Abstract
Pituitary neuroendocrine tumors (PitNETs) are classified according to cell lineage, which requires immunohistochemistry for adenohypophyseal hormones and the transcription factors (TFs) PIT1, SF1, and TPIT. According to the current WHO 2022 classification, PitNETs with co-expression of multiple TFs are termed “plurihormonal”. Previously, PIT1/SF1 co-expression was prevailingly reported in PitNETs, which otherwise correspond to the somatotroph lineage. However, little is known about such tumors and the WHO classification has not recognized their significance. We compiled an in-house case series of 100 tumors, previously diagnosed as somatotroph PitNETs. Following TF staining, histopathological features associated with PIT1/SF1 co-expression were assessed. Integration of in-house and publicly available sample data allowed for a meta-analysis of SF1-associated clinicopathological and molecular features across a total of 270 somatotroph PitNETs. The majority (74%, 52/70) of our densely granulated somatotroph PitNETs (DGST) unequivocally co-expressed PIT1 and SF1 (DGST-PIT1/SF1). None (0%, 0/30) of our sparsely granulated somatotroph PitNETs (SGST) stained positive for SF1 (SGST-PIT1). Among DGST, PIT1/SF1 co-expression was significantly associated with scarce FSH/LH expression and fewer fibrous bodies compared to DGST-PIT1. Integrated molecular analyses including publicly available samples confirmed that DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 represent distinct tumor subtypes. Clinicopathological meta-analyses indicated that DGST-PIT1 respond more favorably towards treatment with somatostatin analogs compared to DGST-PIT1/SF1, while both these subtypes show an overall less aggressive clinical course than SGST-PIT1. In this study, we spotlight that DGST with co-expression of PIT1 and SF1 represent a common, yet underrecognized, distinct PitNET subtype. Our study questions the rationale of generally classifying such tumors as “plurihormonal”, and calls for a refinement of the WHO classification. We propose the term “somatogonadotroph PitNET”.
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Introduction
Pituitary neuroendocrine tumors (PitNETs), formerly termed pituitary adenomas, are common intracranial neoplasms, which originate from adenohypophyseal cells of the anterior pituitary lobe. PitNET classification is based on cell lineage, determined by immunohistochemistry for adenohypophyseal hormones and the pituitary transcription factors (TFs) PIT1, SF1, and TPIT [2, 28]. According to the current WHO classification of 2022, immunopositivity for one of these TFs denotes the PIT1-, gonadotroph, or corticotroph lineage, respectively. PitNETs of the PIT1-lineage can further exhibit somatotroph, mammosomatotroph, lactotroph and/or thyrotroph differentiation. Some PitNETs may express multiple adenohypophyseal hormones and/or TFs, belonging to more than one cell lineage. Currently, the WHO classification subsumes all such tumors under the term “plurihormonal PitNET/adenoma” [31].
First reports on PitNETs demonstrating simultaneous expression of multiple hormones date back to over 40 years ago [9, 22]. In particular, expression of gonadotropins was recurrently described in growth hormone (GH)-secreting PitNET/adenomas causing acromegaly [9, 12, 14, 22], suggesting affiliations with both the somatotroph and gonadotroph lineage in a subset of such tumors.
Through the routine implementation of TFs in pituitary diagnostics, multilineage PitNETs regained increased attention over the last few years. Several studies have reported concurrent PIT1 and SF1 immunopositivity in unusual PitNETs, which otherwise reflected somatotroph tumors [3, 16, 24, 29]. Previous transcriptome-based studies confirmed occasional SF1 expression in somatotroph PitNETs, predominantly in the densely granulated subtype [17, 20]. Moreover, SF1 expression was recently linked to distinct transcriptomic signatures and methylation profiles among somatotroph PitNETs [11, 20]. In summary, the current literature suggests that PIT1/SF1 co-expression associates with the somatotroph lineage. This finding is, however, yet underappreciated by the WHO classification and challenges the precept of declaring any and all PitNETs with co-expression of multiple TFs as “plurihormonal”. Currently, little is known about the clinical, histopathological, and molecular features linked to PIT1/SF1 co-expression in somatotroph PitNETs and such tumors are considered rare.
In this study, we aimed to demonstrate the prevalence of PIT1/SF1 co-expression in PitNETs, which otherwise correspond to somatotroph PitNETs, as defined by the WHO 2022 classification. We further explored the clinical, histopathological, epigenomic and genomic features associated with PIT1/SF1 co-expression in somatotroph tumors in a large meta-analysis by integrating molecular in-house data and publicly available data deposits.
Methods
Case series and tissue assembly
The archive of the University Medical Center Hamburg-Eppendorf was searched for PitNET/adenomas diagnosed between 2017 and 2023 as either sparsely or densely granulated somatotroph PitNET/adenomas or plurihormonal PitNET/adenomas. PitNETs fulfilling the essential diagnostic criteria for sparsely or densely granulated somatotroph PitNETs, as defined by the WHO 2022 Classification of Endocrine and Neuroendocrine Tumours (5th ed.) [31] were included in this study. In detail, inclusion criteria for DGST were adopted as follows: (i) diffuse GH expression (arbitrarily required in at least 30% of tumor cells), (ii) absence of other pituitary cell differentiation was disregarded owing to the purpose of this study, (iii) perinuclear LMWCK staining (perinuclear cytoplasmic CAM5.2 immunopositivity arbitrarily required in at least 30% of tumor cells), and (iv) acidophilic cytoplasm on H&E-stained sections (evaluated as either moderate or strong cytoplasmic eosinophilia). Six PitNETs were entirely immunonegative for CAM5.2 and thus did not fulfill the third WHO criterion. They were nevertheless included in the case series, due to a lack of reasonable differential diagnoses other than DGST and with the aim to further explore these unusual tumors.
