 157, 717–724. [Google Scholar] [CrossRef] [Green Version]
  32. Tateno, T.; Izumiyama, H.; Doi, M.; Akashi, T.; Ohno, K.; Hirata, Y. Defective expression of prohormone convertase 1/3 in silent corticotroph adenoma. Endocr. J. 2007, 54, 777–782. [Google Scholar] [CrossRef] [Green Version]
  33. Iino, K.; Oki, Y.; Matsushita, F.; Yamashita, M.; Hayashi, C.; Miura, K.; Nishizawa, S.; Nakamura, H. Immunohistochemical properties of silent corticotroph adenoma and Cushing’s disease. Pituitary 2007, 10, 35–45. [Google Scholar] [CrossRef]
  34. Ohta, S.; Nishizawa, S.; Oki, Y.; Yokoyama, T.; Namba, H. Significance of absent prohormone convertase 1/3 in inducing clinically silent corticotroph pituitary adenoma of subtype I--immunohistochemical study. Pituitary 2002, 5, 221–223. [Google Scholar] [CrossRef] [PubMed]
  35. Neou, M.; Villa, C.; Armignacco, R.; Jouinot, A.; Raffin-Sanson, M.L.; Septier, A.; Letourneur, F.; Diry, S.; Diedisheim, M.; Izac, B.; et al. Pangenomic Classification of Pituitary Neuroendocrine Tumors. Cancer Cell 2020, 37, 123–134.e125. [Google Scholar] [CrossRef]
  36. Zhang, N.; Lin, J.K.; Chen, J.; Liu, X.F.; Liu, J.L.; Luo, H.S.; Li, Y.Q.; Cui, S. MicroRNA 375 mediates the signaling pathway of corticotropin-releasing factor (CRF) regulating pro-opiomelanocortin (POMC) expression by targeting mitogen-activated protein kinase 8. J. Biol. Chem. 2013, 288, 10361–10373. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Tani, Y.; Sugiyama, T.; Izumiyama, H.; Yoshimoto, T.; Yamada, S.; Hirata, Y. Differential gene expression profiles of POMC-related enzymes, transcription factors and receptors between non-pituitary and pituitary ACTH-secreting tumors. Endocr. J. 2011, 58, 297–303. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Bertagna, X. Proopiomelanocortin-derived peptides. Endocrinol. Metab. Clin. N. Am. 1994, 23, 467–485. [Google Scholar] [CrossRef]
  39. Righi, A.; Faustini-Fustini, M.; Morandi, L.; Monti, V.; Asioli, S.; Mazzatenta, D.; Bacci, A.; Foschini, M.P. The changing faces of corticotroph cell adenomas: The role of prohormone convertase 1/3. Endocrine 2017, 56, 286–297. [Google Scholar] [CrossRef] [PubMed]
  40. Zheng, G.; Lu, L.; Zhu, H.; You, H.; Feng, M.; Liu, X.; Dai, C.; Yao, Y.; Wang, R.; Zhang, H.; et al. Clinical, Laboratory, and Treatment Profiles of Silent Corticotroph Adenomas That Have Transformed to the Functional Type: A Case Series with a Literature Review. Front. Endocrinol. 2020, 11, 558593. [Google Scholar] [CrossRef] [PubMed]
  41. Cooper, O.; Ben-Shlomo, A.; Bonert, V.; Bannykh, S.; Mirocha, J.; Melmed, S. Silent corticogonadotroph adenomas: Clinical and cellular characteristics and long-term outcomes. Horm. Cancer 2010, 1, 80–92. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Umeoka, K.; Sanno, N.; Osamura, R.Y.; Teramoto, A. Expression of GATA-2 in human pituitary adenomas. Mod. Pathol. 2002, 15, 11–17. [Google Scholar] [CrossRef] [PubMed]
  43. Mete, O.; Kefeli, M.; Çalışkan, S.; Asa, S.L. GATA3 immunoreactivity expands the transcription factor profile of pituitary neuroendocrine tumors. Mod. Pathol. 2019, 32, 484–489. [Google Scholar] [CrossRef]
