Possible Good News! Effects of Tubastatin A on Adrenocorticotropic Hormone Synthesis and Proliferation of Att-20 Corticotroph Tumor Cells

  • Rie HagiwaraDepartment of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan
  • Kazunori KageyamaDepartment of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan
  • Yasumasa IwasakiSuzuka University of Medical Science, Suzuka 510-0293, Japan
  • Kanako NiiokaDepartment of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan
  • Makoto DaimonDepartment of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan
Abstract

Cushing’s disease is an endocrine disorder characterized by hypercortisolism, mainly caused by autonomous production of ACTH from pituitary adenomas. Autonomous ACTH secretion results in excess cortisol production from the adrenal glands, and corticotroph adenoma cells disrupt the normal cortisol feedback mechanism. Pan-histone deacetylase (HDAC) inhibitors inhibit cell proliferation and ACTH production in AtT-20 corticotroph tumor cells. A selective HDAC6 inhibitor has been known to exert antitumor effects and reduce adverse effects related to the inhibition of other HDACs. The current study demonstrated that the potent and selective HDAC6 inhibitor tubastatin A has inhibitory effects on proopiomelanocortin (Pomc) and pituitary tumor-transforming gene 1 (Pttg1) mRNA expression, involved in cell proliferation. The phosphorylated Akt/Akt protein levels were increased after treatment with tubastatin A. Therefore, the proliferation of corticotroph cells may be regulated through the Akt-Pttg1 pathway. Dexamethasone treatment also decreased the Pomc mRNA level. Combined tubastatin A and dexamethasone treatment showed additive effects on the Pomc mRNA level. Thus, tubastatin A may have applications in the treatment of Cushing’s disease.

Access the PDF at https://www.jstage.jst.go.jp/article/endocrj/advpub/0/advpub_EJ21-0778/_pdf/-char/en

 

Difference in miRNA Expression in Functioning and Silent Corticotroph Pituitary Adenomas Indicates the Role of miRNA in the Regulation of Corticosteroid Receptors

Abstract

Corticotroph pituitary adenomas commonly cause Cushing’s disease (CD), but some of them are clinically silent. The reason why they do not cause endocrinological symptoms remains unclear. We used data from small RNA sequencing in adenomas causing CD (n = 28) and silent ones (n = 20) to explore the role of miRNA in hormone secretion and clinical status of the tumors. By comparing miRNA profiles, we identified 19 miRNAs differentially expressed in clinically functioning and silent corticotroph adenomas. The analysis of their putative target genes indicates a role of miRNAs in regulation of the corticosteroid receptors expression. Adenomas causing CD have higher expression of hsa-miR-124-3p and hsa-miR-135-5p and lower expression of their target genes NR3C1 and NR3C2. The role of hsa-miR-124-3p in the regulation of NR3C1 was further validated in vitro using AtT-20/D16v-F2 cells. The cells transfected with miR-124-3p mimics showed lower levels of glucocorticoid receptor expression than control cells while the interaction between miR-124-3p and NR3C1 3′ UTR was confirmed using luciferase reporter assay. The results indicate a relatively small difference in miRNA expression between clinically functioning and silent corticotroph pituitary adenomas. High expression of hsa-miR-124-3p in adenomas causing CD plays a role in the regulation of glucocorticoid receptor level and probably in reducing the effect of negative feedback mediated by corticosteroids.

1. Introduction

Pituitary adenomas (also referred to as pituitary neuroendocrine tumors, PitNETs) represent about 10–20% of intracranial neoplasms in adults. They may originate from different kinds of secretory pituitary cells including corticotroph ACTH-secreting cells. Corticotroph adenomas commonly cause ACTH-dependent Cushing’s disease, but a significant proportion of these tumors are endocrinologically non-functioning and classified as subclinical/silent corticotroph adenomas (SCAs) [1].
CD-causing ACTH tumors are commonly small microadenomas with approximately 50% being smaller than 5 mm, which is challenging for MRI diagnostics [2]. In contrary, SCAs are commonly diagnosed due to neurological symptoms related to tumor mass at the stage of large macroadenomas. Frequently they show invasive growth and increased proliferation index [1]. According to current recommendations, SCAs are now referred to as “high-risk” pituitary adenomas which refers to their fast and invasive growth, high risk of recurrence and resistance to medical therapy [3,4]. They are recognized to be more aggressive than other clinically nonfunctioning pituitary tumors such as those of gonadotroph origin or null-cell adenomas [5].
The mechanism underlying the difference in secretory activity of CD-causing and subclinical tumors is unclear and only a few studies focused on this issue were published. The results indicated a role of the expression levels of particular genes/proteins involved in the regulation of POMC expression and pro-hormone conversion into ACTH as well as genes involved in pituitary differentiation [6,7,8,9,10,11,12,13]. However, it also appears that both active and silent corticotroph adenomas share a similar overall gene expression profile [14,15].
The aim of this study was to compare the profiles of microRNA (miRNA) expression in clinically functioning and silent corticotroph adenomas and to identify miRNAs that play a role in different ACTH secretory activity.

2. Results

2.1. Patients Characteristics

The study included 28 patients with CD and 20 patients suffering from SCA. All patients with CD had clear clinical signs and symptoms of hypercortisolism verified according to biochemical criteria including elevated midnight cortisol levels and 24 h urinary free cortisol (UFC). Patients with SCA had no clinical or biochemical signs of hypercortisolism and showed normal levels of midnight cortisol and 24 h UFC. Patients with CD had significantly higher morning serum cortisol levels than patients with SCAs (p = 0.0002) while no significant difference was observed in the morning serum ACTH levels. No difference in cortisol/ACTH ratio was observed between CD and SCA patients.
All the adenoma samples were ACTH-positive upon immunohistochemical staining against pituitary hormones (ACTH, GH, TSH, FSH, LH, α-subunit) and had characteristic ultrastructural features of corticotroph adenoma. Forty-one adenomas were positive only for ACTH, while seven ACTH-positive adenomas showed additional moderate/weak immunoreactivity for α-subunit. Increased proliferation assessed by Ki67 index ≥ 3% was observed in a similar proportion of CD and SCA patients, seven tumors causing CD and five SCAs. A higher proportion of sparsely vs. densely granulated adenomas was observed in SCAs than in CD-related adenomas, but the difference did not cross a significance threshold (p = 0.0787). No difference in the proportion of invasive/noninvasive adenomas was observed in clinically functioning and silent corticotroph adenomas.
All SCAs were macroadenomas, while tumors causing CD included 17 macroadenomas and 11 microadenomas. No significant differences in preoperative clinical parameters, including 24 h UFC, morning serum ACTH level, morning and midnight serum cortisol level, cortisol/ACTH ratio, were observed between CD patients with micro- and macroadenomas. Irrespectively, a correlation between tumors size and ACTH level (Spearman R= 0.4678; p = 0.0121) and a negative correlation between cortisol/ACTH ratio (Spearman R= −0.4015; p = 0.0342) was observed in CD patients.
No correlation was found between the remaining biochemical parameters and tumor size. Overall, the patients’ characteristics are presented in Table 1, while details including both the clinical and histopathological data are shown in Supplementary Table S1.
Table 1. Summary of clinical features of patients with Cushing’s disease and silent corticotroph adenomas.
Table

2.2. Identification of miRNAs Differentially Expressed in Corticotroph Adenomas Causing CD and Subclinical Cortiotroph Adenomas

NGS data on miRNA expression of 48 corticotroph adenomas from previous investigation were used to compare miRNA expression levels between adenomas causing CD (n = 24) and subclinical corticotroph adenomas (n = 20). Sequencing of small RNA libraries produced approximately 2,497,367 reads per sample, which were mapped to the human genome (hg19) and used for quantification of expression levels of known miRNAs, according to miRBase 22 release. Sequencing reads were annotated to 1917 miRNAs. Measurements of 1902 mature miRNAs expression were included in the analysis, after filtering out the miRNAs with low expression.
When miRNA profiles of adenomas causing CD and SCAs were compared, a total of 19 differentially expressed miRNAs were found that met the criteria of adjusted p-value < 0.05. This set included 16 miRNAs with higher expression in tumors causing CD: hsa-miR-129-2-3p, hsa-miR-129-5p, hsa-miR-124-3p, hsa-miR-132-5p, hsa-miR-129-1-3p, hsa-miR-135b-5p, hsa-miR-27a-3p, hsa-miR-10b-5p, hsa-miR-9-3p, hsa-miR-6506-3p, hsa-miR-6864-5p, hsa-let-7b-5p, hsa-miR-670-3p, hsa-miR-22-5p, hsa-miR-346 and hsa-miR-9-5p, Three miRNAs with lower expression in CD patients were found: hsa-miR-1909-3p, hsa-miR-4319 and hsa-miR-181b-3p. Details are presented in Table 2 and Figure 1A,B.
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Figure 1. MiRNA expression profiling in corticotroph adenomas. (A). Difference in miRNA expression between functioning and silent corticotroph adenomas. Volcano plot showing differentially expressed miRNAs. Significance and fold change thresholds are marked with dashed lines. (B). Heat map representing the expression of differentially expressed miRNAs and clustering the samples of adenomas causing Cushing’s disease (CD) and silent corticotroph adenomas (SCA). (C). The correlation between the expression levels of differentially expressed miRNAs and POMC expression or hormonal laboratory measurements in patients: morning plasma ACTH level, morning and midnight plasma cortisol levels and 24 h urinary free cortisol; * indicate p-value < 0.05; ** indicate p-value < 0.01; *** indicate p-value < 0.001
Table 2. The list of miRNAs differentially expressed in corticotroph pituitary adenomas causing CD and silent corticotroph adenomas.
Table

2.3. The Correlation of miRNA Expression and Patients’ Clinical Data

Since the clustering of the tumors based on the expression of differentially expressed miRNAs did not clearly separate functioning and silent adenomas, we determined whether the expression of the identified differentially expressed miRNAs is directly related to the results of patients’ laboratory tests as well as POMC expression, measured in tumor samples with qRT-PCR. For this purpose, Spearman’s correlation was applied to calculate a correlation matrix. We observed a significant positive correlation between 13 miRNAs out of 19 differentially expressed miRNAs and at least one of clinical laboratory parameters: serum ACTH, morning cortisol level, midnight cortisol level or 24 h UFC. For 11 miRNAs, with higher expression in patients with CD a positive correlation was observed, while a negative correlation was observed for 3 miRNAs that have lower expression in patients with CD. Four of the differentially expressed miRNAs, hsa-miR-9-3p, hsa-miR-9-5p, hsa-miR-27a-3p and hsa-miR-6506-3p, are correlated with POMC expression level in tumor tissue. The absolute value of correlation coefficient ranged between 0.31 and 0.55 which indicates a weak/moderate relationship. Details are presented in Figure 1C.

2.4. Funtional Enrichment Analysis of Differentially Expressed miRNAs

To investigate the possible functional role of the identified miRNAs with different expression levels in CD tumors and SCAs, we used the information on experimentally validated miRNA targets gathered in the miRtarbase release 8.0 database. High confidence known miRNA targets that were validated with luciferase reporter assay, reported in miRtarbase, were included in the analysis. The enrichment of the genes reported as miRNA targets of our 19 miRNAs of interest was determined with gene set over-representation analysis (GSOA) based on Gene Ontology (GO) Molecular Function and GO Biological Processes. The list of all the genes reported in miRTarbase as validated with reporter gene assay was used as reference. As a result, we found 30 GO Molecular Function terms and 293 GO Biological Processes terms as significantly enriched with genes that are targets of the 19 differentially expressed miRNAs. Top 10 enriched terms were related mainly to steroid hormone activity, regulation of transcription and regulation of stem cell differentiation, as shown in Figure 2. Details are presented in Supplementary Table S2. We paid special attention to the terms that refer to steroid hormone action, i.e., steroid hormone receptor activity (GO:0003707), nuclear receptor activity (GO:0004879), ligand-activated transcription factor activity (GO:0098531), as well as steroid hormone-mediated signaling pathway (GO:0043401) and hormone-mediated signaling pathway (GO:0009755). Importantly, the miRNA target genes that were overrepresented in these terms included NR3C1 and NR3C2 that encode for adrenal hormones glucocorticoid receptor (GR) and mineralocorticoid receptor (MR), respectively. According to the miRtarbase 9.0 database, hsa-miR-124-3p is a negative regulator of NR3C1 gene [16] while both hsa-miR-124-3p and hsa-miR-135b-5p downregulate MR [17].
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Figure 2. Gene set over-representation analysis of putative target genes of miRNAs differentially expressed in clinically functioning and silent corticotroph adenomas.
Using the PubMed search, we found additional evidence strongly supporting the role of hsa-miR-124-3p in the regulation of NR3C1 [18,19,20,21] as well as the role of hsa-miR-135b-5p in downregulating NR3C2 [22,23].

2.5. Comparison of the Expression of NR3C1 and NR3C2 in Corticotroph Adenomas Causing CD and Silent Adenomas

We determined the expression levels of NR3C1 and NR3C2 in corticotroph adenomas with qRT-PCR. We observed a significantly lower expression of both genes in samples from CD patients (n = 24) as compared to SCAs (n = 24); fold change (FC) 0.49 p = 0.0166 and FC 0.37 p = 0.0132, for NR3C1 and NR3C2, respectively. However, the observed difference is rather slight and a notable dispersion of the results was observed (Figure 3). The differences in NR3C1 and NR3C2 expression correspond to the differences in hsa-miR-124-3p and hsa-miR-135b-5p levels. Patients with CD have higher levels of both miRNAs and lower levels of NR3C1 and NR3C2 mRNA (Figure 3). Unfortunately, we did not find a direct correlation between the expression levels of hsa-miR-124-3p and NR3C1 or hsa-miR-135b-5p and NR3C2.
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Figure 3. The expression levels of NR3C1 and NR3C2 measured with qRT-PCR as well as hsa-miR-124-3p and hsa-miR-135b-5p measured with small RNA sequencing in tumor samples from CD patients and silent corticotroph adenomas; * indicate p-value < 0.05

2.6. Investigtion of miRNA-Related Regulation of NR3C1 In Vitro

Transfecting the cultured cells with miRNA mimics is the commonly used approach of in vitro validation of specific miRNA–mRNA interaction. We used mice corticotroph tumor AtT-20/D16v-F2 cells for in vitro experiment and initially verified whether these cells do express Nr3c1 and Nr3c2 genes using deposited RNAseq data from a previous experiment on AtT-20 cells (GSE132324; Gene Expression Omnibus) and qRT-PCR. This showed that the AtT-20/D16v-F2 have relatively high expression of Nr3c1 but do not express Nr3c2. Thus, we focused on the regulatory role of miR-124-3p on Nr3c1 expression. We used miRBase [24] and Targetscan [25] to determine whether miR-124-3p is evolutionarily conserved in humans and mice and whether it targets NR3C1 in both species. It confirmed that miR-124-3p is broadly conserved and it shares the same sequence of mature miRNA in humans and mice. Importantly, GR is among highly rated miR-124-3p predicted targets in both humans and mice and two highly conserved miR-124-3p binding motifs in 3′UTR of this gene were identified in these two species (Figure 4A).
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Figure 4. Role of mir-124-3p in regulation of glucocorticoid receptor gene. (A). Putative hsa-mir-124-3p target sites in 3′UTR of NR3C1. (B). Reduced expression of Nr3c1 gene expression and glucocorticoid receptor (GR) protein level in AtT-20/D16v-F2 cells treated with hsa-miR-124-3p mimics. (C). Results of luciferase reporter gene assay, showing the interaction between Nr3c1 3′UTR site 2 and mir-124-3p; * indicate p-value < 0.05; ns—not significant.
When we transfected AtT-20/D16v-F2 cells with miR-124-3p miRNA mimic and unspecific negative control miRNA mimic, we observed a significant decrease in Nr3c1 expression in cells treated with miR-124-3p miRNA mimic (Figure 4B). It was significantly lower than in cells treated with unspecific miRNA mimic. This difference was also clearly visible at the protein level. The GR level was reduced in cells treated with miR-124-3p miRNA mimic as compared to control (Figure 4B).
Two fragments of Nr3c1 3′UTR including each of putative miR-124-3p binding motifs were cloned in plasmid vector into 3′ region of the firefly luciferase gene. AtT-20/D16v-F2 cells were transfected with empty vector, vector with miR-124-3p binding site 1 and vector miR-124-3p binding site 2. Each of the three variants of the cells were cotransfected with miR-124-3p miRNA mimic or unspecific miRNA mimic that served as a negative control. Luminescence was developed 48 h after transfection and detected with microplate reader. As a result, we observed a significant decrease in luminescence in the cells with introduced plasmid with miR-124-3p binding site 2 treated with miR-124-3p mimic as compared to the cells transfected with the same plasmid construct but with control miRNA mimic. This observation confirms the interaction between miR-124-3p and 3′ UTR of Nr3c1 at putative binding site 2 (Figure 4C). The experiment did not confirm an interaction between miR-124-3p and 3′ UTR of Nr3c1 at binding site 1 since no significant difference of luminescence was found in cells transfected with plasmid vector harboring this binding motif treated with miR-124-3p mimic and the same cells treated with negative miRNA mimic (Figure 4C).

