Metformin Inhibits Cell Proliferation and ACTH Secretion In AtT20 Cells Via Regulating the Mapk Pathway

Posted on January 16, 2024 by MaryO

Abstract

We investigated the impact of metformin on ACTH secretion and tumorigenesis in pituitary corticotroph tumors. The mouse pituitary tumor AtT20 cell line was treated with varying concentrations of metformin. Cell viability was assessed using the CCK-8 assay, ACTH secretion was measured using an ELISA kit, changes in the cell cycle were analyzed using flow cytometry, and the expression of related proteins was evaluated using western blotting. RNA sequencing was performed on metformin-treated cells. Additionally, an in vivo BALB/c nude xenograft tumor model was established in nude mice, and immunohistochemical staining was conducted for further verification. Following metformin treatment, cell proliferation was inhibited, ACTH secretion decreased, and G1/S phase arrest occurred. Analysis of differentially expressed genes revealed cancer-related pathways, including the MAPK pathway. Western blotting confirmed a decrease in phosphorylated ERK1/2 and phosphorylated JNK. Combining metformin with the ERK1/2 inhibitor Ulixertinib resulted in a stronger inhibitory effect on cell proliferation and POMC (Precursors of ACTH) expression. In vivo studies confirmed that metformin inhibited tumor growth and reduced ACTH secretion. In conclusion, metformin inhibits tumor progression and ACTH secretion, potentially through suppression of the MAPK pathway in AtT20 cell lines. These findings suggest metformin as a potential drug for the treatment of Cushing’s disease.

Introduction

Pituitary neuroendocrine tumors (PitNETs) are common intracranial tumors with an incidence of 1/1000, and pituitary corticotroph tumors (corticotroph PitNETs) account for approximately 15% of all PitNETs. Most corticotroph PitNETs are functional tumors with clinical manifestations of Cushing’s disease characterized by central obesity, hypertension, diabetes mellitus, and psychosis (Cui et al., 2021). The increased cortisol due to the overproduction of adrenocorticotropic hormone (ACTH) significantly reduces the overall quality of survival and life expectancy of patients (Sharma et al., 2015; Barbot et al., 2018). Currently, treatment of corticotroph PitNETs mainly relies on surgery resection, pharmacologic therapy or radiotherapy may be considered for patients with residual tumors or those who are unable to undergo surgery. While several agents, such as cabergoline and pasireotide, are clinically approved, the effect is unsatisfactory, and potentially serious side effects exist. Therefore, there is an urgent need to develop novel therapeutic drugs for corticotroph PitNETs.

Metformin is a biguanide hypoglycemic agent for the treatment of type 2 diabetes. In addition to its hypoglycemic effect, numerous studies identified the therapeutic role of metformin in the prevention and treatment of various tumors including small cell lung cancer, colorectal cancer, breast cancer, ovarian cancer, and neuroendocrine tumors (Lu et al., 2022; Kamarudin et al., 2019; Wang et al., 2019; Thakur et al., 2019), making metformin a promising adjuvant drug in the therapy of cancers. Besides, it has been reported that metformin improves metabolic and clinical outcomes in patients treated with glucocorticoids. However, to date, limited studies explore the potential anti-cancer effect of metformin in corticotroph PitNETs. Recent studies report the use of metformin for blood glucose and body weight control in patients with Cushing’s disease (Ceccato et al., 2015), while the role of metformin on ACTH secretion and tumor growth in corticotroph PitNETs remains to be elucidated.

In the current study, we investigated the effect of metformin in corticotroph PitNETs and performed RNA-sequencing to identify the potential mechanisms of metformin. We found that metformin inhibited cell proliferation and ACTH secretion of AtT20 cells in a dose-dependent manner. Besides, metformin induced cell cycle arrest via decreased ERK1/2 phosphorylation and increased P38 phosphorylation. Our results revealed that metformin is a potential drug for corticotroph PitNET therapy.

Section snippets

Cell culture

The ACTH-secreting mouse pituitary tumor cell line AtT-20 was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). Cells were cultured in F-12K medium (ATCC; Catalog No. 30-2004), supplemented with 15% fetal bovine serum (FBS; Gibco), and 2.5% horse serum (Gibco) as suggested. AtT20 cells were cultured in a humidified incubator at 37 °C in 5% CO2.

Reagents and drugs

Metformin and Ulixertinib were purchased from MedChemExpress (MCE), Metformin was dissolved in sterile H2O and prepared as a

Results

Metformin inhibits cell proliferation and ACTH secretion, and leads to cell cycle arrest in AtT20 cells.

We used CCK-8 assay to detect the cell viability of AtT20 cells after treatment with different concentrations of metformin at 24 h, 48 h, and 72 h. The results showed that metformin significantly inhibited the proliferation of AtT20 cells in a dose-dependent manner (Fig. 1A). Similarly, prolonged (6 days) treatment of AtT20 cells with a lower concentration (400 μM) of metformin also inhibited

Discussion

Metformin, acting by binding to PEN2 and initiating the subsequent AMPK signaling pathway in lysosomes, is the most commonly used oral hypoglycemic agent (Hundal et al., 2000; Ma et al., 2022). Previous reports demonstrated metformin as a potential anti-tumor agent in cancer therapy (Evans et al., 2005). Metformin, either alone or in combination with other drugs, has been shown to reduce cancer risk in a variety of tumors including pituitary neuroendocrine tumors (PitNETs) (Thakur et al., 2019;

Conclusion

Our study demonstrated that metformin suppressed cell proliferation and decreased ACTH secretion in AtT20 cells via the MAPK pathway. Our results revealed that metformin is a potential anti-tumor drug for the therapy of corticotroph PitNETs, which deserves further study.

Funding

This study was supported by the National Natural Science Foundation of China (82072804, 82071559).

CRediT authorship contribution statement

Yingxuan Sun: Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Jianhua Cheng: Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing. Ding Nie: Formal analysis, Writing – review & editing. Qiuyue Fang: Data curation, Formal analysis, Writing – review & editing. Chuzhong Li: Conceptualization, Supervision, Writing – original draft, Writing – review & editing, Funding acquisition. Yazhuo Zhang:

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

We thank Mr. Hua Gao (Cell Biology Laboratory, Beijing Neurosurgical Institute, China) for support with the techniques.

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From https://www.sciencedirect.com/science/article/abs/pii/S0303720723002915

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High-resolution Contrast-enhanced MRI With Three-Dimensional Fast Spin Echo Improved the Diagnostic Performance for Identifying Pituitary Microadenomas In Cushing’s Syndrome

Posted on July 26, 2023 by MaryO

Abstract

Objectives

To assess the diagnostic performance of high-resolution contrast-enhanced MRI (hrMRI) with three-dimensional (3D) fast spin echo (FSE) sequence by comparison with conventional contrast-enhanced MRI (cMRI) and dynamic contrast-enhanced MRI (dMRI) with 2D FSE sequence for identifying pituitary microadenomas.

Methods

This single-institutional retrospective study included 69 consecutive patients with Cushing’s syndrome who underwent preoperative pituitary MRI, including cMRI, dMRI, and hrMRI, between January 2016 to December 2020. Reference standards were established by using all available imaging, clinical, surgical, and pathological resources. The diagnostic performance of cMRI, dMRI, and hrMRI for identifying pituitary microadenomas was independently evaluated by two experienced neuroradiologists. The area under the receiver operating characteristics curves (AUCs) were compared between protocols for each reader by using the DeLong test to assess the diagnostic performance for identifying pituitary microadenomas. The inter-observer agreement was assessed by using the κ analysis.

Results

The diagnostic performance of hrMRI (AUC, 0.95–0.97) was higher than cMRI (AUC, 0.74–0.75; p ≤ .002) and dMRI (AUC, 0.59–0.68; p ≤ .001) for identifying pituitary microadenomas. The sensitivity and specificity of hrMRI were 90–93% and 100%, respectively. There were 78% (18/23) to 82% (14/17) of the patients, who were misdiagnosed on cMRI and dMRI and correctly diagnosed on hrMRI. The inter-observer agreement for identifying pituitary microadenomas was moderate on cMRI (κ = 0.50), moderate on dMRI (κ = 0.57), and almost perfect on hrMRI (κ = 0.91), respectively.

Conclusions

The hrMRI showed higher diagnostic performance than cMRI and dMRI for identifying pituitary microadenomas in patients with Cushing’s syndrome.

Key Points

• The diagnostic performance of hrMRI was higher than cMRI and dMRI for identifying pituitary microadenomas in Cushing’s syndrome.

• About 80% of patients, who were misdiagnosed on cMRI and dMRI, were correctly diagnosed on hrMRI.

• The inter-observer agreement for identifying pituitary microadenomas was almost perfect on hrMRI.

Introduction

Cushing’s syndrome, caused by excessive exposure to glucocorticoids, is associated with considerable morbidity and increased mortality [1]. Cushing’s syndrome has diverse manifestations, including central obesity, moon facies, purple striae, and hypertension [2]. Cushing’s disease, due to adrenocorticotropic hormone (ACTH) hypersecretion from pituitary adenomas, is the most common etiology of ACTH-dependent Cushing’s syndrome [1, 2]. According to the Endocrine Society Clinical Practice Guideline, transsphenoidal surgery is the first-line treatment for Cushing’s disease [3]. The identification of pituitary adenomas on preoperative MRI can significantly increase the postoperative remission rate from 50 to 98% [4]. Therefore, it is critical to identify pituitary adenomas on MRI before surgery.

However, there are considerable challenges in identifying ACTH-secreting pituitary adenomas. This is because about 90% of the tumors are microadenomas (less than 10 mm in size) and the median diameter at surgery is about 5 mm [5, 6]. Conventional contrast-enhanced MRI (cMRI) using a two-dimensional (2D) fast spin echo (FSE) sequence has been routinely used to acquire images with 2- to 3-mm slice thickness, but some microadenomas are difficult to be identified on cMRI, resulting in false negatives reported in up to 50% of patients with Cushing’s disease [7]. Dynamic contrast-enhanced MRI (dMRI) increases the sensitivity of identifying pituitary adenomas to 66% [8], but it also increases false positives at the same time [9, 10]. The 3D spoiled gradient recalled (SPGR) sequence has been introduced in high-resolution contrast-enhanced MRI (hrMRI) to acquire images with 1- to 1.2-mm slice thickness. It is reported that the 3D SPGR sequence is superior to the 2D FSE sequence in the identification of pituitary adenomas with a sensitivity of up to 80% [11,12,13], but it cannot satisfy the clinical needs that about 20% of the lesions are still missed. Therefore, techniques are needed that can help better identify pituitary adenomas, particularly microadenomas. Previously, the 3D FSE sequence was recommended in patients with hyperprolactinemia [14]. Recently, the 3D FSE sequence has developed rapidly and can provide superior image quality with diminished artifacts [15]. Sartoretti et al demonstrated in a very effective fashion that the 3D FSE sequence is a reliable alternative for pituitary imaging in terms of image quality [16]. However, to our knowledge, few studies have investigated the diagnostic performance of 3D FSE sequences for identifying ACTH-secreting pituitary adenomas, particularly microadenomas.

The aim of our study was to assess the diagnostic performance of hrMRI with 3D FSE sequence by comparison with cMRI and dMRI with 2D FSE sequence for identifying ACTH-secreting pituitary microadenomas in patients with Cushing’s syndrome.

Materials and methods

This single-institutional retrospective study was approved by the Institutional Review Board of our hospital. The study was conducted in accordance with the Helsinki Declaration. The informed consent was waived due to the retrospective nature of the study.

Study participants

We retrospectively reviewed the medical records and imaging studies of 186 consecutive patients with ACTH-dependent Cushing’s syndrome, who underwent a combined protocol of cMRI, dMRI, and hrMRI from January 2016 to December 2020. Postoperative patients with Cushing’s disease (n = 97), patients with ectopic ACTH syndrome who underwent pituitary exploration (n = 2), and patients with macroadenomas (n = 5) or lack of pathology (n = 13) were excluded from the study. Finally, 69 patients with ACTH-dependent Cushing’s syndrome were included in the current study (Fig. 1) and the patients included were all surgically confirmed.

Fig. 1
figure 1

Flowchart of patient inclusion/exclusion process and image analysis. ACTH adrenocorticotropic hormone, CD Cushing’s disease, EAS ectopic ACTH syndrome, T1WI T1-weighted imaging, T2WI T2-weighted imaging

MRI protocol

All the patients were imaged on a 3.0 Tesla MR scanner (Discovery MR750w, GE Healthcare) using an 8-channel head coil. The MRI protocol included coronal T2-weighted imaging, coronal T1-weighted imaging, and sagittal T1-weighted imaging before contrast injection. After contrast injection of gadopentetate dimeglumine (Gd-DTPA) at 0.05 mmol/kg (0.1 mL/kg) with a flow rate of 2 mL/s followed by a 10-mL saline solution flush, dMRI and cMRI with 2D FSE sequence were obtained first, and hrMRI with 3D FSE sequence using variable flip angle technique was performed immediately afterward. Detailed acquisition parameters are presented in Table S1.

Image analysis: diagnostic performance

Image interpretation was independently conducted by two experienced neuroradiologists (F.F. and H.Y. with 25 and 16 years of experience in neuroradiology, respectively), who were blinded to patient information. The evaluation order of cMRI, dMRI, and hrMRI sequences was randomized. The identification of pituitary microadenomas on images was scored based on a three-point scale (0 = poor; 1 = fair; 2 = excellent). Scores of 1 or 2 represented the identification of the lesion. Reference standards were established by using all available imaging, clinical, surgical, and pathological resources, with a multidisciplinary team approach.

Image analysis: image quality

Two readers (Z.L. and B.H. with 4 years of experience in radiology, respectively) were asked to assess the image quality of cMRI, dMRI, and hrMRI. Before exposure to images used in the current study, these readers underwent a training session to make sure that they were comparable to the experienced neuroradiologists in terms of image quality assessment. Images were presented in a random order. Image quality was assessed by using a 5-point Likert scale [17], including overall image quality (1 = non-diagnostic; 2 = poor; 3 = fair; 4 = good; 5 = excellent), sharpness (1 = non-diagnostic; 2 = not sharp; 3 = a little sharp; 4 = moderately sharp; 5 = satisfyingly sharp), and structural conspicuity (1 = non-diagnostic; 2 = poor; 3 = fair; 4 = good; 5 = excellent). An example of image quality assessment is shown in Table S2. Final decision was made through a consensus agreement.

The mean signal intensity of pituitary microadenomas, pituitary gland, and noise on cMRI, dMRI, and hrMRI was measured using an operator-defined region of interest. For noise, a 10-mm2 region of interest was placed in the background, and noise was defined as the standard deviation of the signal intensity of the background [17]. For pituitary microadenomas and pituitary gland, the region of interest should include a representative portion of the structure. The mean signal intensity of the pituitary microadenoma was replaced with that of the pituitary gland when no microadenoma was identified. A signal-to-noise ratio (SNR) was defined as the mean signal intensity of the pituitary microadenoma divided by noise. A contrast-to-noise ratio (CNR) was defined as the absolute difference of the mean signal intensity between the normal pituitary gland and pituitary microadenomas divided by noise [17]. Supplementary Fig. 1 shows how to measure the SNR and CNR with the region of interest in a contrast-enhanced pituitary MRI. Supplementary Fig. 2 shows the selection of images for the SNR and CNR calculation.

Statistical analysis

The κ analysis was conducted to assess the inter-observer agreement for identifying pituitary microadenomas. The κ value was interpreted as follows: below 0.20, slight agreement; 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, substantial agreement; greater than 0.80, almost perfect agreement.

To assess the diagnostic performance of cMRI, dMRI, and hrMRI for identifying pituitary microadenomas, the receiver operating characteristic curves were plotted and the area under curves (AUCs) were compared between MR protocols for each reader by using the DeLong test. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated. The Mann–Whitney U test was used to evaluate the difference in image quality scores and the Wilcoxon signed-rank test was used to evaluate SNR and CNR measurements between MR protocols. A p value of less than 0.05 was considered statistically significant. Statistical analysis was performed using MedCalc Statistical Software (version 20.0.15; MedCalc Software) and SPSS Statistics (version 22.0; IBM).

Results

Clinical characteristics

A total of 69 patients (median age, 39 years; interquartile range [IQR], 29–54 years; 38 women [55%]) with ACTH-dependent Cushing’s syndrome were included in the study and their clinical characteristics are shown in Table 1. Among the 69 patients, 60 (87%) patients were diagnosed with Cushing’s disease and 9 (13%) were ectopic ACTH syndrome. The median disease course was 36 months (IQR, 12–78 months). The median serum cortisol, ACTH, and 24-h urine free cortisol level before surgery were 33.0 μg/dL (IQR, 25.1–40.1 μg/dL; normal range 4.0–22.3 μg/dL), 77.2 ng/L (IQR, 55.0–124.0 ng/L; normal range 0–46 ng/L), and 422.0 μg (IQR, 325.8–984.6 μg; normal range 12.3–103.5 μg), respectively. The median serum cortisol and 24-h urine free cortisol level after surgery were 3.0 μg/dL (IQR, 1.8–18.4 μg/dL) and 195.6 μg (IQR, 63.5–1240.3 μg), respectively. The median diameter of pituitary microadenomas was 5 mm (IQR, 4–5 mm), ranging from 3 to 9 mm.

Table 1 Clinical characteristics of the patients

Diagnostic performance of cMRI, dMRI, and hrMRI for identifying pituitary microadenomas

The inter-observer agreement for identifying pituitary microadenomas by κ statistic between two readers was moderate on cMRI (κ = 0.50), moderate on dMRI (κ = 0.57), and almost perfect on hrMRI (κ = 0.91), respectively.

The diagnostic performance for identifying pituitary microadenomas on cMRI, dMRI, hrMRI, and combined cMRI and dMRI is summarized in Table 2. For reader 1, the diagnostic performance of hrMRI (AUC, 0.95; 95%CI: 0.87, 0.99) was higher than that of cMRI (AUC, 0.75; 95%CI: 0.63, 0.85; p = 0.002), dMRI (AUC, 0.59; 95%CI: 0.47, 0.71; p < 0.001), and combined cMRI and dMRI (AUC, 0.65; 95%CI: 0.53, 0.76; p = 0.001). For reader 2, the diagnostic performance of hrMRI (AUC, 0.97; 95%CI: 0.89, 1.00) was higher than that of cMRI (AUC, 0.74; 95%CI: 0.63, 0.84; p = 0.001), dMRI (AUC, 0.68; 95%CI: 0.56, 0.79; p = 0.001), and combined cMRI and dMRI (AUC, 0.70; 95%CI: 0.58, 0.80; p = 0.003).

Table 2 Diagnostic performance of cMRI, dMRI, and hrMRI for identifying pituitary microadenomas

For reader 1, 23 of the 69 patients (33%) were misdiagnosed on both cMRI and dMRI, but 18 of the 23 misdiagnosed patients (78%) were correctly diagnosed on hrMRI. For reader 2, 17 of the 69 patients (25%) were misdiagnosed on both cMRI and dMRI, but 14 of the 17 misdiagnosed patients (82%) were correctly diagnosed on hrMRI.

Figure 2 shows that a 5-mm pituitary microadenoma was identified on preoperative pituitary MRI. The margin of the lesion was fully delineated on hrMRI, but not on cMRI and dMRI. Figure 3 shows that a 3-mm pituitary microadenoma was missed on cMRI, but identified on dMRI and hrMRI. Figure 4 shows that a 5-mm pituitary microadenoma was correctly diagnosed on hrMRI, but missed on cMRI or dMRI. Figure 5 shows that a 4-mm pituitary microadenoma was evident on coronal images as well as reconstructed axial and reconstructed sagittal images on hrMRI.

Fig. 2

figure 2

Images in a 56-year-old man with Cushing’s disease. The 5-mm pituitary microadenoma (arrow) can be identified on (a) coronal contrast-enhanced T1-weighted image and (b) coronal dynamic contrast-enhanced T1-weighted image obtained with two-dimensional (2D) fast spin echo (FSE) sequence, but the margin is not fully delineated. The lesion (arrow) is well delineated on (c) coronal contrast-enhanced T1-weighted image on high-resolution MRI obtained with 3D FSE sequence. d Intraoperative endoscopic photograph during transsphenoidal surgery after exposure of the sellar floor shows a round pituitary microadenoma (arrow)

Fig. 3

figure 3

Images in a 34-year-old woman with Cushing’s disease. No tumor is identified on (a) coronal contrast-enhanced T1-weighted image obtained with two-dimensional (2D) fast spin echo (FSE) sequence. The 3-mm pituitary microadenoma (arrow) with delayed enhancement is identified on the left side of the pituitary gland on (b) coronal dynamic contrast-enhanced T1-weighted image obtained with 2D FSE sequence and (c) coronal contrast-enhanced T1-weighted image on high-resolution MRI obtained with 3D FSE sequence. d Intraoperative endoscopic photograph during transsphenoidal surgery shows a 3-mm pituitary microadenoma (arrow)

Fig. 4

figure 4

Images in a 43-year-old man with Cushing’s disease. The lesion is missed on (a) coronal contrast-enhanced T1-weighted image and (b) coronal dynamic contrast-enhanced T1-weighted image obtained with two-dimensional (2D) fast spin echo (FSE) sequence. c Coronal contrast-enhanced T1-weighted image on high-resolution MRI obtained with 3D FSE sequence shows a round pituitary microadenoma (arrow) measuring approximately 5 mm with delayed enhancement on the left side of the pituitary gland. d Intraoperative endoscopic photograph for microsurgical resection of the 5-mm pituitary microadenoma (arrow)

Fig. 5

figure 5

Images in a 48-year-old woman with Cushing’s disease. Preoperative high-resolution contrast-enhanced MRI using three-dimensional fast spin echo sequence shows a 4-mm pituitary microadenoma (arrow) with delayed enhancement is well delineated on the left side of the pituitary gland on (a) coronal, (b) reconstructed axial, and (c) reconstructed sagittal contrast-enhanced T1-weighted images. d Intraoperative endoscopic photograph during transsphenoidal surgery after exposure of the sellar floor shows a round pituitary microadenoma (arrow)

Image quality of cMRI, dMRI, and hrMRI

Image quality scores of cMRI, dMRI, and hrMRI are presented in Table 3. Scores for overall image quality, sharpness, and structural conspicuity on hrMRI (overall image quality, 5.0 [IQR, 5.0–5.0]; sharpness, 5.0 [IQR, 4.5–5.0]; structural conspicuity, 5.0 [IQR, 5.0–5.0]) were higher than those on cMRI (overall image quality, 4.0 [IQR, 3.5–4.0]; sharpness, 4.0 [IQR, 3.0–4.0]; structural conspicuity, 4.0 [IQR, 4.0–4.0]; p < 0.001 for all) and dMRI (overall image quality, 4.0 [IQR, 4.0–4.0]; sharpness, 4.0 [IQR, 4.0–4.0]; structural conspicuity, 4.0 [IQR, 4.0–4.5]; p < 0.001 for all).

Table 3 Image quality scores on cMRI, dMRI, and hrMRI

The SNR and CNR measurements are shown in Table 4. The SNR of the pituitary microadenomas on hrMRI (67.5 [IQR, 51.2–92.1]) was lower than that on cMRI (82.3 [IQR, 61.8–127.2], p < 0.001), but higher than that on dMRI (53.9 [IQR, 35.2–72.6], p = 0.001). The CNR on hrMRI (26.2 [IQR, 15.1–41.0]) was higher than that on cMRI (10.6 [IQR, 0–42.6], p = 0.023) and dMRI (11.2 [IQR, 0–29.8], p < 0.001).

Table 4 SNR and CNR on cMRI, dMRI, and hrMRI

Discussion

The identification of pituitary microadenomas is considerably challenging but critical in patients with ACTH-dependent Cushing’s syndrome. Our study demonstrated that hrMRI with 3D FSE sequence had higher diagnostic performance (AUC, 0.95–0.97) than cMRI (AUC, 0.74–0.75; p ≤ 0.002) and dMRI (AUC, 0.59–0.68; p ≤ 0.001) for identifying pituitary microadenomas. To our knowledge, there are no previous studies specifically evaluating the identification of pituitary microadenomas on hrMRI with 3D FSE sequence by comparison with cMRI and dMRI in patients with ACTH-dependent Cushing’s syndrome, and this is the largest study conducted in ACTH-secreting microadenomas with a sensitivity of more than 90%.

Recently, techniques for pituitary evaluation have developed rapidly. Because of false negatives and false positives on cMRI and dMRI using 2D FSE sequence [7, 9, 10], a 3D SPGR sequence was introduced for identifying pituitary adenomas. Previous studies demonstrated that the 3D SPGR sequence performed better than the 2D FSE sequence in the identification of pituitary adenomas with a sensitivity of up to 80% [11,12,13]. In patients with hyperprolactinemia, the 3D FSE sequence was recommended [14] and the 3D FSE sequence has rapidly developed recently with superior image quality [15, 16], suggesting that the 3D FSE sequence may be a reliable alternative for identifying pituitary adenomas. However, to our knowledge, few studies have investigated the diagnostic performance of the 3D FSE sequence for identifying ACTH-secreting pituitary adenomas. To fill the gaps, we conducted the current study and revealed that images obtained with the 3D FSE sequence had higher sensitivity (90–93%) in identifying pituitary microadenomas, than that in previous studies using the 3D SPGR sequence [8, 11,12,13].