  44. Ricklefs, F.L.; Fita, K.D.; Rotermund, R.; Piffko, A.; Schmid, S.; Capper, D.; Buslei, R.; Buchfelder, M.; Burkhardt, T.; Matschke, J.; et al. Genome-wide DNA methylation profiles distinguish silent from non-silent ACTH adenomas. Acta Neuropathol. 2020, 140, 95–97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Salomon, M.P.; Wang, X.; Marzese, D.M.; Hsu, S.C.; Nelson, N.; Zhang, X.; Matsuba, C.; Takasumi, Y.; Ballesteros-Merino, C.; Fox, B.A.; et al. The Epigenomic Landscape of Pituitary Adenomas Reveals Specific Alterations and Differentiates Among Acromegaly, Cushing’s Disease and Endocrine-Inactive Subtypes. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2018, 24, 4126–4136. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Melmed, S. Pathogenesis of pituitary tumors. Nat. Rev. Endocrinol. 2011, 7, 257–266. [Google Scholar] [CrossRef]
  47. Mete, O.; Ezzat, S.; Perry, A.; Yamada, S.; Uccella, S.; Grossman, A.B.; Asa, S.L. The Pangenomic Classification of Pituitary Neuroendocrine Tumors: Quality Histopathology is Required for Accurate Translational Research. Endocr. Pathol. 2021, 32, 415–417. [Google Scholar] [CrossRef] [PubMed]
  48. Pînzariu, O.; Georgescu, B.; Georgescu, C.E. Metabolomics—A Promising Approach to Pituitary Adenomas. Front. Endocrinol. 2018, 9, 814. [Google Scholar] [CrossRef]
  49. Shah, S.S.; Aghi, M.K. The Role of Single-Nucleotide Polymorphisms in Pituitary Adenomas Tumorigenesis. Cancers 2019, 11, 1977. [Google Scholar] [CrossRef] [Green Version]
  50. Tatsi, C.; Stratakis, C.A. The Genetics of Pituitary Adenomas. J. Clin. Med. 2019, 9, 30. [Google Scholar] [CrossRef] [Green Version]
  51. Metin-Armagan, D.; Comunoglu, N.; Bulut, G.; Kadioglu, P.; Kameda, H.; Gazioglu, N.; Tanriover, N.; Ozturk, M. A Novel Expression Profile of Cell Cycle and DNA Repair Proteins in Nonfunctioning Pituitary Adenomas. Endocr. Pathol. 2020, 31, 2–13. [Google Scholar] [CrossRef]
  52. Thodou, E.; Argyrakos, T.; Kontogeorgos, G. Galectin-3 as a marker distinguishing functioning from silent corticotroph adenomas. Hormones (Athens Greece) 2007, 6, 227–232. [Google Scholar] [PubMed]
  53. Cheng, S.; Xie, W.; Miao, Y.; Guo, J.; Wang, J.; Li, C.; Zhang, Y. Identification of key genes in invasive clinically non-functioning pituitary adenoma by integrating analysis of DNA methylation and mRNA expression profiles. J. Transl. Med. 2019, 17, 407. [Google Scholar] [CrossRef] [PubMed]
  54. Kober, P.; Boresowicz, J.; Rusetska, N.; Maksymowicz, M.; Paziewska, A.; Dąbrowska, M.; Kunicki, J.; Bonicki, W.; Ostrowski, J.; Siedlecki, J.A.; et al. The Role of Aberrant DNA Methylation in Misregulation of Gene Expression in Gonadotroph Nonfunctioning Pituitary Tumors. Cancers 2019, 11, 1650. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. García-Martínez, A.; Sottile, J.; Sánchez-Tejada, L.; Fajardo, C.; Cámara, R.; Lamas, C.; Barberá, V.M.; Picó, A. DNA Methylation of Tumor Suppressor Genes in Pituitary Neuroendocrine Tumors. J. Clin. Endocrinol. Metab. 2019, 104, 1272–1282. [Google Scholar] [CrossRef]
  56. Uraki, S.; Ariyasu, H.; Doi, A.; Takeshima, K.; Morita, S.; Inaba, H.; Furuta, H.; Fukuhara, N.; Inoshita, N.; Nishioka, H.; et al. MSH6/2 and PD-L1 Expressions Are Associated with Tumor Growth and Invasiveness in Silent Pituitary Adenoma Subtypes. Int. J. Mol. Sci. 2020, 21, 2831. [Google Scholar] [CrossRef] [Green Version]
  57. Torregrosa-Quesada, M.E.; García-Martínez, A.; Sánchez-Barbie, A.; Silva-Ortega, S.; Cámara, R.; Fajardo, C.; Lamas, C.; Aranda, I.; Pico, A. The silent variants of pituitary tumors: Demographic, radiological and molecular characteristics. J. Endocrinol. Investig. 2021, 44, 1637–1648. [Google Scholar] [CrossRef] [PubMed]
