Phosphoproteomic dysregulation drives tumor proliferation in Cushing’s disease

Phosphoproteomic dysregulation drives tumor proliferation in Cushing’s disease | 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 Phosphoproteomic dysregulation drives tumor proliferation in Cushing’s disease David T. Asuzu , Dustin Mullaney , Debjani Mandal , Diana Nwokoye , Sheelu Varghese , Daniela Tortoza Lopez , Dhruval Bhatt , Kory Johnson , Abdel Elkahloun , View ORCID Profile Zied Abdullaev , Kenneth Aldape , Dragan Maric , Clarisse Quignon , Nasir S. Malik , Joseph P. Steiner , Yan Li , Susan Wray , Lynnette K Nieman , Christina Tatsi , View ORCID Profile Prashant Chittiboina doi: https://doi.org/10.1101/2025.09.28.679056 David T. Asuzu 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD 2 Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke , Bethesda, MD 3 Department of Neurological Surgery, University of Virginia , Charlottesville, VA MD, PhD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dustin Mullaney 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Debjani Mandal 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diana Nwokoye 4 Department of Neurosurgery, University of Texas Southwestern , Dallas, TX MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sheelu Varghese 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniela Tortoza Lopez 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dhruval Bhatt 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kory Johnson 5 DIR Bioinformatics Section, National Institute of Neurological Disorders and Stroke , Bethesda, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abdel Elkahloun 6 Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute , Bethesda, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zied Abdullaev 7 Laboratory of Pathology, National Cancer Institute , Bethesda, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zied Abdullaev Kenneth Aldape 7 Laboratory of Pathology, National Cancer Institute , Bethesda, MD MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dragan Maric 8 Flow and Imaging Cytometry Core Facility, National Institute of Neurological Disorders and Stroke , Bethesda, MD, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clarisse Quignon 9 Cellular and Developmental Neurobiology Section, National Institute of Neurological Disorders and Stroke , Bethesda, MD, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nasir S. Malik 10 Translational Neuroscience Center, National Institute of Neurological Disorders and Stroke , Bethesda, MD, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joseph P. Steiner 10 Translational Neuroscience Center, National Institute of Neurological Disorders and Stroke , Bethesda, MD, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yan Li 11 Proteomics Core Facility, National Institute of Neurological Disorders and Stroke , Bethesda, MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan Wray 9 Cellular and Developmental Neurobiology Section, National Institute of Neurological Disorders and Stroke , Bethesda, MD, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lynnette K Nieman 12 Section on Translational Endocrinology, National Institute of Diabetes and Digestive and Kidney Diseases , Bethesda, MD MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christina Tatsi 13 Section on Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development , Bethesda, MD MD, MSc, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Prashant Chittiboina 1 Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke , Bethesda, MD 2 Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke , Bethesda, MD MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Prashant Chittiboina For correspondence: prashant.chittiboina{at}nih.gov Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Pituitary adenomas constitute up to 20% of primary brain tumors, yet somatic mutations are only found in 15% of pituitary adenomas. Epigenomic dysregulation has been proposed as a tumorigenic mechanism in pituitary adenomas causing Cushing’s disease (CD). We created paired datasets of human CD adenomas and en-route margin adult human pituitary glands and assayed their chromatin accessibility, DNA methylation, transcriptomic, proteomic and phospho-proteomic landscapes. In CD adenomas, we found epigenetic reactivation of a neurodevelopmental phosphoprotein program typically lost