Inclusion criteria for SGST were adopted as follows: (i) variable GH expression (any immunostaining pattern was accepted), (ii) absence of other pituitary cell differentiation was disregarded owing to the purpose of this study, (iii) abundant fibrous bodies (in more than 70% of cells), and (iv) absence of acidophilic cytoplasm on H&E-stained sections (evaluated as either none or weak cytoplasmic eosinophilia).
Cases of insufficient formalin-fixed paraffin-embedded (FFPE) material quantity or quality were excluded. To avoid inclusion of mammosomatotroph, mixed GH-PRL and plurihormonal PIT1-lineage tumors, cases with marked expression of estrogen receptor, prolactin or TSH were excluded from this study. Scattered intratumoral PRL expression up to 5% was tolerated for study inclusion, owing to the high prevalence of this finding, the possibility that PRL expression may stem from intratumorally entrapped residual adenohypophyseal cells and the aim to further explore this feature.
The use of all tissue specimens for research upon anonymization was in accordance with local and national ethical standards and with the 1964 Helsinki declaration and its later amendments.
Histology and immunohistochemistry
FFPE tissue samples were sectioned into 2 µm thick slices, according to standard laboratory protocols. Immunohistochemical stainings were performed on an automated staining machine (Ventana BenchMark TX, Roche Diagnostics, Mannheim, Germany). The following primary antibodies were used: SF1 (ab217317, Abcam, 1:250), TPIT (AMAb91409, Atlas antibodies, 1:1000), PIT1 (ab272639, Abcam, 1:1000), GH (ABIN6950857, antibodies-online, 1:100), PRL (ab11301, Abcam, 1:1000), TSH (MS1453-P, Thermo scientific, 1:10,000), alpha-subunit (bs-1912R, Bioss Antibodies, 1:1000), CAM5.2 (AB_2800363, BD Biosciences, 1:1000), FSH (M3504, Dako, 1:200), LH (M3502, Dako, 1:100), Ki67 (SP6, Cell Marque, 1:750). Detection was performed with secondary antibodies and diaminobenzidine (DAB) as a chromogen. PitNETs were considered either positive or negative for TFs. No equivocal cases were encountered. A tissue microarray of non-pituitary NETs was kindly provided by Guido Sauter (Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany).
DNA methylation analysis
DNA was isolated from FFPE tissue using the ReliaPrep™ FFPE gDNA Miniprep System (Promega). Around 100–500 ng of DNA was bisulfite-converted using the EZ DNA Methylation Kit (Zymo Research). The DNA Clean & Concentrator-5 kit (Zymo Research) and the Infinium HD FFPE DNA Restore Kit (Illumina) were used to clean and restore the converted DNA. Finally, the Illumina Infinium Methylation EPIC BeadChip Kit was used to quantify the methylation status of 850,000 CpG sites on an iScan device (Illumina).
Raw methylation array data (idat files) from this study and publicly available deposits [Capper et al. (GSE109381) [6]; Neou et al. (E-MTAB-7762) [17], Kober et al. (GSE226764) [11], and Silva-Júnior et al. (GSE207937) [23]] were processed using the minfi package [1] in R [19]. Probes on sex chromosomes, probes with a detection p value of or above 0.01, probes with SNPs at the CpG site, and cross-reactive probes were excluded. When combining different types of arrays, probes, which were not represented in both the EPIC and the 450 k array were excluded.
Consensus cluster analyses were performed using the ConsensusClusterPlus package [32]. Uniform Manifold Approximation and Projection (UMAP) transformation and plotting were performed using the umap package [15].
Cumulative copy number profiles (CNPs) were calculated using the GenVisR package [26]. To reduce CNP noise, three consecutive segments were required to surpass the cutoffs set to − 0.35 and 0.35.
Methylation-based classification was performed using the R package of the MNP brain tumor methylation classifier v12.5 [6].
Data integration and annotation of external samples
For evaluation of clinicopathological parameters in DGST-PIT1/SF1, DGST-PIT1, and SGST-PIT1 across studies, we compiled an extended case series consisting of our in-house samples, and publicly available data of somatotroph PitNETs derived from Capper et al. [6] (GSE109381), Neou et al. [17] (E-MTAB-7762, E-MTAB-7768), Silva-Júnior et al. [23] (GSE207937, GSE209903), Kober et al. [11] (GSE226764) and Rymuza et al. [20] (E-MTAB-11889). Extended sample data of the latter study were kindly provided by Mateusz Bujko (Dept. of Molecular and Translational Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland). For preparation of the integrated dataset, in-house samples were classified as either DGST-PIT1/SF1, DGST-PIT1, or SGST-PIT1, based on histomorphology and SF1-IHC. External samples were reclassified according to NR5A1 (SF1) RNA expression levels and/or methylation classifier results. Normalized NR5A1 counts above or below 160 were considered high, or low, respectively. External samples of Rymuza et al. lacked both DNA methylation and RNA expression data and were reclassified according to the previously described qPCR-based transcriptomic subgroups [20]. To maintain reclassification accuracy, samples of Rymuza et al. were excluded if histomorphology was not in line with the qPCR-based transcriptomic subgroup.