3. Discussion

Based on the clinical manifestation and biochemical tests results, pituitary corticotroph adenomas can be divided into functioning adenomas causing Cushing’s disease and SCAs. These two subtypes of tumors also differ in terms of some characteristics in MRI [2,26] and pathological features [27]. In contrast to CD-causing adenomas which are commonly small microadenomas, SCAs are diagnosed as macroadenomas due to neurological symptoms related to tumor mass. They are characterized by invasive growth, high risk of recurrence and resistance to medical therapy and are therefore referred to as “high-risk” pituitary adenomas according to current classification [3,4]. In our study, the SCAs were larger than functioning counterparts, as expected. A clear prevalence of women is observed among CD patients according to literature data [28], while it is not observed in patients suffering from SCAs. Our SCA group contained near equal representation of women and men as in previous reports [29,30]; however, some studies indicated female prevalence in SCAs [31].
Comparing functioning and silent corticotroph adenomas, we did not observe difference in patients’ age as well as differences in invasive growth status, ratio of adenomas with increased proliferation index and proportions of sparsely and densely granulated adenomas that may suggest the lack of difference in the tumors’ “aggressiveness”. Importantly, limitations for generalization of our results should be noted. The number of patients included in the analysis is relatively low and the group is not representative of the general population, especially in the case of patients suffering from Cushing’s disease. Since the main goal of our study was a molecular profiling of tumor tissue, we intentionally preselected large adenomas, which allowed us to have enough tissue for DNA/RNA isolation and successful molecular procedures.
In our investigation, we observed a negative correlation between cortisol/ACTH ratio and tumor volume in functioning corticotroph adenomas as described previously [32]. However, we did not observe any difference between micro- and macroadenomas causing CD as compared to SCAs (data not shown) as was found in previous studies [12].
The reason why some of corticotroph adenomas exhibit excessive hormone secretion and the others remain clinically silent is unclear and only few attempts have been made to determine the possible molecular mechanism underlying this difference in secretory activity. They were mainly focused on investigating the expression of the selected genes or proteins by comparing subclinical and functioning corticotroph adenomas. These studies indicated different expression levels of prohormone convertase 1/3 POMC, genes encoding somatostatin receptors, corticotropin releasing hormone receptor 1, vasopressin receptor (V1BR), corticosteroid 11-beta-dehydrogenase as well as NEUROD1 and TPIT [6,7,8,9,10,11,12,13]. However, whole transcriptome studies indicated that adenomas causing CD and subclinical corticotroph adenomas share a very common gene expression profile and a very low number of differentially expressed genes can be found by comparing transcriptome of silent and CD-causing ACTH tumors [14,15].
In our study, we determined the miRNA expression profile of 28 clinically functioning adenomas and 20 SCAs with next-generation sequencing of small RNA fraction. This allowed for the quantification of over 1900 miRNA annotated to current version of miRbase database and comparing their expression in two groups of tumor samples. We found a significant difference only in the expression levels of 19 miRNAs, that represent less than 1% of the miRNAs included in the analysis. This result resembles the observation from previous comparison of whole transcriptome profiles in functioning adenomas and SCAs where only 34 differentially expressed genes were found. Generally, both observations indicate a very common molecular profile of corticotroph adenomas, regardless of the functional status.
In our study, the expression levels of 13 out of 19 identified differentially expressed miRNAs were also correlated with peripheral ACTH/cortisol levels, further supporting the role of these miRNAs in secretory activity of corticotroph adenomas.
The possible role of miRNA in subclinical nature of SCAs was addressed in only one previous study by García-Martínez A et al. [33]. The authors compared the expression of 5 miRNAs in 24 functioning and 23 silent adenomas and observed a difference in hsa-miR-200a and hsa-miR-103 levels [33]. Their results were not confirmed by our investigation since these two miRNAs were not found among differentially expressed miRNAs. In our data, very a similar expression level of hsa-miR-200a was observed in clinically functioning and silent adenomas. In turn, a slightly higher expression of hsa-miR-103a-3p was observed in SCAs as previously reported, but the difference did not cross the significance threshold level. We should note that different methods were used for these two studies and technical and analytical differences could result in this discrepancy.
Since miRNAs play a role in gene regulation, their effect should be investigated in the context of the function of targeted genes. The interaction between miRNA and its target mRNA 3′UTR can be predicted with in silico tools. Unfortunately, prediction results can be very difficult to interpret since a huge number of predicted interactions can be found for some miRNAs. For example, when using the Targetescan (http://www.targetscan.org; accessed on 28 February 2022) prediction tool [25], over 4000 target genes were predicted for each hsa-miR-9-3p, hsa-miR-1909-3p, hsa-miR-22-5p and hsa-miR-181b-3p that we found as differentially expressed in CD and SCA. Therefore, to investigate a possible functional relevance of differentially expressed miRNAs we used a database of experimentally validated miRNA targets [34]. Gene set over-representation analysis of miRNA target genes indicated their enrichment in the pathways of steroid hormone nuclear receptors functioning. This result indicates that miRNAs that have different expression levels in CD and SCAs play a role in the regulation of expression of genes involved in steroid hormone signaling at hormone receptor level. It is especially interesting since this group of compounds includes adrenal hormones that play a role in the regulation of the hypothalamic–pituitary–adrenal (HPA) axis.
The particular enriched miRNA target genes included NR3C1 and NR3C2 that encode for corticosteroid hormone receptors (GR and MR, respectively). Both receptors are located in the cytoplasm where they bind glucocorticoids. Upon ligand binding, they are translocated to nucleus where they form dimers on DNA at glucocorticoid response elements (GREs). Glucocorticoid and mineralocorticoid receptors directly regulate the expression of target genes and/or influence the expression indirectly through the interaction with other transcription factors [35].
Glucocorticoids play a role in the basic mechanism of negative feedback of HPA axis. They act on hypothalamus, where high cortisol levels reduce secretion of corticotropin-releasing hormone (CRH), thus they directly reduce stimulation of ACTH secretion by anterior pituitary lobe. Glucocorticoids also inhibit the activity of pituitary cells indirectly. Corticotroph cells express GRs and their activation results in the reduction of POMC expression and secretion of ACTH [36,37]. In pituitary corticotroph adenomas, NR3C1 point mutations and loss of heterozygosity in NR3C1 locus were identified [38]. These mutations seem to affect the secretory activity and result in tumor resistance to corticosteroids [39]. Reduced expression of corticosteroid receptors in corticotroph adenomas has been reported in patients with resistance to high doses of dexamethasone [40]. These data indicate a role of GR in secretory activity of clinically functioning corticotroph adenomas. The expression of corticosteroid genes was previously investigated in CD-causing tumors and SCAs and no significant differences were found. However, it is worth noting that a low number of SCA patients was included in these studies: n = 9 [13], n = 8 [11] and n = 2 [41].
According to previously published results, hsa-miR-124-3p is a negative regulator of NR3C1 [16,18,19,20,21]. This was observed in acute lymphoblastic leukemia [19], adipocytes [20] and human embryonic kidney cells [21], where the reduced expression of NR3C1 upon an increase in hsa-miR-124-3p as well as a direct interaction between this miRNA and 3′UTR of GR gene were observed. Some additional clinical observations also suggest the role of hsa-miR-124-3p in the regulation of the response to cortiosteroids in patients with acute-on-chronic liver failure [18] and lymphoblastic leukemia [19]. Hsa-miRNA-124 also mediates corticosteroid resistance in T-cells of sepsis patients through the downregulation of GR [42].
Our analysis of the expression level of NR3C1 in corticotroph adenomas showed that tumors causing CD have lower gene expression and accordingly they exhibit higher levels of hsa-miR-124-3p. Subsequently, the role of hsa-miR-124-3p in NR3C1 downregulation was confirmed in mice AtT-20/D16v-F2 corticotroph cells using miRNA mimics and reporter gene assay. Transfection of AtT-20/D16v-F2 cells with hsa-miR-124-3p mimics resulted in reduced NR3C1 mRNA expression and GR protein level. We also confirmed the interaction between hsa-miR-124-3p and one of two predicted binding motifs in 3′UTR of NR3C1 with luciferase reporter gene assay. Since sequences of hsa-miR-124-3p and target sequence in 3′UTR of NR3C1 mRNA are the same in mice and in humans, we believe that results showing the regulation of the GR-encoding gene in mice AtT-20/D16v-F2 cells are also relevant to humans. Together, the available data indicate that in pituitary corticotrophs, hsa-miR-124-3p downregulates the expression of the GR gene. Since this receptor mediates the response of pituitary cells to cortisol, the expression of hsa-miR-124-3p appears to be an important element in the regulation of secretory activity of corticotroph cells. Based on these results, we can hypothesize that in CD, a high level of hsa-miR-124-3p contributes to lowering of GR expression and in consequence it plays a role in lowering the effect of glucocorticoid feedback on the activity of corticotroph adenoma. Hsa-miR-124-3p and hsa-miR-135b-5p can downregulate the expression level of MR, as proven in model HeLa cells [17]. Expression of both miRNAs is higher in corticotroph adenomas causing CD which corresponds to the lower expression of the NR3C2 gene in these tumors as compared to SCAs. Since the role of the MR receptor expression in pituitary cells is poorly understood, the functional implication of this observation is much less clear than in the case of GR downregulation. MR and GR have similar amino acid sequences, especially in DNA-binding domain, but they differ in affinity to corticosteroids. MR is specific for both mineralocorticoids and glucocorticoids while GR is specific predominantly for glucocorticoids. MRs have much higher affinity for glucocorticoids than GRs and are activated at basal glucocorticoid conditions, while GR occupancy is increased when glucocorticoid levels rise during the circadian peak or stress. Due to these differences, these two receptors play slightly different roles, despite the fact that they share a number of target genes [43]. MR expression is considered more tissue-specific than GR and was reported to be the most prevalent in kidney and adipose tissue but also in the hippocampus and hypothalamus [44]. However, the available databases of human expression pattern such as the Genotype-Tissue Expression project (https://gtexportal.org; accessed on 10 December 2021) or Protein atlas (https://www.proteinatlas.org; accessed on 10 December 2021) indicate that MR is widely expressed in multiple human tissues and organs including the pituitary gland. Unfortunately, a role of MR receptor in pathogenesis of pituitary tumors remains unknown.
AtT-20 cells, which are the only available cell line model of corticotroph adenoma, do not express MR receptor, thus the procedure of experimental validation of the role of miRNA in NR3C2 silencing is not applicable. With a lack of experimental data on the exact role of MR, we can only hypothesize that miRNA-mediated silencing of NR3C2 may have the similar effect on HPA axis feedback as silencing of NR3C1. It may enhance ACTH secretion by reducing the direct inhibitory effect of glucocorticoids on neoplastic pituitary corticotrophs.
The difference in expression of hsa-miR-124-3p and hsa-miR-135b-5p between subclinical and CD-causing adenomas is not big, thus we suppose that high expression of these miRNAs is not the only cause of difference in ACTH secretion. Presumably this is one of the mechanisms in the regulation of corticotrophs’ secretory activity. The model of miRNA-based corticosteroid receptor regulation does not undermine the role of previously described differences in the expression of convertase 1/3, POMC, somatostatin receptors or corticotropin releasing hormone receptor 1 or genes involved in differentiation of pituitary cells [6,7,8,9,10,11,12,13]. When considering the complex nature of the regulation of ACTH secretion, it can be assumed that multiple mechanisms may be involved in the silent character of subclinical adenomas. The low number of identified differentially expressed miRNAs or genes in silent and clinically functioning adenomas probably results from the intertumoral molecular heterogeneity of SCAs. This is also in line with clinical evidence indicating that some silent corticotroph adenomas can transform into clinically functioning ones while the others remain silent [1].
The misregulation of GR expression or NR3C1 mutation may have important therapeutical implications in CD patients. Non-selective GR antagonist Mifepristone was officially approved for treatment in patients with Cushing’s syndrome [45] while another new GR inhibitor, Relacorilant (CORT125134), is under clinical investigation for its use in this group of patients [46]. The further studies will be required to assess the role of GR abnormalities in response to GR-targeting treatment in CD.
In our study, we focused mainly on the role of hsa-miR-124-3p and hsa-miR-135b-5p in the regulation of corticosteroid receptors, but the role of other differentially expressed miRNAs can also be elucidated, based on the function of putative target genes. In the pathways enrichment analysis of the putative targets, molecular functions related to transcriptional regulation were found among the top processes. Interestingly, five miRNAs, i.e., hsa-miR-132-5p, hsa-miR-135b-5p, hsa-miR-27a-3p, hsa-miR-9-3p and hsa-miR-9-5p, were previously reported to downregulate the expression of FOXO1 transcription factor [47,48,49,50,51]. FOXO1 plays an important role in the differentiation of pituitary cells [52] and secretion of gonadotropic hormones [53,54] and prolactin [55]. The role of FOXO1 in pituitary corticotroph cells was not investigated but it was shown to regulate POMC expression in POMC hypothalamic neurons [56]. In POMC, neurons of arcuate nucleus FOXO1 directly suppresses POMC expression. A similar mechanism was also observed in prolactin pituitary adenomas where FOXO1 suppresses the promoter of PRL gene [55]. It is possible that high expression of hsa-miR-132-5p, hsa-miR-135b-5p, hsa-miR-27a-3p, hsa-miR-9-3p and hsa-miR-9-5p in pituitary corticotroph adenomas reduces the level of FOXO1 and eventually contributes to the upregulation of POMC expression. In our data from corticotroph adenomas, we observed the correlation between levels of hsa-miR-9-3p/hsa-miR-9-5 and POMC expression, which also supports this concept, but the exact role of miRNAs in possible FOXO1-related regulation of secretory activity of corticotroph cells requires further functional investigation.

4. Materials and Methods

4.1. Patients and Tissue Samples

Pituitary tumor samples from 48 patients were collected during transsphenoidal surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue samples, including 28 samples from patients with Cushing’s disease and 20 samples of SCA were used for the study. Diagnosis of hypercortisolism was based on standard hormonal criteria: increased UFC in three 24 h urine collections, disturbances of cortisol circadian rhythm, increased serum cortisol levels accompanied by increased or not suppressed plasma ACTH levels at 8.00 and a lack of suppression of serum cortisol levels to <1.8 µg/dL during an overnight dexamethasone suppression test (1 mg at midnight). The pituitary etiology of Cushing’s disease was confirmed based on the serum cortisol levels or UFC suppression < 50% with a high-dose dexamethasone suppression test (2 mg q.i.d. for 48 h) or a positive result of a corticotrophin-releasing hormone stimulation test (100 mg i.v.) and positive pituitary magnetic resonance imaging.
ACTH levels were assessed using IRMA (ELSA-ACTH, CIS Bio International, Gif-sur-Yvette Cedex, France). The analytical sensitivity was 2 pg/mL (reference range: 10–60 pg/mL). Serum cortisol concentrations were determined by the Elecsys 2010 electrochemiluminescence immunoassay (Roche Diagnostics, Mannheim, Germany). Sensitivity of the assay was 0.02 μg/dL (reference range: 6.2–19.4 μg/dL). UFC was determined after extraction (liquid/liquid with dichloromethane) by electrochemiluminescence immunoassay (Elecsys 2010, Roche Diagnostics)—reference range: 4.3–176 μg/24 h.
All the tumors underwent detailed histopathological diagnosis including immunohistochemical staining with antibodies against particular pituitary hormones (ACTH, GH, TSH, FSH, LH, α-subunit) and Ki67 as well as ultrastructural analysis with electron microscopy.
The SCAs were characterized by the following clinicopathological criteria: positive immunohistochemical staining for ACTH, lack of signs and symptoms of hypercortisolism (Cushing’s syndrome), negative hormonal evaluation and non-compliance with diagnostic criteria of the CD.
Macroadenoma was defined as an adenoma with at least one diameter exceeding 10 mm, and the tumor volume was assessed with the diChiro Nelson formula (height × length × width × π/6). Invasive growth of the tumors was evaluated using Knosp grading [57]. Adenomas with Knosp grades 0, 1 and 2 were considered non-invasive, while those with Knosp 3 and 4 were considered invasive.
Forty-three patients had a clear history of not using any drugs that control the overproduction of the cortisol or ACTH (ketoconazole, mitotane, metyrapone, osilodrostat, mifepristone, pasireotide) before surgical treatment. The information on preoperative pharmacological treatment was not available for 5 patients.
Tumor tissue content of each FFPE sample ranged between 80 and 100% (median 99%), as assessed with histopathological examination. Patients’ characteristics are presented in Table 1 and details on each patient’s data are available in Supplementary Table S1.
The study was approved by the local Ethics Committee of Maria Sklodowska-Curie National Research Institute of Oncology in Warsaw, Poland. Each patient provided informed consent for the use of tissue samples for scientific purposes.
Total RNA from FFPE samples was purified with RecoverAll™ Total Nucleic Acid Isolation Kit for FFPE tissue (Thermo Fisher Scientific, Waltham, MA, USA) and measured using NanoDrop 2000 (Thermo Fisher Scientific). RNA was stored at −70 °C.

4.2. Micro RNA Expression Profiling

For comparing the miRNA expression profiles in CD-causing and clinically silent adenomas, NGS data from our previous investigation of miRNA expression in corticotroph adenomas were used. The dataset is available at Gene Expression Omnibus, accession no GSE166279. Sequencing of small RNA fraction was performed in 48 tumor samples (28 CD patients and 20 SCA patients) with ion semiconductor sequencing technology, as described previously [58]. Briefly, Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific) was used for sequencing library construction, Ion Xpress™ RNA-Seq Barcode Kit was used for hybridization and ligation of RNA adapters. RNA reverse transcription and subsequent cDNA purification and library size selection were performed using Nucleic Acid Binding Beads and verified using Bioanalyzer 2100 with High Sensitivity DNA Kit (Agilent, Santa Clara, CA, USA). Ion Chef instrument, with Ion PI™ Hi-Q™ Chef Kit (Thermo Fisher Scientific) and Ion Proton sequencer (Thermo Fisher Scientific) were used for library preparation and sequencing, respectively.
BamToFastq package was applied for converting unmapped bam files into fastq files. miRDeep2 was applied for read mapping to known human miRNAs (according to miRBase release 22) and reads quantification. Data normalization and differential expression analysis were performed using DESeq2. Filtration for low-expression miRNAs was applied as described previously. FC of expression calculated as the ratio of the normalized read-count value in CD-causing and silent adenomas was used as a measure of expression difference. Adjusted p-value < 0.05 was used as significance threshold. MiRtarbase release 9.0 database [34] was used to identify known miRNA target genes. PANTHER (http://pantherdb.org; accessed on 10 December 2021) [59] was used for gene set over-representation analysis.

4.3. qRT-PCR gene Expression Analysis

One microgram of RNA was subjected to reverse transcription with Transcriptor First Strand cDNA Synthesis Kit (Roche Diagnostics). qRT-PCR reaction was carried out in 384-well format using 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) and Power SYBR Green PCR Master Mix (Thermo Fisher Scientific) in a volume of 5 μL, containing 2.25 pmol of each primer. The samples were amplified in triplicates. GAPDH was used as reference gene. Delta Ct method was used to calculate the relative expression level. PCR primers’ sequences are presented in Supplementary Table S3.

4.4. Cell Line Culture and miRNA Mimic Transfection

AtT-20/D16v-F2 cells were purchased from ATCC collection and cultured in DMEM medium supplemented with 10% FBS, as recommended. MiRCURY LNA miRNA Mimics including hsa-miR-124-3p mimic (YM00471256, Qiagen, Hilden, Germany) and negative control mimic (YM00479902-ADB, Qiagen) were used. AtT-20/D16v-F2 cells were seeded at 5 × 104 per well of a 24-well plate in culture medium and transfected with 50 nM miRNA with 1% (v/v) HiPerFect Transfection Reagent (Qiagen), according to the manufacturer’s instructions. The next day, the culture medium was changed. In total, 48 h after transfection the cells were harvested and subjected to isolation of total RNA with RNeasy Mini Kit (Qiagen). The expression of the putative hsa-miR-124-3p target gene was determined with qRT-PCR.

4.5. Luciferase Reporter Gene Assay

Hsa-miR-124-3p target sites in 3′UTR of NR3C1 were determined with Targetscan [25]. Each of two predicted hsa-miR-124-3p target sites were cloned into pmirGLO Dual-Luciferase miRNA Target Expression Vector (Promega, Madison, WI, USA). AtT-20/D16v-F2 cells (2 × 104/well) were seeded onto a 96-well plate in 100 µL culture medium. The next day, the cells were transfected with 100 ng of each plasmid vector, independently using 0.25% (v/v) lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) in 10 µL of DMEM. The cells were subsequently transfected with either hsa-miR-124-3p mimic (YM00471256, Qiagen) or negative control mimic (YM00479902-ADB, Qiagen) in a final concentration of 50 nM using HiPerfectReagent (Qiagen). Culture medium was changed on the next day. Luciferase activity was measured with One-Glo Luciferase Assay System (Promega) 48 h after transfection.

4.6. Western Blotting

Cells were lysed in ice cold RIPA buffer, incubated for 30 min in 4 °C and centrifuged at 12,500× g rpm for 20 min at 4 °C. Samples were resolved using SDS-PAGE and electrotransferred to polyvinylidene fluoride membranes (PVDF) (Thermo Fisher). GR protein was detected with monoclonal anti-Glucocorticoid Receptor antibody (ab183127, Abcam, Cambridge, UK), and secondary anti-rabbit antibody conjugated to HRP (#7074, Cell Signaling, Beverly, MA, USA). Glyceraldehyde-3-Phosphate Dehydrogenase (#MAB374, Millipore, Bedford, MA, USA) detected with mouse HRP-conjugated antibody (#7076 Cell Signaling) served as control. Visualization was performed with SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific) and CCD digital imaging system Alliance Mini HD4 (UVItec Limited, Cambridge, UK).

4.7. Statistical Analysis

A two-sided Mann–Whitney U-test was used for analysis of continuous variables. The Spearman correlation method was used for correlation analysis. Significance threshold of α = 0.05 was adopted. Data were analyzed using GraphPad Prism 6.07 (GraphPad Software, La Jolla, CA, USA). Hierarchical clustering analysis was carried out with Cluster 3.0, and the results were visualized using TreeView 1.6 software (Stanford University School of Medicine, Stanford, CA, USA).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23052867/s1.

Author Contributions

Conceptualization, M.M. and M.B.; Methodology, M.B. and B.J.M.; Software, J.B.; Formal analysis, P.K., B.J.M. and M.B.; Investigation, B.J.M., P.K., N.R., M.B. and M.P.; Resources, J.K., G.Z., A.S. and T.M.; Data curation, J.B., B.J.M. and M.B.; Writing—original draft preparation, M.B., P.K. and B.J.M.; Writing—review and editing, all the authors; Visualization, M.B. and B.J.M.; Supervision, M.M.; Project administration M.B.; Funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Science Centre, Poland, grant number 2021/05/X/NZ5/01874.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the local Ethics Committee of Maria Sklodowska-Curie Institute—Oncology Center in Warsaw, Poland; approval no. number 44/2018, date of approval 26 July 2018.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data from next-generation sequencing of small RNA fraction of 48 corticotroph adenoma samples are available at Gene Expression Omnibus, accession no GSE166279.

Conflicts of Interest

The authors declare no conflict of interest.

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Covid-19 and Cushing’s Disease in a Patient with ACTH-secreting Pituitary Carcinoma

Abstract

Summary

The pandemic caused by severe acute respiratory syndrome coronavirus 2 is of an unprecedented magnitude and has made it challenging to properly treat patients with urgent or rare endocrine disorders. Little is known about the risk of coronavirus disease 2019 (COVID-19) in patients with rare endocrine malignancies, such as pituitary carcinoma. We describe the case of a 43-year-old patient with adrenocorticotrophic hormone-secreting pituitary carcinoma who developed a severe COVID-19 infection. He had stabilized Cushing’s disease after multiple lines of treatment and was currently receiving maintenance immunotherapy with nivolumab (240 mg every 2 weeks) and steroidogenesis inhibition with ketoconazole (800 mg daily). On admission, he was urgently intubated for respiratory exhaustion. Supplementation of corticosteroid requirements consisted of high-dose dexamethasone, in analogy with the RECOVERY trial, followed by the reintroduction of ketoconazole under the coverage of a hydrocortisone stress regimen, which was continued at a dose depending on the current level of stress. He had a prolonged and complicated stay at the intensive care unit but was eventually discharged and able to continue his rehabilitation. The case points out that multiple risk factors for severe COVID-19 are present in patients with Cushing’s syndrome. ‘Block-replacement’ therapy with suppression of endogenous steroidogenesis and supplementation of corticosteroid requirements might be preferred in this patient population.

Learning points

  • Comorbidities for severe coronavirus disease 2019 (COVID-19) are frequently present in patients with Cushing’s syndrome.
  • ‘Block-replacement’ with suppression of endogenous steroidogenesis and supplementation of corticosteroid requirements might be preferred to reduce the need for biochemical monitoring and avoid adrenal insufficiency.
  • The optimal corticosteroid dose/choice for COVID-19 is unclear, especially in patients with endogenous glucocorticoid excess.
  • First-line surgery vs initial disease control with steroidogenesis inhibitors for Cushing’s disease should be discussed depending on the current healthcare situation.

Background

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a significant impact on the health care systems to date. The clinical presentation of coronavirus disease 2019 (COVID-19) is diverse, ranging from asymptomatic illness to respiratory failure requiring admission to the intensive care unit (ICU). Risk factors for severe course include old age, male gender, comorbidities such as arterial hypertension, diabetes mellitus, chronic lung-, heart-, liver- and kidney disease, malignancy, immunodeficiency and pregnancy (1). Little is known about the risk of COVID-19 in patients with rare endocrine malignancies, such as pituitary carcinoma.

Case presentation

This case concerns a 43-year-old man with adrenocorticotrophic hormone (ACTH)-secreting pituitary carcinoma (with cerebellar and cervical drop metastases) with a severe COVID-19 infection. He had previously received multiple treatment modalities including surgery, radiotherapy, ketoconazole, pasireotide, cabergoline, bilateral (subtotal) adrenalectomy and temozolomide chemotherapy as described elsewhere (2). His most recent therapy was a combination of immune checkpoint inhibitors consisting of ipilimumab (3 mg/kg) and nivolumab (1 mg/kg) (anti-CTLA-4 and anti-PD-1, respectively) every 3 weeks for four cycles, after which maintenance therapy with nivolumab (240 mg) every 2 weeks was continued. Residual endogenous cortisol production was inhibited with ketoconazole 800 mg daily. He had stabilized disease with a decrease in plasma ACTH, urinary free cortisol and stable radiological findings (2). Surgical resection of the left adrenal remnant was planned but was not carried out due to the development of a COVID-19 infection.

In March 2021, he consulted our emergency department for severe respiratory complaints. He had been suffering from upper respiratory tract symptoms for one week, with progressive dyspnoea in the last three days. He tested positive for SARS-CoV-2 the day before admission. On examination, his O2 saturation was 72%, with tachypnoea (40/min) and bilateral pulmonary crepitations. His temperature was 37.2°C, blood pressure 124/86 mmHg and pulse rate 112 bpm. High-flow oxygen therapy was initiated but yielded insufficient improvement (O2 saturation of 89% and tachypnoea 35/min). He was urgently intubated for respiratory exhaustion.

Investigation

Initial investigations showed type 1 respiratory insufficiency with PaO2 of 52.5 mmHg (normal 75–90), PaCO2 of 33.0 mmHg (normal 36–44), pH of 7.47 (normal 7.35–7.45) and a P/F ratio of 65.7 (normal >300). His inflammatory parameters were elevated with C-reactive protein level of 275.7 mg/L (normal <5·0) and white blood cell count of 7.1 × 10⁹ per L with 72.3% neutrophils. His most recent morning plasma ACTH-cortisol level (measured using the Elecsys electrochemiluminescence immunoassays on a Cobas 8000 immunoanalyzer [Roche Diagnostics]) before his admission was 213 ng/L (normal 7.2–63) and 195 µg/L (normal 62–180) respectively, while a repeat measurement 3 weeks after his admission demonstrated increased cortisol levels of 547 µg/L (possibly iatrogenic due to treatment with high-dose hydrocortisone) and a decreased ACTH of 130 ng/L.

Treatment

On admission, he was started on high-dose dexamethasone therapy for 10 days together with broad-spectrum antibiotics for positive sputum cultures containing Serratia, methicillin-susceptible Staphylococcus aureus and Haemophilus influenzae. Thromboprophylaxis with an intermediate dose of low molecular weight heparin (tinzaparin 14 000 units daily for a body weight of 119 kg) was initiated. A ‘block-replacement’ regimen was adopted with the continuation of ketoconazole (restarted on day 11) in view of his endocrine treatment and the supplementation of hydrocortisone at a dose depending on the current level of stress. The consecutive daily dose of hydrocortisone and ketoconazole is shown in Fig. 1.

Figure 1View Full Size
Figure 1
‘Block-replacement’ therapy with ketoconazole and hydrocortisone/dexamethasone. Dexamethasone 10 mg daily was initially started as COVID-19 treatment, followed by hydrocortisone at a dose consistent with current levels of stress. Ketoconazole was restarted on day 11 and titrated to a dose of 800 mg daily to suppress endogenous glucocorticoid production.

Citation: Endocrinology, Diabetes & Metabolism Case Reports 2022, 1; 10.1530/EDM-21-0182

Outcome and follow-up

He developed multiple organ involvement, including metabolic acidosis, acute renal failure requiring continuous venovenous hemofiltration, acute coronary syndrome type 2, septic thrombophlebitis of the right jugular vein, and critical illness polyneuropathy. He was readmitted twice to the ICU, for ventilator-associated pneumonia and central line-associated bloodstream infection respectively. He eventually recovered and was discharged from the hospital to continue his rehabilitation.

Discussion

We describe the case of a patient with severe COVID-19 infection with active Cushing’s disease due to pituitary carcinoma, who was treated with high-dose dexamethasone followed by ‘block-replacement’ therapy with hydrocortisone in combination with off-label use of ketoconazole as a steroidogenesis inhibitor. His hospitalization was prolonged by multiple readmissions to the ICU for infectious causes. Our case illustrates the presence of multiple comorbidities for a severe and complicated course of COVID-19 in a patient with active Cushing’s disease.

Dexamethasone was initially chosen as the preferred corticosteroid therapy, in analogy with the RECOVERY trial, in which dexamethasone at a dose of 6mg once daily (oral or i.v.) resulted in lower 28-day mortality in hospitalized patients with COVID-19 requiring oxygen therapy or invasive mechanical ventilation (3). However, the optimal dose/choice of corticosteroid therapy is unclear, especially in a patient population with pre-existing hypercortisolaemia. A similar survival benefit for hydrocortisone compared to dexamethasone has yet to be convincingly demonstrated. This may be explained by differences in anti-inflammatory activity but could also be due to the fact that recent studies with hydrocortisone were stopped early and were underpowered (45).

Multiple risk factors for a complicated course of COVID-19 are present in patients with Cushing’s syndrome and might increase morbidity and mortality (67). These include a history of obesity, arterial hypertension and impaired glucose metabolism. Prevention and treatment of these pre-existing comorbidities are essential.

Patients with Cushing’s syndrome also have an increased thromboembolic risk, which is further accentuated by the development of severe COVID-19 infection (67). Thromboprophylaxis with low molecular weight heparin is associated with lower mortality in COVID-19 patients with high sepsis‐induced coagulopathy score or high D-dimer levels (8) and is presently widely used in the treatment of severe COVID-19 disease (9). Subsequently, this treatment is indicated in hospitalized COVID-19 patients with Cushing’s syndrome. It is unclear whether therapeutic anticoagulation dosing could provide additional benefits (67). An algorithm based on the International Society on Thrombosis and Hemostasis-Disseminated Intravascular Coagulation score was proposed to evaluate the ideal anticoagulation therapy in severe/critical COVID-19 patients, with an indication for therapeutic low molecular weight heparin dose at a score ≥5 (9).

Furthermore, the chronic cortisol excess induces suppression of the innate and adaptive immune response. Patients with Cushing’s syndrome, especially when severe and active, should be considered immunocompromised and have increased susceptibility for viral and other (hospital-acquired) infections. Prophylaxis for Pneumocystis jirovecii with trimethoprim/sulfamethoxazole should therefore be considered (67).

Additionally, there is a particular link between the pathophysiology of COVID-19 and Cushing’s syndrome. The SARS-CoV-2 virus (as well as other coronaviruses) enter human cells by binding the ACE2 receptor. The transmembrane serine protease 2 (TMPRSS2), expressed by endothelial cells, is additionally required for the priming of the spike-protein of SARS-CoV-2, leading to viral entry. TMPRSS2 was studied in prostate cancer and found to be regulated by androgen signalling. Consequently, the androgen excess frequently associated with Cushing’s syndrome might be an additional risk factor for contracting COVID-19 via higher TMPRSS2 expression (10), especially in women, in whom the effect of excess androgen would be more noticeable compared to male patients with Cushing’s syndrome.

Treating Cushing’s syndrome with a ‘block-replacement’ approach, with suppression of endogenous steroidogenesis and supplementation of corticosteroid requirements, is an approach that should be considered, especially in severe or cyclic disease. The use of this method might decrease the need for monitoring and reduce the occurrence of adrenal insufficiency (7). Our patient was on treatment with ketoconazole, which was interrupted at initial presentation and then restarted under the coverage of a hydrocortisone stress regimen. Ketoconazole was chosen because of its availability. Advantages of ketoconazole over metyrapone include its antifungal activity with the potential for prevention of invasive pulmonary fungal infections, as well as its antiandrogen action (especially in female patients) and subsequent inhibition of TMPRSS2 expression (10). Regular monitoring of the liver function (every month for the first 3 months, at therapy initiation or dose increase) is necessary. Caution is needed due to its inhibition of multiple cytochrome P450 enzymes (including CYP3A4) and subsequently greater risk of drug-drug interactions vs metyrapone (710). Another disadvantage of ketoconazole is the need for oral administration. In our patient, ketoconazole was delivered through a nasogastric tube. i.v. etomidate is an alternative in case of an unavailable enteral route.

Finally, as a general point, the first-line treatment of a patient with a novel diagnosis of Cushing’s disease is transsphenoidal surgery. Recent endocrine recommendations pointed out the possibility of initial disease control with steroidogenesis inhibitors in patients without an indication for urgent intervention during a high prevalence of COVID-19 (7). This would allow the optimalization of metabolic parameters; emphasizing that the short-to mid-term prognosis is related to the cortisol excess and not its cause. Surgery could then be postponed until the health situation allows for safe elective surgery (7). This decision depends of course on the evolution of COVID-19 and the healthcare system in each country and should be closely monitored by policymakers and physicians.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Patient consent

Written informed consent for publication of their clinical details and/or clinical images was obtained from the patient.

Author contribution statement

J M K de Filette is an endocrinologist-in-training and was the main author. All authors were involved in the clinical care of the patient. All authors contributed to the reviewing and editing process and approved the final version of the manuscript.

References

Lung Neuroendocrine Tumors With Cushing Syndrome Not Biologically Aggressive

Neuroendocrine pulmonary tumors in people with Cushing syndrome (CS) are associated with increased nodal metastasis, higher recurrence, and lower disease-free survival compared with quiescent bronchopulmonary tumors, according to results from an observational case series published in JAMA Network Open. Researchers said their study shows these tumors are not biologically aggressive and underlying carcinoid biology may not be as important as symptomatic hormonal physiology.

Patients (n=68) with CS who underwent curative-intent pulmonary surgery at the National Cancer Institute (NCI) between 1982 and 2020 were retrospectively reviewed for clinical outcomes on the basis of tumor etiology. Outcomes were compared among groups of patients with adrenocorticotropic hormone-secreting carcinoid tumors who were treated at the National Institutes of Health in 2021 (n=68), Hôpital Européen Georges-Pompidou in 2011 (n=14), the Mayo Clinic in 2005 (n=23), and Massachusetts General Hospital in 1997 (n=7).

Patients who underwent surgery at the NCI were aged median 41 years (range, 17-80 years), 42.6% were men, 81.8% were White, and mean follow-up after surgery was 16 months (range, 0.1-341 months).

Most patients had T status 1a (55.9%). The pathological stages were IA1 (37.3%), IA2 (23.7%), IA3 (1.7%), IIB (16.9%), IIIA (20.3%), or unknown (13.2%).

The patients with typical carcinoid tumors (83.8%) underwent lobectomy (70.2%), wedge (22.8%), segmentectomy (5.3%), and pneumonectomy (1.7%) surgical approaches. Patients with atypical carcinoid tumors (16.2%) underwent lobectomy (72.7%) and wedge (27.3%) approaches. Stratified by surgical approach, lobectomy recipients were younger (P =.01) and more had node-positive atypical carcinoid tumors (P =.01).

After surgery, morbidity occurred among 19.1% of patients; overall mortality was 1.5%.

Disease-free survival at 5 years following surgery was 73.4% (95% CI, 48.7%-87.6%) and 55.1% (95% CI, 26.3%-76.5%) at 10 years. Disease-free survival was 75.4% (95% CI, 49.2%-89.3%) at 5 years and 50.2% (95% CI, 18.3%-75.7%) at 10 years for typical carcinoid tumors and remained stable at 75.0% among those with atypical carcinoid tumors. Median follow-up after surgery was 16 months (range, 0.1-341 months). At the time of last follow-up, 76.4% of the patient population was alive and tumor free.

The overall incidence of persistence/recurrence was 16.2%. Recurrent disease occurred in 7 patients and persistent disease in 4 patients. Only one of this group had an atypical carcinoid tumor. Mean time to recurrence in patients with recurrent disease was 76 months with a median of 55 months.

The adrenocorticotropic hormone-secreting carcinoid cohort from multiple institutions was aged median 39 years, 46.4% were men, 72.3% underwent lobectomy or pneumonectomy, 18.7% had morbidity, and 0.9% mortality. The majority of these groups had typical carcinoid tumors (83.9%) with a mean size of 1.1 cm (range, 0.1-10 cm) and 39.4% had lymph node positivity. Recurrence occurred among 12.6% of patients and persistence among 5.4% of patients. Among the recurrence cohort, 85.7% had typical carcinoid tumors. Time to recurrence was >6 years. Disease-free survival was 73% at five years and 55% at 10 years.

This study was limited by the small group sizes, however, due to the rarity of this cancer it was not possible to include more individuals.

“Ectopic adrenocorticotropic hormone secreting carcinoid tumors with Cushing syndrome appear to be associated with increased metastasis to lymph nodes, higher recurrence (mostly local), and lower overall disease-free survival at 5 and 10 years than quiescent bronchial carcinoid tumors, irrespective of histologic subtype,” the researchers wrote. “Nevertheless, we contend these tumors are not biologically aggressive since these patients have distinct, prolonged survival and delayed time to recurrence.”

The researchers also noted that “the current staging system applied to these tumors raises questions about prognostic accuracy. Extrapolation may suggest that the underlying carcinoid biology may not be as important as the symptomatic hormonal physiology.” They suggested future studies may test “whether a lung-sparing surgical approach coupled with routine lymphadenectomy is an optimal intervention in this scenario when normal endocrine functioning is restored and CS sequelae resolve.”

Reference

Seastedt KP, Alyateem GA, Pittala K, et al. Characterization of outcomes by surgical management of lung neuroendocrine tumors associated with Cushing syndrome. JAMA Netw Open. 2021;4(9):e2124739. doi:10.1001/jamanetworkopen.2021.24739

From https://www.endocrinologyadvisor.com/home/topics/general-endocrinology/cushing-syndrome-and-lungs-and-neuoendocrine-tumors/

Unique Cell in Rare Tumor Tied to Ectopic Cushing’s

Single-cell transcriptome analysis identifies a unique tumor cell type producing multiple hormones in ectopic ACTH and CRH secreting pheochromocytoma

Abstract

Ectopic Cushing’s syndrome due to ectopic ACTH&CRH-secreting by pheochromocytoma is extremely rare and can be fatal if not properly diagnosed. It remains unclear whether a unique cell type is responsible for multiple hormones secreting. In this work, we performed single-cell RNA sequencing to three different anatomic tumor tissues and one peritumoral tissue based on a rare case with ectopic ACTH&CRH-secreting pheochromocytoma. And in addition to that, three adrenal tumor specimens from common pheochromocytoma and adrenocortical adenomas were also involved in the comparison of tumor cellular heterogeneity. A total of 16 cell types in the tumor microenvironment were identified by unbiased cell clustering of single-cell transcriptomic profiles from all specimens. Notably, we identified a novel multi-functionally chromaffin-like cell type with high expression of both POMC (the precursor of ACTH) and CRH, called ACTH+&CRH + pheochromocyte. We hypothesized that the molecular mechanism of the rare case harbor Cushing’s syndrome is due to the identified novel tumor cell type, that is, the secretion of ACTH had a direct effect on the adrenal gland to produce cortisol, while the secretion of CRH can indirectly stimulate the secretion of ACTH from the anterior pituitary. Besides, a new potential marker (GAL) co-expressed with ACTH and CRH might be involved in the regulation of ACTH secretion. The immunohistochemistry results confirmed its multi-functionally chromaffin-like properties with positive staining for CRH, POMC, ACTH, GAL, TH, and CgA. Our findings also proved to some extent the heterogeneity of endothelial and immune microenvironment in different adrenal tumor subtypes.

Editor’s evaluation

The study described an extremely rare type of adrenal pheochromocytoma that secretes both ACTH and CRH, in addition to catecholamines. Single-cell RNA sequencing of the tumor and other tumors revealed a group of cells that are responsible for the hormone secretion. We believe that this work will provide an interesting example of functional endocrine tumors and how they are formed.

https://doi.org/10.7554/eLife.68436.sa0

 

Introduction

Cushing’s syndrome (CS) is a rare disorder caused by long-term exposure to excessive glucocorticoids, with an annual incidence of about 0.2–5.0 per million (Lacroix et al., 2015Newell-Price et al., 2006Lindholm et al., 2001Steffensen et al., 2010Bolland et al., 2011Valassi et al., 2011). About 80% of CS cases are due to ACTH secretion by a pituitary adenoma, about 20% are due to ACTH secretion by nonpituitary tumors (ectopic ACTH syndrome [EAS]), and 1% are caused by corticotropin-releasing hormone (CRH)-secreting tumors (Alexandraki and Grossman, 2010Ejaz et al., 2011Ballav et al., 2012). Most EAS tumors (~60%) are more common intrathoracic tumors, only 2.5–5% of all EAS are caused by a pheochromocytoma (Alexandraki and Grossman, 2010Isidori et al., 2006Ilias et al., 2005Aniszewski et al., 2001). Pheochromocytoma, a catecholamine-producing tumor, becomes even rarer when it is capable of both secreting ACTH and CRH (Lenders et al., 2005Zelinka et al., 2007). By 2020, only two cases with pheochromocytoma secreted both ACTH and CRH were reported (Elliott et al., 2021O’Brien et al., 1992Jessop et al., 1987). As one of the largest adrenal tumor treatment centers in China, our hospital, Peking Union Medical College Hospital (PUMCH) receives more than 500 adrenal surgery performed per year, with almost 100 cases undergoing pheochromocytoma surgery. But so far, we have encountered only one case of pheochromocytoma secreting both ACTH and CRH, which was first reported in this study.

Since the combination of dual ACTH/CRH secreting pheochromocytoma with CS is extremely rare, there is limited knowledge about the diagnosis and management of this disease. Ectopic secretion hormones ACTH and CRH may complicate the presentation of pheochromocytoma, and this tumor usually leads to CS, which can be fatal if not properly diagnosed and managed (Ballav et al., 2012Ilias et al., 2005Lenders et al., 2014Lase et al., 2020). Surgical resection of the pheochromocytoma is the primary treatment option. Although previous studies have reported ectopic ACTH and CRH secreting pheochromocytomas, it was unclear whether a unique cell type that produces multiple hormones influences CS. The concept of ‘one cell, one hormone, and one neuron one transmitter,’ which is known as Dale’s Principle (Dale in 1934; for detailed discussion, see Burnstock, 1976), has dominated the understanding of neurotransmission for many years (Burnstock, 1976). Currently, single-cell RNA-sequencing (scRNA-seq) can examine the expression profiles of a single cell and is recognized as the gold standard for defining cell states and phenotypes (Tang et al., 2009Tammela and Sage, 2020Kolodziejczyk et al., 2015Patel et al., 2014Tirosh et al., 2016bTirosh et al., 2016aPuram et al., 2017Venteicher et al., 2017Young et al., 2018Bernard et al., 2019Segerstolpe et al., 2016Reichert and Rustgi, 2011). It can reveal the presence of rare and novel unique cell types, such as CFTR-expressing pulmonary ionocytes on lung airway epithelia (Montoro et al., 2018Plasschaert et al., 2018). It also provides an unbiased method to better understand the diversity of immune cells in the complex tumor microenvironment (Papalexi and Satija, 2018Stubbington et al., 2017).

In this study, we reported a rare case of CRH/ACTH-secreting pheochromocytoma infiltrating the kidney and psoas muscle tissue. scRNA-seq identified a unique chromaffin-like cell type, called ACTH+&CRH + pheochromocyte, with both high expression of POMC (precursor for ACTH) and CRH pheochromocyte as well as TH (tyrosine hydroxylase, a key enzyme for catecholamine synthesization). Immunocytochemical and immunofluorescence staining showed all for these markers, which confirmed the tumor capable of multiple hormones secreting characteristics. We determined that the expression of POMC directly causes the secretion of ACTH, and the expression of CRH indirectly promotes the secretion of ACTH hormone, which ultimately leads to CS. After the tumor resection, clinical manifestations also showed complete remission of CS. For comparison, other adrenal tumor subtypes were also collected and studied, namely, a common pheochromocytoma (without ectopic ACTH or CRH secretion function) and two adrenocortical adenomas. We used a scRNA-seq approach to obtain transcriptomic profiles for all collected samples and identified a list of differentially expressed genes (DEGs) through cell clustering and markers finding. Notably, GAL, co-expressed with ACTH and CRH, could be a new candidate marker to detect the rare ectopic ACTH+&CRH + secreting pheochromocytes by comparing ACTH+&CRH + pheochromocyte with common pheochromocyte and cortical cell clusters. It suggested that GAL, which encodes small neuroendocrine peptides, may be locally involved in the regulation of the hypothalamic-pituitary-adrenal (HPA) axis.

Results

Single-cell profiling and unbiased clustering of collecting specimens

We applied scRNA-seq methods to perform large-scale transcriptome profiling of seven prospectively collected samples from tumors and peritumoral tissue of three adrenal tumor patients (Figure 1A). Case 1 suffered from a rare pheochromocytoma with typical Cushingoid features. The laboratory results showed high levels of cortisol, ACTH, and catecholamines. The abdominal contrast-enhanced computer tomography scanning revealed bilateral adrenocortical hyperplasia and irregular tumor within the left adrenal. After the resection, we collected three dissected tumor specimens (esPHEO_T1, esPHEO_T2, and esPHEO_T3) from different anatomic sites of the tumor and an adrenal tissue adjacent to the tumor (esPHEO_Adj). For comparison, we also collected other adrenal tumors, namely, a common pheochromocytoma (PHEO_T) from Case 2 and two adrenocortical adenomas (ACA_T1 and ACA_T2) from Case 3. Case 2 showed elevated catecholamines and normal levels of cortisol and ACTH. Case 3 showed a high level of cortisol, a low level of ACTH, and an intermediate level of catecholamines. The detailed clinical information for the three cases was summarized in Appendix 1—table 1. To investigate the difference of the secretory function, we performed the immunohistochemistry (IHC) staining of selected markers, CgA (chromogranin A) and ACTH in esPHEO_T1, PHEO_T, and esPHEO_Adj samples (Figure 1B). We observed that CgA positive cells were present in both pheochromocytomas (esPHEO_T1 and PHEO_T), but ACTH positive cells were only observed in the rare pheochromocytoma (esPHEO_T1) with the ACTH-secreting cellular characteristics. As expected, there were no CgA and ACTH positive cells in the adjacent sample (esPHEO_Adj). Thus, at the clinical stage, our histopathology results confirmed that Case 1 was a rare ectopic ACTH secreting pheochromocytoma which stained positively for both ACTH and CgA.

Clinical sample collection of adrenal tumor and adjacent specimen for scRNA-seq analysis.

(A) scRNA-seq workflow for three tumor specimens (esPHEO_T1, esPHEO_T2, and esPHEO_T3) and one adjacent specimen (esPHEO_Adj) from the rare pheochromocytoma with ectopic ACTH and CRH secretion (Case … see more

Then, we applied scRNA-seq approaches to selected seven specimen samples (six tumors and one sample adjacent to the tumor). The tissues after resection were rapidly digested into a single-cell suspension, and the 3′-scRNA-seq protocol (Chromium Single Cell 3′ v2 Libraries) was performed for each sample unbiasedly. After quality control filtering to remove cells with low gene detection, high mitochondrial gene coverage, and doublets filtration, we compiled a unified cells-by-genes expression matrix of a total of 44,511 individual cells (Supplementary file 1Appendix 1—figure 2). Then the SCT-transformed normalization, principal component analysis (PCA), was employed to perform unsupervised dimensionality reduction. Then, the cells were clustered based on the graph-based clustering analysis, and visualized in the distinguished diagram using the Uniform Manifold Approximation and Projection (UMAP) method. The marker genes were calculated to identify each cell cluster by performing differential gene expression analysis (Supplementary file 2).

As shown in Figure 2A, the distinct cell clusters were identified and the conventional cell lineage gene markers were employed to annotate the clusters, such as CHGA and CHGB for adrenal chromaffin cell, cytochrome P450 superfamily for adrenocortical cell, S100B for sustentacular cell, GNLY for NK cell, MS4A1 for B cell, CD8A for CD8+ T cell, and IL7R for CD4+ T cell. Based on the expression of gene markers, we recognized a total of 16 main cell groups: ACTH+&CRH + pheochromocyte, pheochromocyte, adrenocortical, sustentacular, erythroblast/granulosa, endothelial, fibroblast, neutrophil, monocyte, macrophage, plasma, B, NK, CD8+ T&NKT, CD8+ T, and CD4+ T, among which the endothelial cell group was composed of four endothelial cell subgroups. The heatmap showed the expression levels of specific cluster markers for each cell phenotype that we identified (Figure 2B). For this analysis, we specifically focused on the four types of adrenal cells and showed their markers in a heatmap (Appendix 1—figure 3). Additionally, we detected the transcription factors alongside their candidate target genes, which are jointly called regulons. The analysis scored the activity of regulon for each cell (Appendix 1—figure 4A) and yielded specific regulons for each cellular cluster (Appendix 1—figure 4B). We also specifically focused on the adrenal cells and found XBP1 as the top regulons for ACTH+&CRH + pheochromocyte and adrenocortical cell type (Appendix 1—figure 4C).

Different cell types and their highly expressed genes through single-cell transcriptomic analysis.

(A) The t-distributed stochastic neighbor embedding (t-SNE) plot shows 16 main cell types from all specimens. (B) Heatmap shows the scaled expression patterns of the top 10 marker genes in each cell … see more

Identification of a previously unrecognized cell type

The presence of heterogeneous cell populations in different adrenal tumor specimens and the peritumoral sample (Figure 3A) prompted us to investigate their cellular compositions and characteristics. As shown in Figure 3B, different sources of specimens represented distinct cell type compositions. Notably, although the size of the cell clusters of the adrenal gland was relatively small, four distinct subtypes of adrenal cells were observed, including ACTH+&CRH + pheochromocyte, pheochromocyte, adrenocortical cells, and sustentacular cells. The ACTH+&CRH + pheochromocytoma cell subtype was specific to three tumor samples, esPHEO_T1, esPHEO_T2, and esPHEO_T3 from Case 1, but was not observed in the peritumoral sample (esPHEO_Adj) and other adrenal tumor samples from Case 2 (PHEO_T) and Case 3 (ACA_T1 and ACA_T2). This result was consistent with the clinical symptoms in our earlier reports that ACTH was only over-secreted in pheochromocytoma of Case 1. The cell cluster of ACTH+&CRH + pheochromocyte was supported by the specific expression of the markers POMC (proopiomelanocortin) and CRH (corticotropin-releasing hormone) (Figure 3C). POMC is a precursor of ACTH, and CRH is the most important regulator of ACTH secretion. We also detected another specific expression signal, GAL, for the cell cluster of ACTH+&CRH + pheochromocyte (Figure 3C). GAL encodes small neuroendocrine peptides and can regulate diverse physiologic functions, including growth hormone, insulin release, and adrenal secretion (Ottlecz et al., 1988McKnight et al., 1992Murakami et al., 1989Hooi et al., 1990). A study found that GAL and ACTH were co-expressed in human pituitary and pituitary adenomas, and suggested that GAL may be locally involved in the regulation of the HPA axis (Hsu et al., 1991). We demonstrated that GAL was expressed in the ACTH+&CRH + pheochromocyte and might participate in the regulation ATCH secretion (Figure 3C). Then we examined the known adrenal chromaffin cell markers (CHGA and CHGB) and the markers for catecholamine-synthesizing enzymes (TH and PNMT) (Figure 3C). These known markers and another new candidate marker CARTPT were observed in both ACTH+&CRH + pheochromocyte and pheochromocyte cell subtypes. The CYP17A1 and CYP21A2, the typical markers of the adrenal cortical cell subtype, were also investigated (Figure 3C). They are members of the cytochrome P450 superfamily, encoding key enzymes, and maybe the precursors of cortisol in the adrenal glucocorticoids biosynthesis pathway (Auchus et al., 1998Petrunak et al., 2014). Finally, a subtype of cells with positive expression of S100B was identified, called sustentacular cells. Sustentacular cells were found near chromaffin cells and nerve terminations. Several studies have shown that sustentacular cells exhibit stem-like characteristics (Pardal et al., 2007Fitzgerald et al., 2009Poli et al., 2019Scriba et al., 2020).

A unique tumor cell type was revealed by the composition analysis of cell types in each sample.

The results validated an ectopic ACTH and CRH secreting pheochromocytoma. (A) Cell clusters shown in UMAP map can be subdivided by different specimens. (B) Frequency distribution of cell types among … see more

Our scRNA-seq analysis validated that the mRNA expression of POMC (precursor for ACTH) and CRH in pheochromocyte triggered the pathophysiology of ectopic ACTH and CRH syndromes, thereby stimulating the adrenal glands to release cortisol. The overexpression of TH and PNMT was responsible for the excessive secretion of catecholamines in the ACTH+&CRH + pheochromocyte and pheochromocyte cell subtypes. Tumor samples (esPHEO_T1, esPHEO_T2, and esPHEO_T3) from Case 1 and PHEO_T from Case 2 were demonstrated to have the function of producing catecholamine. These genes related to catecholamine secretion were all negative for adrenocortical cell subtypes because the catecholamine-producing pheochromocytomas originated from chromaffin cells in the adrenal medulla rather than the adrenal cortex. Our laboratory tests were consistent with these results, that is, both Case 1 and Case 2 had a high level of catecholamines in plasma and 24 hr urine while Case 3 had a normal level. We also found CARTPT was similar to PNMT and can be used as a marker for ACTH+&CRH + pheochromocyte and pheochromocyte. Chromaffin cell markers CHGA and CHGB were mainly characterized in PHEO_T and three tumor samples from Case 1. Adrenocortical cell clusters mainly existed in ACA_T1 and ACA_T2, but a few existed in esPHEO_Adj. S100B was specifically identified in PHEO_T. An absence of S100-positive sustentacular cells has been previously confirmed in most malignant adrenal pheochromocytomas, and the locally aggressive or recurrent group usually contains a large number of these cells (Unger et al., 1991). It suggests that PHEO_T from Case 2 might be a locally aggressive case, while Case 1 is the opposite. To validate this finding, we performed additional IHC staining experiments on paraffin-embedded serial slices with similar tissue regions from the tumor specimen esPHEO_T3 using antibodies against CgA, ACTH, POMC, CRH, TH, and GAL. We did find that these markers were all positive in the tumor tissue, which further indicated that the special rare pheochromocytoma exhibited multiple hormone-secreting characteristics, including ACTH, CRH, and catecholamines (Figure 3DAppendix 1—figure 8). We also prepared two serial slices for immunofluorescence co-staining for POMC&CRH and POMC&TH. The legible co-localization signals were observed, where the green signal was for POMC, and the red signal was for CRH and TH (Figure 3EAppendix 1—figure 9). This result confirmed the ACTH and CRH secreting pheochromocytoma from Case 1 contained a unique multi-functional chromaffin-like cell type, which was consistent with the analysis result by scRNA-seq.

Differential expression genes show adrenal tumor cell-type specificity

Next, we analyzed the DEGs between ACTH+&CRH + pheochromocyte and the other two subtypes of adrenal tumor cells (pheochromocyte and adrenocortical cells). It is worth noting that many genes were dramatically upregulated specifically in ACTH+&CRH + pheochromocyte when compared with the other tumor cell types, such as GAL, POMC, PNMT, and CARTPT (Figure 4A). Using these upregulated or downregulated genes, we performed functional enrichment analysis based on gene ontology (GO) annotation to further characterize the molecular characteristics of different tumor cell types. In comparison with adrenocortical cell types, the highly upregulated genes of ACTH+&CRH + pheochromocyte were mainly enriched in the neuropeptide signaling pathway, hormone secretion, and transport, while the downregulated genes were mostly enriched in the pathway of adrenocortical hormones (Figure 4B). Comparing the two types of pheochromocyte, GO functional enrichment analysis for the biology process (BP) revealed that the upregulated genes for ACTH+&CRH + pheochromocyte were also enriched in the neuropeptide signaling pathway, while the enrichment of the downregulated genes from the GO functional result hardly reach statistical significance. Interestingly, compared with adrenocortical cells, a total of 248 upregulated and 198 downregulated genes were detected in ACTH+&CRH + pheochromocyte, while only 95 upregulated and 111 downregulated genes were detected in ACTH+&CRH + pheochromocyte when compared with pheochromocyte (Figure 4C), which suggested that the difference between ACTH+&CRH + pheochromocyte and pheochromocyte was relatively small. The known adrenal chromaffin cell markers (CHGA and CHGB) were differential expressed significantly between ACTH+&CRH + pheochromocyte and adrenocortical cells, but not observed significant difference between two subtypes of pheochromocytes. Besides, the co-upregulated genes, such as CARTPT, PNMT, POMC, GAL, and CRH, were responsible for the production of a variety of hormones and involved in neuropeptide signaling pathways. Of which, the product of PNMT catalyzes the last step of the catecholamine biosynthesis pathway, methylating norepinephrine to form epinephrine. The overexpression of PNMT was responsible for the significantly elevated epinephrine (Appendix 1—table 1) of the rare Case 1 with ectopic ACTH and CRH secretory pheochromocytoma. The elevated plasma ACTH (Appendix 1—table 1) of the rare Case 1 could be explained by specific high expression signals of GAL, POMC, and CRH. In details, POMC is the precursor of ACTH; CRH is the most important regulator of ACTH secretion; and GAL was co-expressed in the ACTH+&CRH + pheochromocyte, which might be locally involved in the regulation of the HPA axis. Therefore, we concluded that the tumor cell type of ACTH+&CRH + pheochromocyte from Case 1 had multiple hormone secretion functions, namely, CRH secretion function, ACTH secretion function, and catecholamine secretion function. Furthermore, we believed that the rare Case 1 harbor the ACTH-dependent CS is due to the presence of the identified novel tumor cell type of ACTH+&CRH + pheochromocyte, which secretes both ACTH and CRH. The secretion of ACTH had a direct effect on the adrenal gland to produce cortisol, while the secretion of CRH can indirectly stimulate the secretion of ACTH from the anterior pituitary (Figure 4D).

Altered functions in POMC+&CRH + pheochromocyte revealed by differential gene expression analysis.

(A) Volcano plot of changes in gene expression between POMC+&CRH + pheochromocytes and other adrenal cell types (pheochromocytes and adrenocortical cells). The x-axis specifies the natural logarithm … see more

RNA velocity analysis

To investigate dynamic information in individual cells, we performed RNA velocity analysis using velocyto.py for spliced or unspliced transcripts annotation followed by scVelo pipeline for RNA dynamics modeling. RNA velocity is the time derivative of the measured mRNA abundance (spliced/unspliced transcripts) and allows to estimate the future developmental directionality of each cell (La Manno et al., 2018). We observed the ratios of spliced and unspliced mRNA, and sustentacular cell type was ranking first with 36% unspliced proportions among non-immune cell types (Figure 5A and B). The balance of unspliced and spliced mRNA abundance is an indicator of the future state of mature mRNA abundance, and thus the future state of the cell (Bergen et al., 2020). Previously study had observed unspliced transcripts were enriched in genes involved in DNA binding and RNA processing in hematopoietic stem cells (Bowman et al., 2006). For the high proportions of unspliced/spliced transcripts, stem-like characteristics of sustentacular cells were supported. There were more spliced transcripts proportions in POMC+&CRH + pheochromocytes than in pheochromocytes (Figure 5B). Then, we estimated pseudotime grounded on transcriptional dynamics and generated velocity streamlines that account for speed and direction of motion. As observed in the pseudotime of four adrenal cell subtypes, medullary cells are earlier than cortical cells (Figure 5C). From velocity streamlines, we found the four adrenal cell subtypes, that is, POMC+&CRH + pheochromocytes, pheochromocytes adrenocortical cells, and sustentacular cells, were independent respectively and not directed toward other cell types (Figure 5D). Newly transcribed, unspliced pre-mRNAs were distinguished from mature, spliced mRNAs by detecting the presence of introns. Genes, like POMC and CRH, only contain one coding sequence (CDS) region, were all detected as spliced (Appendix 1—figure 5). It indicated that the actual values of RNA velocity for POMC+&CRH + pheochromocytes might be larger than the predicted ones. Furthermore, the spliced versus unspliced phase for CHGA, CHGB, and TH demonstrated a clear more dynamics expression in POMC+&CRH + pheochromocytes than in pheochromocytes (Appendix 1—figure 5).

RNA velocity analysis supported sustentacular cells as root and indicated four adrenal cell subtypes were independent respectively and not directed toward other cell types.

RNA velocity is the time derivative of the measured mRNA abundance (spliced/unspliced transcripts) and allows to estimate the future developmental directionality of each cell. (A) The total ratios … see more

Lineage tracing analysis confirms the plasticity of adrenal tumor cell subsets

We performed the pseudotime analysis for the adrenal tumor cell subsets to determine the pattern of the dynamic cell transitional states. We used the recommended strategy of Monocle to order cells based on genes that differ between clusters. The sustentacular cells were in an early state in pseudotime analysis (Figure 6A, B and C), which was in accordance with their exhibited stem-like properties and the highest unspliced proportion among non-immune cell types in the RNA velocity analysis. The results also showed a transition from sustentacular cells to pheochromocytes and then to ACTH+&CRH + pheochromocyte, and adrenocortical cells were on another branch (Figure 6A, B and C). To determine whether specific gene modules might be responsible for this cell plasticity, we calculated the expression levels of all the genes in the single-cell transcriptome identified the DEGs on the different paths through the entire trajectory (Figure 6D), which showed the dynamic changes of each gene over pseudotime.

Pseudotime analysis of adrenal cells inferred by Monocle.

We ran reduce dimension with t-SNE for four types of adrenal cells and sorted cells along pseudotime using Monocle. The single-cell pseudotime trajectories by ordering cells were constructed based … see more

scRNA-seq reveals distinct immune and endothelial cell type in the tumor microenvironment

scRNA-seq allowed us to use an unbiased approach to discover the composition of immune cell populations of the adrenal tumor specimens. Analysis of our transcriptional profiles revealed that from the frequency distribution of cell clusters, immune cells accounted for more than ~50% of total cells (Figure 3B). We identified and annotated the immune cell types based on the expression of conventional markers, such as B cells with MS4A1, NK cells with GNLY, and Neutrophil with S100A8 and S100A9 (Figure 7A). The various frequency distribution of immune cell sub-clusters was observed among different samples (Figure 7B). Due to the identical tumor microenvironment, all three tumor specimens one peritumoral specimen from the rare case had similar immune cell composition. Interestingly, the CD4 T cells, B cells, and macrophages are mainly presented in two adrenal cortical adenomas (ACA_T1 and ACA_T2), while the CD8 T cells mostly resided in the microenvironment of other pheochromocytoma tumor and the peritumoral specimen. We found the heterogeneity of T cells in different adrenal tumor subtypes, that is, compared with CD4 T cells in adrenocortical adenomas, the pheochromocytoma types were mostly manifested by activated CD8+, especially in the anatomic specimens from the ectopic ACTH&CRH secreting pheochromocytoma.

Diverse immune microenvironments in different adrenal tumor subtypes and tumor-adjacent tissue.

(A) The UMAP diagram shows the expression levels of well-known marker genes of immune cell types. (B) Frequency distribution of immune cell sub-clusters in different adrenal tumors and … see more

Endothelial cells consisted of four distinct sub-clusters: vascular endothelial cells, lymphatic endothelial cells, cortical endothelial cells, and other endothelial cells, as shown in the cell cluster distribution map highlighted by endothelial cells (Figure 8ASupplementary file 3). Various adrenal tumor subtypes had different endothelial compositions (Figure 8B). Vascular endothelial cells were mainly identified in pheochromocytoma samples (esPHEO_T1, esPHEO_T2, esPHEO_T3, and PHEO_T), because pheochromocytoma is a tumor arising in the adrenal medulla, and vascular endothelial cells might be detected from the medullary capillary. Cortical endothelial cells were mainly detected in adrenocortical adenomas (ACA_T1 and ACA_T2). Lymphatic endothelial cells were found in the adjacent adrenal specimen of the rare ACTH+&CRH + pheochromocytoma (esPHEO_Adj). Then, by comparing vascular endothelial cells with two other subclusters (lymphatic endothelial cells and cortical endothelial cells), we found the markers across the subclusters of endothelial cells and annotated GO function of differentially expressed genes (Figure 8C and D). Vascular endothelial cells are the barrier between the blood and vascular wall and have the functions of organizing the extracellular matrix and regulating the metabolism of vasoactive substances. Lymphatic endothelial cells are responsible for chemokine-mediated pathways. Cortical endothelial cells express TFF3 and FABP4, which are involved in repairing and maintaining stable functions.

Differential gene expression analysis shows changes in endothelial cell functions.

(A) The UMAP diagram shows four different endothelial cell sub-clusters. (B) Frequency distribution of endothelial cell sub-clusters among different adrenal tumors and tumor-adjacent specimen. (C) … see more

Discussion

Both CS and pheochromocytoma are serious clinical conditions. In this study, we reported an extremely rare patient (Case 1) with ATCH-dependent CS due to an ectopic ACTH&CRH secreting pheochromocytoma. Surgery is the most common treatment strategy for this type of tumor. After the operation, our clinical manifestations of Case 1 showed the complete remission of CS. The IHC of the dissected tumor confirmed the diagnosis with positive staining for CRH and ACTH. In this study, scRNA-seq was used for the first time to identify the rare ACTH+&CRH + pheochromocyte cell subset. Compared with other subtypes of adrenal tumors, the common pheochromocytoma (from Case 2) and adrenal cortical cells (from Case 3), the DEGs in Case 1 were further characterized. Case 2 was examined to have normal levels of cortisol and ACTH, but Case 3 showed a Cushingoid appearance. The molecular mechanism of CS in Case 3 was different, which was attributed to two cortical adenomas on the left adrenal, showing ACTH-independent hypercortisolemia. In addition, to investigate the genetic driver for Case 1, we supplemented whole-exome sequencing experiments for all rest specimens, that is, tumors (esPHEO_T2 and esPHEO_T3) and controls (esPHEO_Adj and esPHEO_Blood) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma. Filtered germline and somatic mutations were listed in Supplementary file 4 including detailed annotations. Genetic mutations of phaeochromocytoma and paraganglioma are mainly classified into two major clusters, that is, pseudo hypoxic pathway and kinase signaling pathways (Pillai et al., 2016Nölting and Grossman, 2012). We did not find any gene mutations that were related to these two major clusters. We only identified one shared somatic variant of ACAN (c.5951T > A:p.L1984Q) comparing variants in tumor samples to controls but Sanger sequencing only confirmed the presence in esPHEO_T3 which was not observed in esPHEO_T2 (Appendix 1—figure 7). ACAN, encoding a major component of the extracellular matrix, is a member of the aggrecan/versican proteoglycan family. Mutations of ACAN were reported related to steroid levels (Yousri et al., 2018). It is well-established that circulating steroid levels are linked to inflammation diseases such as arthritis, because arthritis as well as most autoimmune disorders results from a combination of several predisposing factors including the stress response system such as hypothalamic-pituitary-adrenocortical axis (Cutolo et al., 2003). But no direct evidence related to ACAN to phaeochromocytoma. Therefore, no obvious genetic driver was found to explain the rare case of ACTH/CRH-secreting phaeochromocytoma. Further investigations would be needed to uncover the relation between ACAN and phaeochromocytoma.

For many years, the understanding of neurotransmission has been dominated by the concept of ‘one cell, one hormone, and one neuron one transmitter,’ which is known as Dale’s Principle (Dale in 1934; for detailed discussion, see Burnstock, 1976Burnstock, 1976). Sakuma et al., 2016 reported an ectopic ACTH pheochromocytoma case and proved that ACTH and catecholamine were produced by two functionally distinct chromaffin-like tumor cell types through immunohistochemical analysis Sakuma et al., 2016. However, more and more evidence has emerged that Dale’s principle is incorrect because existing studies have shown that these cells are multi-messenger systems (Hakanson and Sundler, 1983Apergis-Schoute et al., 2019Svensson et al., 2018). Based on scRNA-seq results, we concluded that the tumor cells from Case 1 had multiple hormone secretion functions, namely, CRH secretion function, ACTH secretion function, and catecholamine secretion function. CRH is the most important regulator of ACTH secretion. Therefore, we believed that the secretion of both CRH and ACTH of this tumor led to ACTH-dependent CS. Besides, the secretion of ACTH had a direct impact on the adrenal gland to produce cortisol, and the secretion of CRH indirectly stimulated the secretion of ACTH by the anterior pituitary. Jessop et al., 1987 also draw the same conclusion in their report in 1987. However, in the reported case, the histological immunostained result was shown only for the corticotropin-releasing factor (CRF-41), but not for ACTH (Jessop et al., 1987).

Adrenal glands are composed of two main tissue types, namely, the cortex and the medulla, which are responsible for producing steroid and catecholamine hormones, respectively. The inner medulla is derived from neuroectodermal cells of neural crest origin, while the outer cortex is derived from the intermediate mesoderm. In the adrenal pheochromocytomas, a third cell type with the positive expression of S100B was identified, called ‘sustentacular’ cells (Suzuki and Kachi, 1995Lloyd et al., 1985). By evaluating 17 malignant and recurrent or locally aggressive adrenal pheochromocytomas, Unger et al., 1991 found that sustentacular cells were absent in most malignant cases (Unger et al., 1991). Because there are no sustentacular cells in ACTH&CRH secreting pheochromocytoma, ACTH&CRH secreting pheochromocytoma is more serious than the common pheochromocytoma. Furthermore, several studies have demonstrated that sustentacular cells exhibit stem-like characteristics (Pardal et al., 2007Fitzgerald et al., 2009Poli et al., 2019Scriba et al., 2020). A unique case of a tumor originating from S100-positive sustentacular cells was previously reported (Lau et al., 2006). The RNA velocity estimation and pseudo-time analysis of different adrenal cell subtypes supported the sustentacular cells exhibiting stem-like properties. Although pheochromocyte was prior to ACTH&CRH secreting pheochromocyte in pseudotime order, the RNA velocity prediction of POMC+&CRH+ pheochromocytes might be under-estimated because the transcripts of POMC and CRH were all predicted as spliced ones. Based on the spliced versus unspliced phase for CHGA, CHGB, and TH, it showed a clear more dynamics expression in POMC+&CRH+ pheochromocytes than in pheochromocytes. We assumed that ACTH&CRH secreting pheochromocyte have more hormone-producing functions, retain stem- and endocrine-differentiation ability. But further experiments are needed to validate our hypothesis.

There are bidirectional communications between the immune system and the neuroendocrine system (Blalock, 1989). Hormones produced in the endocrine system, especially glucocorticoids, affect the immune system to modulate its function (Imura and Fukata, 1994). Other hormones, such as growth hormone (GH) and prolactin (PRL), also modulate the immune system (Blalock, 1989). It has been proved that the exogenous production of cytokines can stimulate and mediate the release of multiple hormones including ACTH, CRH (Rivier et al., 1989Bernton et al., 1987), and induce the activation of the HPA axis (Gisslinger et al., 1993Fukata et al., 1994Kakucska et al., 1993Murakami N Fukata et al., 1992). Human T cells coordinate the adaptive immunity of different anatomic compartments by producing cytokines and effector molecules (Szabo et al., 2019). The activation of naive T cells through the antigen-specific T cell receptor (TCR) can initiate transcriptional programs that can drive the differentiation of lineage-specific effector functions. CD4+ T cells secrete cytokines to recruit and activate other immune cells, while CD8+ T cells have cytotoxic functions and can directly kill infected or tumor cells. Recent studies have shown that the composition of the T cell subset is related to the specific tissue locations (Carpenter et al., 2018Thome et al., 2014). scRNA-seq can be used to deconvolve the immune system heterogeneity with high resolution. Compared with adrenocortical adenomas which were in CD4+ (with the expression of cytokine receptors, such as the IL-7R) state, T cells in pheochromocytoma, especially T cells in the ectopic ACTH&CRH secreting pheochromocytoma were inactivated CD8+ state, suggesting different tumor microenvironments between adrenocortical adenomas and pheochromocytoma. Previous studies have shown that signaling through IL-7R is essential in the developmental process and regulation of lymphoid cells (Kondrack et al., 2003Tan et al., 2001Tan et al., 2002Lenz et al., 2004Li et al., 2003Seddon et al., 2003), and disruption of the IL-7R signaling pathway may lead to skewed T cell distribution and cause immunodeficiency (Maraskovsky et al., 1996Kaech et al., 2003Carini et al., 1994). Our results indicated the heterogeneity of the immune system between different samples, and CD4+ T cells with the high expression level of IL-7R might be related to adrenal tumor progression, apoptosis, or factors influencing progression such as immune activation. Although we have shown the heterogeneity of immune cell types in different adrenal tumor subtypes, it is unclear how T cells influence different markers, including effector states and interferon-response states. In addition to composition differences, a deeper understanding of the complex interactions between adrenal tumor tissues and immune systems is a key issue in neuroendocrine tumor research.

Overall, we reported a rare case in which ectopic ACTH&CRH-secreting pheochromocytoma on the left adrenal that infiltrated around the kidney and psoas major tissues. We applied scRNA-seq to identify this rare and special adrenal tumor cell. Thus, the majority of our analysis focused on the validation of novel tumor cell type and their multiple hormones-secreting functions, namely, CRH secretion function, ACTH secretion function, and catecholamine secretion function. Also, GAL could be a candidate marker to detect the rare ectopic ACTH+&CRH + secreting pheochromocytes. For future studies, on one hand, we are very concerned about similar suspicious cases in the clinic. On the other hand, we are going for following research for further downstream experiments to validate the molecular mechanism for secreting multiple hormones.

Materials and methods

Clinical specimens collection

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Our study included three adrenal tumor patients, that is, pheochromocytoma with ectopic ACTH and CRH secretion, common pheochromocytoma, and adrenocortical adenoma. All three patients had signed the consent forms at the General Surgery Department of Peking Union Medical College Hospital (PUMCH). The enhanced CT scanning images and laboratory test (ACTH, 24 hr urine-free cortisol, Catecholamines) of relevant patients are listed in Appendix 1. Fresh tumor specimens were collected during surgical resection. For the case of ACTH and CRH secreting pheochromocytoma, we performed the surgical resection of the tumor at left adrenal (esPHEO_T1) and its infiltrating tissues located in the kidney (esPHEO_T3) and masses (esPHEO_T2), and obtained three tumor specimens. The peritumor sample (esPHEO_Adj) was collected from the left adrenal tissue under the supervision of a qualified pathologist. The other two patients underwent left adrenalectomy and provided the other three tumor specimens. In details, one tumor specimen was obtained from the patient with common pheochromocytoma and two tumor specimens were obtained from the patient with adrenocortical adenoma. A total of seven specimens were carefully dissected under the microscope and confirmed by a qualified pathologist.

Single-cell transcriptome library preparation and sequencing

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After the resection, tissue specimens were rapidly processed for single-cell RNA sequencing.

Single-cell suspensions were prepared according to the protocol of Chromium Single Cell 3′ Solution (V2 chemistry). All specimens were washed two times with cold 1× phosphate-buffered saline (PBS). Haemocytometer (Thermo Fisher Scientific) was used to evaluate cell viability rates. Then, we used Countess (Thermo Fisher Scientific) to count the concentration of single-cell suspension, and adjust the concentration to 1000 cells/μl. Samples that were lower than the required cell concentration defined in the user guide (i.e., <400 cells/µl) were pelleted and re-suspended in a reduced volume; and then the concentration of the new solution was counted again. Finally, the cells of the sample were loaded, and the libraries were constructed using a Chromium Single-Cell Kit (version 2). Single-cell libraries were submitted to 150 bp paired-end sequencing on the Illumina NavoSeq platform.