  58. Drummond, J.; Roncaroli, F.; Grossman, A.B.; Korbonits, M. Clinical and Pathological Aspects of Silent Pituitary Adenomas. J. Clin. Endocrinol. Metab. 2019, 104, 2473–2489. [Google Scholar] [CrossRef] [Green Version]
  59. Nih, L.R.; Gojgini, S.; Carmichael, S.T.; Segura, T. Dual-function injectable angiogenic biomaterial for the repair of brain tissue following stroke. Nat. Mater. 2018, 17, 642. [Google Scholar] [CrossRef]
  60. Yamada, S.; Ohyama, K.; Taguchi, M.; Takeshita, A.; Morita, K.; Takano, K.; Sano, T. A study of the correlation between morphological findings and biological activities in clinically nonfunctioning pituitary adenomas. Neurosurgery 2007, 61, 580–584, discussion 584–585. [Google Scholar] [CrossRef] [PubMed]
  61. Langlois, F.; Lim, D.S.T.; Varlamov, E.; Yedinak, C.G.; Cetas, J.S.; McCartney, S.; Dogan, A.; Fleseriu, M. Clinical profile of silent growth hormone pituitary adenomas; higher recurrence rate compared to silent gonadotroph pituitary tumors, a large single center experience. Endocrine 2017, 58, 528–534. [Google Scholar] [CrossRef] [PubMed]
  62. Kim, D.; Ku, C.R.; Park, S.H.; Moon, J.H.; Kim, E.H.; Kim, S.H.; Lee, E.J. Clinical Parameters to Distinguish Silent Corticotroph Adenomas from Other Nonfunctioning Pituitary Adenomas. World Neurosurg. 2018, 115, e464–e471. [Google Scholar] [CrossRef] [PubMed]
  63. Cho, H.Y.; Cho, S.W.; Kim, S.W.; Shin, C.S.; Park, K.S.; Kim, S.Y. Silent corticotroph adenomas have unique recurrence characteristics compared with other nonfunctioning pituitary adenomas. Clin. Endocrinol. 2010, 72, 648–653. [Google Scholar] [CrossRef]
  64. Alahmadi, H.; Lee, D.; Wilson, J.R.; Hayhurst, C.; Mete, O.; Gentili, F.; Asa, S.L.; Zadeh, G. Clinical features of silent corticotroph adenomas. Acta Neurochir. 2012, 154, 1493–1498. [Google Scholar] [CrossRef] [PubMed]
  65. Kim, J.; Yoon, S.J.; Moon, J.H.; Ku, C.R.; Kim, S.H.; Lee, E.J.; Kim, S.H.; Kim, E.H. Clinical Significance of Radical Surgery in the Treatment of Silent Corticotroph Adenoma. J. Korean Neurosurg. Soc. 2019, 62, 114–122. [Google Scholar] [CrossRef] [PubMed]
  66. Cooper, O. Silent corticotroph adenomas. Pituitary 2015, 18, 225–231. [Google Scholar] [CrossRef] [Green Version]
  67. Guttenberg, K.B.; Mayson, S.E.; Sawan, C.; Kharlip, J.; Lee, J.Y.; Martinez-Lage, M.; Loevner, L.A.; Ewanichak, J.; Grady, M.S.; Snyder, P.J. Prevalence of clinically silent corticotroph macroadenomas. Clin. Endocrinol. 2016, 85, 874–880. [Google Scholar] [CrossRef] [PubMed]
  68. Cazabat, L.; Dupuy, M.; Boulin, A.; Bernier, M.; Baussart, B.; Foubert, L.; Raffin-Sanson, M.L.; Caron, P.; Bertherat, J.; Gaillard, S. Silent, but not unseen: Multimicrocystic aspect on T2-weighted MRI in silent corticotroph adenomas. Clin. Endocrinol. 2014, 81, 566–572. [Google Scholar] [CrossRef] [PubMed]
  69. Kasuki, L.; Antunes, X.; Coelho, M.C.A.; Lamback, E.B.; Galvao, S.; Silva Camacho, A.H.; Chimelli, L.; Ventura, N.; Gadelha, M.R. Accuracy of microcystic aspect on T2-weighted MRI for the diagnosis of silent corticotroph adenomas. Clin. Endocrinol. 2020, 92, 145–149. [Google Scholar] [CrossRef] [PubMed]
  70. Fountas, A.; Lavrentaki, A.; Subramanian, A.; Toulis, K.A.; Nirantharakumar, K.; Karavitaki, N. Recurrence in silent corticotroph adenomas after primary treatment: A systematic review and meta-analysis. J. Clin. Endocrinol. Metab. 2018, 104, 1039–1049. [Google Scholar] [CrossRef] [PubMed]