There is a trade-off between spatial resolution and image noise. The reduced slice thickness can overcome the partial volume averaging effect, but it is associated with increased image noise [17]. Strikingly, our study showed that hrMRI had higher image quality scores than cMRI and dMRI, in terms of overall image quality, sharpness, and structural conspicuity. The SNR of the pituitary microadenomas on cMRI was slightly higher than that on hrMRI in our study. This is because the SNR was calculated as the mean signal intensity of the pituitary gland (instead of the pituitary microadenoma) divided by noise when no microadenoma was identified, and the mean signal intensity of the pituitary gland is higher than that of the pituitary microadenoma. About 40% of pituitary microadenomas were missed on cMRI, whereas less than 10% of pituitary microadenomas were missed on hrMRI. Given the situation mentioned above, the SNR on hrMRI was lower than that on cMRI. However, the CNR on hrMRI was significantly higher than that on cMRI and dMRI. Therefore, hrMRI in our study can dramatically improve the spatial resolution with high CNR, enabling the better identification of pituitary microadenomas.

The identification of pituitary adenomas on preoperative MRI in patients with ACTH-dependent Cushing’s syndrome could help the differential diagnosis of Cushing’s syndrome and aids surgical resection of lesions. It should be noted that most of the pituitary adenomas in patients with Cushing’s disease are microadenomas [5, 6]. In our study, all the tumors are microadenomas with a median diameter of 5 mm (IQR, 4–5 mm), making the diagnosis more challenging. The sensitivity of identifying pituitary adenomas decreased from 80 to 72% after excluding macroadenomas in a previous study [12], whereas the sensitivity of identifying pituitary microadenomas in our study was 90–93% on hrMRI. In the current study, hrMRI performed better than cMRI, dMRI, and combined cMRI and dMRI, with high AUC (0.95–0.97), high sensitivity (90–93%), and high specificity (100%), superior to previous studies [8, 11,12,13]. The high sensitivity of hrMRI for identifying pituitary adenomas will help surgeons improve the postoperative remission rate [4]. The high specificity of hrMRI will assist clinicians to consider ectopic ACTH syndrome, and then perform imaging to identify ectopic tumors. Besides, the inter-observer agreement for identifying pituitary microadenomas was almost perfect on hrMRI (κ = 0.91), which was moderate on cMRI (κ = 0.50) and dMRI (κ = 0.57). Therefore, hrMRI using the 3D FSE sequence is a potential alternative that can significantly improve the identification of pituitary microadenomas.

Limitations of the study included its retrospective nature and the relatively small sample size in patients with ectopic ACTH syndrome as negative controls. The bias may be introduced in the patient inclusion process. Only those patients who underwent all the cMRI, dMRI, and hrMRI scans were included. In fact, some patients will bypass hrMRI when obvious pituitary adenomas were detected on cMRI and dMRI. These patients were not included in the current study because of lack of hrMRI findings. Given the situation, the sensitivity of identifying pituitary adenomas will be higher with the enrollment of these patients. Besides, the timing of the sequence acquisition after contrast injection is essential [16] and bias may be introduced due to the postcontrast enhancement curve of both the pituitary gland and the microadenoma [14]. In the future, a prospective study with different sequence acquisition orders is needed to minimize possible interference caused by the postcontrast enhancement curve. Moreover, a larger sample size is also needed to verify the diagnostic performance of hrMRI using 3D FSE sequence for identifying pituitary microadenomas and to determine whether it can replace 2D FSE or 3D SPGR sequences for routinely evaluating the pituitary gland.

In conclusion, hrMRI with 3D FSE sequence showed higher diagnostic performance than cMRI and dMRI for identifying pituitary microadenomas in patients with Cushing’s syndrome.

Abbreviations

ACTH:
Adrenocorticotropic hormone
AUC:
Area under the receiver operating characteristics curve
cMRI:
Conventional contrast-enhanced MRI
CNR:
Contrast-to-noise ratio
dMRI:
Dynamic contrast-enhanced MRI
FSE:
Fast spin echo
hrMRI:
High-resolution contrast-enhanced MRI
IQR:
Interquartile range
SNR:
Signal-to-noise ratio
SPGR:
Spoiled gradient re

called

References

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Acknowledgements

We thank Dr. Kai Sun, Medical Research Center, Peking Union Medical College Hospital, for his guidance on the statistical analysis in this study.

Funding

This study has received funding from the National Natural Science Foundation of China (grant 82071899), the National Key Research and Development Program of China (grants 2016YFC1305901, 2020YFA0804500), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grants 2017-I2M-3–008, 2021-I2M-1–025), the Beijing Natural Science Foundation (grant L182067) and National High Level Hospital Clinical Research Funding (2022-PUMCH-B-067, 2022-PUMCH-B-114).

Author information

Author notes

  1. Zeyu Liu and Bo Hou contributed equally to this work and share first authorship
  2. Hui You and Feng Feng contributed equally to this work and share corresponding authorship

Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing Dongcheng Distinct, Beijing, 100730, China

    Zeyu Liu, Bo Hou, Hui You, Mingli Li & Feng Feng

  2. Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing Dongcheng Distinct, Beijing, 100730, China

    Lin Lu, Lian Duan & Huijuan Zhu

  3. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing Dongcheng Distinct, Beijing, 100730, China

    Kan Deng & Yong Yao

  4. State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan Wangfujing Dongcheng Distinct, Beijing, 100730, China

    Yong Yao, Huijuan Zhu & Feng Feng

Corresponding authors

Correspondence to Hui You or Feng Feng.

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Guarantor

The scientific guarantor of this publication is Feng Feng.

Conflict of interest

The authors of this manuscript declare no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Supplementary Information

Below is the link to the electronic supplementary material.

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Cushing’s Disease Patients are More Likely to Have Ocular Hypertension

Posted on December 29, 2022 by MaryO

The following is the summary of “Increased Risk of Ocular Hypertension in Patients With Cushing’s Disease” published in the December 2022 issue of Glaucoma by Ma, et al.

Ocular hypertension was more common in people with Cushing’s illness. The usage of steroids in the body is a major contributor to high intraocular pressure (IOP). Topical or systemic glucocorticoid use may increase the prevalence of ocular hypertension in the general population from 30–40%. The prevalence of ocular hypertension in endogenous hypercortisolemia and the ophthalmological consequences following endocrine remission after surgical resection are unknown. During the period of January 2019 through July 2019, all patients with Cushing’s disease (CD) who were hospitalized at a tertiary pituitary facility for surgical intervention had their intraocular pressure (IOP), vision field, and peripapillary retinal nerve fiber layer thickness recorded.

Nonfunctioning pituitary adenoma (NFPA) patients and acromegaly patients from the same time period were used as comparison groups. Researchers showed postoperative changes in IOP, estimated the odds ratio (OR), and identified risk variables for the development of ocular hypertension. About 52 patients with CD were included in the study (mean age 38.4±12.4 years). Patients with CD had an IOP that was 19.4±5.4 mm Hg in the left eye and 20.0±7.1 mm Hg in the right eye, which was significantly higher than that of patients with acromegaly (17.5±2.3 mm Hg in the left eye and 18.6±7.0 mm Hg in the right eye, P=0.033) and NFPA (17.8±2.6 mm Hg in the left eye and 17.4±2.4 mm Hg in the right eye, Ocular hypertension was diagnosed in 21 eyes (20.2%) of CD patients, but only 4 eyes (4.7%) of acromegaly patients and 4 eyes (4.5%) of NFPA patients. Patients with CD had an odds ratio (OR) of 5.1 [95% CI, 1.3-25.1, P=0.029] and 6.6 [95% CI, 1.8-30.3, P=0.007] for developing ocular hypertension compared with the 2 control groups.

Higher levels of urine-free cortisol were associated with an increased risk of ocular hypertension in CD patients (OR=19.4, 95% CI, 1.7-72.6). Patients with CD saw a decrease in IOP at 1 month following surgery, and this improvement was maintained for another 2 months. Researchers conclude that endogenous hypercortisolemia should be included as part of the glaucoma assessment due to the increased risk of ocular hypertension in CD. Ophthalmologists and neuroendocrinologists should use their judgment in light of this finding.

Source:  journals.lww.com/glaucomajournal/Fulltext/2022/12000/Increased_Risk_of_Ocular_Hypertension_in_Patients.3.aspx

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Development of Human Pituitary Neuroendocrine Tumor Organoids to Facilitate Effective Targeted Treatments of Cushing’s Disease

Posted on November 7, 2022 by MaryO

Abstract

(1) Background: Cushing’s disease (CD) is a serious endocrine disorder caused by an adrenocorticotropic hormone (ACTH)-secreting pituitary neuroendocrine tumor (PitNET) that stimulates the adrenal glands to overproduce cortisol. Chronic exposure to excess cortisol has detrimental effects on health, including increased stroke rates, diabetes, obesity, cognitive impairment, anxiety, depression, and death. The first-line treatment for CD is pituitary surgery. Current surgical remission rates reported in only 56% of patients depending on several criteria. The lack of specificity, poor tolerability, and low efficacy of the subsequent second-line medical therapies make CD a medical therapeutic challenge. One major limitation that hinders the development of specific medical therapies is the lack of relevant human model systems that recapitulate the cellular composition of PitNET microenvironment.
(2) Methods: human pituitary tumor tissue was harvested during transsphenoidal surgery from CD patients to generate organoids (hPITOs).
(3) Results: hPITOs generated from corticotroph, lactotroph, gonadotroph, and somatotroph tumors exhibited morphological diversity among the organoid lines between individual patients and amongst subtypes. The similarity in cell lineages between the organoid line and the patient’s tumor was validated by comparing the neuropathology report to the expression pattern of PitNET specific markers, using spectral flow cytometry and exome sequencing. A high-throughput drug screen demonstrated patient-specific drug responses of hPITOs amongst each tumor subtype. Generation of induced pluripotent stem cells (iPSCs) from a CD patient carrying germline mutation CDH23 exhibited dysregulated cell lineage commitment.
(4) Conclusions: The human pituitary neuroendocrine tumor organoids represent a novel approach in how we model complex pathologies in CD patients, which will enable effective personalized medicine for these patients.

1. Introduction

Cushing’s disease (CD) is a serious endocrine disorder caused by an adrenocorticotropic hormone (ACTH)-secreting pituitary neuroendocrine tumor (PitNET) that stimulates the adrenal glands to overproduce cortisol [1,2,3,4]. The WHO renamed pituitary adenomas as PitNETs [5]. While PitNETs have been defined as benign, implying that these tumors cause a disease that is not life threatening or harmful to health, in fact chronic exposure to excess cortisol has wide-ranging and detrimental effects on health. Hypercortisolism causes increased stroke rates, diabetes, obesity, depression, anxiety, and a three-fold increase in the risk of death from cardiovascular disease and cancer [4,6,7,8].
The first-line treatment for CD is pituitary surgery, which is followed by disease recurrence in 50% of patients during the 10-year follow-up period after surgery in the hands of an experienced surgeon [9,10,11]. Studies have demonstrated that surgical failures and recurrences of CD are common, and despite multiple treatments, biochemical control is not achieved in approximately 30% of patients. This suggests that in routine clinical practice, initial and long-term disease remission is not achieved in a substantial number of CD patients [7,12]. Hence, medical therapy is often considered in the following situations: when surgery is contraindicated or fails to achieve remission, or when recurrence occurs after apparent surgical remission. While stereotactic radiosurgery treats incompletely resected or recurrent PitNETs, the main drawbacks include the longer time to remission (12–60 months) and the risk of hypopituitarism [3,13,14]. There is an inverse relationship between disease duration and reversibility of complications associated with the disease, thus emphasizing the importance of identifying an effective medical strategy to rapidly normalize cortisol production by targeting the pituitary adenoma [4,7,12]. Unfortunately, the lack of current standard of care treatments with low efficacy and tolerability makes CD a medical therapeutic challenge.
The overall goal of medical therapy for CD is to target the signaling mechanisms to lower cortisol levels in the body [15,16]. The drugs offered for treatment of CD vary in the mechanism of action, safety, tolerability, route of administration, and drug–drug interactions [15,16]. In the era of precision medicine [17], where it is imperative to identify effective therapies early, there is an urgent need to accelerate the identification of therapies targeted to the ACTH-secreting pituitary tumor which are tailored for each individual patient. The absence of preclinical models that replicate the complexity of the PitNET microenvironment has prevented us from acquiring the knowledge to advance clinical care by implementing therapies specifically targeting the tumor, which would have a higher efficacy and tolerability for CD patients. In this instance, organoids can replicate much of the complexity of an tumor. An “organoid” is defined as a three-dimensional cell structure, grown from primary cells of dissociated pituitary tumors in Matrigel matrix, which proliferate, and differentiate in three dimensions, eventually replicating key biological properties of the tissue [18]. While pituitary cell lines predominantly represent hormonal lineages, these cultures do not reproduce the primary pituitary tissue because of the tumor transformation and non-physiological 2D culture conditions [19,20,21]. Pituitary tissue-derived organoids have been generated from mouse models [22,23]. While several human and rat pituitary spheroid/aggregate/tumoroid models have been reported, these cultures consist of poorly differentiated cells with high replicative potential which can affect drug response and produce data that poorly translate to the clinic [24,25]. In this study, we developed an organoid model derived from human PitNETs that replicated much of the cellular complexity and function of the patient’s tumor. Organoids derived from corticotroph PitNETs retained the genetic alterations of the patient’s primary tissue.