Single-cell RNA-seq data pre-processing and quality control

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After obtaining the paired-end raw reads, we used CellRanger (10× Genomics, v3.1.0) to pre-process the single-cell RNA-seq data. Cell barcodes and unique molecular identifiers (UMIs) of the library were extracted from read1. Then, the reads were split according to their cell (barcode) IDs, and the UMI sequences from read2 were simultaneously recorded for each cell. Quality control on these raw readings was subsequently performed to eliminate adapter contamination, duplicates, and low-quality bases. After filtering barcodes and low-quality readings that were not related to cells, we used STAR (version 2.5.1b) to map the cleaned readings to the human genome (hg19) and retained the uniquely mapped readings for UMIs counts. Next, we estimated the accurate molecular counts and generated a UMI count matrix for each cell by counting UMIs for each sample. Finally, we generated a gene-barcode matrix that showed the barcoded cells and gene expression counts.

Based on the number of total reads, the number of detected gene features, and the percentage of mitochondrial genes, we performed quality control filtering through Seurat (v3.1.5) (Butler et al., 2018Stuart et al., 2019) to discard low-quality cells. Briefly, mitochondrial genes inside one cell were calculated lower than 20%, and total reads in one cell were below 40,000. Also, the cells were further filtered according to the following criteria: PHEO, ACA, and esPHEO samples with no more than 5000, 3000, and 2500 genes were detected, respectively, and at least 200 genes were detected per cell in any sample. Low-quality cells and outliers were discarded, and the single cells that passed the QC criteria were used for downstream analyses. Doublets were predicted by DoubletFinder (v2.0) (McGinnis et al., 2019) and DoubletDecon (v1.1.6) (DePasquale et al., 2019Appendix 1—figure 2).

Clustering analysis and cell phenotype recognition

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Seurat (Butler et al., 2018Stuart et al., 2019) software package was used to perform cell clustering analysis to identify major cell types. All Seurat objects constructed from the filtered UMI-based gene expression matrixes of given samples were merged. We first applied ‘SCTransform’ function to implement normalization, variance stabilization, and feature selection through a regularized negative binomial model. Then, we reduced dimensionality through PCA. According to standard steps implemented in Seurat, highly variable numbers of principal components (PCs) 1–20 were selected and used for clustering using the t-distributed stochastic neighbor embedding method (t-SNE). We identified cell types of these groups based on the expression of canonic cell type markers or inferred by CellMarker database (Zhang et al., 2019). Finally, the four groups of endothelial cells were combined to a larger endothelial cell cluster for downstream analysis. Cellular cluster statistics were added in Supplementary file 2, which presented cell counts for each cellular cluster in different samples and top 10 gene markers. Endothelial cell cluster statistics were added in Supplementary file 3, which presented cell counts for each endothelial cell cluster in different samples and top 10 gene markers.

DEG analysis

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The cell-type-specific genes were identified by running Seurat (Butler et al., 2018Stuart et al., 2019) containing the function of ‘FindAllMarkers’ on a log-transformed expression matrix with the following parameter settings: min.pct=0.25, logfc.threshold=0.25 (i.e., there is at least 0.25 log-scale fold change between the cells inside and outside a cluster), and only.pos=TRUE (i.e., only positive markers are returned). For heatmap and violin plots, the SCT-transformed data from Seurat pipeline were used. Using the Seurat ‘FindMarkers’ function, we found the DEGs between two cell types. We also used R package of clusterProfiler with default parameters to identify gene sets that exhibited significant and consistent differences between two given biological states.

RNA velocity estimation

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We used the velocyto python package (v0.17.17) (La Manno et al., 2018) for distinguishing transcripts as spliced or unspliced mRNAs based on the presence or absence of intronic regions in the transcript. We took aligned reads of BAM file for each sample as input. After per sample abundance estimation, it generated a LOOM file with the loompy package. Then, we used the scVelo (v0.2.3; Bergen et al., 2020) to combine each sample abundance data as well as cell cluster information from the Seurat object. We showed the proportions of abundances for each sample using scvelo.pl.proportions function. The RNA velocity was estimated for each cell for an individual gene at a given time point based on the ratio of its spliced and unspliced transcript. RNA velocity graph was visualized on a UMAP plot, with vector fields representing the averaged velocity of nearby cells. We also visualized some marker genes dynamics portraits with scv.pl.velocity to examine their spliced versus unspliced phase in different cell types.

Pseudotime analysis

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The Monocle2 packages (v2.14.0) (Trapnell et al., 2014) for R were used to determine the pseudotimes of the differentiation of four different cell subtypes, that is, POMC+/CRH + pheochromocytoma, pheochromocytoma, adrenocortical, and sustentacular cells. We converted a Seurat3 integrated object into a Monocle cds object and distributed the composed cell clusters to the Monocle cds partitions. Then, we used Monocle2 to perform trajectory graph learning and pseudotemporal sorting analysis by specifying the sustentacular cells as the root nodes. To identify genes that are significantly regulated as the cells differentiate along the cell-to-cell distance trajectory, we used the differentialGeneTest() function implemented in Monocle2 (Trapnell et al., 2014). Finally, we selected the genes that were differentially expressed on different paths through the trajectory and plotted the pseudotime_heatmap.

Gene regulatory network (regulon) analysis

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We used R package SCENIC (v1.1.2) (Aibar et al., 2017) for gene regulatory network inference. Normalized log counts were used as input to identify co-expression modules by the GRNBoost2 algorithm. Following which, regulons were derived by identifying the direct-binding TF target genes while pruning others based on motif enrichment around transcription start site (TSS) with cisTarget databases. Using aucell, the regulon activity score was measured as the area under the recovery curve (AUC). Additionally, regulon specificity score (RSS) was used for the detection of the cell-type-specific regulons.

Cell-cell communication analysis

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Given the diverse immune and endothelial cell types in the tumor microenvironment, we performed cell-cell communication analysis using CellPhoneDB Python package (2.1.7) (Efremova et al., 2020). We visualized the potential cell-cell interactions among various immune cells, endothelial cells, and other cell types in the different tumor microenvironment (esPHEO, esPHEO_Adj, PHEO, and ACA) (Appendix 1—figure 6).

Whole-exome sequencing

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Genomic DNA extracted from whole blood (esPHEO_Blood), esPHEO_T2, esPHEO_T3, and esPHEO_Adj of the rare Case 1 were sent for whole-exome sequencing. The exomes were captured using the Agilent SureSelect Human All Exon V6 Kit and the enriched exome libraries were constructed and sequenced on the Illumina NovaSeq 6000 platform to generate WES data (150 bp paired-end reads, >100×) according to standard manufacturer protocols. The cleaned reads were aligned to the human reference genome sequence NCBI Build 38 (hg38) using Burrows-Wheeler Aligner (BWA) (v0.7.17) (Li and Durbin, 2009). All aligned BAM were then performed through the same bioinformatics pipeline according to GATK Best Practices (v4.2) (McKenna et al., 2010). We obtained germline variants shared by all tumors and control samples based on variant calling from GATK-HaplotypeCaller. We then used GATK-MuTect2 to call somatic variants in tumors and obtained a high-confidence mutation set after rigorous filtering by GATK-FilterMutectCalls. All variants were annotated using ANNOVAR (v2018Apr16) (Wang et al., 2010). The criteria for filtering variants were as follows: (1) only retained variants located on exon or splice site, and excluded synonymous variants; (2) retained rare variants with minor allele frequencies <5% in any ancestry population groups from public databases (1000 Genomes, ESP6500, ExAC, or the GnomAD); (3) For germline variants, excluded common variants in dbSNP (Build 138) and predicted benign missense variants by SIFT, Polyphen2, and Mutation Taster.

Immunocytochemistry and Immunofluorescence

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Immunocytochemical and immunofluorescent staining experiments were conducted according to standard protocols using antibodies against malinfixed paraffin-embedded (FFPE) tissue specimens. The antibodies and reagents used in the experiments are listed as follows: ACTH (Abcam, ab199007), POMC (ProteinTech, 66358-1-Ig), TH (Abcam, ab112), CRH (ProteinTech, 10944-1-AP), CgA (ProteinTech, 60135-1-Ig), and Human Galanin Antibody (R&D, MAB5854).

Appendix 1

Clinical samples description

Case 1: A 39-year-old lady underwent laparoscopic left adrenal tumor resection in July 2012 at a local hospital. She had a 2-year history of headache, generalized swelling, and palpitations. She was noted to have hypertensive (BP 240/120 mmHg) and typical Cushingoid characteristics, including asthenia, supraclavicular fat deposits, bruises, purple striae, proximal myopathy, and hyperpigmentation. Histopathology confirmed an adrenomedullary chromaffin tumor. During tumor immunostaining, the tumor stained positively for ACTH. After the adrenal surgery, her Cushingoid characteristics, hypokalemia, and hypertension were all relieved.

However, the patient experienced recurrence of symptoms and signs in January 2019 and was admitted to our hospital. It was found that urine and plasma metanephrine were significantly elevated, and plasma ACTH was also high. Enhanced CT scanning of the abdomen revealed bilateral adrenocortical hyperplasia and multiple masses in the left adrenal and around the left kidney. The largest mass lesion was 2.3×1.6 cm2, which invaded upper pole of left kidney. But the I123-MIBG scintigraphy was negative. We performed a surgery to remove left adrenal, kidney, and masses. After the surgery, the patient’s clinical features and symptoms were improved, and the excessive hypercortisolemia and catecholamine eventually returned to normal. IHC revealed positive staining for chromogranin A, ACTH, and CRH, confirming the diagnosis of pheochromocytoma secreting both ACTH and CRH.

Case 2: A 42-year-old male with a 3-year history of headache and palpitations, and a 6-month history of hypertension was admitted to our hospital. Laboratory tests showed that the plasma and urine catecholamines and their metabolites were elevated, and cortisol and ACTH were at the normal level. Enhanced CT showed a 67×70 mm2 left adrenal tumor, and I123-MIBG scintigraphy exhibited positive. We performed a surgery to remove the left adrenal gland. After the surgery, the patient’s clinical features and symptoms were relieved. IHC confirmed the diagnosis of pheochromocytoma.

Case 3: A 50-year-old female came to our hospital with hypertension, hyperkalemia, and Cushingoid symptoms (moon face and central obesity). Enhanced CT scanning revealed a 19×36 mm2 irregular mass in left adrenal gland. The laboratory tests showed ACTH-independent hypercortisolemia. The left adrenal gland was removed, and Cushing’s syndrome was relieved. Resected specimen revealed two tumors in the left adrenal gland, and IHC confirmed the diagnosis of adrenal adenoma.

Appendix 1—table 1
Summary of laboratory test for three cases.
Laboratory test Case 1 Case 2 Case 3 Reference range
ACTH 519.0 24.0 <5 0–46.0 pg/ml
24 hr urine-free cortisol 2024.4 332.4 12.3–103.5 μg/24 hr
Catecholamines
Plasma metanephrines
Normetanephrine 3.28 10.81 0.4 <0.9 nmol/L
Metanephrine 3.44 11.55 0.2 <0.5 nmol/L
24 hr urine
Epinephrine 397.63 56.23 1.92 1.74–6.42 μg/24 hr
Norepinephrine 475.43 82.29 26.17 16.69–40.65 μg/24 hr
Dopamine 432.21 301.71 240.5 120.93–330.5 μg/24 hr
Appendix 1—figure 1

Enhanced CT scanning image for three cases.

(A) Enhanced CT scanning for Case 1 with pheochromocytoma secreting both ACTH and CRH. The abdomen revealed bilateral adrenocortical hyperplasia and multiple masses in the left adrenal and around … see more

Appendix 1—figure 2

Quality control plots and doublet detection for this scRNA-seq study.

Violin plots showing number of total RNAs (A), number of genes (B), and percentage of mitochondrial (mito) genes (C) for cells in seven samples. Doublets were predicted by DoubletFinder (D) and … see more

Appendix 1—figure 3

Four adrenal cell types and their highly expressed genes through single-cell transcriptomic analysis.

Heatmap shows the scaled expression patterns of top 10 marker genes in each cell type. The color keys from white to red indicate relative expression levels from low to high.

Appendix 1—figure 4

Transcription factors detection using SCENIC pipeline.

(A) Binarized heatmap showing the AUC score (area under the recovery curve, scoring the activity of regulons) of the identified regulons plotted for each cell. (B) For each cellular cluster, dot … see more

Appendix 1—figure 5

The spliced versus unspliced phase for marker genes in four types of adrenal cells.

Transcripts were marked as either spliced or unspliced based on the presence or absence of intronic regions in the transcript. For each gene, the scatter plot shows spliced and unspliced ratios in a … see more

Appendix 1—figure 6

Ligand-receptor interaction analysis for CD4+ T cells, CD8+ T cells, and endothelial cells in different tumor microenvironments.

Overview of ligand-receptor interactions between the CD4+ T cells (A), CD8+ T cells (B), endothelial (C), and the other cell types in the different tumor microenvironments. p-values are represented … see more

Appendix 1—figure 7

Whole-exome sequencing identified one shared somatic variant of ACAN comparing variants in tumor samples to controls and Sanger sequencing only confirmed the presence in esPHEO_T3 but not observed in esPHEO_T2.

(A) Distribution of somatic mutations for the rare case with ectopic ACTH&CRH-secreting pheochromocytoma. OncoPrint plots were generated using the R package Maftools for somatic mutations from five … see more

Appendix 1—figure 8

Immunohistochemistry of CgA, ACTH, POMC, CRH, TH, or GAL on serial biopsies from tumor specimen infiltrating tissues located in the kidney (esPHEO_T3).

We observed positive staining signal at tumor left in each slice, while the adjacent kidney was un-stained could be negative controls. The magnification is 0.5×, 2.5×, 10×, and 40× from left to … see more

Appendix 1—figure 9

Immunofluorescence co-staining for POMC&CRH and POMC&TH on two serial biopsies from tumor specimen esPHEO_T3.

The magnification is 10× (top) and 40× (bottom). Red rectangular indicates the magnified area of the location, as shown in Figure 3E.

Data availability

The raw data of scRNA-seq sequencing reads generated in this study were deposited in The National Genomics Data Center (NGDC, https://bigd.big.ac.cn/) under the accession number: PRJCA003766.

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    https://doi.org/10.1530/EJE-11-0272

Decision letter

  1. Murim Choi
    Reviewing Editor; Seoul National University, Republic of Korea
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States
  3. Murim Choi
    Reviewer; Seoul National University, Republic of Korea

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your work entitled “Single-cell transcriptome analysis identifies a unique tumor cell type producing multiple hormones in ectopic ACTH and CRH secreting pheochromocytoma” for further consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Mone Zaidi as the Senior Editor.

Reviewer #1:

The authors identified an extremely rare case of ATCH-dependent Cushing syndrome due to ACTH&CRH secreting pheochromocytoma. They retrieved sugically resected samples from the tumor and subjected them to scRNA-seq, which led them to identify a group of cells that are double-positive for ACTH&CRH. They then performed a series of expriments to confirm that the cells are indeed present in the tissue, and attempted to identify genes that may lie upstream of the process.

Perhaps the most important point of the study is the identification of the double-positive (DP) cells from the patient. However, evidence supporting this observation is relatively scarce other than showing a cell cluster that express POMC, CRH etc (as displayed in Figure 3A, C). Gene expression pattern shown in Figure 3C supports that the DP cells share molecular characteristics with those of pheochromocytes. But in the t-SNE plot, these cells are located far from pheochromocytes in PHEO_T. Rather, the DP cell cluster seems to be branched out from immune cells. If I didn’t read the t-SNP plot wrong, I wonder why the identity of DP cells is closer to the immune cells. Also, it needs to be clarified if the DP cells could be doublets? The authors did not show basic statistics and QA/QC data of the scRNA-seq experiment (as supplementary data for example). They should show that the DP cells are not technical doublet cells.

Another critical question would be what is the genetic driver that induces expression of both hormones in the DP cells? They propose GAL, but the evidence supporting its direct role is not strong and remains speculative.

Comments for the authors:

Overall, this study requires more carefully designed expriments and interpretation. Otherwise, it remains as a descriptive study with vague conclusions, leaving the uniqueness of the sample being the only strength of the study.

1. Colors in Figure 3A are confusing.

2. Figure 5 does not add much to the molecular mechanism. Rather it merely describes physiological consequences by the presence of DP cells. Please consider strengthen or remove it.

3. Isn’t Figure 7B a duplication of Figure 3B?

4. IHC data in Figure 3E, F lack negative controls. And the readers need additional markers to be guided of its anatomical location.

5. Figure 4 compared DEGs between DP cells and other tumor cells. Since the cell groups that were being compared are too different, observing such dramatic differences is not unexpected and hard to coin physiological relevance. Wouldn’t it be more meaningful to compare them to pheochromocytes?

6. The pseudotime analysis in Figure 6 does not answer the question of how the DP cells originated. It should be performed in a such way to suggest genes that marks critical points during the pseudotime branching or proceeding.

Reviewer #2:

In this manuscript Zhang et al. generated single cell RNA sequencing data for the adrenal gland tumors including extremely rare type of tumor, ACTH & CRH-secreting pheochromocytoma. Unbiased clustering analysis discovered a unique tumor cell type that expresses multiple hormones unlike normal adrenal gland cells and other tumor cell types that produce a single hormone. By comparing with other type of tumor cells, they identified specific marker genes of the novel tumor cell type. They also revealed the distinct immune and endothelial cell populations in the microenvironment of different tumor samples.

Although the gene expression profiles of novel cell type can be utilized to reveal the molecular mechanism of this rare tumor associated with Cushing’s syndrome, the data was generated from only a single patient and have not validated in other samples. In addition, the results only provide the list of genes that were specifically expressed in the novel tumor cell type and their potentially related biological pathways, but not detail molecular and cellular characters of the cells. The single cell gene expression profiling data are definitely useful for the researches.

Comments for the authors:

I have several concerns and suggestions, which if addressed would improve the manuscript.

1. The major finding of this manuscript is the presence of multi-functional tumor cell type which produce multiple hormones such as POMC, the precursor of ACTH and CRH. But, this finding was only derived from a single sample and experimentally validated using the same tissue. I understand the sample is very rare, but could the authors validate the result in different tumor samples at least using IHC or IF? If sample is not available, the limitation of the study should be mentioned.

2. Please consider providing full list of marker genes that were used for cell type annotation.

3. Figure 3C does not seem to support the statement “We demonstrated that GAL was expressed in the ACTH+&CRH+ pheochromocyte and ‘regulated the secretion of ACTH'”.

4. The authors identified a unique and important multi-functional cell type but current analyses (differentially expressed genes identification and gene ontology analysis) seem insufficient to characterize molecular feature of ACTH+&CRH+ pheochromocyte. The authors could perform additional comprehensive analysis such as SCENIC analysis in order to identify the master transcription regulator of the cell type.

5. The pseudo-time analysis indicated that sustentacular cells transform to ACTH+&CRH+ pehochromocytes and then to pheochromocyte. The authors utilized Monocle3 in which user has to define the starting points. The authors can validate the result using RNA velocity analysis which also predicts cell transition without the need of prior knowledge about starting point cell type.

6. Given the diverse immune and endothelial cell type in the tumor microenvironment, it would be interesting to perform the cell-cell interaction analysis using the programs such as CellPhoneDB to see if they have distinct regulatory role in different tumor microenvironment.