  71. Bradley, K.J.; Wass, J.A.; Turner, H.E. Non-functioning pituitary adenomas with positive immunoreactivity for ACTH behave more aggressively than ACTH immunonegative tumours but do not recur more frequently. Clin. Endocrinol. 2003, 58, 59–64. [Google Scholar] [CrossRef]
  72. Pappy, A.L., 2nd; Savinkina, A.; Bicknese, C.; Neill, S.; Oyesiku, N.M.; Ioachimescu, A.G. Predictive modeling for pituitary adenomas: Single center experience in 501 consecutive patients. Pituitary 2019, 22, 520–531. [Google Scholar] [CrossRef]
  73. Righi, A.; Agati, P.; Sisto, A.; Frank, G.; Faustini-Fustini, M.; Agati, R.; Mazzatenta, D.; Farnedi, A.; Menetti, F.; Marucci, G.; et al. A classification tree approach for pituitary adenomas. Hum. Pathol. 2012, 43, 1627–1637. [Google Scholar] [CrossRef]
  74. Manojlovic-Gacic, E.; Engstrom, B.E.; Casar-Borota, O. Histopathological classification of non-functioning pituitary neuroendocrine tumors. Pituitary 2018, 21, 119–129. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Trouillas, J.; Roy, P.; Sturm, N.; Dantony, E.; Cortet-Rudelli, C.; Viennet, G.; Bonneville, J.F.; Assaker, R.; Auger, C.; Brue, T.; et al. A new prognostic clinicopathological classification of pituitary adenomas: A multicentric case-control study of 410 patients with 8 years post-operative follow-up. Acta Neuropathol. 2013, 126, 123–135. [Google Scholar] [CrossRef]
  76. Raverot, G.; Dantony, E.; Beauvy, J.; Vasiljevic, A.; Mikolasek, S.; Borson-Chazot, F.; Jouanneau, E.; Roy, P.; Trouillas, J. Risk of Recurrence in Pituitary Neuroendocrine Tumors: A Prospective Study Using a Five-Tiered Classification. J. Clin. Endocrinol. Metab. 2017, 102, 3368–3374. [Google Scholar] [CrossRef]
  77. Park, S.H.; Jang, J.H.; Lee, Y.M.; Kim, J.S.; Kim, K.H.; Kim, Y.Z. Function of cell-cycle regulators in predicting silent pituitary adenoma progression following surgical resection. Oncol. Lett. 2017, 14, 7121–7130. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Lenders, N.; McCormack, A. Malignant transformation in non-functioning pituitary adenomas (pituitary carcinoma). Pituitary 2018, 21, 217–229. [Google Scholar] [CrossRef] [PubMed]
  79. Rotman, L.E.; Vaughan, T.B.; Hackney, J.R.; Riley, K.O. Long-Term Survival After Transformation of an Adrenocorticotropic Hormone-Secreting Pituitary Macroadenoma to a Silent Corticotroph Pituitary Carcinoma. World Neurosurg. 2019, 122, 417–423. [Google Scholar] [CrossRef]
  80. Zaidi, H.A.; De Los Reyes, K.; Barkhoudarian, G.; Litvack, Z.N.; Bi, W.L.; Rincon-Torroella, J.; Mukundan, S., Jr.; Dunn, I.F.; Laws, E.R., Jr. The utility of high-resolution intraoperative MRI in endoscopic transsphenoidal surgery for pituitary macroadenomas: Early experience in the Advanced Multimodality Image Guided Operating suite. Neurosurg. Focus 2016, 40, E18. [Google Scholar] [CrossRef]
  81. Yamamoto, M.; Nakao, T.; Ogawa, W.; Fukuoka, H. Aggressive Cushing’s Disease: Molecular Pathology and Its Therapeutic Approach. Front. Endocrinol. 2021, 12, 650791. [Google Scholar] [CrossRef]
  82. Prendergast, N.; Aldana, P.R.; Rotondo, R.L.; Torres-Santiago, L.; Beier, A.D. Pediatric silent corticotroph pituitary adenoma and role for proton therapy: Case report. J. Neurosurg. Pediatr. 2018, 23, 214–218. [Google Scholar] [CrossRef] [PubMed]