Due to the purpose of this study, all external sample with a classifier match or histomorphology other than somatotroph PitNET (e.g., mixed GH/PRL) were excluded. Furthermore, samples demonstrating an inconclusive mismatch between SF1 status and classifier result, as well as insufficient data for accurate reclassification were excluded. The final extended case series comprised a total of 270 tumors (99 in-house, 38 cases from Capper et al., 43 cases from Kober et al., 23 cases from Neou et al., 59 cases from Rymuza et al., 8 cases from Silva-Júnior et al.).
Statistical analyses
Statistical analyses of clinicopathological parameters were performed using the stats R package (v4.1.3) [19]. Continuous scale data were analyzed using the student’s t test. Ranked/ordinal data were analyzed using the Wilcoxon rank sum test. Binary categorical data were analyzed using the chi-square test. For detection of significant recurrent chromosomal alterations, the Genomic Identification of Significant Targets in Cancer (GISTIC) procedure [4] was applied using the CNVRanger R package [25]. Population ranges were computed based on reciprocal overlap between genomic regions with a cutoff of 0.51. In order to increase specificity for significant recurrent chromosomal alterations, at least 5 Mbp of genomic regions were required to show p values < 0.05 for each chromosome.
Results
We searched the archive of the University Medical Center Hamburg-Eppendorf for PitNETs, which fulfilled the essential diagnostic criteria for densely (DGST) or sparsely granulated somatotroph PitNET (SGST) as defined by the WHO 2022. Owing to the purpose of this study, hormone, and TF expression outside of the PIT1-lineage were overlooked as exclusion criteria.
PIT1/SF1 co-expression is highly prevalent in DGST
A total of 100 PitNETs were assembled (Supplementary Table 1). The series comprised 70 DGST (64 of which had been previously diagnosed as DGST, 6 of which had also been termed “plurihormonal” due to detection of FSH, LH and/or SF1 expression during diagnostic workup) and 30 SGST (all previously diagnosed as SGST). The 100 PitNETs were stained for PIT1, SF1, and TPIT. The vast majority of DGSTs displayed strong and unequivocal co-expression of PIT1 and SF1 (52/70 (74.3%), Fig. 1a–i, subsequently referred to as DGST-PIT1/SF1). The remaining DGSTs demonstrated nuclear staining for PIT1 and were clearly void of SF1 expression (18/70 (25.7%), subsequently referred to as DGST-PIT1, Fig. 1a). All 30 SGST (subsequently referred to as SGST-PIT1) showed strong nuclear staining for PIT1 and were clearly immunonegative for SF1 (Fig. 1a). None of the 100 PitNETs (i.e., DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1) expressed TPIT. Sensitivity and specificity of our TF antibodies was validated by appropriate and expected staining results of further non-PIT1 lineage PitNETs (n = 60; Supplementary Fig. 1a–h) [2] and non-pituitary NETs (n = 151; Supplementary Fig. 1i) [30].
Histopathological feature comparison
We continued to compare histopathological features of DGST-PIT1/SF1, DGST-PIT1, and SGST-PIT. As expected, CAM5.2 immunopositive fibrous bodies were highly abundant in SGST-PIT1 (median 90% of tumor cells, compared to 3% in DGST-PIT1/SF1 and 25% in DGST-PIT1, p < 0.001; Fig. 2a, e–g), whereas DGST-PIT1/SF1 and DGST-PIT1 displayed more prominent cytoplasmic CAM5.2 immunoreactivity (median 70% and 50%, respectively, compared to 0% in SGST-PIT1; p < 0.001 ***; Fig. 2b, e–g). Comparisons within the group of DGST revealed that fibrous bodies were significantly less prevalent in DGST-PIT1/SF1 compared to DGST-PIT1 (p < 0.001), whereas the extend of cytoplasmic CAM5.2 staining was similar in both groups (p = 0.10 ns).
Since SF1 is a marker of the gonadotroph lineage, we assessed expression of the gonadotropins FSH and LH. Only 9 of 52 (17.3%) DGST-PIT/SF1 expressed FSH or LH, whereas immunopositivity for these hormones was not found in DGST-PIT1 (0/14) or SGST-PIT1 (0/25) (Fig. 2c, h–j). Overall expression levels were rather low among FSH/LH-positive DGST-PIT1/SF1 with a median of 3% (range: 1–30%) immunopositive tumor cells (Fig. 2d, h–j). Based on the WHO 2022 diagnostic criteria for somatotroph tumors, we moreover assessed cytoplasmic eosinophilia, growth hormone (GH) expression, and alpha-subunit expression. As expected, both DGST-PIT1/SF1 and DGST-PIT1 showed strong eosinophilia (Fig. 2k), GH expression (Fig. 2l) and alpha-subunit expression (Fig. 2m) compared to SGST-PIT1 (p < 0.001 in all three analyses). We discovered no significant differences between DGST-PIT1/SF1 and DGST-PIT1 in these three analyses (p = 0.22, p = 0.09, p = 0.11, respectively). Of note, intratumoral prolactin (PRL) expression was found in the majority of PitNETs (40/52 (77%) in DGST-PIT1/SF1, 16/18 (89%) in DGST-PIT1, 23/30 (77%) in SGST-PIT1) with insignificant differences between the three groups (Fig. 2n). No tumors stained positive for TSH (0/52 DGST-PIT1/SF1, 0/18 DGST-PIT1, 0/30 SGST-PIT1). In summary, histopathological investigations comparing DGST-PIT1/SF1 and DGST-PIT1 revealed that DGST-PIT1/SF1 show infrequent and scarce expression of FSH or LH and harbor less fibrous bodies than DGST-PIT1.