in the post-natal pituitary gland. CD cells overexpressed PPP1R17 , a potent endogenous inhibitor of the ubiquitous protein phosphatase PP2A. Mechanistically, PPP1R17 overexpression in normal murine pituitary cells recapitulated the adenoma phenotype, and PPP1R17-mediated tumorigenesis was reversible using an FDA-approved small molecule PP2A agonist both in-vitro and in-vivo. Our findings highlight aberrant peptide phosphorylation as a targetable mechanism in CD. Download figure Open in new tab Introduction Eukaryotic cellular homeostasis is tightly governed by the balance of protein kinases and phosphatases, which control phosphorylation levels and enzymatic activity of key intracellular proteins 1 , 2 . Disruption of this kinase-phosphatase balance has been reported underlying malignant tumors driven by discrete mutations 3 , 4 . In non-malignant tumors, kinase dysregulation has been described as a mechanism of neoplastic tumor progression following acquisition of mutations 5 . Little is known about early aberrant phosphorylation events in non-malignant tumor formation, or about epigenetic mechanisms underlying such dysregulation. The PP2A family of serine/threonine phosphatases plays a critical role in the regulation of signal transduction cascades, cell cycle regulation, cell morphology and development 6 . PP2A is regulated by the relative abundance of its regulatory subunits which control its substrate specificity, subcellular localization and catalytic activity 7 – 10 . The catalytic subunit (PP2Ac) is further regulated by methylation or phosphorylation. Several endogenous PP2A inhibitors have been identified, which either bind directly to PP2Ac or target specific PP2A holoenzymes and prevent dephosphorylation of a variety of PP2A substrates 11 . PPP1R17 is an endogenous PP2A inhibitor both in its phospho-PPP1R17 and dephospho-PPP1R17 forms 8 , however its role in pituitary tumorigenesis has not been previously investigated. The pituitary gland is the site of sporadic adenomas (benign tumors) in up to 35% of the general population 12 , 13 . Despite its small size (∼500mg), adenomas of the pituitary gland comprise 20% of all primary brain tumors 14 . Even after successful removal, histologically benign adenomas can recur in up to 30% of patients, causing long-term morbidity 15 . Cushing’s disease (CD) is caused by pituitary adenomas hypersecreting adrenocorticotropin (ACTH). CD adenomas are often small (2 - 10 mm) at diagnosis, minimally distort the pituitary architecture and have a predictable effect on the hormonal axes (biochemical testing confirms the presence of and remission from CD) 16 – 18 . Non-surgical treatments are limited, therefore, understanding mechanisms of tumorigenesis may improve our ability to target these tumors 19 . A minority of CD adenomas (total ∼40%) have somatic mutations in USP8 (∼30%), USP48 or BRAF 20 – 22 . Epigenetic dysregulation has been hypothesized to contribute to CD tumorigenesis via promoter hypomethylation of POMC , ESR1 and CASP8 23 , 24 , via downregulation of antiproliferative micro-RNAs miR-15a, miR-16 and miR-26a 25 , 26 , and via increased glucocorticoid resistance triggered by loss of HDAC2 and Swi/Snf chromatin remodeling complex subunit Brg1 27 . However, the chromatin accessibility landscape of CD remains unknown. Previously, we applied advances in surgical sampling and in-vitro modeling to identify anti-apoptotic mechanisms in wildtype CD adenomas 28 . Here, we investigated common epigenetic mechanisms underlying CD adenomas using a multi-omics approach. Results Reactivation of neurodevelopmental gene PPP1R17 is restricted to adenomas causing Cushing’s disease We performed single nucleus (sn) multiome analysis on nuclei extracted from surgically annotated ( Supplemental video ) freshly dissociated CD adenomas versus en-route, adjacent cells in the tumor margin (total 37,263 nuclei; Figure 1A , Supplemental Table S1 ). We included wildtype and USP8 -mutated CD adenomas in this study ( Figure 1B , Supplemental Table S1 ) and used marker-based cell-type classification of single nuclei 29 , 30 that corresponded well with unsupervised clustering 31 , 32 . Using a validated set of expression markers 28 , we identified canonical pituitary cell classes in each sample and generated snRNAseq gene signatures for corticotrophs, lactotrophs, somatotrophs, endothelial cells, gonadotrophs, leukocytes and folliculostellate cells (FSCs; Figure 1C Supplemental Figure S1, Supplemental Table S2 ). Importantly, we distinguished between core CD corticotrophs versus adjacent corticotrophs in the tumor margin ( Figure 1C ). We found marked similarities between cell-type specific gene signatures derived from snRNAseq compared to single cell RNAseq ( Supplemental Table S3 ) 28 . Download figure Open in new tab Figure 1. PPP1R17 reactivation in post-natal pituitary adenomas. A. Study schema. Pituitary adenomas and en-route adjacent margin tissues (when available) were surgically annotated and processed separately for single-nucleus multiome (ATACseq plus RNAseq) analysis, Mass spectrometry (MS), multiplex immunocytochemistry (mIHC) and DNA methylation. B. Summary of human surgical samples and investigations included in the current study. C. Marker gene expression in the integrated multiome dataset and their corresponding cell classes (see also: Supplemental Figure S1 and Supplemental Table S2). D. Left panel: UMAP projections of distinct nuclei from snRNAseq analysis using patient-derived samples including post-natal pituitary cells in the CD tumor core versus margin. Nuclei were clustered according to their cell-tyle identity. Middle panel: CD core specimens are comprised mostly of corticotrophs. Right panel: PPP1R17 expression is largely restriction to core CD corticotrophs. E. scRNAseq analysis 28 evaluating PPP1R17 expression (right column) mapped over pituitary cell types (left column). Top row: post-natal pituitary cells from the tumor margin. Middle row: CD core adenomas. Bottom row: non-CD adenomas (1 growth hormone and 1 non-functioning pituitary adenoma). F. Volcano plot from snRNAseq data showing differentially expressed genes between CD core versus margin corticotrophs. G. UpSet plot with pairwise comparisons between syngeneic CD core versus margin corticotrophs from the same patients evaluated by snRNAseq (P1 versus P2) or scRNAseq (P6 versus P7). H. Representative mIHC image of a whole-mounted CD adenoma and its adjacent margin tissues (i-ii) corticotroph-specific POMC and TBX19 expression. PPP1R17 (iii), and its canonical substrate PP2A (iv) co-expression (v) assessed within the identified adenoma. Scale bar = 50µM. I. i: Alpha GSU staining in mouse E14.5 embryos highlights pars tuberalis (PT) and anterior lobe (AL). ii – iii: Pomc staining (brown) is mostly restricted to the AL, and ppp1R17 staining (blue)is found in the PT and AL (blue arrowheads). Iv: By E18.5, ppp1R17 staining (blue) is mostly restricted to the intermediate lobe (IL) and PT, with only few select AL Cells retaining ppp1R17 positivity. V: Pomc (green) staining AL and IL, and ppp1R17 (red) restricted to the IL. Vi-vii: minimal Ppp1R17 expression in the adult mouse (red) in the AL, with residual staining in the IL. Scale bar = 50µM. CD = Cushing’s adenoma. In the integrated snRNAseq dataset, most cells within the tumor core were corticocotrophs, whereas corticotrophs comprised only a small fraction of cells in the tumor margin ( Figure 1D , left two panels). PPP1R17 was predominantly expressed in CD core corticotrophs by snRNAseq ( Figure 1D , right panel), a finding which was replicated in our previously published scRNAseq datastet 28 ( Figure 1E ). The cell of origin for pituitary adenomas remains unknown. Previously, we had shown that stem-like folliculostellate cells (FSCs) were spatially separated from the adenoma compartment 28 . We leveraged a combination of trajectory inference and RNA velocity using CellRank 33 of tumor-margin pairs and showed that adenoma cells are derived from pituitary corticotrophs rather than FSCs ( Supplemental Fig S2 ). The fate probability transition from margin to tumor phenotype was strongly linked to PPP1R17 overexpression ( Supplemental Fig S2). Compared to margin corticotrophs, PPP1R17 was highly overexpressed in CD core corticotrophs by snRNAseq ( Figure 1F ; Supplemental Table S3 ). Indeed, PPP1R17 was the only gene differentially overexpressed in pairwise analysis between syngeneic CD core versus margin corticotrophs from the same patients in both snRNAseq and scRNAseq analysis ( Figure 1G ; Supplemental Table S4 ). The peptide product of PPP1R17 is an endogenous inhibitor of the ubiquitous serine-threonine phosphatase PP2A 34 , a tumor suppressor 35 . Within surgical samples, PPP1R17 protein expression was limited to the CD adenoma compartment by mIHC ( Figure 1H , Supplemental Fig S3 ). We then asked whether the lack of PPP1R17 in the post-natal pituitary gland was physiologic versus a pathologic response to adjacent adenomas. PPP1R17 expression is found in neural progenitor cells during human fetal development but is lost in adulthood except for cerebellar Purkinje cells and occasional cells in the hypothalamus and pons/medulla 36 , 37 . We found robust Ppp1r17 expression in E14.5 mouse embryonic pituitary glands, which decreased markedly by E18.5 ( Figure 1H iv - v ) and diminished further in the adult mouse anterior pituitary lobe where we found only sparse and low expression ( Figure 1H vi - vii ). Taken together, these findings suggest that PPP1R17 is reactivated in Cushing’s disease adenomas. Activation of a proliferative chromatin accessibility program in adenoma corticotrophs To better understand the mechanisms underlying tumorigenesis and PPP1R17 reactivation, we leveraged our snATACseq data and mapped the corticotroph-restricted fragment coverage (binned to 1 Mb) over the entire genome. We found increased chromatin accessibility in adenoma corticotrophs compared to margin corticotrophs ( Figure 2A ). We then mapped fragments to ‘peaks’ specific to corticotrophs, and individual genes. Upon differential analysis, we found increased coverage at peak regions, regions proximal to transcription start sites and distal to transcription end sites in CD adenoma corticotrophs ( Figure 2B ). Download figure Open in new tab Figure 2: Altered global and PPP1R17 locus-specific chromatin accessibility in adenoma cells. A. Fragment coverage over the entire genome in single nucleus multiome dataset. Fragment coverage of corticotroph cells is averaged over 4 syngeneic core-margin pairs and two additional adenomas, binned to 1mBase, and mapped at the same scale for adenoma corticotrophs (red) and adjacent margin corticotrophs (blue). B. Waterfall plots mapping reads per kilobase million (RPKM) normalized fragment coverage in corticotrophs over peak (left), and gene (right) regions. In each panel, right column is adenoma corticotroph map and left is for corticotrophs derived from adjacent margin. C. Volcano plots illustrating differentially accessible peaks (left), and differentially accessible genes (right) in comparison of corticotrophs derived from adenomas (n=6) and adjacent margin (n=4, all paired with at least one adenoma). Significance levels set at 0.05 for p value and 0.5 for absolute log base 2 fold change. D. Transcription factor motifs for NFY and SP-1 are differentially accessible in CD core versus margin corticotrophs. E. Chromatin accessibility at PPP1R17 locus in adenoma corticotrophs (red track) versus corticotrophs derived from the adjacent margin (blue track), plotted at the same scale. Chromatin accessibility data derived from 4 syngeneic core-margin pairs and two additional CD adenomas and averaged across compartments. F. Schematic for reverse-ChIP performed using CRISPR dCas9-3XFLAG-biotin and a gRNA targeting the PPP1R17 promoter to pull down promoter-bound peptides for mass spectrometry. GSEA analysis for proteins bound to the PPP1R17 promoter reveals enrichment for RNA splicing and RNA processing. CD = Cushing’s disease. TSS = transcription start site. TES = transcription end site. We identified 1,094 differentially accessible peaks (606 up in the tumor core, 488 up in the adjacent margin) and 517 differentially accessible genes (150 up in the tumor core, 367 up in the adjacent margin; Figure 2C , Supplemental Figure S4, Supplemental Table S3 ). Comparative GSEA analyses of gene expression and chromatin accessibility profiles revealed a significant overlap in pathways (n = 67 GO pathways) including cytoplasmic translation and oxidative phosphorylation ( Supplemental Figure S4, Supplemental Table S5 ). Several transcription factor motifs were differentially accessible in CD adenomas, including NF-Y and SP1 ( Figure 2D , Supplemental Table S6 ). NF-Y is involved in chromatin remodeling, promotes cell proliferation and declines during differentiation 38 , 39 . SP1 is an essential embryonic transcription factor responsible for regulating cell cycle, methylation-free CpG islands, and formation of active chromatin structures 40 . Correspondingly, several NF-Y and SP1 targets were differentially overexpressed in adenoma corticotrophs ( Supplemental Figure S4, Supplemental Table S7 ). We focused our attention to the PPP1R17 locus and identified increased chromatin accessibility at the PPP1R17 promoter ( Figure 2E ). We also found increased chromatin accessibility at an internal enhancer from which a novel lncRNA PPP1R17-203 is transcribed ( Supplemental Figure S5 ). PPP1R17-203 shares sequence similarity with MAP7 which plays a role in several cancer types 41 , and BLASTN revealed interactions between PPP1R17-203 and several other lncRNAs ( Supplemental Table S8 ). The role of PPP1R17-203 in CD tumorigenesis remains to be explored. We assessed proteins associated with the accessible PPP1R17 promoter in CD adenomas by reverse-ChIP (rChIP) using CRISPR dCas9-3XFLAG and a gRNA specific to the PPP1R17 promoter to pull down promoter-associated proteins for Mass spectrometry 42 ( Figure 2F ). Control experiments excluded the PPP1R17 targeting gRNA. Several proteins bound to the PPP1R17 promoter in CD adenomas ( Supplemental Table S9) were enriched for pathways involving mRNA splicing and processing ( Figure 2F ). Several histone variants including H1.2, H1.4, H2A.2, H3.2 were enriched at the PPP1R17 promoter, as well as HP1BP3 which plays a key role in heterochromatin organization. Several members of the RAS oncogene family were also enriched at the PPP1R17 promoter, including RAB10, RAB15, RAB1A, RAB2B and RAB3B. Our snRNAseq also showed RAB3B overexpression in CD corticotrophs ( Supplemental Table S2 ). Taken together, these findings suggest that increased RAS signaling 43 and chromatin remodeling events underly PPP1R17 reactivation in CD corticotrophs. PPP1R17 interacts with PP2A in CD adenomas Pituitary adenomas can be clustered according to their DNA methylation profiles 23 . We asked if promoter DNA hypomethylation was coordinated with increased accessibility at the PPP1R17 locus. DNA methylation profiling on tissues from 6 CD adenomas versus 11 autopsy-derived pituitary adenomas revealed widespread DNA methylation changes ( Figure 3A , Supplemental Table S10 ). Bisulfite conversion and methylation-specific PCR from a subset of DNA methylation samples including 5 CD adenomas versus 5 autopsy-derived human pituitaries identified DNA hypomethylation at the PPP1R17 promoter (P = 0.03; Figure 3B ). Analysis of TCGA data similarly found PPP1R17 promoter hypomethylation in several tumor types including hepatocellular carcinoma and head and neck squamous cancer ( Supplemental Figure S6A) . These data suggest that benign tumors including CD adenomas may share epigenetic dysregulation of kinase-phosphatase balance with solid cancers. Download figure Open in new tab Figure 3: PPP1R17 promoter hypomethylation and increased transcription factor binding in CD. A. Differentially methylated regions in CD adenomas (n = 6) versus autopsy-derived normal pituitary glands (n = 11). B. Bisulfite conversion followed by methylation-specific PCR evaluating methylation at the PPP1R17 promoter in CD adenomas (n = 5) versus autopsy-derived normal pituitary glands (n = 5, subset from A). C. Thermofluor protein thermal shift assay using purified recombinant human PPP1R17 and PP2Ac demonstrating a concentration-dependent decrease in the PPP1R17 melting temperature, with a half-maximal effect between 1.5 and 5 µg of PP2A. D. AlphaFold Multimer modeling predicting binding of human PPP1R17 (K66 and T119) to PP2A catalytic subunit (PP2Ac H118, D88). E. Representative proximity ligation assay in margin (left) versus CD adenoma core (right) using antibodies for PP2Ac and PPP1R17. Red puncta indicate peptides in proximity. F. Representative mIHC staining of a CD adenoma and its adjacent margin. POMC (red) and TBX19 (green) staining localized primarily to tumor cells. Lower panels evaluating phosphorylation of PP2A targets AKT, ERK and EGFR. CD = Cushing’s disease. The most common mode of PP2A suppression in human tumors is via overexpression of other known endogenous PP2A inhibitors including CIP2A, PME1 and SET 44 – 46 . Other than PPP1R17 , we did not find transcriptional overexpression of other endogenous PP2A inhibitors in adenoma corticotrophs ( Supplemental Figure S6B ). We purified recombinant human PPP1R17 and PP2Ac and performed Thermofluor protein thermal shift assays. We titrated purified PPP1R17 and obtained reliable melting temperature measurements at 1μg of protein. Addition of increasing concentrations of PP2Ac revealed a concentration-dependent decrease in the PPP1R17 melting temperature, with a half-maximal effect between 1.5 and 5 μg of PP2A ( Figure 3C ), verifying direct interaction between PPP1R17 and PP2Ac. Purified PPP1R17 also inhibited PP2A serine/threonine phosphatase activity in a cell-free assay ( Supplemental Figure S6C ), although this finding did not reach statistical significance. AlphaFold Multimer 47 , 48 predicted binding of human PPP1R17 (K66 and T119) to the catalytic subunit of PP2A (H118, D88 of PP2Ac; Figure 3D ). Known endogenous inhibitors – PME1 and SET interact with PP2Ac at Arg89 and share an additional interaction site at Tyr127 with PPP1R17 ( Supplemental Figure S6B ). Finally, PPP1R17 colocalized with PP2Ac in human CD adenomas by proximity ligation assay ( Figure 3E ) and by mIHC ( Figure 1G ). As a functional consequence of this interaction, we found that several PP2A targets were hyperphosphorylated in CD adenomas including pAKT, pERK and pEGFR ( Figure 3F ). PPP1R17 reactivation induces tumorigenic signaling in adenomas We further explored the downstream effects of PP2A inhibition by performing quantitative phophoproteomic profiling where we labeled syngeneic, paired CD adenomas and their adjacent margin with Tandem Mass Tag (TMT) reagents, enriched phosphopeptides using TiO2 and iMac methods, and acquired LC-MS/MS data on peptides with and without phosphoenrichment. We normalized phosphopeptides to their corresponding peptides. We identified hyperphosphorylation of peptides involved in MYC signaling and G2-M checkpoint regulation ( Figure 4A ; Supplemental Table S11 ). We performed MoMo motif analysis in CD adenomas versus adjacent margin 49 , 50 and identified preferential hyperphosphorylation of peptides with motifs xxSPxx and DxxxSxx ( Figure 4B ). Download figure Open in new tab Figure 4: PPP1R17 inhibits ubiquitous tumor suppressor PP2A. A. TMT LC-MS/MS quantification of phosphopeptide/peptide ratios in core CD adenomas versus margin (n = 3/group) shows several hyperphosphorylated and dephosphorylated targets. B. MoMo motif analysis of preferentially hyperphosphorylated and dephosphorylated motifs in CD adenoma core versus margin (n = 3/group). C. TMT LC-MS/MS quantification of phosphopeptide/peptide ratios in mCort PPP1R 17 versus mCort GFP cells (n = 5/group). D. MoMo motif analysis of preferentially hyperphosphorylated and dephosphorylated motifs in mCort PPP1R 17 versus mCort GFP cells (n = 5/group). E. GSEA analysis of pathways differentially phosphorylated in mCort PPP1R 17 versus mCort GFP cells (n = 5/group). F. Representative Western blots of mCort cells and ATT-20 cells transiently transfected with lentiviral plasmids encoding PPP1R17 versus GFP evaluating phosphorylation of PP2A targets AKT, ERK and S6. CD = Cushing’s disease. We next validated the activation of tumorigenic pathways in-vitro using mouse anterior pituitary cells (mCort) to serve as baseline normal cells. We stably overexpressed PPP1R17 tagged to GFP in mCort cells (mCort PPP1R 17 ; Supplemental Figure S7A ). mCort PPP1R 17 cells exhibited hyperphosphorylation of proteins involved in mRNA splicing and mRNA processing ( Figure 4C ; Supplemental Table S12 ). Motif analysis in mCort PPP1R 17 versus mCort GFP cells also identified preferential hyperphosphorylation of peptides with the xxSPxx motif ( Figure 4D ). Over-representation analysis revealed enrichment of phoshoproteins associated with RNA splicing and mRNA processing ( Supplemental Table S12 ). We confirmed our phosphoproteome findings with western blotting. mCort cells transiently transfected using PPP1R17 lentiviral plasmids showed increased pAKT, pERK and pS6 compared to mCort cells transfected with GFP ( Figure 4E , left panel ). In ATT-20 cells, a model for CD 51 , 52 , transient transfection using PPP1R17 lentiviral plasmids similarly resulted in increased pAKT and pERK compared to GFP controls ( Figure 4E , right panel ). PPP1R17 overexpression derepresses transcription in CD In addition to its role as a cytoplasmic phosphatase, PP2A can dephosphorylate nuclear RNA polymerase II via the INTAC complex and globally attenuate transcription 53 . We therefore hypothesized that chronic PPP1R17 overexpression activates RNA PolII, resulting in increased transcription and differential protein abundance in CD adenomas. mCort cells stably overexpressing PPP1R17 (mCort PPP1R 17 ) showed increased pRNA PolII (S2, S5, S7) and increased total RNA PolII compared to mCort GFP cells ( Figure 5A ) suggesting transcriptional derepression. Bulk RNAseq analysis of mCort PPP1R 17 compared to mCort GFP cells revealed upregulation of genes associated with MYC activation, unfolded protein response and epithelial-mesenchymal transition ( Figure 5B , Supplemental Table S13 ), pathways previously associated with CD adenomas 19 , 28 , 54 . We observed significant overlap between genes overexpressed in human CD adenomas and mCort PPP1R 17 cells, especially in genes involved in protein translation and peptide biosynthesis ( Supplemental Table S14 ). Download figure Open in new tab Figure 5: PPP1R17 overexpression in-vitro recapitulates tumorigenic programs. A. Representative Western blots evaluating phosphorylation of RNA PolII (S2, S5 and S7) as well as total RNA PolII in mCort PPP1R 17 versus mcort GFP cells. B. GSEA analysis of bulk RNAseq comparison of mCort PPP1R 17 versus mCort GFP cells (n = 3/group) showing differential enrichment of several pathways. C. Proteomic analysis of mCort PPP1R 17 versus mCort GFP cells (n = 5/group). D. GSEA analysis of peptides differentially abundant in mCort PPP1R 17 versus mCort GFP cells (n = 5/group) highlighting enrichment of pathways for hypoxia, protein secretion and epithelial-mesenchymal transition. E. Proteomic analysis of CD adenoma core versus margin (n = 3/group). F. GSEA analysis of peptides differentially abundant in CD adenoma core versus margin (n = 3/group) highlighting enrichment of pathways for epithelial-mesenchymal transition, hypoxia and protein secretion. Proteomic analysis of mCort PPP1R 17 compared to mCort GFP cells ( Figure 5C ) showed upregulation of peptides involved in epithelial-mesenchymal transition, hypoxia and protein secretion ( Figure 5D , Supplemental Table S15 ). Similarly, we found increased EGFR staining in CD adenomas ( Supplemental Figure S8 ). Proteomic analysis of CD adenomas versus their adjacent margin tissues ( Figure 5E ) revealed enrichment of other proteins involved with epithelial-mesenchymal transition, hypoxia and protein secretion ( Figure 5F , Supplemental Table S16 ), suggesting that PPP1R17 overexpression potentiates a hormone secreting tumor program. PPP1R17 overexpression is a targetable mechanism of adenoma pathogenesis PP2A agonists interrupt the interaction between PP2A and its endogenous inhibitors, a strategy that may be of clinical benefit 55 – 57 . However, this strategy has not been explored in the context of hormone-secreting adenomas. Of the PP2A agonists tested (DT061, ABL127, and fingolimod), we found that only fingolimod suppressed POMC transcription ( Figure 6A ). mCort PPP1R 17 cells showed hyperproliferation and acceleration of the cell cycle compared to mCort GFP cells, with fewer cells in the S-phase (16.4% versus 24.1%, P < 0.001; Supplemental Figure S7B ). Fingolimod rescued this phenotype and inhibited cell proliferation in mCort PPP1R 17 cells ( Figure 6B ) but not in mCort GFP cells ( Supplemental Figure S7B ). Phosphoproteomic analysis showed that fingolimod treatment in mCort PPP1R 17 cells dephosporylated peptides involved in cell cycle regulation and RNA polymerase II transcription ( Figure 6C ; Supplemental Table S17 ), including specific reversal of PPP1R17-mediated hyperphosphorylation in proteins involved in MYC signaling and G2-M checkpoint regulation ( Supplemental Figure 7C , Supplemental Table S17 ). Treatment of mCort PPP1R 17 cells with fingolimod selectively decreased phosphorylation of peptides with the xxSPxx motif ( Figure 6D ). We confirmed that fingolimod treatment partially reversed AKT and ERK hyperphosphorylation in mCort PP1R 17 cells ( Figure 6E ) and induced apoptotic markers Bid, Bbc3 and Hrk ( Figure 6F ). Download figure Open in new tab Figure 6: PPP1R17 upregulation is a targetable mechanism in CD. A. qRT-PCR of POMC normalized to b-actin in mCort PPP1R 17 cells before and after treatment with 10μM fingolimod, DT061 or ABL127 (n = 3 technical replicates, representative image from n = 3 biological replicates; *** P < 0.05). B. Comparison of proliferation between mCort PPP1R 17 cells treated with DMSO controls versus low dose (2.5μM) fingolimod (n = 8/group). C. GSEA analysis revealed dephosphorylation of peptides involved in cell cycle signaling and RNA Polymerase II transcription in mCort PPP1R 17 cells after 48 hours of fingolimod 10μM. D. MoMo analysis showed differential dephosphorylation of peptides with the xxSPxx motif in mCort PPP1R 17 cells after treatment