  83. Xu, Z.; Ellis, S.; Lee, C.C.; Starke, R.M.; Schlesinger, D.; Lee Vance, M.; Lopes, M.B.; Sheehan, J. Silent corticotroph adenomas after stereotactic radiosurgery: A case-control study. Int. J. Radiat. Oncol. Biol. Phys. 2014, 90, 903–910. [Google Scholar] [CrossRef]
  84. Salehi, F.; Scheithauer, B.W.; Kovacs, K.; Horvath, E.; Syro, L.V.; Sharma, S.; Manoranjan, B.; Cusimano, M. O-6-methylguanine-DNA methyltransferase (MGMT) immunohistochemical expression in pituitary corticotroph adenomas. Neurosurgery 2012, 70, 491–496, discussion 496. [Google Scholar] [CrossRef] [PubMed]
  85. Cohen-Inbar, O.; Xu, Z.; Lee, C.C.; Wu, C.C.; Chytka, T.; Silva, D.; Sharma, M.; Radwan, H.; Grills, I.S.; Nguyen, B.; et al. Prognostic significance of corticotroph staining in radiosurgery for non-functioning pituitary adenomas: A multicenter study. J. Neuro-Oncol. 2017, 135, 67–74. [Google Scholar] [CrossRef] [PubMed]
  86. Decaroli, M.C.; Ansaloni, A.; Monzani, M.L.; Losa, M.; Zunarelli, E.; Rochira, V.; Madeo, B. Long-Term Use of Temozolomide as Safe and Effective Therapy for an Aggressive Corticotroph Adenoma in a Very Old Patient. J. Endocr. Soc. 2021, 5, bvab065. [Google Scholar] [CrossRef] [PubMed]
  87. Lizzul, L.; Lombardi, G.; Barbot, M.; Ceccato, F.; Gardiman, M.P.; Regazzo, D.; Bellu, L.; Mazza, E.; Losa, M.; Scaroni, C. Long-course temozolomide in aggressive pituitary adenoma: Real-life experience in two tertiary care centers and review of the literature. Pituitary 2020, 23, 359–366. [Google Scholar] [CrossRef]
Table 1. Case series of SCAs.
Table 1. Case series of SCAs.
ReferencesYearSCA CasesNFPA
Control
Group
SCAs Percentage of NFPAsClassification with TPITTPIT+
ACTH+
TPIT+
ACTH-
HypopituitarismRecurrenceProgressionFollow-Up TimeRecurrence Predictors
PreoperativePostoperative
1 [14]20158351616%YES5132NANANANANANA
2 [62]2018373419.7%NONANANANA0/303/717.95 ± 14.86 MNA
3 [63]20102813417.2%NONANA13167NA5.6 ± 4.9 YYounger age, male gender,
multiple recurrences
4 [12]20123312620.8%NONANA25242/168/1742.5 MNA
5 [72]20194131911.3%NONANANANA2NA1.7 YNA
6 [64]201220305–6%NONANANANARecurrence/re-growth 14%41 MNA
7 [71]20032860NANONANANANATotal recurrence 9/28 (32%)7.4 YNA
8 [17]20216223820.6%YES5755 Hypocortisolism 6 Hypocortisolism1/1919/3948.4 MMale gender; MIB index ≥ 3%;
SCA tumor pathology
9 [57]20212013712.7%YES146NANANANANANA
10 [65]201955411 HNANANONANA64.7%25/340/357/2049 MNA
11 [10]202110084111.9%NONANA24NEW 230/4212/5834.8 MNA
12 [15]202010575724.3%YES396638822/46NA17.2 ± 7.6 MNA
13 [9]2021112198 SGAs30.2%YES793320331/7410/3814.1 ± 4.6 MNA
HNA: hormone negative adenomas; SGAs: silent gonadotroph adenomas.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Jiang, S.; Chen, X.; Wu, Y.; Wang, R.; Bao, X. An Update on Silent Corticotroph Adenomas: Diagnosis, Mechanisms, Clinical Features, and Management. Cancers 2021, 13, 6134. https://doi.org/10.3390/cancers13236134

AMA Style

Jiang S, Chen X, Wu Y, Wang R, Bao X. An Update on Silent Corticotroph Adenomas: Diagnosis, Mechanisms, Clinical Features, and Management. Cancers. 2021; 13(23):6134. https://doi.org/10.3390/cancers13236134

Chicago/Turabian Style

Jiang, Shenzhong, Xiaokun Chen, Yinzi Wu, Renzhi Wang, and Xinjie Bao. 2021. "An Update on Silent Corticotroph Adenomas: Diagnosis, Mechanisms, Clinical Features, and Management" Cancers 13, no. 23: 6134. https://doi.org/10.3390/cancers13236134

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop