Transcriptomic perspective on acromegaly- related PIT-1/SF-1 positive pituitary tumors

Transcriptomic perspective on acromegaly- related PIT-1/SF-1 positive pituitary tumors | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Transcriptomic perspective on acromegaly- related PIT-1/SF-1 positive pituitary tumors View ORCID Profile Julia Rymuza , Qilin Zhang , View ORCID Profile Mateusz Bujko doi: https://doi.org/10.1101/2025.05.28.656330 Julia Rymuza a Department of Molecular and Translational Oncology, Maria Sklodowska-Curie National Research Institute of Oncology , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julia Rymuza Qilin Zhang b Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University , Shanghai 200040, China c National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University , Shanghai 200040, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mateusz Bujko a Department of Molecular and Translational Oncology, Maria Sklodowska-Curie National Research Institute of Oncology , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mateusz Bujko For correspondence: mateusz.bujko{at}nio.gov.pl Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Neuroendocrine pituitary tumors (PitNETs) are classified based on clinical manifestation and expression of pituitary cell lineage-specific transcription factors (TFs) and hormones. A subtype of acromegaly-related tumors was found to express PIT-1 and SF-1 TFs, two markers of distinct pituitary cells. They were considered as multilineage or “somatogonadotoph” tumors. The aim of our study was to determine their identity and cell type origin by extensive transcriptomic analysis. For this purpose, we analyzed the RNA sequencing (RNAseq) data from 546 PitNETs (including 193 acromegaly-related tumors) and single cell RNAseq data from somatotroph and gonadotroph tumors and normal pituitary. Somatrotroph PitNETs co-expressing PIT-1 and SF-1 TFs were found in each included RNAseq dataset. Their transcriptomic profile and activity of pituitary TFs closely resembles other somatotroph tumors, but they substantially differ from gonadotroph PitNETs yet retain activity of NR5A1 (SF-1) and expression of some of SF-1-regulated genes ( LHB and GNHRH ). SF-1 apparently regulates slightly different set of genes in double positive somatotroph PitNETs and gonadotroph tumors. Based on scRNAseq data a subcluster of normal gonadotroph cells with POU1F1 (PIT-1) expression was identified, but tumor cells of PIT-1/SF-1 PitNETs were not similar to this subtype of normal gonadotrophs. A difference in genes expression profiles between 3 subtypes of somatotroph tumors was determined by analysis of both bulk- and scRNAseq data. From transcriptomic perspective of genes-coregulation and the activity of pituitary transcription factors, acromegaly-related PitNETs co-expressing PIT-1 and SF-1 are subtype of PIT-1 lineage tumors and molecular data do not support considering them as multilineage. Introduction Neuroendocrine pituitary tumors (PitNETs) also named pituitary adenomas are among the most common intracranial tumors in adults. They are a heterogenous group of tumors, that originates from distinct pituitary secretory cells. Pituitary tumors are classified according to the recommendations of fifth edition of Classification of Endocrine and Neuroendocrine Tumors [ 1 ]. The principle of this classification is the paradigm that mature pituitary cell that secrete specified tropic hormones originate from common progenitor cells through the differentiation process that is orchestrated by a set of particular transcription factors. The key transcription factors for development of mature corticotroph and gonadotroph pituitary lineages are TPIT (encoded by TBX19 gene) and SF-1 ( NR5A1 ), respectively. In turn, PIT-1 ( PUO1F1 ) transcription factor was found pivotal in maturation of somatotroph, lactotroph and tyrotroph cells. Thus, somatotroph pituitary tumors (sPitNETs) are by definition those that express PIT-1 transcription factor and produce growth hormone. In addition to somatotroph tumors, other subtypes of GH- and PIT-1-positive tumors cause acromegaly: mammosomatotroph tumors and mature plurihormonal tumors (characterized by expression of GH and additional hormones (prolactin (PRL) and thyrotropic hormone, respectively), mixed somatolactotroph tumors (consisting of two distinct populations of somatotroph and lactotroph tumor cells), or low-differentiated PIT-1 tumors [ 2 , 3 ]. According to a recently published data, somatotroph pituitary tumors that cause acromegaly fall into 3 distinct molecular subtypes [ 4 – 7 ]. They differ in gene expression, DNA methylation and DNA copy number profiles, as well as in the frequency of GNAS mutations, granularity scores on electron microscopy images, percentage of invasive tumors and tumor size [ 4 – 6 , 8 ]. By analyses of genes expression, we found that one of these three subtypes (referred as Subtype 1 in our previous report [ 7 ]) is characterized by the expression of NR5A1 (SF-1), a marker of gonadotroph PitNETs. Such tumors with expression of both PIT-1 and SF-1 TFs were occasionally reported by the others through the years [ 9 – 15 ] and they were pointed as a matter of special interest. These PIT-1/SF-1 positives sPitNETs are different in many ways as compared to the other acromegaly-related PitNETs without SF-1 expression. They are densely granulated adenomas without GNAS mutation, with ectopic GIPR expression, distinguished DNA methylation profile and high level of genomic instability [ 4 , 6 , 7 ]. Recent discussions of PIT-1/SF-1 tumors have been marked by confusion, as these two TFs are the markers specific to separate pituitary linages according to official classification. These tumors were commonly referred to as multilineage pituitary tumors [ 16 , 17 ], gonado/somatotroph tumors [ 6 ], somatogonadotroph [ 18 ] or plurihormonal tumors [ 9 , 11 ]. In this article we made an attempt to reveal the identity of double positive PIT-1/SF-1 tumors by in depth analysis of combination of our in house generated and publically available transcriptomic data on pituitary tumors as well as the available data from scRNAseq experiments in PitNETs and normal pituitary. The main question we addressed are: does PIT-1/SF-1 are more related to gonadotrop or somatotroph PitNETs and does experimental data justifies considering them as multilineage; are there normal counterparts of double positive PIT-1/SF-1 cells in pituitary; what distinguishes PIT-1/SF-1 and other PIT-1 somatotroph tumors at genes expression level. We are convinced that transcriptomic point of view with a focus on the transcription factors activity is the perspective underlying official WHO classification of pituitary tumors. Methods Bulk RNASeq data We used RNAseq data previously generated by our research groups HRA003588 [ 19 ] and E-MTAB-11889 [ 8 ]. Through the literature review we also identified additional 3 datasets that contain bulk transcriptomic data of PitNETs including E-MTAB-7768 [ 15 ], GSE213527 [ 20 ], GSE209903 [ 21 ]. Depending on available data, we used either Salmon [ 22 ] to map reads to human genome version hg38 and taximport [ 23 ] to extract row counts from row reads or featuresCounts [ 24 ] to count reads from already mapped reads. In the analysis, we used genes detected in all studies that had more than 10 reads in over 25% of samples. The dataset was then variance stabilized transformed (VST) using DESeq2 [ 25 ] and batch effects were removed with the removeBatchEffect function from the limma package [ 26 ]. Differentially expressed genes were detected using linear model based on transformed, batch corrected data with false discovery rate (FDR) p-value correction. To identify previously established transcriptomic subtypes of sPitNETs in the analyzed cohort, we focused on samples which were diagnosed with acromegaly or mixed acromegaly and hyperprolactinemia or had serum IGF-1 level above 400 ng/mL. We clustered samples using k-means clustering, hierarchical clustering and Gaussian Mixture Model. We assign samples to a cluster by choosing the most abounded cluster based on three clustering methods. For samples with discordant results among algorithms, we assign them based on previously established marker genes for sPitNET subtypes [ 8 ]. To further investigate expression patterns, we used WGCNA [ 27 ] to find modules of highly correlated genes and relate them to chosen data features. We also computed regulons, which are groups of genes co-regulated by a specific transcription factor (TF) with the RTN package [ 28 ]. For TF with documented function in pituitary (list in Supplementary Table S1) we calculated their activity in the samples. We used two-tailed GSEA to check if DEGs are enriched with regulons [ 29 , 30 ]. Single cell RNAseq data We utilized the previously obtained scRNAseq data PRJCA009690 [ 31 ] as well as the other publicly available single cell transcriptomes of both adult pituitary gland (APG) and PitNETs: GSE208108 [ 32 ]; HRA003110 [ 33 ]; HRA003483 [ 34 ]; PRJNA1137596 [ 35 ] as well as fetal pituitary (FPG) GSE142653 [ 36 ]. Raw reads were processed with alvin-fry [ 37 ] through simpleleaf framework using GRCh38 genome assembly. All the data was further analyzed using Seurat package (v5.1.0) [ 38 ]. Low quality cells were filtered based on nFeature_RNA, nCount_RNA, percent.mt. Doublets were detected using scDblFinder [ 39 ] and only singlets were kept for further analysis. Red blood cells were identified by calculating RBC_score, with HBB, HBA1, HBG2, HBA2 used as marker genes. We selected highly variable features, scaled and did principal component analysis (PCA) of the filtered count matrix. Depending on data complexity, we integrated the data using either CCA [ 40 ] or FastMNN [ 41 ] algorithms. Cluster cell types were identified based on expression of classical marker genes [ 31 , 32 ]. We assessed the similarity of tumors, using AddModuleScore with 20 top DEG specific to given clusters from APG. We used Slingshot[ 42 ] and tradeSeq [ 43 ] to construct lineage trajectory of integrated APG and FPG gonadotroph cells and to examine gene expression with pseudotime. For integrated data from APG and somatotroph tumors, we use Monocle3[ 44 ] to calculate pseudotime-related trajectory. Using decoupleR [ 45 ] we constructed pseudobulks of somatotroph cells in each sample. We applied pyDESeq2 [ 46 ] to calculate DEGs between somatotroph cells from each subtype and APG. For resulting gene sets, we conducted GSEA with decoupleR using KEGG database [ 47 ]. To compare regulons of important TF between somatotroph cells from Subtype 1 and gonadotrhop cells from gonadotrhop tumors, we used hdWGCNA [ 48 ]. We selected genes for each TF which met criteria of regulatory score above 0.75. We inferred copy numbers of each sample with CopyKAT [ 49 ]. We used somatotroph cells from APG as normal cells. For each sample, we calculated CN profiles as average CN ration for chromosome arm. Each cell was then assigned to a stability cluster depending on aneuploidy score (AS) calculated as a number of chromosome arms with average CN ration above 0.1 after normalization by subtracting CN ration of chr22, to remedy previously showed shortcomings of only depth based CN calculations [ 4 ]. If AS was above 2, we designated cells as unstable, and if they had deletion of chr11 and less than 4 amplifications of chromosomal arms as del11. Results Clustering of pituitary tumor samples Three molecular subtypes of somatotroph tumors causing acromegaly were identified in previous studies, therefore we started our analysis with an attempt of assigning each sample in study cohort comprising 193 acromegaly-causing tumors to one of these subtypes. Dimensionality reduction of transcriptomic data with UMAP showed a clustering of acromegaly-related tumors into three groups ( Fig. 1A ). Based on consensus of 3 clustering algorithms, 98% of the samples was assigned to one of these groups. Each group was identified as representing one of previously characterized subtypes (subtypes 1,2 and 3) [ 4 , 6 , 7 ] based on the presence of the samples from previous research with known transcriptome subtype assignment. Three samples (1.5% of all acromegaly-related PitNETs) with unclear annotation due to lack of consensus between 3 algorithms were assigned manually based on previously proposed marker genes [ 7 ] (Supplementary Fig. S1). Together, it allowed for the annotation of each acromegaly-related tumor sample in the study cohort with one of the known, previously described subtype of somatotroph tumors. Subtype 1, previously identified as composed of densely granulated SF-1/PIT-1 double positive tumors was represented by 43/193 (22%) of the samples; 83/193 (43%) samples were assigned to Subtype 2 previously identified as enriched for GNAS -mutated DG tumors; whereas 67/193 (35%) were of Subtype 3 known to be enriched for SG somatotroph PitNETs. According to previous observations, the co-expression of POU1F1 and NR5A1 was clearly confirmed in the tumors of Subtype 1 ( Fig 1B ). All these tumors were originally diagnosed as GH-omas or plurihirmonal tumors co expressing GH and PRL with one case of mixed GH/PRL and one of unknown diagnosis. Download figure Open in new tab Fig. 1 Transcriptomic profile of acromegaly-related pituitary tumors as well as all the PitNETs included in study cohort. A) UMAP of acromegaly-related tumors with assigned transcriptomic subtypes. B) Expression of POU1F1 and NR5A1 in acromegaly-related tumors samples colored according to transcriptomic subtype. C) UMAP of the whole PitNET study cohort labeled according to hormone staining by immunohistochemistry (IHC) with distinguished samples from acromegaly-related Subtype 1 tumors. D) Hierarchical clustering and similarity matrix of the transcriptomes of the entire study cohort with summary of molecular, histological and clinical characteristics. E) Results of Weighted Gene Co-expression Network Analysis (WGCNA) analysis, showing correlation (cor) between calculated gene modules and given features. Having the acromegaly-related tumors characterized, we performed the unsupervised clustering of all the PitNETs in the study cohort (n=546) (Supplementary Table S2). We paid a special attention to the similarity of double positive POU1F1 -and NR5A1 -expressing tumors to the other histological types. The samples clustered according to the expression of lineage-specific TFs into PIT-1, TPIT and SF-1 tumors separately ( Fig. 1C ). Most of the tumors described as negative for TF expression (null cell tumors) (13/24) clustered with SF-1 group; 8/24 null cell tumors clustered with TPIT samples and 3/24 with PIT-1 cluster. The combined study cohort included 45 non-functioning PitNETs without reported immunohistochemical staining status i.e. no information on hormone nor transcription factor expression. These samples clustered mostly with SF-1 group (32 samples); 11 of them grouped with TPIT tumors and 2 with PIT-1. Importantly, all the acromegaly-related Subtype 1 tumors (positive for both POU1F1 and NR5A1 ) form a separate cluster located closely to the PIT-1 but not to SF-1 tumors reflecting their much higher similarity to PIT-1 positive than gonadotroph PitNETs. This clustering pattern obtained with UMAP was confirmed with hierarchical clustering and similarity matrix ( Fig. 1D ). The dendrogram that represent the similarity of the samples contain the main 3 branches of PIT-1, SF-1 and TPIT tumors. With the exception of 3 samples, all double positive PIT-1/SF-1 tumors were grouped as a separate cluster within PIT-1 dendrogram branch. Of note, none of these tumors clustered within the same branch as gonadotroph SF-1-positive PitNETs. Genes co-expression analysis Following the observation of much higher similarity of double positive PIT-1/SF-1 tumors to the other PIT-1 lineage tumor than SF-1 gonadotroph tumors, we looked whether the genes expression profile reflect their pituitary lineage identity. We used a Weighted Gene Co-expression Network Analysis (WGCNA) of the RNAseq data of the entire study cohort to generate gene co-expression network and to identify the groups of co-expressed genes. A total of 27 modules of co-expressed genes labeled with a distinct color names were found. We determined the relationship between each module and the expression of genes encoding for lineage specific transcription factor ( NR5A1, POU1F1 and TBX19 ). We also checked the relationship between modules and given subtypes of tumor sample including, SF-1 positive gonadotrophs and TPIT-positive corticotrophs and general PIT-1 lineage tumors (including those POU1F1/NR5A1 expressing) as well as each of 3 transcriptomic subtypes of acromegaly-related tumors ( Fig. 1E ). We observed that the tumors of basic histological subtypes, defined by the expression of lineage-specific TF, have similar relationship with the co-expression modules as the expression of the gene encoding these respective TFs. PIT-1 tumors have similar relationship with WGCNA modules as POU1F1 expression, while SF-1 gonadotroph tumors and TPIT corticotroph tumors have a similar relationship with the expression of NR5A1 and TBX19 , respectively. All three subtypes of acromegaly – related tumors were clearly most similar to PIT1-lineage tumors and POU1F1 expression level in terms of the relationship with the WGCNA modules ( Fig. 1E ). Subtype 1 double positive tumors were similar to SF-1 - positive goadotropinomas in the relationship to only one module (grey). In general, this unsupervised analysis of genes co-expression confirmed the observation from sample clustering, that the double positive Subtype 1 tumors are much more similar to other tumors of PIT1-lineage than that to SF-1-positive gonadotroph. There is a very little similarity in genes co-expression between PIT-1/SF-1- and SF-1-positive tumors. Regulon analysis Transcriptomic data allows for the evaluation of the transcription factors activity. As the pituitary lineage-specific TF are substantial for official PitNETs classification and a number of TFs that orchestrate pituitary development were identified, we recognized regulon analysis as especially valuable. Regulon (set of genes regulated by known transcription factor) was established for the entire dataset based on mutual information and subsequently the activity of all the known transcript factors was calculated for each tumor sample. Based on the literature data, we selected 25 TFs known to be relevant for differentiation and functioning of anterior pituitary cells and the regulons of 20 of these TFs were identified (5 TFs didn’t pass a default filtration due to small regulon size) (Supplementary Table S3). As the result, we established the regulons patterns characteristic for basic PitNETs histological subtypes with a distinct TFs activity in PIT-1 linage tumors, SF-1 gonadotropinomas and TPIT-corticotropinomas ( Fig. 2A ). Interestingly, the results suggest that each TF regulate mostly distinct set of genes with relatively low number of genes regulated by more than one TF ( Fig. 2B ). Download figure Open in new tab Fig. 2 Result of regulon analysis. A) Regulon activity of chosen transcription factors (TF) orchestrating the differentiation of the pituitary secretory cells lineages in PitNET samples with summary of molecular and histological characteristics. B) Network of genes regulated by chosen TF. C) Result of Gene Set Enrichment Analysis (GSEA) showing enrichment of differentially expressed genes (DEGs) between Subtype 1 (PIT-1/SF-1 positive) somatotroph tumors and gonadotroph tumors in genes from regulons of POU1F1 and NR5A1 . D) Expression levels of chosen differentially expressed genes. E) Result of GSEA showing enrichment of selected DEGs between Subtype 1 (PIT-1/SF-1 positive) somatotroph tumors and Subtype 1/2 somatotroph tumors in genes from regulons of POU1F1 and NR5A1 . Especially, notable difference in TFs activity was observed in comparison of PIT-1 and SF-1 PitNETs. Double positive acromegaly-related tumors are characterized by a TFs activity pattern characteristic to PIT-1 not SF-1 tumors, with an exception of the activity of NR5A1 , which is common for double positive and gonadotroph PitNETs. The other TFs specific for gonadotroph tumors such as GATA2, GATA3 are inactive in double positive PIT-1/SF-1 Subtype 1 somatotroph tumors. The difference in activity of the TFs in each subtype of somatotroph and gonadotroph tumors reflects their distinct expression levels, as well as the difference in expression of the genes involved in secretory activity of pituitary cells ( Fig. 2A ). We complemented the regulon analysis with a differential gene expression analysis. Genes differentially expressed (DEGs) between double positive Subtype 1 acromegaly-related tumors and gonadotroph SF-1 tumors were determined (Supplementary Table S4). Next, we analyzed the enrichment of DEGs with the genes from POU1F1 and NR5A1 regulons with GSEA. We observed that DEGs are highly enriched in genes regulated by POU1F1 , but they are also enriched in the regulon of NR5A1 . Most of the genes which are transcriptionally activated by NR5A1 , according to the calculated regulon, have lower expression in double positive sPitNETs than in gnadotroph tumors ( Fig. 2C ). This result indicates that, to some extent, NR5A1 regulates different set of genes in gonadotroph and Subtype 1-acromegaly tumors ( Fig. 2C ). Indeed, some known NR5A1 -depenedent, experimentally validated genes as CYP11A1, NR0B1 , or FSHB are expressed on lower level in double positive somatotroph PitNETs and gonadotroph tumors while the others as GNRHR or LHB have comparable expression ( Fig. 2D ). On the other hand, some NR5A1 -regulated genes as STAR have higher expression in Subtype 1 somatotroph than gonadotroph PitNETs. We also compared Subtype 1 somatotroph tumors to other acromegaly-related tumors (combined Subtype 2 and 3) and looked for the enrichment of DEGs in the regulons of NR5A1 and POU1F1 (Supplementary Table S4). DEGs were highly enriched for NR5A1 -regulated genes but very modestly enriched for the genes regulated by POU1F1 ( Fig. 2E ) confirming the specificity of regulation by NR5A1 to Subtype 1 tumors and showing only slightly different activity patter of POU1F1 . We also used the regulon data to determine the TFs that are the most specific to double positive PIT-1/SF-1tumors over the rest of the PitNETs (Supplementary Table S5). This showed NKX2-2 activity was the most specific (AUC 0.98) confirming the previous suggestion that this TF may serve as reliable marker for this specific transcriptomic subtype [ 50 ]. Interestingly, the genes specific to somatotroph cells as GH1, GH2 and GHRHR were annotated with the regulon of NKX2-2 ( Fig. 2B ). Analysis of scRNAseq data from normal pituitary for identification of normal double positive NR5A1 / POU1F1 cells We analyzed the available single-cell RNAseq data from 10 samples of normal adult pituitary gland (APG). Unsupervised clustering detected 19 clusters, which were classified into 12 cell types based on marker genes and cluster similarity: 4 PIT1 lineage (PIT1.Pro, Somato, Lacto, Thyro), 2 SF1 lineage (Gonado, Gonado.PIT), 1 TPIT lineage (Cortico), 3 immune cells (Macrophages, Monocytes, CD8+ T cells), 1 stem cells (Stem), 2 stroma cells (Endothelial cells, Fibroblasts) ( Fig. 3A ). Next, we checked whether the identified clusters contain the cells expressing both POU1F1 and NR5A1 , and we found such cell to be restricted to clusters of PIT-1 and SF-1 cell lineages. We found a total of 91 NR5A1/POU1F1 double positive cells (0.6% of all cells) including 49 and 42 in PIT-1 and SF-1 clusters respectively ( Fig. 3B ). Interestingly, NR5A1/POU1F1 double positive cells forms a specified subcluster among SF-1 gonadotroph cells, while they are dispersed through the entire cell population PIT-1-positive lineage ( Fig. 3C ). Download figure Open in new tab Fig. 3 Singel-cell RNAseq data from adult pituitary gland (APG). A) UMAP of cells from APG samples with assigned cell types. B) Co-expression of POU1F1 and NR5A1 in cells from APG labeled according to main clusters identified as particular cell subtypes. C) Expression of POU1F1 and NR5A1 in cells from APG. D) Expression of POU1F1 and NR5A1 in gonadotroph cells from integrated data of APG and fetal pituitary. E) Clustering and differentiation trajectory of gonadotroph cells from integrated data of APG and fetal pituitary. F) POU1F1 and NR5A1 expression along differentiation trajectory of gonadotroph cells from APG and fetal pituitary. Additionally, we verified the presence of such cells in publicly available scRNA data of human fetal pituitary (FPG) as well as mouse and rat pituitary gland. Such double positive cells were observed in human APG (see Figure 3 ), FPG as well as in mouse and rat (Supplementary Fig. S2). To understand the developmental origin of NR5A1/POU1F1 double positive cells, we integrated scRNAseq data from human APG and FPG, and processed the data to identify cells from the basic pituitary lineages and performed trajectory analysis focused on cluster of gonadotroph cells. It showed that double positive cells form a subcluster among SF-1 cells ( Fig. 3D ). They are at the end of separate differentiation trajectory ( Fig. 3E ). In the cells of this lineage, the expression of POU1F1 increases, while NR5A1 decreases with time ( Fig. 3F ). Neoplastic and normal somatotroph cells Intriguingly, NR5A1/POU1F1 double positive-cells were identified among both PIT-1 and gonadotroph lineages of normal pituitary gland but RNAseq data from tumors shows that double positive PIT-1/SF-1 tumors are in general similar to PIT-1 positive not gonadotroph PitNETs. To focus specifically on the pituitary tumor cells, we acquired and analyzed the publicly alliable scRNAseq data of somatotroph and gonadotroph PitNETs (25 and 5 samples respectively, Supplementary Table S6). We assigned each sample of somatotroph tumors to one of the previously defined transcriptomic subtypes based on classification of their pseudobulk expression profile. Three tumors were identified as corresponding to Subtype 1 (SF-1/PIT1-positive), 14 as Subtype 2 while 8 as Subtype 3 ( Fig. 4A ). Then, we integrated scRNAseq data from somatotroph PitNETs with gonadotroph PitNETs (gPitNETs) and APG (total of 40 samples, 142188 cells) and assigned the tumor and non-neoplastic cell populations to five main clusters ( Fig. 4B ) (Supplementary Fig. S3). Tumor cells of gonadotroph PitNETs were closely similar to normal gonadotroph cell while all the tumor cells of somatotroph tumors, including those PIT-1/SF-1 positive, were similar to normal cells of PIT-1 lineage ( Fig. 4C ). Importantly, tumor cells of double positive PIT-1/SF-1 somatotroph tumors were definitely unrelated to a subcluster of normal NR5A1 / POU1F1 double positive gonadotroph cells (Gonado.PIT1) observed in scRNAseq data on normal adult. They were unambiguously similar to tumor cells of the other somatotroph tumors as well as cells of normal PIT-1 lineage not to gonadotroph tumors cell nor normal gonadotrophs as also reflected by similarity in the expression of key pituitary cell-related genes (Supplementary Fig. S4). Download figure Open in new tab Fig. 4 Integrated single-cell RNAseq data of three subtypes of somatotroph PitNETs (sPitNETs), gonadothroph PitNETs (gPitNETs) and adult pituitary gland (APG). A) UMAP of combined pseudobulk counts of samples from scRNAseq of somatotroph PitNETs and bulk RNAseq data of all the somatotroph tumors from study cohort colored by three transcriptomic subtypes of sPitNETs. B) UMAP from integrated data of sPitNETs, gPitNETs and APG with assigned main cell clusters. C) Similarity of cells in specific samples to cell clusters from APG. D) Main cell clusters representation in samples. E) Co-expression of POU1F1 and NR5A1 with assigned main clusters in specific sample groups. F) Comparison of overlap in genes from NR5A1 regulon calculated based on gonadotroph cells from gPitNETs and somatotroph cells from Subtype 1 (PIT-1/SF-1 positive) sPitNETs. The gonadotroph tumors were basically composed of cells of gonadotroph nature, while each of the subtype of somatotroph tumors included mostly cells of PIT-1 lineage ( Fig. 4D ). Some low amount of the cells expressing both NR5A1 and POU1F1 was detected in gonadotroph tumors (these cells belonged to cluster of SF-1 gonadotroph cells) as wells as in two subtypes of somatotroph tumor subtypes without NR5A1 expression (these cells belonged to cluster of PIT-1 cluster) ( Fig. 4E ). We also looked for the abundance of nonneoplastic cells as immune, stromal and stem-like cells in individual tumor samples and APG. A notable intertumoral heterogeneous was observed. A high content of immune cells was identified in 1 of 3 samples of double positive (SF-1/PIT-1) somatotroph PitNETs, that possibly affect the analysis ( Fig. 4D ). In general, no difference in the frequency of subtypes of nonneoplastic cells between somatotroph tumor subtypes, were found. Lower content of stemm-like cells and monocytes was found in somatotroph tumors in general as compared to APG as detailed in Supplementary Fig. S5. To verify the observation that a distinct set of genes can be regulated by SF-1 in double positive somatotroph and gonadotroph PitNETs we established regulons of NR5A1 in gonadotroph tumor cells and double positive somatotroph tumor cell independently. Analyzes of genes in regulons confirmed results from comparison of bulk samples of Subtype 1 and gonadotrhop tumors that NR5A1 regulates different genes in those PitNET types ( Fig. 4F , Supplementary Table S7). Determining abnormal genes expression in somatotroph tumors by comparison with normal pituitary somatotroph is unavailable with bulkRNAseq due to a complexed cellular composition of pituitary gland. This can be in turn achieved with scRNAseq data. We compared genes expression in tumor cells of each subtype of somatotroph PitNETs with normal somatotrophs of APG. We identified 1149, 1853 and 2021 DEGs for Subtype 1, 2 and 3 respectively, with a set of DEGs unique for each subtype (Fig.5A, Supplementary Table S8). The result confirmed a difference in the expression of important genes related to pituitary functioning and pathogenesis between subtypes, as well as differences between tumor and normal cells. Showing abnormal expression of TFs ( POU1F1, NR5A1 ) as wells genes encoding for GH ( GH1, CSH1, CSHL1 ), in genes regulating GH secretion ( GHRHR, GIPR, GHSR, SSTRs and DRDs ) ( Fig. 5B ). Results confirmed abnormal increase of the expression of genes induced by SF-1 in Subtype 1 somatotroph tumors (PIT-1/SF-1 positive) as steroidogenesis-related genes and GNRHR , but not in LHB and FSHB . The results also confirmed a tumor-specific decrease of the expression of cell adhesion and cell cycle-related genes in Subtype 3 tumors. Download figure Open in new tab Fig. 5 Comparison of tumor somatotroph cells of each particular transcriptomic subtypes of somatotroph PitNETs (sPitNETs) and normal somatotroph cells of adult pituitary gland (APG). A) Overlap of differentially expressed genes (DEGs) identified in comparison of tumor somatotroph cell of each transcriptomic subtype of sPitNETs and normal somatotrophs of APG. B) The expression of selected DEGs in each transcriptomic subtype of sPitNETs and normal somatotrophs. C) Result of gene set enrichment analysis (GSEA) of DEGs from comparison of cells from specific transcriptomic subtypes of sPitNETs to APG based on KEGG database. D) UMAP of somatotroph cells from integrated APG and sPitNET data colored by Monocle3-inferred pseudotime. E) Comparison of distribution of pseudotime in cells from specific cell groups. F) UMAP of somatotroph cells from integrated APG and sPitNET data, classified according to genomic instability into stable, unstable and chromosome 11 deleted. KEGG pathways enriched for a DEGs found in the comparison of neoplastic cells of each tumor subtype and normal somatotropes were identified (Supplementary Table S9) including the pathways specific for distinct subtypes ( Fig. 5C ). Aberrant genes expression influence upregulation of galactose and glucose metabolism and downregulation of TGF beta signaling in Subtype 1 tumors; upregulaiton of TCA cycle, in Subtype 2 and downregulation of cell adhesion in Subtype 3. ScRNAseq data were also used to calculate psudotime-related trajectory of the PIT.Pro and somatotroph cells in APG and somatotroph tumor cells to trace the differences between tumor samples of known subtypes ( Fig. 5D ). We observed a notable variation of pseudotime among cells from specific Subtypes without a clear relationship to development of normal pituitary cells, however we noted the individual samples of Subtype 3 were the most distant as compared to normal somatotroph ( Fig. 5E ). Determining copy number profiles with scRNAseq data showed loss of chromosome 11 in these samples ( Fig. 5F , Supplementary Fig. S5). This chromosomal loss was already identified as recurrent CNV in subtype 3 tumors [ 4 , 6 ]. Discussion The principles of the classification of pituitary tumors is determining from which developmental pituitary lineage they originate [ 51 ]. Basically, the lineage of corticotroph cells is characterized by the expression of TPIT transcription factor, gonadotroph cells express SF-1 TF and PIT-1-positive lineage includes somatotropes, lactotropes and tyreotropes. In general, PitNETs falls in the three basic categories of TPIT, SF-1 and PIT-1 tumors and the tumors developed from distinct pituitary cell lineages have distinct molecular profile as observed in transcriptomic, epigenetic and proteomic levels [ 15 , 19 , 21 ]. PitNETs of a particular type are still characterized by a degree of heterogeneity in clinical, histological and molecular features. Clinically functioning somatotroph pituitary tumors fall into 3 distinct molecular subtypes which are not clearly distinguished in official WHO classification [ 4 – 7 ] and one of these subtypes is characterized by the expression of NR5A1 (SF-1) TF. Such double positive PIT-1 and SF-1-expressing sPitNETs were also occasionally, previously reported by others [ 9 – 15 ] and they were pointed as a matter of interest. Preliminary goal of our study was to identify the tumors of this subtype in each publicly available RNAseq data set from sPitNETs. Indeed, using unsupervised transcriptomic analysis of 193 tumor samples, we found that ∼20% of sPitNETs are double positive tumors, confirming their previously observed frequency among acromegaly-related tumors. Importantly, they were detected in each of 5 data sets of various patients’ population (Asian, European, South American) included in the analysis. The developmental identity of tumors co-expressing PIT-1 and SF-1 has been recently discussed in the literature, and they were differently referred by the authors as multilineage pituitary tumors [ 16 , 17 , 50 , 52 , 53 ], gonado/somatotroph tumors [ 6 ], somatogonadotroph [ 18 ] or plurihormonal tumors [ 9 , 11 , 54 ]. We addressed the question whether these acromegaly-related sPitNETs truly have molecular features of pituitary gonadotropes and to what extent they resemble SF-1 tumors. The results of our analysis comprising 546 pituitary tumor samples showed that, in terms of genes expression, double positive sPitNETs are closely similar to the other PIT-1 not to gonadotroph tumors. By the use of scRNAseq data, we were able to compare tumor cells of each subtype of somatotroph tumors and gonadotroph PitNETs with normal pituitary cells of both PIT-1 and SF-1 lineages. It clearly confirmed that double positive Subtype 1 sPitNETs are counterparts of normal PIT-1 cells, not pituitary gonadotropes. Results of comparing the activity of known TFs involved in differentiation of pituitary cell lineages is in line with the above observation. PIT-1/SF-1 sPitNETs are characterized by activity of TFs known to be involved in development of PIT-1, not SF-1 lineage as POU1F1, PROP1, CEBPD or FOXO1 [ 55 ]. Beside the activity of SF-1 ( NR5A1 ), these tumors do not show the activity of the other known gonadotroph lineage-related TFs as GATA2 or GATA3 [ 56 ]. Part of PIT-1/SF-1 positive sPitNETs were found to express LH in immunohistichemical staining [ 6 , 7 , 18 ]. Our analysis confirms that these tumors indeed express both LHB and GNRHR at the level similar to gonadotroph tumors. Both these genes are regulated by SF-1 transcription factors in pituitary gonadotroph cells [ 57 , 58 ]. Interestingly, a category of somatotroph pituitary tumors that responds with increased GH secretion to luteinizing hormone-releasing hormone (LHRH) was recently described [ 59 ]. Most of these tumors also showed paradoxical increase of GH in response to oral glucose tolerance test (OGTT) [ 59 ] a phenomenon observed in a specified group of patients with acromegaly [ 60 ] as a result of ectopic expression of GIPR [ 61 ]. GIPR expression is correlated with NR5A1 level in sPitNETs [ 7 ] and it corresponds to demethylation at GIPR promoter that was observed in SF-1/PIT-1 positive sPitNETs [ 5 ]. The presented analysis of both bulk- and scRNAseq confirms that these double positive sPitNETs are those that express high level of GIPR and expression of this gene distinguishes the tumor cells of these subtype from normal pituitary somatotropes, which are GIPR-negative. These tumors also express LHB and GNRHR , therefore we conclude that Subtype 1 sPitNETs are those that increase GH secretions after LHRH or glucose load. Since the expression of LHB and GNRHR is related to NR5A1 in both Subtype 1 sPitNETs and gPitNETs the other genes regulate by this TF turn out to be differentially expressed in these tumor types. The best validated gene positively regulated by SF-1 is STAR , which encodes a protein playing a key role in steroidogenesis in adrenal cortex and ovary [ 62 ]. We clearly observed high expression of STAR in Subtype 1 sPitNETs, but not in gonadotorph tumors. In opposite, the other SF-1 regulated genes such as FSHB, CYP11A1 and NR0B1 (DAX1) have higher expression level in gonadopoph tumors. More general, our analysis of NR5A1 regulons in bulk- and scRNAseq data indicate that this TF regulates somewhat different set of genes in double positive sPitNETs and gonadotropinomas. Our regulon analysis also confirms the previous report that NKX2-2 transcription factor can be considered as specific to PIT-1/SF-1 sPitNETs [ 50 ] as we observed the highest activity of NKX2-2 in this tumor subtype. The analysis scRNAseq from normal APG showed the presence of a few PIT-1/SF-1 cell among both PIT-1 and gonadotroph lineage cells. Similarly, such cells were identified in human fetal pituitary as well as in pituitary from mouse and rat. Interestingly, normal PIT-1/SF-1 double positive cells represented a specified developmental subcluster of gonadotroph cells, but double positive tumor cells from Subtype 1 sPitNETs turn out to be completely dissimilar (unrelated) to this gonadotroph cluster. Thus, we conclude that these tumor cells originate from PIT-1 lineage not from gonadotroph cell subtype. Taking advantage of the scRNAseq data, we compared genes expression in each subtype of sPitNETs with normal somatotrophs to identify tumor-specific aberrantly expressed gens. The profile of aberrant genes expression in tumor cells of each sPitNET subtypes confirms the observation of difference in genes expression, both observed in current bulk RNAseq results and previously reported [ 6 , 7 ]. Additionally, by integrating the scRNAseq data from normal pituitary and sPitNETs we made an attempt of pseudotime analysis to match tumor samples with normal developmental trajectory from ProPit cell to somtotroph lineage. The result shows that sPitNETs are more related to mature somtotroph lineage and the clear difference in development trajectory of tumor of three subtypes could not be found. The interesting results from this analysis was that Subtype 3 tumors are the least related to normal somatotrophs and these are the tumors characterized by chromosome 11 loss. This cytogenetic change was already recognized as recurrent DNA copy number variant in sparsely granulated sPitNETs [ 4 , 6 ]. In this study, we were able to collect a large data set of bulk RNAseq results from PitNETs, but much lower number of scRNAseq data are currently available. Combining the data from 5 studies we identified only 3 PIT-1/SF-1 sPitNETs what should be considered as main limitation of our study. Low number of these sample influence the statistical inference power, while variability within this group prompt us to cautiously interpret the single cell data. In example, the results indicate the increase of SSTR2 in this tumor subtype as also reported previously [ 16 ], but this observation is notably biased by very high expression of this SSTR in 1/3 samples. The role of immune component of tumors environment in sPitNEt has been described [ 63 ] and scRNAseq results allows for an in depth analysis of the microenvironment cell composition. Unfortunately, given intertumoral heterogeneity, we were not able to draw a clear conclusion on difference in microenvironment difference in three subtypes of sPitNETs. Conclusions From the perspective of transcriptomic profiling, we do not find the rationale for considering PIT-1/SF-1 sPitNETs as multilineage or “somatogonadotoph” tumors. They have genes expression pattern and profile of the activity of TFs very similar to the other somatotroph tumors with no similarity to gonadotroph tumors with the exception of NR5A1 expression and expression of some of SF-1-regulated genes as LHB and GNHRH . SF-1 apparently regulates slightly different set of genes in these sPitNETs and gonadotroph tumors, with different STAR expression as an example. Accordingly, tumor cells of PIT-1/SF-1 sPitNETs are closely related to normal pituitary somatotrophs not gonadotrophs, however a subtype of normal gonadotroph cells with POU1F1 expression was also identified. Declarations Availability of data and material Freely available datasets from bulkRNAseq (accession numbers: HRA003588, E-MTAB-11889, E-MTAB-7768, GSE213527, GSE209903) and scRNAseq (accession numbers: PRJCA009690, GSE208108, HRA003110, HRA003483, PRJNA1137596 and GSE142653). Authors’ contributions Author Contributions: Conceptualization, JR, MB; Methodology, JR, MB, QZ; Formal analysis JR, MB, QZ; investigation, JR, MB, QZ; resources, JR, MB, QZ; data curation, JR, MB; writing— original draft preparation, JR, MB; writing—review and editing, all the authors; visualization, JR ; supervision, MB; project administration MB; funding acquisition, MB. Funding This research was funded by Maria Sklodowska-Curie National Institute of Oncology, grant SN/GW5/2023. Ethics approval and consent to participate not applicable; secondary use of data Consent for publication Not applicable Competing interests The authors declare no conflict of interest. Acknowledgements Not applicable Funder Information Declared Maria Sklodowska-Curie National Institute of Oncology , SN/GW5/2023 Footnotes Author information: Julia Rymuza – julia.rymuza{at}nio.gov.pl , Qilin Zhang - zhangqlhuashan{at}163.com References 1. ↵ WHO Classification of Tumours Editorial Board . Endocrine and Neuroendocrine tumours . 5th ed. Lyon : International Agency for Research on Cancer ; 2022 . 2. ↵ Asa SL , Kucharczyk W , Ezzat S. Pituitary acromegaly: Not one disease . Endocr Relat Cancer . 2017 ; 24 : C1 – 4 . OpenUrl Abstract / FREE Full Text 3. ↵ Akirov A , Asa SL , Amer L , Shimon I , Ezzat S. The clinicopathological spectrum of acromegaly . J Clin Med . MDPI ; 2019 . 4. ↵ Rymuza J , Kober P , Maksymowicz M , Nyc A , Mossakowska BJ , Woroniecka R , et al. High level of aneuploidy and recurrent loss of chromosome 11 as relevant features of somatotroph pituitary tumors . J Transl Med . 2024 ; 22 : 994 . OpenUrl CrossRef PubMed 5. ↵ Kober P , Rymuza J , Baluszek S , Maksymowicz M , Nyc A , Mossakowska BJ , et al. DNA methylation pattern in somatotroph pituitary neuroendocrine tumors . Neuroendocrinology . 2023 ; 6. ↵ Dottermusch M , Ryba A , Ricklefs FL , Flitsch J , Schmid S , Glatzel M , et al. Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features . Acta Neuropathol . 2024 ; 147 . 7. ↵ Rymuza J , Kober P , Rusetska N , Mossakowska BJ , Maksymowicz M , Nyc A , et al. Transcriptomic Classification of Pituitary Neuroendocrine Tumors Causing Acromegaly . Cells . 2022 ; 11 . 8. ↵ Rymuza J , Kober P , Rusetska N , Mossakowska BJ , Maksymowicz M , Nyc A , et al. Transcriptomic Classification of Pituitary Neuroendocrine Tumors Causing Acromegaly . Cells . 2022 ; 11 : 3846 . OpenUrl CrossRef 9. ↵ Silva-Ortega S , García-Martinez A , Niveiro de Jaime M , Torregrosa ME , Abarca J , Monjas I , et al. Proposal of a clinically relevant working classification of pituitary neuroendocrine tumors based on pituitary transcription factors . Hum Pathol . 2021 ; 110 : 20 – 30 . OpenUrl CrossRef PubMed 10. Hong SW , Kim SH , Lim SH , Lee EJ , Kim SH , Ku CR , et al. Clinical Relevance of New World Health Organization Classification System for Pituitary Adenomas: A Validation Study With 2-Year Experience . Front Oncol . 2021 ; 11 . 11. ↵ Tordjman KM , Greenman Y , Ram Z , Hershkovitz D , Aizenstein O , Ariel O , et al. Plurihormonal Pituitary Tumor of Pit-1 and SF-1 Lineages, with Synchronous Collision Corticotroph Tumor: a Possible Stem Cell Phenomenon . Endocr Pathol . 2019 ; 30 : 74 – 80 . OpenUrl CrossRef PubMed 12. Yasuno H , Watanabe T , Miyamoto Y , Kandori H , Yamasaki H , Fukuda R. Expression of steroidogenic factor 1 and pituitary specific transcription factor 1 in rat pituitary adenomas . J Toxicol Pathol . 2013 ; 26 : 209 – 13 . OpenUrl CrossRef PubMed 13. Michael McDermott al T , Haugen BR , Gordon DF , Wood WM , Brown NS , Bauer CA , et al. Reverse Transcription Polymerase Chain Reaction Analysis of Pituitary Hormone, Pit-1 and Steroidogenic Factor-1 Messenger RNA Expression in Pituitary Tumors Pit-1 and Steroidogenic Fa ctor-1 in Pituitary Tumors . 14. Asa SL , Puy LA , Lew AM , Sundmark VC , Elsholtz HP . Cell Type-Specific Expression of the Pituitary Transcription Activator Pit-l in the Human Pituitary and Pituitary Adenomas* . 1993 . 15. ↵ Neou M , Villa C , Armignacco R , Jouinot A , Raffin-Sanson ML , Septier A , et al. Pangenomic Classification of Pituitary Neuroendocrine Tumors . Cancer Cell . 2020 ; 37 : 123 - 134.e5 . OpenUrl CrossRef PubMed 16. ↵ Fookeerah P , Varikatt W , Shingde M , Dexter MAJ , McLean M. Somatostatin receptor expression and clinical outcome of multilineage pituitary tumours expressing PIT1 and SF1 . Endocr Connect . 2023 ; 12 . 17. ↵ Asa SL , Mete O , Riddle ND , Perry A. Multilineage Pituitary Neuroendocrine Tumors (PitNETs) Expressing PIT1 and SF1 . Endocr Pathol . 2023 ; 34 . 18. ↵ B K Kleinschmidt-DeMasters CGT . Reassessment of Plurihormonal Pituitary Adenomas/PitNETs . Am J Surg Pathol . 2024 ; 48 : 1600 – 16 . OpenUrl CrossRef 19. ↵ Zhang F , Zhang Q , Zhu J , Yao B , Ma C , Qiao N , et al. Integrated proteogenomic characterization across major histological types of pituitary neuroendocrine tumors . Cell Res . 2022 ; 32 . 20. ↵ Yamato A , Nagano H , Gao Y , Matsuda T , Hashimoto N , Nakayama A , et al. Proteogenomic landscape and clinical characterization of GH-producing pituitary adenomas/somatotroph pituitary neuroendocrine tumors . Commun Biol . 2022 ; 5 : 1304 . OpenUrl CrossRef PubMed 21. ↵ da Silva-Júnior RMP , Bueno AC , Martins CS , Coelli-Lacchini F , Okanobo Ozaki JG , de Almeida E Silva DC , et al. Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors . J Clin Endocrinol Metab [Internet] . 2022 ; Available from: http://www.ncbi.nlm.nih.gov/pubmed/36504388 22. ↵ Patro R , Duggal G , Love MI , Irizarry RA , Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression . Nat Methods . 2017 ; 14 . 23. ↵ Soneson C , Love MI , Robinson MD . Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences . F1000Res . 2015 ; 4 : 1521 . OpenUrl 24. ↵ Liao Y , Smyth GK , Shi W. FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features . Bioinformatics . 2014 ; 30 . 25. ↵ Love MI , Huber W , Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol . 2014 ; 15 : 550 . OpenUrl CrossRef PubMed 26. ↵ Ritchie ME , Phipson B , Wu D , Hu Y , Law CW , Shi W , et al. limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic Acids Res . 2015 ; 43 : e47 – e47 . OpenUrl CrossRef PubMed 27. ↵ Langfelder P , Horvath S. WGCNA: An R package for weighted correlation network analysis . BMC Bioinformatics . 2008 ; 9 . 28. ↵ Fletcher MNC , Castro MAA , Wang X , de Santiago I , O’Reilly M , Chin S-F , et al. Master regulators of FGFR2 signalling and breast cancer risk . Nat Commun . 2013 ; 4 : 2464 . OpenUrl CrossRef PubMed 29. ↵ Campbell TM , Castro MAA , de Santiago I , Fletcher MNC , Halim S , Prathalingam R , et al. FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness . Carcinogenesis . 2016 ; 37 : 741 – 50 . OpenUrl CrossRef PubMed 30. ↵ Castro MAA , de Santiago I , Campbell TM , Vaughn C , Hickey TE , Ross E , et al. Regulators of genetic risk of breast cancer identified by integrative network analysis . Nat Genet . 2016 ; 48 : 12 – 21 . OpenUrl CrossRef PubMed 31. ↵ Zhang Q , Yao B , Long X , Chen Z , He M , Wu Y , et al. Single-cell sequencing identifies differentiation-related markers for molecular classification and recurrence prediction of PitNET . Cell Rep Med . 2023 ; 4 . 32. ↵ Asuzu DT , Alvarez R , Fletcher PA , Mandal D , Johnson K , Wu W , et al. Pituitary adenomas evade apoptosis via noxa deregulation in Cushing’s disease . Cell Rep . 2022 ; 40 . 33. ↵ Lyu L , Jiang Y , Ma W , Li H , Liu X , Li L , et al. Single-cell sequencing of PIT1-positive pituitary adenoma highlights the pro-tumour microenvironment mediated by IFN-γ-induced tumour-associated fibroblasts remodelling . Br J Cancer . 2023 ; 128 . 34. ↵ Yan N , Xie W , Wang D , Fang Q , Guo J , Chen Y , et al. Single-cell transcriptomic analysis reveals tumor cell heterogeneity and immune microenvironment features of pituitary neuroendocrine tumors . Genome Med . 2024 ; 16 . 35. ↵ Zhang Y , Wang L , Yi X , Ma X , Wu H , Zhang M , et al. Single-cell and spatial transcriptomic analyses reveal heterogeneity characteristics and specific cell subtype regulators in growth hormone-secreting pituitary adenomas . Int J Surg [Internet] . 2024 ; Available from: http://www.ncbi.nlm.nih.gov/pubmed/39784532 36. ↵ Zhang S , Cui Y , Ma X , Yong J , Yan L , Yang M , et al. Single-cell transcriptomics identifies divergent developmental lineage trajectories during human pituitary development . Nat Commun . 2020 ; 11 : 5275 . OpenUrl CrossRef PubMed 37. ↵ He D , Zakeri M , Sarkar H , Soneson C , Srivastava A , Patro R. Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data . Nat Methods . 2022 ; 19 : 316 – 22 . OpenUrl CrossRef PubMed 38. ↵ Hao Y , Stuart T , Kowalski MH , Choudhary S , Hoffman P , Hartman A , et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis . Nat Biotechnol . 2024 ; 42 : 293 – 304 . OpenUrl CrossRef PubMed 39. ↵ Germain PL , Robinson MD , Lun A , Garcia Meixide C , Macnair W. Doublet identification in single-cell sequencing data using scDblFinder . F1000Res . 2022 ; 10 . 40. ↵ Butler A , Hoffman P , Smibert P , Papalexi E , Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species . Nat Biotechnol . 2018 ; 36 : 411 – 20 . OpenUrl CrossRef PubMed 41. ↵ Korsunsky I , Millard N , Fan J , Slowikowski K , Zhang F , Wei K , et al. Fast, sensitive and accurate integration of single-cell data with Harmony . Nat Methods . 2019 ; 16 : 1289 – 96 . OpenUrl CrossRef PubMed 42. ↵ Street K , Risso D , Fletcher RB , Das D , Ngai J , Yosef N , et al. Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics . BMC Genomics . 2018 ; 19 . 43. ↵ Van den Berge K , Roux de Bézieux H , Street K , Saelens W , Cannoodt R , Saeys Y , et al. Trajectory-based differential expression analysis for single-cell sequencing data . Nat Commun . 2020 ; 11 . 44. ↵ Cao J , Spielmann M , Qiu X , Huang X , Ibrahim DM , Hill AJ , et al. The single-cell transcriptional landscape of mammalian organogenesis . Nature . 2019 ; 566 . 45. ↵ Badia-I-Mompel P , Vélez Santiago J , Braunger J , Geiss C , Dimitrov D , Müller-Dott S , et al. decoupleR: ensemble of computational methods to infer biological activities from omics data . Bioinformatics Advances . 2022 ; 2 . 46. ↵ Muzellec B , Teleńczuk M , Cabeli V , Andreux M. PyDESeq2: a python package for bulk RNA-seq differential expression analysis . Bioinformatics . 2023 ; 39 . 47. ↵ Kanehisa M , Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes . Nucleic Acids Res . 2000 . 48. ↵ Morabito S , Reese F , Rahimzadeh N , Miyoshi E , Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data . Cell Reports Methods . 2023 ; 3 . 49. ↵ Gao R , Bai S , Henderson YC , Lin Y , Schalck A , Yan Y , et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes . Nat Biotechnol . 2021 ; 39 . 50. ↵ Doğukan FM , Karatay H , Yüzkan S , Burhan Ş , Erkan B , Yılmaz-Özgüven B. Clinicopathological Correlates of PIT1 and SF1-Multilineage Pituitary Neuroendocrine Tumors and the Diagnostic Utility of NKX2.2 Immunohistochemistry in Pituitary Pathology . Arch Pathol Lab Med . 2024 ; 51. ↵ Asa SL , Mete O , Perry A , Osamura RY . Overview of the 2022 WHO Classification of Pituitary Tumors . Endocr Pathol . Springer ; 2022 . p. 6 – 26 . 52. ↵ Wang X , Tang H , Bie Z , Wang Y , Yuan R , Zhang Z , et al. Clinical and Pathological Features of Pit1/SF1 Multilineage Pituitary Neuroendocrine Tumor . Neurosurgery . 2024 ; 95 . 53. ↵ Faraj CA , McCutcheon IE , Gubbiotti MA . PIT-1/SF-1 co-expression in pituitary neuroendocrine tumors (PitNETs) with comprehensive review of the literature: How should we best characterize these neoplasms? Ann Diagn Pathol . 2025 ; 74 . 54. ↵ Mihajlović M , Pekić S , Doknić M , Stojanović M , Rašić D , Miljić D , et al. Plurihormonal Pituitary Neuroendocrine Tumours – A Single Centre Experience . Int J Surg Pathol . 2024 ; 32 . 55. ↵ Ellsworth BS , Stallings CE . Molecular Mechanisms Governing Embryonic Differentiation of Pituitary Somatotropes . Trends in Endocrinology and Metabolism . 2018 . 56. ↵ Le Ciclé C , Cohen-Tannoudji J , L’Hôte D. Recent Advances in the Understanding of Gonadotrope Lineage Differentiation in the Developing Pituitary . Neuroendocrinology. S. Karger AG ; 2024 . 57. ↵ Ngan ESW , Cheng PKW , Leung PCK , Chow BKC . Steroidogenic factor-1 interacts with a gonadotrope-specific element within the first exon of the human gonadotropin-releasing hormone receptor gene to mediate gonadotrope-specific expression . Endocrinology . 1999 ; 140 . 58. ↵ Fowkes RC , Burrin JM . Steroidogenic factor-1: A key regulator of gonadotroph gene expression . Journal of Endocrinology . 2003 ; 177 . 59. ↵ Mitsui Y , Mukai K , Otsuki M , Oshino S , Saitoh Y , Kohara M , et al. Growth hormone increase by luteinizing hormone-releasing hormone reflects gonadotroph-related characteristics in acromegaly . Pituitary . 2024 ; 60. ↵ Hage M , Janot C , Salenave S , Chanson P , Kamenický P. Etiology and outcome of acromegaly in patients with a paradoxical GH response to glucose . Eur J Endocrinol . BioScientifica Ltd .; 2021 . p. R261 – 8 . 61. ↵ Hage M , Chaligné R , Viengchareun S , Villa C , Salenave S , Bouligand J , et al. Hypermethylator Phenotype and Ectopic GIP Receptor in GNAS Mutation-Negative Somatotropinomas . Journal of Clinical Endocrinology and Metabolism . 2019 ; 104 : 1777 – 87 . OpenUrl CrossRef PubMed 62. ↵ Hanley NA , Ikeda Y , Luo X , Parker KL . Steroidogenic factor 1 (SF-1) is essential for ovarian development and function . Mol Cell Endocrinol . 2000 ; 163 . 63. ↵ Chiloiro S , Giampietro A , Gessi M , Lauretti L , Mattogno PP , Cerroni L , et al. CD68+ and CD8+ immune cells are associated with the growth pattern of somatotroph tumors and response to first generation somatostatin analogs . J Neuroendocrinol . 2023 ; 35 . View the discussion thread. Back to top Previous Next Posted May 30, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Transcriptomic perspective on acromegaly- related PIT-1/SF-1 positive pituitary tumors Julia Rymuza , Qilin Zhang , Mateusz Bujko bioRxiv 2025.05.28.656330; doi: https://doi.org/10.1101/2025.05.28.656330 Share This Article: Copy Citation Tools Transcriptomic perspective on acromegaly- related PIT-1/SF-1 positive pituitary tumors Julia Rymuza , Qilin Zhang , Mateusz Bujko bioRxiv 2025.05.28.656330; doi: https://doi.org/10.1101/2025.05.28.656330 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Molecular Biology Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41910) Biophysics (21436) Cancer Biology (18576) Cell Biology (25480) Clinical Trials (138) Developmental Biology (13368) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15598) Genomics (22482) Immunology (17726) Microbiology (40360) Molecular Biology (17163) Neuroscience (88534) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)