Molecular feature comparison
We continued to investigate global DNA methylation profiles in a subset of our series (31/100 cases, including 14 DGST-PIT1/SF1, 12 DGST-PIT1, and 5 SGST-PIT1). Samples were selected based on tissue availability and histopathological features of interest. In detail, this subset included 24 PitNETs with intratumoral prolactin expression, 4 DGST lacking CAM5.2 immunoreactivity, and 3 DGST-PIT1/SF1 with FSH/LH expression.
The methylation data of our 31 in-house samples were integrated into a large reference series of 77 somatotroph PitNETs compiled from three independent publicly available datasets [11, 17, 23]. Integrated methylation data consensus cluster analysis confirmed that somatotroph tumors separate into three distinct epigenomic subclusters, as recently described (Fig. 3a) [11]. SF1 expression depicted by immunoreactivity (this study) and RNA expression levels (external studies) demonstrated that the three epigenomic subclusters relate to the tumors denoted as DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 in this study. The three previously described transcriptome-based molecular somatotroph subgroups of Rymuza et al. [20] adequately affiliated with these three clusters (subgroups 1, 2 and 3, conformed to DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1, respectively). Using the DKFZ brain tumor methylation classifier (www.molecularneuropathology.org; v12.5) [6], DGST-PIT1/SF1 mainly matched with the methylation class (mc) “pituitary adenoma, subtype STH-producing, subclass densely granulated A (novel)” (“DNS-A”), while DGST-PIT matched with the mc “pituitary adenoma, subtype STH-producing, subclass densely granulated B (novel)” (“DNS-B”). SGST-PIT1 matched with the mc “pituitary adenoma, subtype STH-producing, subclass sparsely granulated (novel)” (“SPAR”).
In addition, we investigated how epigenomic profiles of somatotroph PitNETs associate with intratumoral prolactin expression, CAM5.2 immunonegativity and FSH/LH-expression (Supplementary Fig. 2a–f). UMAP analyses indicated that intratumoral PRL expression was not linked to epigenomic distinctness among any subtypes of somatotroph PitNETs (Supplementary Fig. 2d). The extent of cytoplasmic CAM5.2 immunoreactivity also had no impact on the clustering of DGST (Supplementary Fig. 2e). Moreover, FSH/LH expression was not associated with epigenomic distinctness among DGST-PIT1/SF1 (Supplementary Fig. 2f).
We continued to investigate global DNA methylation levels of PitNETs, reclassified as either DGST-PIT1/SF1, DGST-PIT1 or SGST-PIT1. The three subtypes demonstrated global DNA hypomethylation compared to anterior pituitary control tissue, while this finding was particularly pronounced in DGST-PIT1 (Fig. 3b).
Next, cumulative copy number profiles (CNPs) were calculated for the three somatotroph PitNET subtypes and investigated for significant recurrent chromosomal alterations (Fig. 3c–e). We found that DGST-PIT1/SF1 frequently harbored recurrent chromosomal alterations including losses in chromosomes 1, 6, 15, and 16 (Fig. 3c). DGST-PIT1 exhibited occasional gains in chromosome 5 and losses in chromosomes 1, 2, 6, 8, and 11 (Fig. 3d). SGST-PIT1 harbored recurrent losses in chromosome 11 (Fig. 3e).
In summary, DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 were clearly molecularly distinct in terms of epigenomic signatures and CNPs.