2. Materials and Methods

2.1. Generation and Culture of Human Pituitary Neuroendocrine Tumor (PitNET) Organoids

Patients with planned transsphenoidal surgery for pituitary tumors were identified in the outpatient neurosurgery clinics. Tissues were collected under the St. Joseph’s Hospital and Barrow Neurological Institute Biobank collection protocol PHXA-05TS038 and collection of outcomes data protocol PHXA-0004-72-29, with the approval of the Institutional Review Board (IRB) and patient consent. Samples were de-identified and shipped to the Zavros laboratory (University of Arizona) for processing.
Pituitary tumor tissue was collected in Serum-Free Defined Medium (SFDM) supplemented with ROCK inhibitor (Y27632, 10 µM), L-glutamine (2 mM), A83-01 (activin receptor-like kinase (Alk) 4/5/7 inhibitor, 0.5 mM), penicillin/streptavidin (1%), kanamycin (1%), amphotericin/gentamycin (0.2%), CHIR-98014 (4 mM), and thiazovivin (TZV, 2.5 mM). Tissues that contained red blood cells were incubated with Red Blood Cell (RBC) Lysis Buffer according to the manufacturer’s protocol (Thermo Fisher Scientific, San Fransisco, CA, USA). Tissues were dissected into small pieces, transferred to digestion buffer (DMEM/F12 supplemented with 0.4% collagenase 2, 0.1% hyaluronic acid, 0.03% trypsin-EDTA) and incubated for 5–10 min at 37 °C with gentle shaking. Tissue was further incubated with Accutase™ (Thermo Fisher Scientific) for 5 min at 37 °C. Enzymatically dissociated cells were pelleted and washed in DPBS supplemented with antibiotics at a 400 relative centrifugal force (RCF) for 5 min. Dissociated adenoma cells were resuspended in Matrigel™, and Matrigel™ domes containing the cells were then plated in culture dishes and overlaid with pituitary growth media (Supplemental Table S1). The culture was maintained at 37 °C at a relative humidity of 95% and 5% CO2. Organoid growth medium was replenished every 3–4 days and passaged after 15 days in culture.

2.2. Generation of Induced Pluripotent Stem Cells (iPSCs)

Induced pluripotent stem cell lines (iPSC lines) were generated from control individuals (no reported disease) or CD patients according to published protocols by the University of Arizona iPSC Core [26]. All human iPSC lines were tested and found to be negative for mycoplasma contamination using the Mycoalert Mycoplasma testing kits (LT07-318, Lonza), and no karyotype abnormalities were found (KaryoStat+, Thermo).

2.3. Pituitary Organoids Generated from iPSCs

Six well culture plates were coated with 2 mL/well 0.67% Matrigel (diluted in E8 media, UA iPSC core, 151169-01) and incubated at 37 °C at a relative humidity of 95% and 5% CO2 overnight. The iPSC lines were reprogrammed from the blood of either a healthy donor (JCAZ001) or a CD patient (iPSC7 and iPSC1063) at the University of Arizona iPSC Core. Passage 12 iPSCs were plated onto the coated plates and incubated at 37 °C at a relative humidity of 95% and 5% CO2. At 70% confluency, cells were passaged to freshly coated 24 well plates at a ratio of 1:8 and grown to 85–90% confluency before beginning the directed differentiation schedule. From days 0 to 3, cells were cultured in E6 media supplemented with 1% penicillin/streptomycin, 10 μM SB431542, and 5 ng/mL BMP4. BMP4 was withdrawn from the culture at day 3. Starting on day 4, the cells were cultured in E6 media, supplemented with 10 μM SB431542, 30 ng/mL human recombinant SHH, 100 ng/mL FGF8b, 10 ng/mL FGF18, and 50 ng/mL FGF10. Fifteen days after culture, the cells were harvested in cold E6 media by pipetting and resuspended in Matrigel™ (20,000 cells/50 mL Matrigel™). Matrigel™ domes containing the cells were plated in culture dishes and overlaid with differentiation media containing E6 media which was supplemented with 10 μM Y-27632, 30 ng/mL human recombinant SHH, 100 ng/mL FGF8b, 10 ng/mL FGF18, and 50 ng/mL FGF10 (Supplemental Table S2). Organoids were cultured for a further 15 days at 37 °C at a relative humidity of 95% and 5% CO2.

2.4. Spectral Flow Cytometry (Cytek™ Aurora)

The multicolor flow cytometry panel was designed using the Cytek® Full Spectrum Viewer online tool to calculate the similarity index (Supplemental Figure S1). The organoids were harvested in cold SFDM media and centrifuged at 400× g for 5 min. Supernatant was discarded and organoids were dissociated to single cells using Accutase® (Thermo Fisher Scientific 00-4555-56). The enzymatic reaction was stopped using prewarmed DPBS, and cells were then centrifuged at 400× g for 5 min and incubated with fluorochrome-conjugated/unconjugated primary surface or cytoplasmic antibodies (Supplemental Figure S1) at 4 °C for 30 min. Cells were then washed with Cell Staining Buffer (BioLegend # 420-201) and incubated with secondary antibodies (Supplemental Figure S1) at 4 °C for 30 min. Cells were fixed using Cytofix/Cytoperm™ Fixation/Permeabilization Solution (BD Biosciences # 554714) at 4 °C for 20 min, followed by washing with Fixation/Permeabilization wash buffer. Cells were labeled with fluorochrome-conjugated/unconjugated intracellular primary antibodies (Supplemental Figure S1) at 4 °C for 30 min, then washed and incubated with secondary antibodies at 4 °C for 30 min. Cells were resuspended in cell staining buffer and fluorescence and measured using the Cytek Aurora 5 Laser Spectral Flow Cytometer. An unstained cell sample was fixed and used as a reference control. UltraComp eBeads™, Compensation Beads (Thermo Fisher Scientific # 01-2222-42) were stained with the individual antibodies and used as single stain controls for compensation and gating. Data were acquired using the Cytek™ Aurora and analyzed using Cytobank software (Beckman Coulter, Indianapolis, IN, USA).

2.5. Whole Mount Immunofluorescence

Organoids were immunostained using published protocols by our laboratory [27,28,29]. Proliferation was measured by using 5-ethynyl-2′-deoxyuridine (EdU) incorporation according to the Manufacturer’s protocol (Click-IT EdU Alexa Fluor 555 Imaging Kit, Thermo Fisher Scientific C10338). Co-staining was performed by blocking fixed organoids with 2% donkey serum (Jackson Immuno Research, # 017-000-121) diluted in 0.01% PBST for 1hr at room temperature. Organoids were then incubated overnight at 4 °C with primary antibodies, followed by secondary antibodies and Hoechst (Thermo Fisher Scientific H1399, 1:1000 in 0.01% PBST) for 1 h at room temperature. Human specific primary antibodies used included: rabbit anti-ACTH (Thermo Fisher Scientific 701293, 1:250), rabbit anti-Synaptophysin (Thermo Fisher Scientific PA5-27286, 1:100), species PIT1 (Thermo Fisher Scientific PA5-98650, 1:50), rabbit anti-LH (Thermo Fisher Scientific PA5-102674, 1:100), mouse anti-FSH (Thermo Fisher Scientific MIF2709, 1:100), mouse anti-PRL (Thermo Fisher Scientific CF500720, 1:100), Alexa Flour conjugated GH (NB500-364AF647, 1:100), and mouse anti-CAM5.2 (SIGMA 452M-95, 1:250). The secondary antibodies used included Alexa Fluor 488 Donkey Anti Rabbit IgG (H+L) (Thermo Fisher Scientific A21206, 1:100) or Alexa Fluor 647 Donkey Anti Mouse IgG (H+L) (Thermo Fisher Scientific A31571, 1:100). Organoids were visualized and images were acquired by confocal microscopy using the Nikon CrestV2 Spinning Disk (Nikon, Melville, NY, USA). Fluorescence intensity and percentage of EdU positive cells of total cells, were calculated using Nikon Elements Software (Version 5.21.05, Nikon, Melville, NY, USA).

2.6. Nuclear Morphometric Analysis (NMA)

Nuclear Morphometric Analysis (NMA) using treated organoids was performed based on a published protocol that measures cell viability based on the changes in nuclear morphology of the cells, using nuclear stain Hoechst or DAPI [30]. Images of organoid nuclei were analyzed using the ImageJ Nuclear Irregularity Index (NII) plugin for key parameters, which included cell area, radius ratio, area box, aspect, and roundness. Using the published spreadsheet template [30], the NII of each cell was calculated with the following formula: NII = Aspect − Area Box + Radius Ratio + Roundness. The area vs. NII of vehicle-treated cells were plotted as a scatter plot using the template, and was considered as the normal cell nuclei. The same plots were generated for each condition, and the NII and area of treated cells were compared to the normal nuclei, and classified as one of the following NMA populations: Normal (N; similar area and NII), Mitotic (S; similar area, slightly higher NII), Irregular (I; similar area, high NII), Small Regular (SR; apoptotic, low area and NII), Senescent (LR; high area, low NII), Small Irregular (SI; low area, high NII), or Large Irregular (LI; high area, high NII). Cells classified as SR exhibited early stages of apoptosis, and cells classified as either I, SI, or LI exhibited significant nuclear damage. The percentage of cells in each NII classification category were calculated and plotted as a histogram using GraphPad Prism.

2.7. ELISA

Concentration of secreted ACTH in conditioned media that was collected from organoid cultures was measured using the Human ACTH ELISA Kit (Novus Biologicals, NBP2-66401), according to the manufacturer’s protocol. The enzyme–substrate reaction was measured spectrophotometrically (BioTek Gen5 Micro Plate Reader Version 3.11, Santa Clara, CA, USA) at a wavelength of 450 nm, and the ACTH concentration (pg/mL) was interpolated by a standard curve with a 4-parameter logistic regression analysis, using GraphPad Prism (Version 9.2.0, San Diego, CA, USA).

2.8. Drug Assay

Patient adenoma-derived pituitary organoids were grown in 96-well plates and treated with 147 small molecules taken from the NCI AOD9 compound library for 72 h. (https://dtp.cancer.gov/organization/dscb/obtaining/available_plates.html (accessed on 22 August 2021)). Drugs were diluted from 10 mM DMSO stock plates into 100 M DMSO working stocks with a final concentration of 1μM. All vehicle controls were treated with 0.1% DMSO. Organoid proliferation was measured using a CellTiter 96® AQueous One Solution Cell Proliferation Assay kit (MTS, Promega, G3582, Madison, WI, USA) according to the manufacturer’s instruction. Organoid death was calculated based on the absorbance readings at 490 nm, collected from the MTS assay relative to the vehicle controls. Drug screens were performed with biological replicates in the same screen. Drugs were selected based on their ability to target key signaling pathways as well as clinical relevance to the treatment. Drug sensitivity is represented by cell viability, and is significant at <0.5 suppressive effect of the drugs. The percent of cell viability relative to the vehicle control was calculated. Correlation coefficients across each organoid were calculated using the Pearson method to assess confidence in replication. The variance component was detected for each drug across all organoids. A random effect model was run with a single random factor for each drug, and estimated variance was calculated by rejecting the null hypothesis that variation was not present among samples. The drug responses were grouped by variance factor, into large (vc > 100), median (100 > vc > 50), and small (vc < 50). A heatmap was used to display the differential responses in cell viability for the drugs.
Drugs that clustered together and showed response within corticotrophs were investigated further based on their mode of action. Pathways (Kegg and Reactome) and gene ontology mapping were conducted for the genes that were being targeted by the drugs, in order to evaluate the key responses in cellular processes. A network was constructed in Cytoscape v 3.8.2 (San Diego, CA, USA) for the purpose of association between the drugs and genes.

2.9. Drug Dose Responses

Organoids were grown in Matrigel™ domes within 96-well round-bottom culture plates. Recombinant human SHH was removed from the pituitary organoid growth media, 24 h prior to drug treatment. Organoids were treated with either vehicle (DMSO), cabergoline (Selleckchem S5842), ketoconazole (Selleckchem S1353), roscovitine (Selleckchem S1153), GANT61 (Stemcell Technologies 73692), pasireotide (TargetMol TP2207), mifeprostone (Selleckchem S2606), etomidate (Selleckchem S1329), mitotane (Selleckchem S1732), metyropane (Selleckchem S5416), or osilodrostat (Selleckchem S7456) at concentrations of 0, 1, 10, 100, 1000, and 10,000 nM, for 72 h. The percentage of cell viability was measured using an MTS assay (Promega G3580). Absorbance was measured at 490 nm and normalized to the vehicle. Concentrations were plotted in a logarithmic scale, and a nonlinear dose response curve regression was calculated using GraphPad Prism. An IC50 value for each drug treatment was determined based on the dose response curve, using GraphPad Prism analysis software.

2.10. Calculation of Area under the Curve (AUC)

AUC (area under the curve) was determined by plotting the normalized % cell viability versus transformed concentration of the drugs, using a trapezoidal approximation for the area [31]. The formula was based on splitting the curve into trapezoids with bases equal to the % viability (V) and height equal to the interval length (difference in concentrations (C), and then summing the areas of each trapezoid:

∑n0(Vn+Vn−1)2∗(Cn−Cn−1)

2.11. Quantitative RT PCR (qRT-PCR)

RNA was collected from patient-derived organoid cultures using the RNeasy Mini Kit (Qiagen). cDNA was generated from the extracted RNA, and then pre-amplified using TaqMan PreAmp Master Mix (Thermo Fisher Scientific 391128). The primers used were human-specific GAPDH (Thermo Fisher Scientific, Applied Biosystems Hs02786624_g1), NR5A1 (SF1) (Thermo Fisher Scientific, Hs00610436_m1), PIT1 (Thermo Fisher Scientific, Hs00230821_m1), TPit (Thermo Fisher Scientific, Hs00193027), and POMC (Thermo Fisher Scientific, Hs01596743_m1). Each PCR reaction was performed using a final volume of 20 µL, composed of 20X TaqMan Expression Assay primers, 2X TaqMan Universal Master Mix (Applied Biosystems, TaqMan® Gene Expression Systems), and a cDNA template. Amplification of each PCR reaction was conducted in a StepOne™ Real-Time PCR System (Applied Biosystems, Foster City, CA, USA), using the following PCR conditions: 2 min at 50 °C, 10 min at 95 °C, denaturing for 15 s at 95 °C, and annealing/extending for 1 min at 60 °C, for a total of 40 cycles. Relative fold change was calculated using the 2 − ∆∆Ct method [32], where CT = threshold cycle. Results were analyzed as the average fold change in gene expression compared to the control, and GAPDH served as an internal control.

2.12. Whole Exome Sequencing

WES was performed by the University of Arizona Center for Applied Genetics and Genomic Medicine. Isolated DNA from patient adenoma tissue will be quantified using the Qubit quantitation system with standard curve, as per the supplier protocol (Thermo Fisher Scientific). All samples were further tested for quality using the Fragment Analyzer (Advanced Analytical), following the manufacturer-recommended protocols. Whole exome sequencing (WES) was performed by array capture and approximately 60 Mb of exome target sequence, using the SureSelectXT Human All Exon V6 enrichment (Agilent) or equivalent (which one was used). All exome library builds were quantified via qPCR and subsequently sequenced to a minimum 20X coverage, using paired-end chemistry on the Illumina NovaSeq platform. Whole exome sequencing (WES) was performed by hybridization capture of approx. 35 Mb of the exome target sequence, using the Swift Exome Hyb Panel (Swift Biosciences 83216). All exome library builds were quantified via qPCR and subsequently sequenced to a minimum 20X coverage, using paired-end chemistry on the Illumina NextSeq500 or NovaSeq platform (Illumina). DNA reads were trimmed, filtered by quality scores and aligned to the human genome (hg38) with Burrows–Wheeler Aligner with default parameters. Picard (http://broadinstitute.github.io/picard (accessed on 22 December 2021)) was used to mark duplicates. Germline single nucleotide variants (SNV) were called using the Genome Analysis Tool Kit (GATK), using the given guidelines. Mutations were annotated using ANNOVAR for coding sequences. Variants that passed the quality filter were further investigated for similarity. Concordance between tissue and organoids was calculated using Jaccard similarity index (Jij = Mij/(Mi + Mj − Mij) where Mi is the number of variants in tissues, Mj is the number of variants in organoids, and Mij is the number of identical variants in both tissue and organoid.

2.13. Single Cell RNA Sequencing (scRNA-Seq)

Cultures were collected on day 15 of the pituitary directed differentiation schedule, and cells were dissociated into a single-cell suspension using Cell Dissociation Buffer (Thermo Fisher Scientific 13151014). Cells (15,000 cells/sample) were resuspended in the sample buffer (BD Biosciences 65000062), filtered using cell strainer (40 microns), and loaded into a BD Rhapsody cartridge (BD Biosciences 400000847) for single-cell transcriptome isolation. Based on the BD Rhapsody system whole-transcriptome analysis for single-cell whole-transcriptome analysis, microbead-captured single-cell transcriptomes were used to prepare a cDNA library. Briefly, double-stranded cDNA was first generated from the microbead-captured single-cell transcriptome in several steps, including reverse transcription, second-strand synthesis, end preparation, adapter ligation, and whole-transcriptome amplification (WTA). Then, the final cDNA library was generated from double-stranded full-length cDNA by random priming amplification using a BD Rhapsody cDNA Kit (BD Biosciences, 633773), as well as the BD Rhapsody Targeted mRNA and WTA Amplification Kit (BD Biosciences, 633801). The library was sequenced in PE150 mode (paired-end with 150-bp reads) on NovaSeq6000 System (Illumina). A total of 80,000 reads were demultiplexed, trimmed, mapped to the GRCh38 annotation, and quantified using the whole transcriptome analysis pipeline (BD Rhapsody™ WTA Analysis Pipeline v1.10 rev6, San Jose, CA, USA) on the Seven Bridges Genomics platform (https://igor.sbgenomics.com (accessed on 4 April 2022)), prior to clustering analysis in Seurat. For QC and filtration, read counting and unique molecular identifier (UMI) counting were the principal gene expression quantification schemes used in this single-cell RNA-sequencing (scRNA-seq) analysis. The low-quality cells, empty droplets, cell doublets, or multiplets were excluded based on unique feature count (less than 200 or larger than 2500), as they may often exhibit either an aberrantly high gene count or very few genes. Additionally, the mitochondrial QC metrics were calculated, and the cells with >5% mitochondrial counts were filtered out, as the percentage of counts originating from a set of low-quality or dying cells often exhibit extensive mitochondrial contamination. After the removal of unwanted cells from the single cell dataset, the global-scaling normalization method LogNormalize was employed. This method normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000), and log-transforms the result. The molecules per gene per cell, based on RSEC error correction (RSEC_MolsPerCell file) matrix files from iPSCctrl and iPSCCDH23 samples, were imported into Seurat v4, merged, and processed (as stated above) for UMAP reduction, cluster identification, and differential marker assessment using the FindAllMarkers function within Seurat.

2.14. Statistical Analyses

Sample size was based on assessment of power analysis using SigmaStat software. Data collected from each study from at least 4 in vitro technical replicates were analyzed by obtaining the mean ± standard error of the mean (SEM), unless otherwise stated. The significance of the results was then tested using commercially available software (GraphPad Prism, GraphPad software, San Diego, CA, USA).

3. Results

3.1. Generation and Validation of Human PitNET Tissue Derived Organoids

Human PitNET tissue was harvested during endoscopic transsphenoidal pituitary surgery from 35 patients in order to generate organoids. These cultures are referred to as human PitNET tissue derived organoids (hPITOs). Supplementary Table S3 summarizes the neuropathology reports and clinical diagnosis from these cases. In summary, 12 corticotroph (functional, CD), and 3 silent corticotroph tumors (nonfunctional tumors), 9 gonadotroph tumors, 8 lactotroph tumors, and 3 somatotroph tumors (acromegaly) were used to generate hPITOs (Supplementary Table S3).
Bright-field microscopy images of hPITOs that were generated from corticotroph adenomas from patients diagnosed with CD (Figure 1a–e). Silent/nonfunctioning tumors (Figure 1f,g) revealed morphological diversity among the organoid lines between individual patients and amongst subtypes. Confocal microscopy was used to capture a z-stack through the hPITO38, immunofluorescently stained for CAM5.2 (red), ACTH (green), and Hoechst (nuclear staining, blue) and emphasizes the 3D cellular structure of the hPITOs (Supplemental Video S1). Lactotroph, gonadotroph, and somatotroph adenomas were used to generate hPITOs, and showed the same morphological divergence amongst subtypes and between each patient line (Supplemental Figure S2). Proliferation was measured within the cultures using 5-ethynyl-2′-deoxyuridine (EdU) uptake and showed that the percentage of EdU+ve cells/total Hoechst+ve nuclei directly correlated with the pathology MIB-1 (Ki67) score (red, R2 = 0.9256) (Figure 1a–g, Supplemental Figure S2). ACTH concentration, which was measured by ELISA using organoid conditioned culture media collected from each hPITO line, showed the highest expression in the corticotroph adenoma organoids generated from CD patients (Figure 1h).
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Figure 1. Morphology and function of corticotroph hPITOs. (a–g) Brightfield images, immunofluorescence staining using antibodies specific for CAM5.2 (red), ACTH (green), and EdU (magenta, inset) of organoid cultures generated from patients with Cushing’s disease (hPITOs 1, 7, 10, 33, 35) or nonfunctional corticotroph adenomas (hPITO8, 12). Quantification of %EdU positive cells/total cell number is shown and compared to the Ki67 score given in the pathology report (Supplemental Table S3). An ELISA was performed using conditioned media collected from (h) corticotroph hPITO cultures and (i) lactotroph, somatotroph, and gonadotroph hPITO cultures for the measurement of ACTH secretion (pg/mL).

3.2. Characterization of Cell Lineages in Pituitary Adenoma-Derived Organoids by Spectral Cytek™ Aurora Analysis

In order to validate the similarity in cell lineages identified between the organoid line and the patient’s tumor, we compared the immunohistochemistry from the neuropathology report (Supplemental Table S3) to the expression pattern of pituitary adenoma-specific markers, which were measured using Cytek™ Aurora spectral flow cytometry (Figure 2). The location of cells that are found in each cluster based on the highly expressed antigens are shown in the representative tSNE (viSNE) maps (Figure 2a). Compared to nonfunctional adenoma-derived hPITOs, organoids derived from corticotroph adenomas of CD patients highly expressed proliferating (Ki67+) T-Pit+ ACTH cells (Figure 2a). Interestingly, there was an increase in SOX2+ cells within the total cell population, associated with Crooke’s cell adenoma hPITOs (Figure 2a). Within the total cell population, cell clusters expressing CD45 and vimentin were also measured (Figure 2a). Data for the analysis of corticotroph hPITOs, derived from CD patients and individuals with nonfunctional adenomas, were summarized in a heatmap for each subtype organoid line based on quantified cell abundance (percent of total cells) using spectral flow cytometry (Figure 2b).
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Figure 2. Cell heterogeneity of corticotroph hPITOs. (a) viSNE maps define spatially distinct cell populations using pituitary specific cell lineage, stem cell, and transcription factor markers. Cell populations were quantified in organoids generated from CD patients with corticotroph adenomas (sparsely granulated and Crooke’s cell adenoma) or patients with nonfunctional corticotroph adenomas. (b) Quantification of the abundance of cells expressing pituitary specific markers as a percent total. viSNE maps define spatially distinct cell populations in organoid cultures generated from CD patient with (c) corticotroph adenoma (hPITO37, Crooke’s cell adenoma) and adjacent normal tissue (hPITO37N), or (d) sparsely granulated corticotroph adenomas (hPITO38) and adjacent normal tissue (hPITO38N).
Organoid cultures derived from pituitary adenomas (hPITO37 and hPITO38) were compared to organoids derived from adjacent normal pituitary tissue (hPITO37N and hPITO38N) (Figure 2c,d). While Pit1 lineages including cells expressing GH and PRL, as well as SF1 lineages expressing FSH and LH, were detected in the hPITO37N and hPITO38N organoid cultures, these cell populations were significantly reduced within the patient’s matched adenoma tissue (Figure 2c,d). Overall, hPITOs derived from CD patients expressed increased stem and progenitor cell markers, including CXCR4, SOX2, and CD133 (Figure 2). Collectively, our findings of the characterization of the hPITO cultures support our prediction that this in vitro model recapitulates much of the patient’s adenoma pathophysiology.

3.3. Inherent Patient Differences to Drug Response Is Reflected in the Organoid Culture

Tumor recurrence can occur in as many as 30–50% of CD patients after successful surgical treatment [10,33,34]. Unfortunately, bilateral adrenalectomy is the chosen surgical treatment for patients with persistent CD [35]. Bilateral adrenalectomy leads to the increased risk for development of Nelson’s syndrome (progressive hyperpigmentation due to ACTH secretion and expansion of the residual pituitary tumor). Although the risk of developing Nelson’s syndrome following adrenalectomy can be reduced by 50% with stereotactic radiotherapy [35], there is a need to develop medical therapies that directly target the pituitary adenoma. Thus, we established a high-throughput drug screening assay using patient-derived PitNET organoids. After 72 h of treatment, cell viability was measured using an MTS assay, and data were represented as a heatmap whereby blue indicated higher cell death, and red suggested higher cell viability. The replicates behaved consistently with the drug response, with correlation scores of >0.8 for these samples (Figure 3a). We estimated the variance component for each drug across all organoids. Variation among samples was found to be significant (p ≤ 0.05) for each of the 83 drugs. The drug responses were grouped by variance factor into large, median, and small. The larger the variance, the more variable the drug response was across the organoids. We noted a set of drugs that showed a significant differential response across the functional corticotroph organoids. Unsupervised clustering of drug responses across organoids shows a pattern that relates to our statistically calculated results (Figure 3a,c), and the replicates for each independent organoid cluster together. The drugs with higher variance components across all the functional corticotrophs cluster together as a group (Figure 3a). These drugs show cell viability of 10% to 60% across different organoids. Analyzing the pattern more closely, we observe that, within a pathologically defined group, there was a differential organoid response to drugs as well as inherent patient differences to drugs within this group. Figure 3 demonstrates a variation in drug responsiveness amongst the organoid lines generated from individual patients. Importantly, there was further divergence in drug responsiveness amongst the individual organoid lines within each pathologically defined corticotroph subtype. These data clearly demonstrate that the inherent patient difference to drug response which is often observed among CD patients is reflected in the organoid culture.
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Figure 3. Drug screen using hPITOs generated from CD patients. (a) High-throughput drug screening of hPITOs reveals sensitivities to a range of therapeutic agents. Cell viability with high values (indicating resistance) are depicted in red, and low values (indicating sensitivity) are in blue in the clustered heatmap. (b,c) Clusters showing response to therapeutic agents with the most variance across the organoids. (d) Network of drugs from the clusters b and c and their gene targets, showing their participation in signaling pathways and cellular processes.
Drugs that clustered together and showed correlated responses were investigated further for their mode of action based on target genes (Figure 3d). The genes were analyzed for their associations in cellular pathways and gene ontology functional processes. Identified drug–gene pairs were interconnected by cellular pathways that are known to regulate cell cycle, WNT signaling, hedgehog signaling, and neuroactive ligand-receptor interaction signaling pathways (Figure 3d). These identified genes are also known to be influenced by multiple cellular functions, such as cytokine–cytokine receptor interactions and Notch signaling. Proteosome 20S subunit genes PSMAs/PSMBs and the HDAC gene family are involved in many cellular functions. The ephrin receptors (EPHs), adrenoceptor alpha receptors (ADRs), dopamine receptors (DRDs), and the 5-hydroxytryptamine serotonin receptors (HTRs) gene families influence neuronal functions and are targeted by multiple drugs in our focused cluster. These data reveal potential therapeutic pathways for CD patients.
Divergent half maximal inhibitory concentration (IC50) values, as documented by an MTS cell viability assay, were observed in response to drug treatment among hPITOs lines 28, 33, 34, 35, and 37. Note that a shift of the curve to the right indicates a higher IC50 (i.e., more resistant to that drug). Cell viability assays were normalized to vehicle-treated controls in order to ensure that toxicity was specific to the drug effects (Figure 4). Dose response curves for organoid 33 and organoid 34 showed better responses at lower doses for cabergoline compared to Metyrapone and osilodrostat, but different for organoid 35, where Metyrapone and osilodrostat gave better responses than Cabergoline (Figure 4a–h). For the drugs mifepristone and GANT61, 33 and 34 had the same level of response to both the drugs. However, when the two organoid responses were compared, 34 had a better response than 33 (Figure 4a–h). Similar divergent drug responses were observed in hPITO lines 37 and 38 (Figure 4i,k). However, organoids generated from adjacent normal pituitary tissue from patients 37 and 38 were nonresponsive to the same standard of care of investigational drugs for CD (Figure 4j,l). These data were consistent with observation made in the drug screen (Figure 3a–c), and demonstrate that there was an inherent difference to drug response within the organoid cultures of the same corticotroph subtype.
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Figure 4. Drug dose responses by hPITOs generated from CD patients. Dose responses to mifepristone, GANT61, cabergoline, and osilodrostat. (a,e) hPITO28, (b,f) hPITO33, (c,g) hPITO34, and (d,h) hPITO35. Dose responses to cabergoline, ketoconazole, roscovitine, GANT61, pasireotide, mifepristone, etomidate, mitotane, metyrapone, and osilodrostat in (i) hPITO37, (j) organoids generated from adjacent normal pituitary tissue (hPITO37N), (k) hPITO38, (l) hPITO38N, and (m) hPITO39. (n) IC50 and integrated area under the curve in response to mifepristone, ketoconazole, and pasireotide using hPITO39 cultures. Nuclear morphometric analysis of hPITO39 cultures in response to (o,p) vehicle, (q,r) mifepristone, (s,t) pasireotide, and (u,v) ketoconazole. Morphometric classification of NII was based on the normal (N), small (S), small regular (SR), short irregular (SI), large regular (LR), large irregular (LI), and irregular (I) nuclear morphology. Representative Hoechst staining of organoids in response to drug treatments for the calculation of the nuclear irregularity index (NII) are shown in the insets in (p,r,t,v).
In addition to cell viability, Nuclear Morphometric Analysis (NMA) using treated organoids was performed based on a published protocol that measures cell viability according to the changes in nuclear morphology of the cells, using nuclear stain Hoechst or DAPI [30]. Nuclear Irregularity Index (NII) was measured based on the quantification of the morphometric changes in the nuclei in response to the standard-of-care drugs mifepristone, pasireotide, and ketoconazole in hPITO39 (Figure 4o–v). The area vs. NII of vehicle-treated cells were plotted as a scatter plot using the template, and considered as the normal cell nuclei (Figure 4o). The same plots were generated for mifepristone (Figure 4q), pasireotide (Figure 4s), and ketoconazole (Figure 4u). The NII and area of treated cells were compared to those of the normal nuclei, and classified as one of the following NMA populations: Normal (N; similar area and NII), Mitotic (S; similar area, slightly higher NII), Irregular (I; similar area, high NII), Small Regular (SR; apoptotic, low area and NII), Senescent (LR; high area, low NII), Small Irregular (SI; low area, high NII), or Large Irregular (LI; high area, high NII) (Figure 4p,r,t,v). Cells classified as SR exhibited early stages of apoptosis, and cells classified as either I, SI, or LI exhibited significant nuclear damage. Data showed that mifepristone induced significant apoptosis in hPITO39 cultures (Figure 4r), compared to responses to pasireotide (Figure 4t) and ketoconazole (Figure 4v). These responses were consistent with the IC50 and the total area under the curve in response to drugs (Figure 4m,n). Measurement of NII is an approach which may be used to confirm potential drug targets identified from the drug screen.

3.4. Organoid Responsiveness to Pasireotide Correlates with SSTR2 and SSTR5 Expression

Organoid lines hPITO28, 31, 33, 34, and 35 exhibited divergent IC50 values in response to SSTR agonist pasireotide (Figure 5a). hPITO34 was the most responsive to pasireotide, with a low IC50 value of 6.1 nM (Figure 5a). Organoid lines hPITO33 and hPITO35 were the least responsive, with IC50 values of 1.2 µM and 1 µM, respectively, in response to pasireotide (Figure 5a). The expression of SSTR subtypes 1–5 among the different organoid lines were measured by qRT-PCR and IHC (Figure 5b). One of the least responsive organoid lines, hPITO28, exhibited lower differential expression in SSTR2 and SSTR5 compared to the highly responsive hPITO34 line (Figure 5a,b). Gene expression levels of SSTR2 and SSTR5 within hPITO28 and 34 correlated with protein levels within the patient’s tumor tissue (Figure 5c–f). Given the greater binding affinity for SSTR5 compared to SSTR2 by pasireotide, these data were consistent with greater responsiveness to the drug by hPITO34 in comparison to hPITO28 (Figure 5a,c–f). The expression of SSTR subtypes 2 and 5 within the organoid cultures correlated with the expression patterns of the patient’s tumor tissues (Figure 5a,c–f).
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Figure 5. SSTR1-5 expression in hPITOs and patient’s PitNET tissue. (a) Dose response of hPITO28, 31, 33, 34, and 35 lines to pasireotide. (b) Differential expression of SSTR subtypes 1–5 (SSTR1, SSTR2, SSTR3, SSTR4, SSTR5) in hPITO28, hPITO31, hPITO33, hPITO34, and hPITO35. Immunohistochemistry of (c,e) SSTR2 and (d,f) SSTR5 expression in patient PitNET tissue (Pt28 and Pt34), from which hPITO28 and 34 were generated.

3.5. Organoids Derived from Pituitary Corticotroph Adenomas Retain the Genetic Alterations of the Patient’s Primary Tumor

In order to identify the genetic features of the organoids derived from pituitary adenomas of CD patients, we performed whole-exome sequencing (WES) of hPITOs and the corresponding primary adenoma tissues. We performed WES analysis of each hPITO line, and compared the results with those for the corresponding primary adenoma tissues. We showed the concordance rate of exonic variants between the primary tumor tissues obtained from CD patients and the corresponding organoid line. We identified, on average, approximately 5000 mutations across each of the 14 paired samples of organoids and tissues. For the variants detected, all seven pairs showed a Jaccard index ranging from 0.5 to 0.8. Out of seven pairs, five (hPITO24, 25, 28 and 35) pairs had a Jaccard score of 0.8, while hPITO33 and 34 pairs had 0.7, and hPITO1 had 0.5. In order to investigate the similarity across the SNV (single nucleotide variation) sites, we calculated the Jaccard index of exon sites for synonymous and non-synonymous events, and found scores for all pairs ranging from 0.8 to 0.9. Furthermore, for only non-synonymous events, Jaccard scores also ranged from 0.8 to 0.9, except for hPITO1, which showed overall lower concordance, and had a score of 0.4 to 0.5. Figure 6 shows non-synonymous mutations found in organoid and tissue pairs for some of the key genes that are known to be involved in pituitary adenoma disease. Concordance indices between organoids and the matched patient’s adenoma tissues is reported in Figure 6. Therefore, WES data demonstrated that organoids derived from pituitary corticotroph adenomas retained the genetic alterations of the patient’s primary tumor tissue.
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Figure 6. Genomic landscape of hPITOs recapitulates genetic alterations commonly found PitNETs. Overview of single nucleotide variation events detected in hPITOs in genes commonly altered in PitNETs. The mutation frequency across the organoid population is depicted on the right. Color coding of the figure shows that organoid lines are derived from the same patient tumor tissue. ORG: organoid line, TIS: matched patient’s PitNET tissue.