7. How did the authors define the four subclusters of endothelial cells? Please consider providing list of marker genes.

8. In the method part, how did the authors determine different criteria for the maximum number of genes (no more than 5000, 3000, and 2500 genes for PHEO, ACA, and esPHEO samples, respectively)?

Reviewer #3:

Zhang et al. perform single cell RNA sequencing (scRNA-Seq) of one rare ACTH+CRH-secreting phenochromocytoma (3 anatomically distinct sites from the tumor and one peritumoral site), one typical pheochromocytoma, and two typical adrenocortical adenomas.

Their main findings are as follows: (1) They identify a unique cell type, which they term ACTH+CRH+ pheochromocyte, which appears to be the tumor cell present in the rare ACTH+CRH+ tumor (2) Marker gene analysis reveals that while known adrenal chromaffin markers (CHGA, PNMT) are present in both pheochromocytes and ACTH+CRH+ pheochromocyte, the latter has some unique markers such as GAL and POMC. They validate the marker genes with IHC. (3) Profiling of the non-tumor populations reveals distinct immune microenvironment profile and endothelial cell profile to the rare tumor compared with classical pheochromocytoma and adrenalocortical adenoma.

The main strength of this manuscript is that it involves single-cell profiling of an exceptionally rare tumor type and a distinction from the more common adrenal tumors (pheochromocytoma and adrenocortical adenoma). The broader implication of the authors’ findings is with respect to Dale’s principle, which states that a given neuron releases only one type of neurotransmitter. However, in the case of this tumor, single cell analysis clearly shows that ACTH, CRH, and chatacholemines are being released from the same cell. This is quite interesting and significant. The data will also potentially be valuable to others in the field for analysis in future studies.

There remain some unanswered questions – namely:

(1) What is the cell in normal physiology that gives rise to this ACTH+CRH+ pheochromocytoma?

(2) Do conventional phenochromocytomas differ from the ACTH+CRH+ pheochromocytoma in terms of the cell of origin that is transformed, or in the spectrum of genetic alterations that result in transformation?

Comments for the authors:

Overall, I think this study is of broad interest given the rarity of this tumor type. My comments to the authors to improve the manuscript are as follows:

1. Given how rare the ACTH+CRH+ pheochromocytoma is, I think the study would be substantially strengthened if the authors could perform DNA sequencing (WGS or WES) and describe how, if at all, the genomic landscape differs from conventional pheochromocytoma.

2. Can the authors comment on whether the hypothesis is whether the ACTH+CRH+ pheochromocytoma originates from a rare progenitor cell that is distinct from the chromaffin cell giving rise to pheochromocytoma? If so, can the authors stain a panel of normal adrenal glands with some of their marker genes to try and identify this cell in normal tissues?

3. While the tumor type is interesting for its rarity, the analysis performed is quite standard and comes across as a bit superficial in parts. Although it is understandable that the authors have only one ACTH+CRH+ sample I think they can do more with the data and this would significantly strengthen the manuscript. For example, it would be interesting if the authors can point to specific master regulatory factors that drive the distinct programs in pheochromocytes vs. ACTH+CRH+ pheochromocytes. The immune microenvironment analysis, while inherently descriptive, is also somewhat superficial.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your revised article “Single-cell transcriptome analysis identifies a unique tumor cell type producing multiple hormones in ectopic ACTH and CRH secreting pheochromocytoma” for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Murim Choi as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Mone Zaidi as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Although the reviewers thought that many issues were addressed, they still concerned on the superficial analysis results. Nonetheless, they agreed that the manuscript contains a common interest for publication in eLife as the tumor is an extremely rare case. Please address reviewers’ concerns below.

Reviewer #1:

Although the authors could not address all the questions, especially regarding the origin of DP cells and genetic driver for DP cells, it appears reasonable that they are hard to address as the tumor sample was extremely rare.

Reviewer #2:

Although the authors have satisfactorily addressed most of my points, there are remaining concerns about RNA velocity data.

Please cite any reference for the statement “For the high proportions of unspliced/spliced transcripts, stem-like characteristics of sustentacular cells were supported.” Can global ratio of unspliced/spliced transcripts support stem-like characteristics?

Please elaborate Figure 5 C-F. Currently, they don’t seem to add any information.

Reviewer #3:

In the revised manuscript Zhang et al. have included additional data and analyses including more exhaustive QC, RNA velocity analysis, regulome analysis, and have performed WES of the ACTH/CRH-secreting pheochromocytoma. They have generally addressed my technical concerns from the prior review. I maintain that the analysis remains somewhat superficial and descriptive in parts and this may be somewhat of a missed opportunity to more deeply explore the underlying biology of this unique case, understanding the caveats of its rarity. Nonetheless, I think a description of this tumor at single-cell resolution and availability of the dataset is of value to the scientific community.

However, I would like to see a more careful analysis of the WES data prior to publication. I do not see any basic metrics (mutation rate etc.), description of pathogenicity filtering/annotation, or copy number analysis. The mutations shown are primarily missense and I do not really see any obvious driver genes – how many of these are putative driver vs. passenger mutations? ACAN is mentioned, but what is its significance, if any? The somatic landscape should be discussed in comparison to typical phenochromocytomas and adrenocortical carcinomas, which have been more extensively sequenced. If there is no obvious genetic driver of this ACTH/CRH-secreting phenochromocytoma, that should be stated. If the claim is that ACAN alterations are somehow related to this tumor type, that needs to be substantiated. Or if the implication is that ACAN is a passenger alteration, that needs to be stated explicitly also.

https://doi.org/10.7554/eLife.68436.sa1

Author response

Reviewer #1:

The authors identified an extremely rare case of ATCH-dependent Cushing syndrome due to ACTH&CRH secreting pheochromocytoma. They retrieved surgically resected samples from the tumor and subjected them to scRNA-seq, which led them to identify a group of cells that are double-positive for ACTH&CRH. They then performed a series of experiments to confirm that the cells are indeed present in the tissue, and attempted to identify genes that may lie upstream of the process.

We thank the reviewer for carefully reviewing the manuscript. We updated graphs, added supplementary files of raw data QC and cell cluster statistics, and performed RNA velocity analysis, scenic analysis for the single cell RNA sequencing experiments to response the reviewer’s critiques and strengthen the manuscript. In addition, to investigate the genetic driver for Case 1, we supplemented whole-exome sequencing experiments for all rest specimens, that is, tumors (esPHEO_T2, esPHEO_T3) and controls (esPHEO_Adj, esPHEO_Blood) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma.

Perhaps the most important point of the study is the identification of the double-positive (DP) cells from the patient. However, evidence supporting this observation is relatively scarce other than showing a cell cluster that express POMC, CRH etc (as displayed in Figure 3A, C). Gene expression pattern shown in Figure 3C supports that the DP cells share molecular characteristics with those of pheochromocytes. But in the t-SNE plot, these cells are located far from pheochromocytes in PHEO_T. Rather, the DP cell cluster seems to be branched out from immune cells. If I didn’t read the t-SNP plot wrong, I wonder why the identity of DP cells is closer to the immune cells. Also, it needs to be clarified if the DP cells could be doublets? The authors did not show basic statistics and QA/QC data of the scRNA-seq experiment (as supplementary data for example). They should show that the DP cells are not technical doublet cells.

We thank the reviewer for raising the concerns and providing these helpful suggestions. First, we updated the colors mapped to 16 cellular clusters in Figure 2A and Figure 3A to enhance the color difference between doublet-positive (DP) cells and immune cells. Then, the new analysis based on RNA velocity was performed in the revision, and the results showed that DP cluster was isolated and not branched out from other cell types (including immune cells) from velocity streamlines (Figure 5F). In addition, we added the raw data QC and doublet prediction results of the scRNA-seq experiment as shown in Appendix 1—figure 2 and Supplementary File 1. From the doublets predicted by DoubletFinder and DoubletDecon, it is clarified that almost noDP cells were defined as doublets. Cellular cluster statistics were shown in Supplementary File 2, which presented cell counts for each cellular cluster in different samples and top10 gene markers.

Another critical question would be what is the genetic driver that induces expression of both hormones in the DP cells? They propose GAL, but the evidence supporting its direct role is not strong and remains speculative.

We thank the reviewer for raising these important concerns, and we agree with the reviewer that the presentation about the genetic driver in the previous version of the manuscript is not sufficient enough. We changed the conclusion statement “We demonstrated that GAL was expressed in the ACTH+&CRH+ pheochromocyte and regulated the secretion of ACTH” to “We demonstrated that GAL was expressed in the ACTH+&CRH+ pheochromocyte and might participate in the regulation of ACTH secretion”. (Page 7 line 175-182)

We provided more description and additional analysis about putative genetic driver in the DP cells, as follows:

First, we found GAL co-expressed with POMC and CRH, could be a candidate marker to detect the rare ectopic ACTH+&CRH+ secreting pheochromocytes. It might be involved in the regulation of the hypothalamic-pituitary-adrenal axis. (Page 7 line 175-182, Figure 3, Figure 4).

Second, we also found an additional weak signal of transcription regulons for the DP cells (Page 6 line 153-157, Appendix 1—figure 4). It showed XPBP1 as the specific regulons for ACTH+&CRH+ pheochromocyte and adrenocortical cell type.

Third, to investigate the genetic driver, we supplemented whole-exome sequencing experiments for tumors (esPHEO_T2, esPHEO_T3) and controls (esPHEO_Adj, esPHEO_Blood) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma. We identified 1 shared somatic variant of ACAN (c.5951T>A:p.L1984Q) comparing variants in tumor samples to controls but Sanger sequencing only confirmed the presence in esPHEO_T3 which was not observed in esPHEO_T2 (Page 13 line 352-358, Appendix 1—figure 7).

Comments for the authors:

Overall, this study requires more carefully designed experiments and interpretation. Otherwise, it remains as a descriptive study with vague conclusions, leaving the uniqueness of the sample being the only strength of the study.

We thank the reviewer for carefully reviewing and helpful suggestions. We updated graphs and tables, implemented supplementary analysis for the single-cell RNA sequencing data. Because this case is particularly rare, fresh tissue samples are lacking, currently, frozen tissue samples cannot be assayed by flow cytometry. For all rest of the samples, we can only supplement the whole-exome sequencing experiments for tumors (esPHEO_T2, esPHEO_T3) and controls (esPHEO_Adj, esPHEO_Blood) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma to make our results more comprehensive. Lastly, on one hand, we are very concerned about similar suspicious cases in the clinic. On the other hand, we are going for the following research for further downstream experiments to validate the molecular mechanism for secreting multiple hormones.

1. Colors in Figure 3A are confusing.

We have updated the colors mapped to 16 cellular clusters in Figure 2 and Figure 3 to enhance the color difference between doublet-positive (DP) cells and immune cells.

2. Figure 5 does not add much to the molecular mechanism. Rather it merely describes physiological consequences by the presence of DP cells. Please consider strengthen or remove it.

Due to the previous Figure 5 mainly describe the physiological consequences by the presence of DP cells as the reviewer commented. We have moved it to Figure 4D, because the differential expressed genes between DP cells and other adrenal cell types were shown in Figure 4A and Figure 4C. Combining these figures into a group could complement each other and clarify the secreting functions of the DP cells.

3. Isn’t Figure 7B a duplication of Figure 3B?

Figure 3B presents the frequency distribution of all cell types among different samples, while in Figure 7B we specifically focused on the immune microenvironments and showed statistics of immune cell types. To some extent, they are repetitive since both describe the percentage of immune cells. But the denominators are different for percentage calculation, that is, one is the total number of cells in Figure 3B, the other is the total number of immune cells in Figure 7B.

4. IHC data in Figure 3E, F lack negative controls. And the readers need additional markers to be guided of its anatomical location.

We supplemented IHC figures of CgA, ACTH, POMC, CRH, TH or GAL with magnification (0.5x, 2.5x, 10x, 40x) from tumor specimen infiltrating tissues located in the kidney (esPHEO_T3) in Appendix 1—figure 8. We observed positive staining signal at tumor left in each slice, while the adjacent kidney was un-stained could be negative controls. Red rectangular indicates the magnified area of the location as shown in Figure 3D. The. We supplemented the immunofluorescence (IF) co-staining figures with magnification (10x, 40x) for POMC&CRH and POMC&TH from tumor specimen esPHEO_T3 in Appendix 1—figure 9, where red rectangular indicates the magnified area of the location in Figure 3E.

5. Figure 4 compared DEGs between DP cells and other tumor cells. Since the cell groups that were being compared are too different, observing such dramatic differences is not unexpected and hard to coin physiological relevance. Wouldn’t it be more meaningful to compare them to pheochromocytes?

We analyzed the differentially expressed genes (DEGs) between ACTH+&CRH+ pheochromocyte and the other two subtypes of adrenal tumor cells (pheochromocyte and adrenocortical cells) (Page 9 line 241-245). Such dramatic differences were observed because we set the statistically significant differences as a cut-off p-value < 0.05 and a fold change ≥ 1.5 ( which means a log2 fold change |logFC| ≥ 0.585 ) (Figure 4A). It could more strict such as a cut-off p-value <0.01 and a fold change ≥ 2 ( which means a log2 fold change |logFC| ≥ 1 ). But the top significantly differentially expressed genes were POMC, CRH, GAL etc, as marked in Figure 4A. There is a relatively larger difference in gene expression between DP cells and adrenocortical cells than that between DP cells and pheochromocytes (Figure 4C). Since we didn’t identify any pheochromocytes in esPHEO_adj, we could not compare the DP cells to their adjacent pheochromocytes (Supplementary File 2).

Reviewer #2:

In this manuscript Zhang et al. generated single cell RNA sequencing data for the adrenal gland tumors including extremely rare type of tumor, ACTH & CRH-secreting pheochromocytoma. Unbiased clustering analysis discovered a unique tumor cell type that expresses multiple hormones unlike normal adrenal gland cells and other tumor cell types that produce a single hormone. By comparing with other type of tumor cells, they identified specific marker genes of the novel tumor cell type. They also revealed the distinct immune and endothelial cell populations in the microenvironment of different tumor samples.

Although the gene expression profiles of novel cell type can be utilized to reveal the molecular mechanism of this rare tumor associated with Cushing’s syndrome, the data was generated from only a single patient and have not validated in other samples. In addition, the results only provide the list of genes that were specifically expressed in the novel tumor cell type and their potentially related biological pathways, but not detail molecular and cellular characters of the cells. The single cell gene expression profiling data are definitely useful for the researches.

We thank the reviewer for carefully reviewing and raising insightful critiques. In this study, we reported a rare case in which ectopic ACTH&CRH-secreting pheochromocytoma in the left adrenal. To identify the hormones-secreting cells, we sent specimens for single-cell transcriptome sequencing immediately after the resection. Thus, the majority of our analysis focused on the validation of novel tumor cell type and their multiple hormones-secreting functions. For future studies, on one hand, we are very concerned about similar suspicious cases in the clinic. On the other hand, we are going for following research for further downstream experiments to validate the molecular mechanism for secreting multiple hormones.

Comments for the authors:I have several concerns and suggestions, which if addressed would improve the manuscript.

1. The major finding of this manuscript is the presence of multi-functional tumor cell type which produce multiple hormones such as POMC, the precursor of ACTH and CRH. But, this finding was only derived from a single sample and experimentally validated using the same tissue. I understand the sample is very rare, but could the authors validate the result in different tumor samples at least using IHC or IF? If sample is not available, the limitation of the study should be mentioned.

For the case of ACTH and CRH secreting pheochromocytoma, we performed the surgical resection of the tumor at left adrenal (esPHEO_T1) and its infiltrating tissues located in the kidney (esPHEO_T3) and masses (esPHEO_T2), and obtained 3 tumor specimens. The peritumor sample (esPHEO_Adj) was collected from the left adrenal tissue under the supervision of a qualified pathologist. At first, we performed immunohistochemistry (IHC) staining with chromogranin A (CgA) and ACTH markers for esPHEO_T1 and adjacent specimen (esPHEO_Adj) (Figure 1B). To validate our discovery from scRNA-seq data we implemented IHC of CgA, ACTH, POMC, CRH or TH (Figure 3D) on serial biopsies from another tumor specimen (esPHEO_T3) and added immunofluorescence co-staining for POMC&CRH and POMC&TH on two serial biopsies from esPHEO_T3 (Figure 3E). The frozen tissue of esPHEO_T1 is unavailable and a few remaining for esPHEO_T2. For all rest of tissue samples, we supplemented with the whole-exome sequencing experiments for tumors (esPHEO_T2, esPHEO_T3) and controls (esPHEO_Adj) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma.

2. Please consider providing full list of marker genes that were used for cell type annotation.

We add row annotations for top10 marker genes at the heatmap showing different cellular clusters and their highly expressed genes (Figure 2B). Cellular cluster statistics were supplemented in Supplementary File 2, which presented cell counts for each cellular cluster in different samples and top10 gene markers.

3. Figure 3C does not seem to support the statement “We demonstrated that GAL was expressed in the ACTH+&CRH+ pheochromocyte and ‘regulated the secretion of ACTH'”.

We changed the conclusion sentence to “We demonstrated that GAL was expressed in the ACTH+&CRH+ pheochromocyte and might participate in the regulation of ACTH secretion”. We’re trying to express that: [We found GAL co-expressed with POMC and CRH, could be a candidate marker to detect the rare ectopic ACTH+&CRH+ secreting pheochromocytes. As previous research reported, it might be involved in the regulation of the hypothalamic-pituitary-adrenal axis.]

4. The authors identified a unique and important multi-functional cell type but current analyses (differentially expressed genes identification and gene ontology analysis) seem insufficient to characterize molecular feature of ACTH+&CRH+ pheochromocyte. The authors could perform additional comprehensive analysis such as SCENIC analysis in order to identify the master transcription regulator of the cell type.

We have performed additional analysis (Page 18 line 519-570), including RNA velocity analysis, SCENIC analysis etc. In addition, whole-exome sequencing experiments for tumors (esPHEO_T2, esPHEO_T3) and controls (esPHEO_Adj, esPHEO_Blood) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma were performed to make our results more comprehensive.

First, based on differentially expressed genes identification, we mainly found GAL co-expressed with POMC and CRH, could be a candidate marker to detect the rare ectopic ACTH+&CRH+ secreting pheochromocytes. It might be involved in the regulation of the hypothalamic-pituitary-adrenal axis. (Page 7 line 175-182, Figure 3, Figure 4). Second, applied the SCENIC pipeline, we found an additional weak signal of transcription regulons for the DP cells (Page 6 line 153-157, Appendix 1—figure 4). It showed XPBP1 as the specific regulons for ACTH+&CRH+ pheochromocyte and adrenocortical cell type. Third, the spliced vs. unspliced phase for CHGA, CHGB, and TH from RNA velocity analysis demonstrated a clear more dynamics expression in POMC+&CRH+ pheochromocytes than in pheochromocytes (Appendix 1—figure 5). Lastly, to investigate the genetic driver, the whole exome sequencing identified 1 shared somatic variant of ACAN (c.5951T>A:p.L1984Q) comparing variants in tumor samples to controls but Sanger sequencing only confirmed the presence in esPHEO_T3 which not observed in esPHEO_T2 (Page 13 line 352-358, Appendix 1—figure 7).