Clinicopathological meta-analysis
In order to further explore patient and tumor characteristics of DGST-PIT1/SF1, DGST-PIT1, and SGST-PIT1 across studies in a large meta-analysis, we integrated clinicopathological data of our in-house series and public deposits of five previously published studies [6, 11, 17, 20, 23]. All samples were reclassified as either DGST-PIT1/SF1, DGST-PIT1, or SGST-PIT1 based on available histopathological, transcriptome and/or methylome data, resulting in an extended case series of 270 somatotroph tumors (95 DGST-PIT1/SF1, 80 DGST-PIT1, and 95 SGST-PIT1) (Fig. 4a). Median age of patients at primary surgery was 47 (range: 23–84), 49 (range: 18–75), and 40 (range: 22–86) years, respectively (Fig. 4b). SGST-PIT1 showed a significant female predominance (F/M ratio: 2.06, p = 0.001), in contrast to DGST-PIT1/SF1 (F/M: 1.32, p = 0.24) and DGST-PIT1 (F/M: 1.16, p = 0.6) (Fig. 4c). GNAS mutations are recurrent in somatotroph tumors [33], and GNAS statuses have been previously linked to the molecular somatotroph subgroups described by Rymuza et al. [20]. In the integrated data set, DGST-PIT1/SF1 were exclusively GNAS wild type (100%, 35/35), the majority of DGST-PIT1 harbored GNAS mutations (64.4%, 38/59) and most SGST-PIT1 were GNAS wild type (85%, 40/47). (Fig. 4d). SGST-PIT1 showed an increased proliferative rate compared to both DGST-PIT1/SF1 (p < 0.001) and DGST-PIT1 (p = 0.009), whereas differences in proliferation between DGST-PIT1/SF1 and DGST-PIT1 were insignificant (p = 0.8) (Fig. 4e). Tumor sizes were compared between the three subtypes in patients stratified by preoperative therapy with somatostatin analogs (SSA). Median maximum tumor diameters of DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 were 1.5 (range: 0.6–4.46), 1.4 (range: 0.92–3.43), and 1.4 (range: 0.47–5) cm in patients, which had not been treated with SSA prior to surgery. No statistically significant differences were seen between the tumor groups (p > 0.2 in all three comparisons, Fig. 4f). Median maximum tumor diameters of DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 were 1.8 (range: 0.7–4.6), 1.4 (range: 0.5–3.3), and 1.9 (range: 0.51–7.7) cm in patients, which had been treated with SSA prior to surgery. DGST-PIT1 were significantly smaller than DGST-PIT1/SF1 (p = 0.039), and SGST-PIT1 (p = 0.001). No statistically significant difference was seen between DGST-PIT1/SF1 and SGST-PIT1 (p = 0.13, Fig. 4f). Invasive growth (defined as Knosp grade 3–4) was found in 11 of 55 (20%) DGST-PIT1/SF1 and 13 of 54 (24%) DGST-PIT1 with insignificant differences between the two groups (p = 0.61). In comparison, invasive growth was found in 25 of 60 (42%) SGST-PIT1, which was significantly more than in DGST-PIT1/SF1 (p = 0.018) and DGST-PIT1 (p = 0.048) (Fig. 4g). In terms of clinical outcomes, patients with SGST-PIT1 showed lower rates of remission following surgical interventions (46% remission, median follow-up: 390 days), with more frequent disease persistence or relapse, compared to the similar rates of both DGST-PIT1/SF1 (75% remission, median follow-up: 193 days) and DGST-PIT1 (68% remission, median follow-up: 228 days) (Fig. 4h). Clinicopathological and molecular features of the three somatotroph PitNET subtypes are summarized in Fig. 5.
Discussion
In this study, we aimed to explore the histopathological, molecular, and clinical features associated with the emerging role of PIT1/SF1 co-expression in PitNETs. Our study design was centered around the current WHO classification and based on findings of previous publications, which triggered the search for PIT1/SF1 co-expression among PitNETs, which otherwise correspond to the somatotroph PIT1-lineage.
PIT1/SF1 co-expression is highly prevalent among DGST
We were surprised to find that the vast majority (nearly 3/4) of previously diagnosed DGST co-expressed PIT1 and SF1. This unexpectedly high rate can be explained by two circumstances. Firstly, the WHO has only recently dictated necessity for staining all three TFs in routine pituitary diagnostics. Thus, data on unusual TF expression constellations are currently scarce and the prevalence of multilineage PitNETs may be much higher than generally assumed. Secondly, the strict implementation of WHO-based inclusion criteria may have inflated the prevalence of PIT1/SF1 co-expression within the case series. Thus, the frequency of PIT1/SF1 co-expression among PitNETs, which do not fit into the WHO class of somatotroph tumors has yet to be determined. This pertains to pure somatotroph tumors, which do not fulfill the essential criteria for either SGST or DGST, to somatotroph tumors exhibiting elements of mammosomatotroph, mixed somatotroph-lactotroph, or PIT1-plurihormonal differentiation and to further non-somatotroph PitNETs of the PIT1-lineage.
DGST-PIT1/SF1 exhibit distinct molecular, histopathological, and clinical features
Global DNA methylation patterns are considered to reflect the cell of origin, making epigenomic analyses useful for classifying tumors based on their lineage [6, 7, 21]. We compared methylation profiles of DGSTs with and without PIT1/SF1 co-expression and showed that they are epigenomically distinct. This result is in line with a recent publication by Kober et al. [11] and compatible with the notion that transcription factors play an early role in pituitary lineage development. The existence of two epigenomically distinct groups of tumors among DGST has also been described before by Capper et al. [6], and was implemented into the Brain tumor methylation classifier. The two groups had been termed mc “DNS-A” and mc “DNS-B” (v11b4, v12.5, v12.8), while their significance remained unknown. In this report, we clarify that the mc “DNS-A” affiliates with PIT1/SF1 co-expression, whereas the mc “DNS-B” mainly comprises pure PIT1-lineage tumors.
Moreover, we show that histopathological features of most DGST-PIT1/SF1 reflect those of prototypical DGST. In contrast, DGST-PIT1 display fibrous bodies in significantly higher amounts than DGST-PIT1/SF1. We conclude that DGST-PIT1 predominantly pertain to the previously proposed class of “intermediate type” granulated somatotroph PitNET, which were considered within the histomorphological spectrum of DGST [18]. Although “intermediate type” granulation was not exclusively encountered among DGST-PIT1, our results show that this granulation type associates with molecular distinctness from somatotroph PitNETs with densely granulated morphology.