3.6. IPSC Pituitary Organoids Generated from a CD Patients Expressing Familial Mutations Reveal Corticotroph Adenoma Pathology In Vitro

Extensive research has revealed the role of somatic and germline mutations in the development of CD adenomas [36,37]. Pituitary organoids were developed from iPSCs generated from the PBMCs of CD patients and carrying germline mutations that were identified by WES (Supplemental Figure S4). Chromosomal aberrations were not found when comparing against the reference dataset in the iPSCs generated from the CD patients (Supplemental Figure S3a,b). PBMCs isolated from patients diagnosed with CD were analyzed by WES in order to determine the expression of germline mutations. WES revealed the expression of a more recently identified gene predisposing patients to CD, namely cadherin-related 23 [38] (Supplemental Figure S5).
Pituitary organoids were then developed from iPSCs which were generated from the PBMCs of patients with CD (iPSCCDH23 and iPSCMEN1) and a healthy individual (iPSCctrl). Expression of PIT1 (pituitary-specific positive transcription factor 1), ACTH (adrenocorticotropic hormone), GH (growth hormone), FSH (follicle-stimulating hormone), LH (luteinizing hormone), PRL (prolactin), and synaptophysin (synaptophysin) with co-stain Hoechst (nuclei, blue) was measured by immunofluorescence, using chamber slides collected at 15 of the differentiation schedules (Supplemental Figure S6). While pituitary tissue that was differentiated from iPSCctrl expressed all major hormone-producing cell lineages (Supplemental Figure S6a), there was a significant increase in the expression of ACTH and synaptophysin, with a concomitant loss of PIT1, GH, FSH, LH, and PRL in iPSCsMEN1 (Supplemental Figure S6b,c). Interestingly, iPSCCDH23 cultures exhibited a significant increase in the expression of ACTH, GH, LH, and synaptophysin, with a concomitant loss of PIT1, FSH, and PRL (Supplemental Figure S6b,c). Immunofluorescence of iPSCs collected on the fourth day of the differentiation schedule revealed no expression of PIT1, ACTH, GH, FSH, LH, or PRL in (data not shown). Compared to control lines, iPSC lines expressing mutated CDH23 secreted significantly greater concentrations of ACTH earlier in the differentiation schedule (Supplemental Figure S7a). The upregulated expression of pituitary corticotroph adenoma-specific markers in iPSCCDH23 and iPSCMEN1 demonstrates that the iPSC-derived organoids represented the pathology of corticotroph adenomas in vitro.

3.7. ScRNA-seq Reveals the Existence of Unique Proliferative Cell Populations in iPSCCDH23 Cultures When Compared to iPSCsctrl

Using Seurat to identify cell clusters, as well as Uniform Manifold Approximation and Projection 9UMAP, clustering analysis identified 16 distinct cell populations/clusters consisting of known marker genes. Clusters 1, 5, and 7 of the iPSCsCDH23 were distinct from the iPSCctrl cultures (Figure 7a,b). Pituitary stem cells were characterized in iPSCctrl and iPSCCDH23 cultures (Figure 7b). Clusters 1 and 5 expressed markers consistent with the corticotroph subtype cell lineage (Figure 5c). Markers of dysregulated cell cycles and increased proliferation were identified in cell cluster 7 (Figure 7c). Expression of the E2 factor (E2F) family of transcription factors, which are downstream effectors of the retinoblastoma (RB) protein pathway and play a crucial role in cell division control, were identified in distinct cell cluster 7, which was identified within the iPSCCDH23 cultures (Figure 7c). Stem cell markers were also upregulated in cell cluster 7, and identified within the iPSCCDH23 cultures (Figure 7c). Using Cytobank software to analyze organoids collected 30 days post-differentiation, cells were gated on live CK20 positive singlets, and 9000 events per sample were analyzed by the viSNE algorithm. ViSNE plots are shown in two dimensions with axes identified by tSNE- 1 and tSNE-2, and each dot representing a single cell positioned in the multidimensional space (Figure 7d). Individual flow cytometry standard files were concatenated into single flow cytometry standard files, from which 12 spatially distinct populations were identified (Figure 7e). Overlaying cell populations identified by traditional gating strategies onto viSNE plots identified unique cell populations within the iPSCCDH23 cultures (Figure 7e). There were distinct cell populations between the iPSCctrl and iPSCCDH23 organoids, in addition to expression of hormone and cell lineage markers such as ACTH, TPit, PRL, and PIT1 (Figure 7e). The cell populations that exhibited high expression of Ki67 within the iPSCctrl organoid cultures included SOX2+ and PIT1+ populations (Figure 7f). The highly proliferating cell populations within the iPSCCDH23 organoid cultures included those that expressed CD90+/VIM+/CXCR4+ (mesenchymal stem cells), CXCR4+/SOX2+ (stem cells), TPit+ (corticotroph cell lineage), CD133+/CD31+ (endothelial progenitor cells), and CK20+/VIM+/CXCR4+ (hybrid epithelial-mesenchymal stem cells) (Figure 7f). Overall, the iPSCCDH23 organoids were significantly more proliferative compared to the iPSCctrl cultures (Figure 7f). Immunofluorescence staining of iPSCCDH23 organoids revealed increased mRNA expression of TPit and POMC, which correlated with increased ACTH protein compared to iPSCsctrl (Supplemental Figure S6). As shown in Supplemental Figure S6b,c, iPSCCDH23 cultures also exhibited a significant increase in the expression of GH and LH (Supplemental Figure S6b,c).
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Figure 7. Single cell analysis of iPSCctrl and iPSCCDH23 cultures 15 and 30 days post-directed differentiation. (a) UMAP plots showing identified cell clusters 0–16 in iPSCctrl and iPSCCDH23 cultures 15 days post-directed differentiation. (b) Violin plots of representative identified markers of the corticotroph cell lineage, where 2 subpopulations were observed among iPSCctrl and iPSCCDH23 cultures. Arrows highlight clusters 1, 5, and 7. (c) Violin plots showing expression of genes representative of stem cells, Wnt, NOTCH, Hh and SST signaling, anterior pituitary (corticotroph) cell lineage, and cell cycle in clusters 1, 5, and 7 of iPSCCDH23 cultures. Plot width: cell number, plot height: gene expression. (d) viSNE maps showing concatenated flow cytometry standard files for both samples and iPSCctrl and iPSCCDH23 organoids 30 days post-directed differentiation. (e) Overlay of manually gated cell populations onto viSNE plots. (f) Fluorescent intensity of Ki67 of viSNE maps for both samples and iPSCctrl and iPSCCDH23 organoids. iPSCctrl = 22518 events; iPSCCDH23 = 17542 events.
Collectively, Figure 7 demonstrates that the development of pituitary organoids generated from iPSCs of CD patients may reveal the existence of cell populations which, potentially, contribute to the support of adenoma growth and progression, as well as an expansion of stem and progenitor cells that may be the targets for tumor recurrence.

4. Discussion

Our studies demonstrate the development of organoids generated from human PitNETs (hPITOs) can potentially be used to screen for the sensitivity and efficacy of responses to targeted therapies for CD patients that either fail to achieve remission or exhibit recurrence of disease after surgery. In addition, we have documented that induced pluripotent stem cells (iPSCs) generated from a CD patient expressing germline mutation CDH23 (iPSCCDH23) reveals the disease pathogenesis under directed differentiation. Many early in vitro experiments have used pituitary cell lines, spheroids, aggregates, and/or tumoroids that do not replicate the primary PitNET microenvironment [19,20,21], and lack a multicellular identity [39,40]. The development of PitNET tissue-generated organoids is limited to the use of transgenic mouse models as the source [22,23,41]. The recent organoid cultures reported by Nys et al. [42] have been generated from single stem cells isolated from PitNET tissue, and are claimed to be true organoids due to their clonality. However, multicellular complexity was not validated by the protein expression or hormone secretion from pituitary cell lineages in these cultures [42]. According to the National Cancer Institute (NCI, NIH), an ‘organoid’ is defined as “a tiny, 3-dimensional mass of tissue that is made by growing stem cells (cells from which other types of cells develop) in the laboratory” [43]. The hPITOs reported here begin from single and/or 3–4 cell clusters dissociated from the PitNET tissue that harbors the stem cells. Supplemental Video S2 demonstrates a process of ‘budding,’ as well as lumen formation as organoids grow and differentiate. We document differentiation and function by comprehensive spectral flow cytometry, ELISA, and response to standard of care drugs. The growth of PitNET organoids reported in the current study is consistent with that of gastrointestinal tissue derived cultures that begin from cell clusters, crypts, or glands [27,44,45].
Our studies report a PitNET tissue organoid culture with a multicellular identity consisting of differentiated cell lineages, stem/progenitor cells, and immune and stromal cell compartments, which replicates much of the patient’s own adenoma pathology, functionality, and complexity. We have also demonstrated that iPSCs, derived from the blood of a CD patient, can be directly differentiated into pituitary organoids that resemble similar characteristics to the tumor tissue. Many investigators have proposed the use of organoids in personalized medicine, but have focused these efforts on targeted treatment of cancers [27,46,47,48]. The findings reported in these studies are the first to implement this approach for the potential treatment of PitNETs. Collectively, we have developed a relevant human in vitro approach to potentially advance our knowledge as well as our approach to studies in the field of pituitary tumor research. Both the hPITOs and the iPSCCDH23 may be implemented in studies that strive to (1) define the molecular and cellular events that are crucial for the development of PitNETs leading to CD, and (2) accelerate the identification of effective targeted therapies for patients with CD.
While published studies have advanced our understanding of the molecular mechanisms of the pathogenesis of corticotroph adenomas and elucidated candidate therapeutic targets for CD, these reports fall short of directly informing clinical decisions for patient treatment. Using organoids to screen potential drugs and compounds can potentially improve therapeutic accuracy. Figure 3 demonstrated a variation in drug responsiveness amongst the organoid lines generated from individual patients. Importantly, there was further divergence in drug responsiveness amongst the individual organoid lines within each pathologically defined corticotroph subtype. For example, hPITOs generated from patients with sparsely granulated corticotroph adenomas (hPIT0s 10, 25, 34, 35) and Crooke’s cell adenomas (hPITOs 7, 33) showed variable responses regardless of similar pathologically defined subtypes. In addition, the response of the tumor cells within the organoids to the standard of care drugs that directly target the pituitary in the body, including mifepristone and cabergoline, was only 50% in hPITO34 and hPITO35, and almost 0% in the other lines, including hPITO7, 10, and 25. These data clearly demonstrate that the inherent patient difference to drug response that is often observed among CD patients is reflected in the organoid culture. This culture system may be an approach that will provide functional data revealing actionable treatment options for each patient. Patient-derived organoids from several tumors have served as a platform for testing the efficacy of anticancer drugs and predicting responses to targeted therapies in individual patients [27,46,48,49,50]. An example of the use of organoids in identifying drug responsiveness within an endocrine gland is that of papillary thyroid cancer [51]. Organoids developed from PTC patients were used as a preclinical model for studying responsiveness to anticancer drugs in a personalized approach [51]. However, our study is the first report of the use of hPITOs for drug screening. Connecting genetic and drug sensitivity data will further categorize corticotroph subtypes associated with CD. WES analysis of each hPITO line was compared to the results for the corresponding primary adenoma tissues. We showed the concordance rate of exonic variants between the primary tumor tissues obtained from CD patients and the corresponding organoid line. On average, approximately 80% of the variants observed in the CD patients’ adenoma tissues were retained in the corresponding hPITOs.
Pituitary organoids were also developed from iPSCs generated from PBMCs of a CD patient expressing a germline genetic alteration in cadherin-related 23 CDH23 (iPSCCDH23), a CD patient expressing an MEN1 mutation (iPSCMEN1), and a healthy individual (iPSCctrl). Foundational studies performed by investigators at the genome level have revealed significant knowledge regarding the pathophysiology of CD [36,37,52,53]. In some instances, CD is a manifestation of genetic mutation syndromes that include multiple endocrine neoplasia type 1 (MEN1), familial isolated pituitary adenoma (FIPA), and Carney complex [54,55]. CDH23 syndrome is clinically associated with the development of Usher syndrome, deafness, and vestibular dysfunction [56]. Several mutations in CDH23 are associated with inherited hearing loss and blindness [57]. However, none of the variants found in this study were linked to any symptoms of deafness or blindness. A possible explanation is that deafness-related CDH23 mutations are caused by either homozygous or compound heterozygous mutations [57]. In a study that linked mutations in CDH23 with familial and sporadic pituitary adenomas, it was suggested that these genetic alterations could play important roles in the pathogenesis of CD [38]. Genomic screening in a total of 12 families with familial PitNETs, 125 individuals with sporadic pituitary tumors, and 260 control individuals showed that 33% of the families with familial pituitary tumors and 12% of individuals with sporadic pituitary tumors expressed functional or pathogenic CDH23 variants [38]. Consistent with the expected pathology and function of a PitNET from a patient with CD, iPSCCDH23 organoids exhibited hypersecretion of ACTH, and expression of transcription factors and cell markers were reported in the pathology report for corticotroph PitNETs. Collectively, these findings warrant further investigation to determine whether carriers of CDH23 mutations are at a high risk of developing CD and/or hearing loss. Specifically, clinical investigation is required to determine whether pituitary MRI scans should be adopted in the screening of CDH23-related diseases, including Usher syndrome and age-related hearing loss.
Pituitary organoids generated from iPSCs of a CD patient revealed the existence of cell populations that potentially contribute to the support of PitNET growth and disease progression, as well as an expansion of stem and progenitor cells that may be the targets for tumor recurrence. Organoids derived from both pituitary adenomas and iPSCs exhibited increased expression of stem cell and progenitor markers at both the protein and transcriptomic levels. Unique clusters that were proliferative in the iPSCCDH23 organoids expressed a hybrid pituitary cell population which was in an epithelial/mesenchymal state (CK20+/VIM+/CXCR4+/Ki67+). In support of our findings, a similar report of a hybrid epithelial/mesenchymal pituitary cell has been made as part of the normal developmental stages of the human fetal pituitary [58]. Previous studies have suggested that pituitary stem cells undergo an EMT-like process during cell migration and differentiation [59,60,61]. Consistent with our findings are extensive studies using single cells isolated from human pituitary adenomas to show increased expression of stem cell markers SOX2 and CXCR4 [22,23,41,62,63]. Within the clusters identified in the iPSCCDH23 culture were cell populations expressing stem cell markers, including SOX2, NESTIN, CXCR4, KLF4, and CD34. The same iPSCCDH23 cell clusters, 4, 8, 9, and 11, co-expressed upregulated genes of NOTCH, Hedgehog, WNT, and TGFβ signaling, which are pivotal not only in pituitary tumorigenesis and pituitary embryonic development, but also in ‘tumor stemness’ [22,23,41,62,63,64]. We also noted that clusters of cell populations 5 and 14 unique within the iPSCCDH23 cultures expressed upregulated genes which were indicative of high proliferation. We observed upregulated expression of the E2F family of transcription factors (E2Fs) E2F1 and E2F7. These findings are of significance, given that there is evidence to show that upregulation of E2Fs is fundamental for tumorigenesis, metastasis, drug resistance, and recurrence [65]. Within the pituitary adenoma microenvironment, whether these stem cells directly differentiate into pituitary tumors or support the growth of the adenoma is largely unknown. In addition, whether pituitary stem cell populations become activated in response to injury is also understudied. Although the role of stem cells has been identified using a mouse model through implantation of the cells within the right forebrain [66], the identification of pituitary tumor-initiating stem cells using in vivo orthotopic transplantation models is impossible in mice. Pituitary tumors harboring the stem cells may require engraftment within the environment from which the cells are derived in order to enable growth and differentiation of the tumor. However, it is technically impossible to implant cells orthotopically in the murine pituitary. The pituitary tumor organoid cultures presented in these studies may offer an approach by which isolation, identification, and characterization of this stem cell population is possible. Therefore, we would gain knowledge on the mechanisms of pituitary tumor pathogenesis and reveal potential novel targets for therapeutic interventions by using the iPSC generated pituitary organoid culture.
PitNETs associated with the development of CD cause serious morbidity due to chronic cortisol exposure that dysregulates almost every organ system in the body. Overall, existing medical therapies remain suboptimal, with negative impact on health and quality of life, including considerable risk of therapy resistance and tumor recurrence. To date, little is known about the pathogenesis of PitNETs. Here, we present a human organoid-based approach that will allow us to acquire knowledge of the mechanisms underlying pituitary tumorigenesis. Such an approach is essential to identify targeted treatments and improve clinical management of patients with CD.