5. The pseudo-time analysis indicated that sustentacular cells transform to ACTH+&CRH+ pehochromocytes and then to pheochromocyte. The authors utilized Monocle3 in which user has to define the starting points. The authors can validate the result using RNA velocity analysis which also predicts cell transition without the need of prior knowledge about starting point cell type.

At first, we have added RNA velocity analysis (Figure 5B, Page 10 line 268-286). For the high proportions of unspliced/spliced transcripts in Figure 5B, stem-like characteristics of sustentacular cells were supported. We performed the pseudo-time analysis for the adrenal tumor cell subsets to determine the pattern of the dynamic cell transitional states. Then, we re-run the pseudo-time analysis and used the recommended strategy of Monocel to order cells based on genes that differ between clusters. The sustentacular cells were also in an early stage (Figure 6).

6. Given the diverse immune and endothelial cell type in the tumor microenvironment, it would be interesting to perform the cell-cell interaction analysis using the programs such as CellPhoneDB to see if they have distinct regulatory role in different tumor microenvironment.

To investigate the potential cell-cell interactions among various immune cells, endothelial cells, and other cell types in the different tumor microenvironment (esPHEO, esPHEO_Adj, PHEO, and ACA), we performed additional analysis using the CellPhoneDB Python package in the revised version of our manuscript. As shown in the new Appendix 1—figure 6, we observed very distinct patterns of ligand-receptor pairs for cell-cell interactions in the different tumor microenvironments. Notably, the diverse cell clusters within PHEO tumors exhibited a relatively high abundance of cell-cell connections between different cell types, while the cell-cell interactions within esPHEO_Adj samples were totally different. For example, MIF, one of the most enigmatic regulators of innate and adaptive immune responses, was shown as a specific regulator in esPHEO and PHEO, in contrast to ACA.

7. How did the authors define the four subclusters of endothelial cells? Please consider providing list of marker genes.

The four groups of endothelial cells were combined to a larger endothelial cell cluster for downstream analysis. Endothelial cell cluster statistics were added in Supplementary File 3, which presented cell counts for each endothelial cell cluster in different samples and top10 gene markers.

8. In the method part, how did the authors determine different criteria for the maximum number of genes (no more than 5000, 3000, and 2500 genes for PHEO, ACA, and esPHEO samples, respectively)?

We set the different criteria for the maximum number of genes (no more than 5000, 3000, and 2500 genes for PHEO, ACA and esPHEO samples respectively) based on QC violin plot showing the number of detected genes (Appendix 1—figure 2B).

Reviewer #3:

Zhang et al. perform single cell RNA sequencing (scRNA-Seq) of one rare ACTH+CRH-secreting phenochromocytoma (3 anatomically distinct sites from the tumor and one peritumoral site), one typical pheochromocytoma, and two typical adrenocortical adenomas.

Their main findings are as follows: (1) They identify a unique cell type, which they term ACTH+CRH+ pheochromocyte, which appears to be the tumor cell present in the rare ACTH+CRH+ tumor (2) Marker gene analysis reveals that while known adrenal chromaffin markers (CHGA, PNMT) are present in both pheochromocytes and ACTH+CRH+ pheochromocyte, the latter has some unique markers such as GAL and POMC. They validate the marker genes with IHC. (3) Profiling of the non-tumor populations reveals distinct immune microenvironment profile and endothelial cell profile to the rare tumor compared with classical pheochromocytoma and adrenalocortical adenoma.

The main strength of this manuscript is that it involves single-cell profiling of an exceptionally rare tumor type and a distinction from the more common adrenal tumors (pheochromocytoma and adrenocortical adenoma). The broader implication of the authors’ findings is with respect to Dale’s principle, which states that a given neuron releases only one type of neurotransmitter. However, in the case of this tumor, single cell analysis clearly shows that ACTH, CRH, and chatacholemines are being released from the same cell. This is quite interesting and significant. The data will also potentially be valuable to others in the field for analysis in future studies.

There remain some unanswered questions – namely:

(1) What is the cell in normal physiology that gives rise to this ACTH+CRH+ pheochromocytoma?

(2) Do conventional phenochromocytomas differ from the ACTH+CRH+ pheochromocytoma in terms of the cell of origin that is transformed, or in the spectrum of genetic alterations that result in transformation?

We thank the reviewer for carefully reviewing the manuscript and raising insightful questions. To response the reviewer’s questions and strengthen the manuscript, we supplemented analysis and experiments as much as possible.

First, we performed RNA velocity analysis (Figure 5, Page 10 line 268-286) to investigate dynamic information in individual cells. For the high proportions of unspliced/spliced transcripts in Figure 5B, stem-like characteristics of sustentacular cells were supported. Also, the spliced vs. unspliced phase for CHGA, CHGB, and TH from RNA velocity analysis demonstrated a clear more dynamics expression in POMC+&CRH+ pheochromocytes than in pheochromocytes (Appendix 1—figure 5).

Second, we re-run the pseudo-time analysis (Page 10 line 288-300) and used the recommended strategy of Monocel to order cells based on genes that differ between clusters. The sustentacular cells were also in an early state (Figure 6), which was in accordance with their exhibited stem-like properties and the highest unspliced proportion among non-immune cell types in the RNA velocity analysis (Figure 5B). The results also showed a transition from sustentacular cells to pheochromocytes and then to ACTH+&CRH+ pheochromocyte, and adrenocortical cells were on another branch (Figure 6). As we discussed in manuscript (Page 14 line 391-398), although pheochromocyte was prior to ACTH&CRH secreting pheochromocyte in pseudotime order, we assumed that ACTH&CRH secreting pheochromocyte have more hormone-producing functions, retain stem- and endocrine-differentiation ability. But further experiments are needed to validate our hypothesis.

Third, we applied SCENIC analysis pipeline (Page 6 line 153-157, Appendix 1—figure 4) to detect the transcription factors (which are jointly called regulons) alongside their candidate target genes, and yield specific regulons for each cellular cluster. We observed an additional weak signal of transcription regulons (XPBP1) for the ACTH+CRH+ pheochromocytoma and adrenocortical cell type.

Furthermore, to investigate the genetic driver, we supplemented with the whole-exome sequencing (WES) experiments for all rest of tissue samples (esPHEO_T2, esPHEO_T3 and esPHEO_Adj) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma and the blood sample (esPHEO_Blood). Based on WES data, we identified 1 shared somatic variant of ACAN (c.5951T>A:p.L1984Q) comparing variants in tumor samples to controls but Sanger sequencing only confirmed the presence in esPHEO_T3 which not observed in esPHEO_T2 (Page 13 line 352-358, Appendix 1—figure 7).

Overall, additional analyses and experiments have presented more comprehensive results which appropriately address the questions raised by the reviewer. But they also provide new hypothesis remaining unanswered questions. For future studies, on one hand, we are very concerned about similar suspicious cases in the clinic. On the other hand, we are going for following research for further downstream experiments to validate the molecular mechanism for secreting multiple hormones.

Comments for the authors:

Overall, I think this study is of broad interest given the rarity of this tumor type. My comments to the authors to improve the manuscript are as follows:

1. Given how rare the ACTH+CRH+ pheochromocytoma is, I think the study would be substantially strengthened if the authors could perform DNA sequencing (WGS or WES) and describe how, if at all, the genomic landscape differs from conventional pheochromocytoma.

The frozen tissue of esPHEO_T1 and PHEO_T is unavailable and a few remaining for esPHEO_T2. For all rest of tissue samples, we supplemented with the whole-exome sequencing experiments for tumors (esPHEO_T2, esPHEO_T3) and controls (esPHEO_Adj) from the rare case with ectopic ACTH&CRH-secreting pheochromocytoma. (Page 13 line 352-358, Appendix 1—figure 7)

2. Can the authors comment on whether the hypothesis is whether the ACTH+CRH+ pheochromocytoma originates from a rare progenitor cell that is distinct from the chromaffin cell giving rise to pheochromocytoma? If so, can the authors stain a panel of normal adrenal glands with some of their marker genes to try and identify this cell in normal tissues?

(Page 14 line 389-398) The RNA velocity estimation and pseudo-time analysis of different adrenal cell subtypes supported the sustentacular cells exhibiting stem-like properties. Although pheochromocyte was prior to ACTH&CRH secreting pheochromocyte in pseudotime order, the RNA velocity prediction of POMC+&CRH+ pheochromocytes might be under-estimated because the transcripts of POMC and CRH were all predicted as spliced ones. Based on the spliced vs. unspliced phase for CHGA, CHGB and TH it showed a clear more dynamics expression in POMC+&CRH+ pheochromocytes than in pheochromocytes. We assumed that ACTH&CRH secreting pheochromocyte have more hormone-producing functions, retain stem- and endocrine-differentiation ability. But further experiments are needed to validate our hypothesis.

We thank the reviewer for raising good recommendations. We would like to test marker genes in normal tissues. But it is difficult to obtain normal adrenal glands in clinic. We searched POMC, CRH and GAL in Genotype-Tissue Expression Project (GTEx), which launched by the National Institutes of Health (NIH). GTEx has established a database (https://www.gtexportal.org/home/) to study genes in different normal tissues. The results, as shown in Author response images 1-3: POMC is over-expressed in pituitary, but expressed at a very low level in adrenal gland. CRH is overexpressed in brain-hypothalamus, but almost not expressed in adrenal gland. GAL is overexpressed in pituitary and brain-hypothalamus, but almost not expressed in adrenal gland.

Author response image 1

Author response image 2

Author response image 3

3. While the tumor type is interesting for its rarity, the analysis performed is quite standard and comes across as a bit superficial in parts. Although it is understandable that the authors have only one ACTH+CRH+ sample I think they can do more with the data and this would significantly strengthen the manuscript. For example, it would be interesting if the authors can point to specific master regulatory factors that drive the distinct programs in pheochromocytes vs. ACTH+CRH+ pheochromocytes. The immune microenvironment analysis, while inherently descriptive, is also somewhat superficial.

Based on the routine differentially expressed genes analysis, we mainly found GAL co-expressed with POMC and CRH, could be a candidate marker to detect the rare ectopic ACTH+&CRH+ secreting pheochromocytes. As previous research reported, it might be involved in the regulation of the hypothalamic-pituitary-adrenal axis. (Page 7 line 175-182, Figure 3, Figure 4). Second, applied the SCENIC pipeline, we found an additional weak signal of transcription regulons for the DP cells (Page 6 line 153-157, Appendix 1—figure 4). It showed XPBP1 as the specific regulons for ACTH+&CRH+ pheochromocyte and adrenocortical cell type. Furthermore, RNA velocity analysis (Appendix 1—figure 5) demonstrated a clear more dynamics expression in POMC+&CRH+ pheochromocytes than in pheochromocytes.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #2:

Although the authors have satisfactorily addressed most of my points, there are remaining concerns about RNA velocity data.

Please cite any reference for the statement “For the high proportions of unspliced/spliced transcripts, stem-like characteristics of sustentacular cells were supported.” Can global ratio of unspliced/spliced transcripts support stem-like characteristics?

Please elaborate Figure 5 C-F. Currently, they don’t seem to add any information.

(Page 10 line 269-286, Figure 5 and its legend) We thank the reviewer for carefully reviewing and raising this concern about RNA velocity. We have revised our manuscript to add a paragraph and cite the appropriate references in the updated revision. Previously study had observed that the unspliced transcripts were enriched in genes involved in DNA binding and RNA processing in hematopoietic stem cells [1]. And Schwann cell precursors, which can differentiate into chromaffin cells, also had positive unspliced-spliced phase portrait [2]. Therefore, we claimed that, as for the high proportions of unspliced/spliced transcripts, stem-like characteristics of sustentacular cells were supported.

We remove Figure 5 C-D, as the reviewer mentioned, because they don’t seem to add any valuable information. Besides, we added more description about the results for new Figure 5 C-D (old Figure 5 E-F) in Page 10 line 282-288, which showed estimated pseudo-time grounded on transcriptional dynamics and velocity streamlines accounting for speed and direction of motion. These results indicated that medullary cells are earlier than cortical cells (new Figure 5C). From velocity streamlines (new Figure 5D), we found the four adrenal cell subtypes, that is, POMC+&CRH+ pheochromocytes, pheochromocytes adrenocortical cells, and sustentacular cells, were independent respectively and not directed toward other cell types.

Reviewer #3:

In the revised manuscript Zhang et al. have included additional data and analyses including more exhaustive QC, RNA velocity analysis, regulome analysis, and have performed WES of the ACTH/CRH-secreting pheochromocytoma. They have generally addressed my technical concerns from the prior review. I maintain that the analysis remains somewhat superficial and descriptive in parts and this may be somewhat of a missed opportunity to more deeply explore the underlying biology of this unique case, understanding the caveats of its rarity. Nonetheless, I think a description of this tumor at single-cell resolution and availability of the dataset is of value to the scientific community.

However, I would like to see a more careful analysis of the WES data prior to publication. I do not see any basic metrics (mutation rate etc.), description of pathogenicity filtering/annotation, or copy number analysis. The mutations shown are primarily missense and I do not really see any obvious driver genes – how many of these are putative driver vs. passenger mutations? ACAN is mentioned, but what is its significance, if any? The somatic landscape should be discussed in comparison to typical phenochromocytomas and adrenocortical carcinomas, which have been more extensively sequenced. If there is no obvious genetic driver of this ACTH/CRH-secreting phenochromocytoma, that should be stated. If the claim is that ACAN alterations are somehow related to this tumor type, that needs to be substantiated. Or if the implication is that ACAN is a passenger alteration, that needs to be stated explicitly also.

(Page 13 line 359-378; Page 21 line 587-597; Supplementary File 4) We thank the reviewer for carefully reviewing and raising concerns about our WES analysis.

We supplemented the variants filtering criteria in Page 21 line 587-597, and further discussed the WES results in Page 13 line 359-378. Besides, the germline and somatic mutations were listed in Supplementary File 4 including detailed annotations.

Genetic mutations of phaeochromocytoma and paraganglioma are mainly classified into two major clusters, that is, pseudo hypoxic pathway and kinase signaling pathways [3-4]. We did not find any gene mutations or copy number variations that were related to these two major clusters. We only identified 1 shared somatic variant of ACAN mutation (c.5951T>A:p.L1984Q) comparing variants in tumor samples to controls. ACAN, encoding a major component of the extracellular matrix, is a member of the aggrecan/versican proteoglycan family. Mutations of ACAN were reported related to steroid levels [5]. It is well-established that circulating steroid levels are linked to inflammatory diseases such as arthritis, because arthritis as well as most autoimmune disorders result from a combination of several predisposing factors including the stress response system such as the hypothalamic-pituitary-adrenocortical axis [6]. But no direct evidence related to ACAN for phaeochromocytoma. Therefore, no obvious genetic driver was found to explain the rare case of ACTH/CRH-secreting phaeochromocytoma. Further investigations would be needed to uncover the relation between ACAN to phaeochromocytoma.

References:

[1]. Bowman TV, McCooey AJ, Merchant AA, Ramos CA, Fonseca P, Poindexter A, Bradfute SB, Oliveira DM, Green R, Zheng Y, Jackson KA, Chambers SM, McKinney-Freeman SL, Norwood KG, Darlington G, Gunaratne PH, Steffen D, Goodell MA. Differential mRNA processing in hematopoietic stem cells. Stem Cells. 2006. Mar;24(3):662-70.

[2]. La Manno G., Soldatov R., Zeisel A., Braun E., Hochgerner H., Petukhov V., Lidschreiber K., Kastriti M.E., Lönnerberg P., Furlan A. RNA velocity of single cells. Nature. 2018 560:494-498.

[3] Pillai S, Gopalan V, Smith RA, Lam AK. Updates on the genetics and the clinical impacts on phaeochromocytoma and paraganglioma in the new era. Crit Rev Oncol Hematol. 2016. Apr;100:190-208.

[4] Nölting S, Grossman AB. Signaling pathways in pheochromocytomas and paragangliomas: prospects for future therapies. Endocr Pathol. 2012. Mar;23(1):21-33.

[5] Yousri NA, Fakhro KA, Robay A, Rodriguez-Flores JL, Mohney RP, Zeriri H, Odeh T, Kader SA, Aldous EK, Thareja G, Kumar M, Al-Shakaki A, Chidiac OM, Mohamoud YA, Mezey JG, Malek JA, Crystal RG, Suhre K. Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population. Nat Commun. 2018 Jan 23;9(1):333.

[6]. Cutolo M, Sulli A, Pizzorni C, Craviotto C, Straub RH. Hypothalamic-pituitary-adrenocortical and gonadal functions in rheumatoid arthritis. Ann N Y Acad Sci. 2003 May;992:107-17.

https://doi.org/10.7554/eLife.68436.sa2

Article and author information

Author details

  1. Xuebin Zhang

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review and editing

    Contributed equally with

    Penghu Lian and Mingming Su

    Competing interests

    No competing interests declared

  2. Penghu Lian

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

    Contributed equally with

    Xuebin Zhang and Mingming Su

    Competing interests

    No competing interests declared

  3. Mingming Su

    Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

    Contributed equally with

    Xuebin Zhang and Penghu Lian

    Competing interests

    No competing interests declared

    ORCID icon “This ORCID iD identifies the author of this article:”0000-0002-1393-0800

  • Zhigang Ji

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Investigation, Methodology, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

  • Jianhua Deng

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

  • Guoyang Zheng

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

  • Wenda Wang

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

  • Xinyu Ren

    Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Data curation, Visualization

    Competing interests

    No competing interests declared

  • Taijiao Jiang

    1. Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
    2. Suzhou Institute of Systems Medicine, Jiangsu, China
    Contribution

    Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

  • Peng Zhang

    Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
    Contribution

    Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review and editing

    For correspondence

    zhangpengdyx@163.com

    Competing interests

    No competing interests declared

    ORCID icon “This ORCID iD identifies the author of this article:”0000-0002-6218-1885

  • Hanzhong Li

    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
    Contribution

    Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing

    For correspondence

    lihzh@pumch.cn

    Competing interests

    No competing interests declared

Funding

Chinese Academy of Medical Sciences (2017-I2M-1-001)

  • Hanzhong Li

Chinese Academy of Medical Sciences (2021-I2M-1-051)

  • Taijiao Jiang

Chinese Academy of Medical Sciences (2021-I2M-1-001)

  • Taijiao Jiang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by CAMS Innovation Funds for Medical Sciences (CIFMS), which were 2017-I2M-1-001, 2021-I2M-1-051 and 2021-I2M-1-001.

Ethics

Specimen collection was obtained after appropriate research consents (and assents when applicable) and was approved (protocol number: S-K431) by the Institutional Review Board, Peking Union Medical College Hospital. All information obtained was protected and de-identified.

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Murim Choi, Seoul National University, Republic of Korea

Reviewer

  1. Murim Choi, Seoul National University, Republic of Korea

Publication history

  1. Received: March 16, 2021
  2. Accepted: December 13, 2021
  3. Accepted Manuscript published: December 14, 2021 (version 1)
  4. Accepted Manuscript updated: December 15, 2021 (version 2)
  5. Version of Record published: December 31, 2021 (version 3)

Copyright

© 2021, Zhang et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

from https://elifesciences.org/articles/68436

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