Tumor sizes in DGST-PIT1 were significantly smaller than in DGST-PIT1/SF1 and SGST-PIT1 after SSA treatment, indicating that tumor growth of DGST-PIT1 is more efficiently impeded by medical management compared to the other subtypes. In line with this, GNAS mutations and dense granulation patterns were previously linked to favorable SSA responses in somatotroph PitNETs [5, 8, 13, 34]. We find that both these features affiliate with DGST-PIT1.
Somatotroph PitNETs frequently demonstrate intratumoral PRL expression and may be CAM5.2 immunonegative
Handling apparent intratumoral PRL expression in somatotroph PitNETs poses a diagnostic difficulty. Firstly, it can be challenging to histomorphologically differentiate if scarce immunosignal stems from entrapped non-neoplastic pituitary cells or scattered tumor cells. Secondly, in contrast to the WHO 2017, the WHO 2022 states that somatotroph tumors are negative for PRL, raising the question how to classify bona fide somatotroph tumors with little, yet obvious intratumoral PRL expression. In this study, intratumoral PRL expression up to 5% was tolerated for somatotroph PitNET diagnosis. We found that intratumoral PRL expression did not relate to epigenomic distinctness in somatotroph PitNETs. This result clearly showcases that detection of limited PRL expression does not justify exclusion of somatotroph PitNET diagnosis. This should be considered by the WHO classification, which currently states that absence of hormone expression other than GH is an essential diagnostic criterion for somatotroph PitNETs. To further clarify this topic, extended and comprehensive epigenomic investigations on the various histopathological subtypes of PIT1-lineage PitNETs are needed.
Moreover, we epigenomically analyzed four CAM5.2 immunonegative somatotroph PitNETs in our case series. Because CAM5.2-negativity poses a diagnostic dilemma, previous studies refrained from classifying such tumors as sparsely or densely granulated [10, 27]. The epigenomic data presented in this study suggest that lack of CAM5.2 immunoreactivity among DGST does not accompany epigenomic distinctness. In addition, the highly variant extent of cytoplasmic CAM5.2 immunoreactivity (ranging from 30 to 100% of tumor cells) in the CAM5.2-positive DGST of our case series also did not associate with epigenomic distinctness. Taken together, our data suggest that prominent perinuclear cytoplasmic CAM5.2 staining is not a crucial histopathological feature of DGST. Further investigations are needed to clarify if CAM5.2 immunoreactivity may associate with separate clinical features in DGST and whether CAM5.2-negative PitNETs with epigenomic profiles of SGST also exist.
Are DGST-PIT1/SF1 truly plurihormonal tumors?
The question arises, how DGST-PIT1/SF1 should be meaningfully termed and classified. As previously mentioned, the WHO 2022 dictates to bundle all PitNETs with immunopositivity for more than one TF as “plurihormonal PitNET/adenoma”. Consequently, the WHO distinguishes DGST-PIT1/SF1 from pure PIT1-lineage somatotroph PitNETs and formally classifies these tumors together with various PitNETs exhibiting unusual combinations of TF and hormone expression patterns. Our data, however, suggests that DGST-PIT1/SF1 represents a distinct somatotroph PitNET subtype. Moreover, since DGST-PIT1/SF1 rarely expressed FSH or LH in our study, questioning the true plurihormonal identity of these tumors stands to reason. We propose the term “somatogonadotroph PitNET” for DGST-PIT1/SF1.
In conclusion, a substantial proportion of previously diagnosed somatotroph PitNETs co-express PIT1 and SF1 and exhibit clinical, histopathological, and molecular distinctness from other pure PIT1-lineage somatotroph PitNETs. We present a comprehensive meta-analysis of the three emerging molecular subtypes of somatotroph PitNETs, which call for a refinement of the current WHO 2022 classification.
Data availability
The methylation data generated in this study are accessible via the GEO accession number GSE246645.