5. Conclusions

Cushing’s disease (CD) is a serious endocrine disorder caused by an adrenocorticotropic hormone (ACTH)-secreting pituitary neuroendocrine tumor (PitNET), which stimulates the adrenal glands to overproduce cortisol. The absence of preclinical models that replicate the PitNET microenvironment has prevented us from acquiring the knowledge to identify therapies that can be targeted to the tumor with a higher efficacy and tolerability for patients. Our studies demonstrate the development of organoids generated from human PitNETs or induced pluripotent stem cells as an essential approach to identifying targeted therapy methods for CD patients.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells11213344/s1, Figure S1: Antibodies used and Cytek® Full Spectrum Viewer showing calculated similarity indices; Figure S2: Morphology and proliferation of lactotroph, somatotroph, and gonadotroph hPITOs; Table S1: Pituitary Growth Media; Table S2: Components used for pituitary organoids generated from iPSCs; Table S3: clinical characteristics of pituitary adenoma samples used for the generation of organoids; Table S4: Average correlation of replicates reported in Figure 3; Table S5: pituitary cell lineage or stem cell markers used in the scRNA-seq analysis; Video S1: hPITO38 EdU ACTH 3.

Author Contributions

Conceptualization, Y.Z.; methodology, J.C., Y.Z., J.M.C., B.N.S., S.M. and K.W.P.; software, J.C., Y.Z., J.M.C., S.M., Y.C., P.M. and R.P.; validation, Y.Z., J.C., J.M.C., A.S.L., K.C.J.Y. and R.P.; formal analysis, J.C., Y.Z., J.M.C., R.P., Y.C., S.M. and P.M.; investigation, Y.Z.; resources, Y.Z., J.C., J.E., C.A.T., B.H. and A.S.L.; data curation, J.C., Y.Z., J.M.C., R.P. and S.M.; writing—original draft preparation, Y.Z., J.C, S.M., J.M.C., Y.C., B.H. and R.P.; writing—review and editing, Y.Z., J.C., J.M.C., A.S.L., K.C.J.Y., S.M., J.E., C.A.T., K.W.P., B.H., Y.C., P.M., B.N.S. and R.P.; visualization, Y.Z., J.C., J.M.C., A.S.L., K.C.J.Y. and R.P.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Department of Cellular and Molecular Medicine (University of Arizona College of Medicine) startup funds (Zavros). This research study was also partly supported by the National Cancer Institute of the National Institutes of Health under award number P30 CA023074 (Sweasy).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of St. Joseph’s Hospital and Barrow Neurological Institute Biobank collection protocol PHXA-05TS038, and collection of outcomes data protocol PHXA-0004-72-29, and patient consent (protocol date of approval).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated during the analysis of the present study are available in the ReDATA repository, https://doi.org/10.25422/azu.data.19755244.v1. The datasets generated in the current study are also available from the corresponding author on reasonable request. All data generated or analyzed during this study are included in this published article (and its Supplementary Information Files).

Acknowledgments

We acknowledge the technical support of Maga Sanchez in the Tissue Acquisition and Cellular/Molecular Analysis Shared Resource (TACMASR University of Arizona Cancer Center) for assistance with embedding and sectioning of organoids. We would also like to acknowledge Patty Jansma (Marley Imaging Core, University Arizona) and, Douglas W Cromey (TACMASR imaging, University of Arizona Cancer Center) for assistance in microscopy. The authors thank the patients who consented to donate pituitary tumor tissues and blood for the development of the organoids. Without their willingness to participate in the study, this work would not be possible.

Conflicts of Interest

The authors declare no conflict of interest.

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Updated Cushing’s disease guideline highlights new diagnosis, treatment ‘roadmap’

Posted on November 15, 2021 by MaryO

An updated guideline for the treatment of Cushing’s disease focuses on new therapeutic options and an algorithm for screening and diagnosis, along with best practices for managing disease recurrence.

Despite the recent approval of novel therapies, management of Cushing’s disease remains challenging. The disorder is associated with significant comorbidities and has high mortality if left uncontrolled.

Adrenal transparent _Adobe
Source: Adobe Stock

“As the disease is inexorable and chronic, patients often experience recurrence after surgery or are not responsive to medications,” Shlomo Melmed, MB, ChB, MACP, dean, executive vice president and professor of medicine at Cedars-Sinai Medical Center in Los Angeles, and an Endocrine Today Editorial Board Member, told Healio. “These guidelines enable navigation of optimal therapeutic options now available for physicians and patients. Especially helpful are the evidence-based patient flow charts [that] guide the physician along a complex management path, which usually entails years or decades of follow-up.”

Shlomo Melmed

The Pituitary Society convened a consensus workshop with more than 50 academic researchers and clinical experts across five continents to discuss the application of recent evidence to clinical practice. In advance of the virtual meeting, participants reviewed data from January 2015 to April 2021 on screening and diagnosis; surgery, medical and radiation therapy; and disease-related and treatment-related complications of Cushing’s disease, all summarized in recorded lectures. The guideline includes recommendations regarding use of laboratory tests, imaging and treatment options, along with algorithms for diagnosis of Cushing’s syndrome and management of Cushing’s disease.

Updates in laboratory, testing guidance

If Cushing’s syndrome is suspected, any of the available diagnostic tests could be useful, according to the guideline. The authors recommend starting with urinary free cortisol, late-night salivary cortisol, overnight 1 mg dexamethasone suppression, or a combination, depending on local availability.

If an adrenal tumor is suspected, the guideline recommends overnight dexamethasone suppression and using late-night salivary cortisol only if cortisone concentrations can also be reported.

The guideline includes several new recommendations in the diagnosis arena, particularly on the role of salivary cortisol assays, according to Maria Fleseriu, MD, FACE, a Healio | Endocrine Today Co-editor, professor of medicine and neurological surgery and director of the Pituitary Center at Oregon Health & Science University in Portland.

Maria Fleseriu

“Salivary cortisol assays are not available in all countries, thus other screening tests can also be used,” Fleseriu told Healio. “We also highlighted the sequence of testing for recurrence, as many patients’ urinary free cortisol becomes abnormal later in the course, sometimes up to 1 year later.”

The guideline states combined biochemical and imaging for select patients could potentially replace petrosal sinus sampling, a very specialized procedure that cannot be performed in all hospitals, but more data are needed.

“With the corticotropin-releasing hormone stimulation test becoming unavailable in the U.S. and other countries, the focus is now on desmopressin to replace corticotropin-releasing hormone in some of the dynamic testing, both for diagnosis of pseudo-Cushing’s as well as localization of adrenocorticotropic hormone excess,” Fleseriu said.

The guideline also has a new recommendation for anticoagulation for high-risk patients; however, the exact duration and which patients are at higher risk remains unknown.

“We always have to balance risk for clotting with risk for bleeding postop,” Fleseriu said. “Similarly, recommended workups for bone disease and growth hormone deficiency have been further structured based on pitfalls specifically related to hypercortisolemia influencing these complications, as well as improvement after Cushing’s remission in some patients, but not all.”

New treatment options

The guideline authors recommended individualizing medical therapy for all patients with Cushing’s disease based on the clinical scenario, including severity of hypercortisolism. “Regulatory approvals, treatment availability and drug costs vary between countries and often influence treatment selection,” the authors wrote. “However, where possible, it is important to consider balancing cost of treatment with the cost and the adverse consequences of ineffective or insufficient treatment. In patients with severe disease, the primary goal is to treat aggressively to normalize cortisol concentrations.”

Fleseriu said the authors reviewed outcomes data as well as pros and cons of surgery, repeat surgery, medical treatments, radiation and bilateral adrenalectomy, highlighting the importance of individualized treatment in Cushing’s disease.

“As shown over the last few years, recurrence rates are much higher than previously thought and patients need to be followed lifelong,” Fleseriu said. “The role of adjuvant therapy after either failed pituitary surgery or recurrence is becoming more important, but preoperative or even primary medical treatment has been also used more, too, especially in the COVID-19 era.”

The guideline summarized data on all medical treatments available, either approved by regulatory agencies or used off-label, as well as drugs studied in phase 3 clinical trials.

“Based on great discussions at the meeting and subsequent emails to reach consensus, we highlighted and graded recommendations on several practical points,” Fleseriu said. “These include which factors are helpful in selection of a medical therapy, which factors are used in selecting an adrenal steroidogenesis inhibitor, how is tumor growth monitored when using an adrenal steroidogenesis inhibitor or glucocorticoid receptor blocker, and how treatment response is monitored for each therapy. We also outline which factors are considered in deciding whether to use combination therapy or to switch to another therapy and which agents are used for optimal combination therapy.”

Future research needed

The guideline authors noted more research is needed regarding screening and diagnosis of Cushing’s syndrome; researchers must optimize pituitary MRI and PET imaging using improved data acquisition and processing to improve microadenoma detection. New diagnostic algorithms are also needed for the differential diagnosis using invasive vs. noninvasive strategies. Additionally, the researchers said the use of anticoagulant prophylaxis and therapy in different populations and settings must be further studied, as well as determining the clinical benefit of restoring the circadian rhythm, potentially with a higher nighttime medication dose, as well as identifying better markers of disease activity and control.

“Hopefully, our patients will now experience a higher quality of life and fewer comorbidities if their endocrinologist and care teams are equipped with this informative roadmap for integrated management, employing a consolidation of surgery, radiation and medical treatments,” Melmed told Healio.

From https://www.healio.com/news/endocrinology/20211029/updated-cushings-disease-guideline-highlights-new-diagnosis-treatment-roadmap

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