References
Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD et al (2014) Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics 30:1363–1369. https://doi.org/10.1093/bioinformatics/btu049
Asa SL, Mete O, Perry A, Osamura RY (2022) Overview of the 2022 WHO classification of pituitary tumors. Endocr Pathol 33:6–26. https://doi.org/10.1007/S12022-022-09703-7
Asa SL, Mete O, Riddle ND, Perry A (2023) Multilineage pituitary neuroendocrine tumors (PitNETs) expressing PIT1 and SF1. Endocr Pathol. https://doi.org/10.1007/S12022-023-09777-X
Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D et al (2007) Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA 104:20007–20012. https://doi.org/10.1073/PNAS.0710052104/SUPPL_FILE/IMAGE1804.GIF
Bhayana S, Booth GL, Asa SL, Kovacs K, Ezzat S (2005) The implication of somatotroph adenoma phenotype to somatostatin analog responsiveness in acromegaly. J Clin Endocrinol Metab 90:6290–6295. https://doi.org/10.1210/JC.2005-0998
Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D et al (2018) DNA methylation-based classification of central nervous system tumours. Nature 555:469–474. https://doi.org/10.1038/nature26000
Dottermusch M, Schüller U, Hagel C, Saeger W (2023) Unveiling the identities of null cell tumours: epigenomics corroborate subtle histological cues in pituitary neuroendocrine tumour/adenoma classification. Neuropathol Appl Neurobiol 49:e12870. https://doi.org/10.1111/NAN.12870
Efstathiadou ZA, Bargiota A, Chrisoulidou A, Kanakis G, Papanastasiou L, Theodoropoulou A et al (2015) Impact of gsp mutations in somatotroph pituitary adenomas on growth hormone response to somatostatin analogs: a meta-analysis. Pituitary 18:861–867. https://doi.org/10.1007/S11102-015-0662-5/FIGURES/3
Heitz PU (1979) Multihormonal pituitary adenomas. Horm Res 10:1–13. https://doi.org/10.1159/000178983
Iacovazzo D, Carlsen E, Lugli F, Chiloiro S, Piacentini S, Bianchi A et al (2016) Factors predicting pasireotide responsiveness in somatotroph pituitary adenomas resistant to first-generation somatostatin analogues: an immunohistochemical study. Eur J Endocrinol 174:241–250. https://doi.org/10.1530/EJE-15-0832
Kober P, Rymuza J, Baluszek S, Maksymowicz M, Nyc A, Mossakowska BJ et al (2023) DNA methylation pattern in somatotroph pituitary neuroendocrine tumors. Neuroendocrinology. https://doi.org/10.1159/000533692
Kreutzer J, Vance ML, Lopes MBS, Laws ER (2001) Surgical management of GH-secreting pituitary adenomas: an outcome study using modern remission criteria. J Clin Endocrinol Metab 86:4072–4077. https://doi.org/10.1210/JCEM.86.9.7819
Larkin S, Reddy R, Karavitaki N, Cudlip S, Wass J, Ansorge O (2013) Granulation pattern, but not GSP or GHR mutation, is associated with clinical characteristics in somatostatin-naïve patients with somatotroph adenomas. Eur J Endocrinol 168:491–499. https://doi.org/10.1530/EJE-12-0864
Mazal PR, Czech T, Sedivy R, Aichholzer M, Wanschitz J, Klupp N et al (2001) Prognostic relevance of intracytoplasmic cytokeratin pattern, hormone expression profile, and cell proliferation in pituitary adenomas of akromegalic patients. Clin Neuropathol 20:163–171
McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. https://doi.org/10.48550/arxiv.1802.03426
Mete O, Cintosun A, Pressman I, Asa SL (2018) Epidemiology and biomarker profile of pituitary adenohypophysial tumors. Modern Pathology. Nature Publishing Group, pp 900–909
Neou M, Villa C, Armignacco R, Jouinot A, Raffin-Sanson ML, Septier A et al (2020) Pangenomic classification of pituitary neuroendocrine tumors. Cancer Cell 37:123-134.e5. https://doi.org/10.1016/j.ccell.2019.11.002
Obari A, Sano T, Ohyama K, Kudo E, Qian ZR, Yoneda A et al (2008) Clinicopathological features of growth hormone-producing pituitary adenomas: difference among various types defined by cytokeratin distribution pattern including a transitional form. Endocr Pathol 19:82–91. https://doi.org/10.1007/S12022-008-9029-Z
R Core Team (2020) R: a language and environment for statistical computing
Rymuza J, Kober P, Rusetska N, Mossakowska BJ, Maksymowicz M, Nyc A et al (2022) Transcriptomic classification of pituitary neuroendocrine tumors causing acromegaly. Cells. https://doi.org/10.3390/CELLS11233846
Salomon MP, Wang X, Marzese DM, Hsu SC, Nelson N, Zhang X et al (2018) The epigenomic landscape of pituitary adenomas reveals specific alterations and differentiates among acromegaly, Cushing’s disease and endocrine-inactive subtypes. Clin Cancer Res 24:4126–4136. https://doi.org/10.1158/1078-0432.CCR-17-2206
Scheithauer BW, Horvath E, Kovacs K, Laws ER, Randall RV, Ryan N (1986) Plurihormonal pituitary adenomas. Semin Diagn Pathol 3:69–82
da Silva-Júnior RMP, Bueno AC, Martins CS, Coelli-Lacchini F, Ozaki JGO, de Almeida-E-Silva DC et al (2023) Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors. J Clin Endocrinol Metab 108:1452–1463. https://doi.org/10.1210/CLINEM/DGAC703
Silva-Ortega S, García-Martinez A, Niveiro de Jaime M, Torregrosa ME, Abarca J, Monjas I et al (2021) Proposal of a clinically relevant working classification of pituitary neuroendocrine tumors based on pituitary transcription factors. Hum Pathol 110:20–30. https://doi.org/10.1016/J.HUMPATH.2020.12.001
Da Silva V, Ramos M, Groenen M, Crooijmans R, Johansson A, Regitano L et al (2020) CNVRanger: association analysis of CNVs with gene expression and quantitative phenotypes. Bioinformatics 36:972–973. https://doi.org/10.1093/BIOINFORMATICS/BTZ632
Skidmore ZL, Wagner AH, Lesurf R, Campbell KM, Kunisaki J, Griffith OL et al (2016) GenVisR: genomic visualizations in R. Bioinformatics 32:3012–3014. https://doi.org/10.1093/BIOINFORMATICS/BTW325
Swanson AA, Erickson D, Donegan DM, Jenkins SM, Van Gompel JJ, Atkinson JLD et al (2021) Clinical, biological, radiological, and pathological comparison of sparsely and densely granulated somatotroph adenomas: a single center experience from a cohort of 131 patients with acromegaly. Pituitary 24:192–206. https://doi.org/10.1007/S11102-020-01096-2
Taniguchi-Ponciano K, Andonegui-Elguera S, Peña-Martínez E, Silva-Román G, Vela-Patiño S, Gomez-Apo E et al (2020) Transcriptome and methylome analysis reveals three cellular origins of pituitary tumors. Sci Rep 10:1–11. https://doi.org/10.1038/s41598-020-76555-8
Tebani A, Jotanovic J, Hekmati N, Sivertsson Å, Gudjonsson O, Edén Engström B et al (2021) Annotation of pituitary neuroendocrine tumors with genome-wide expression analysis. Acta Neuropathol Commun. https://doi.org/10.1186/S40478-021-01284-6
Uccella S, Leoni E, Kaiser S, Maragliano R, Valerio A, Libera L et al (2023) Heterogeneity of TPIT expression in ACTH-secreting extra-pituitary neuroendocrine tumors (NETs) supports the existence of different cellular programs in pancreatic and pulmonary NETs. Virchows Arch. https://doi.org/10.1007/S00428-023-03642-2
WHO Classification of Tumours Editorial Board. Endocrine and Neuroendocrine tumours [Internet]. Lyon (France): International Agency for Research on Cancer; 2022 [cited 27 Nov 2023]. (WHO classification of tumours series, 5th ed.; vol. 8). https://tumourclassification.iarc.who.int/chapters/36
Wilkerson MD, Hayes DN (2010) ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26:1572–1573. https://doi.org/10.1093/BIOINFORMATICS/BTQ170
Yamato A, Nagano H, Gao Y, Matsuda T, Hashimoto N, Nakayama A et al (2022) Proteogenomic landscape and clinical characterization of GH-producing pituitary adenomas/somatotroph pituitary neuroendocrine tumors. Commun Biol 51(5):1–14. https://doi.org/10.1038/s42003-022-04272-1
Yang I, Park S, Ryu M, Woo J, Kim S, Kim J et al (1996) Characteristics of gsp-positive growth hormone-secreting pituitary tumors in Korean acromegalic patients. Eur J Endocrinol 134:720–726. https://doi.org/10.1530/EJE.0.1340720
Acknowledgements
We thank the laboratory team of the Institute of Neuropathology and Institute of Pathology (University Medical Center Hamburg-Eppendorf, Hamburg, Germany) for excellent technical support.
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Open Access funding enabled and organized by Projekt DEAL. US was supported by the Fördergemeinschaft Kinderkrebszentrum Hamburg.
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MD and US conceived the study. MD, AR, FLR, JF, SS, MG, WS, JEN and US collected and supplied essential data. MD and AR analyzed the data. MD drafted the manuscript. All authors, MD, AR, FLR, JF, SS, MG, WS, JEN and US helped shape the manuscript.
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401_2024_2686_MOESM1_ESM.png
Supplementary Figure 1: PIT1, SF1 and TPIT immunostaining of non-PIT1-lineage PitNETs and non-pituitary NETs. a-d) Gonadotroph PitNETs (n=30) stained with the antibodies PIT1, SF1 and TPIT used in this study stained exclusively and unequivocally for SF1. e-h) Corticotroph PitNETs (n=30) stained with the antibodies PIT1, SF1 and TPIT used in this study stained exclusively and unequivocally for TPIT. Scale bar is 50µm in b-d and f-h. i) Nearly all non-pituitary NETs were immunonegative for PIT1, SF1 and TPIT. In line with a recent study by Uccella et al. (2023), one NET of the lung (1/19) demonstrated moderate TPIT expression in roughly 5% of tumor cells (PNG 3921 KB)
401_2024_2686_MOESM2_ESM.png
Supplementary Figure 2: Intratumoral expression of prolactin, CAM5.2 immunonegativity or expression of gonadotropins does not indicate epigenomic distinctness among somatotroph PitNETs. a-f) Dimension reduction of global DNA methylation data via UMAP illustrates epigenomic similarities between PitNETs. Somatotroph PitNETs with evidence of intratumoral prolactin expression by immunostaining were not epigenomically distinct from somatotroph PitNETs without prolactin immunopositivity and did not affiliate with lactotroph PitNETs (d). Extent of perinuclear cytoplasmic CAM5.2 immunostaining was not associated with epigenomic distinctness among DGST. Of note, CAM5.2 immunonegative DGST did not separate from CAM5.2 immunopositive DGST (e). Among DGST-PIT1/SF1, expression of FSH or LH did not associate with epigenomic distinctness from FSH/LH immunonegative cases. Moreover, FSH/LH-expressing DGST did not affiliate with gonadotroph PitNETs (f). Shown plots in a – f are based on the beta values of the top 10,000 most variant CpGs (PNG 979 KB)
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Dottermusch, M., Ryba, A., Ricklefs, F.L. et al. Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features. Acta Neuropathol 147, 16 (2024). https://doi.org/10.1007/s00401-024-02686-1
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DOI: https://doi.org/10.1007/s00401-024-02686-1