with 48 hours of fingolimod 10μM (n = 5/group). E. Evaluation of phosphorylation status using Western immunoblotting of known PP2A targets EGFR, AKT, ERK and S6 in mCort PPP1R 17 cells versus mCort GFP cells (representative of 3 biological replicates). F. Custom PCR panel evaluation of apoptotic pathway markers in mCort PPP1R 17 cells after treatment with DMSO control versus 10μM fingolimod, DT061 or ABL127 (n = 3 technical replicates). G. Comparison of proliferation between ATT-20 PPP1R 17 cells versus ATT-20 GFP cells after treatment with DMSO control versus 5μM fingolimod (n = 8/group). H. Representative Western immunoblotting of phosphorylation status of PP2A targets EGFR and AKT in ATT-20 PPP1R 17 versus ATT-20 GFP cells. I. Comparison of tumor volume in ATT-20 xenografts implanted in athymic NU/NU mice and treated 17 days with intraperitoneal fingolimod 1mg/Kg versus normal saline controls (n = 5/group). J. Comparison of tumor weights after treatment with fingolimod 1mg/Kg versus normal saline controls (n = 5/group; * P < 0.05). K. Comparison of tumor volumes after treatment with fingolimod 1mg/Kg versus normal saline controls (n = 5/group). In ATT-20 cells, stable PPP1R17 overexpression (ATT-20 PPP1R 17 ) also induced hyperproliferation compared to ATT-20 GFP cells, and this was reversed by 5μM fingolimod treatment ( Figure 6F ). Since ATT-20 cells have a tumor-like phenotype, we found that fingolimod also reduced viability of ATT-20 GFP cells. Similarly to our findings in mCort PPP1R 17 cells, fingolimod treatment in ATT-20 PPP1R 17 rescued hyperphosphorylation of EGFR and AKT ( Figure 6H ). We generated ATT-20 xenografts in athymic NU/NU mice which resulted in adrenal hypertrophy in the mice ( Supplemental Figure S9A ). Fingolimod treatment (1mg/Kg) led to significant decrease in tumor weights (100 vs 249 mg, P = 0.05; Figures 6I and 6J ) and tumor volumes (final volume 168 vs 463 mm 3 , P = 0.05; Figure 6K ; Supplemental Figure S9B ). There was no significant change in skin thickness or body weights in tumor-bearing mice treated with fingolimod versus saline control for 14 days ( Supplemental Figure S9C ). Taken together, these findings demonstrate that PP2A agonism can attenuate the pro-tumorigenic effects of PPP1R17 overexpression in CD adenomas. Discussion PPP1R17 is an endogenous PP2A inhibitor in pituitary adenomas causing Cushing’s disease Our findings demonstrate that epigenetic dysregulation of the kinase-phosphatase balance can underlie human tumors. In this study we identified epigenetic reactivation of PPP1R17, an endogenous inhibitor of the protein phosphatase PP2A, as a driver of CD tumorigenesis. PPP1R17 activity was associated with widespread dysregulation of the phosphoproteome. Reversal of this phenotype using the PP2A agonist fingolimod rescued cellular hyperproliferation and induced apoptosis in a tumor model. Kinase-phosphatase imbalance due to somatic gene amplification of PRKAR1A gene has been implicated in adrenal tumors causing Cushing’s syndrome 58 . Here, we find convergent epigenetic dysregulation along a different pathway leading to kinase-phosphatase imbalance in pituitary adenomas causing Cushing’s disease. Epigenetic modulation of gene expression is a tumorigenic mechanism whereby environmental cues impinge upon the transcriptome without requiring changes in the DNA coding sequence 59 . The majority of CD adenomas are not associated with canonical mutations of known targets BRAF , USP8 or USP48 22 , 60 , and studies on CD epigenomics are lacking. Our multiomic approach combining single-nucleus RNA and ATACseq with DNA methylation profiling identified epigenetic PPP1R17 reactivation in CD adenomas, highlighting a role for gene-environment interactions in CD. Our reverse-ChIP results suggested histone subunit recruitment and active chromatin remodeling at the PPP1R17 promoter, which agrees with other reports on histone partitioning as a mechanism of tumor progression 61 . Further studies are needed to determine precisely how environmental factors modify histone recruitment in CD. PPP1R17 reactivation as a targetable mechanism of CD PPP1R17, also known as G-substrate due to its preferential phosphorylation by cGMP-dependent protein kinase, is found almost exclusively in human cerebellar Purkinje cells where it plays a role in the induction of long-term depression 7 , 62 . In the rat brain, Ppp1r17 is also restricted almost entirely to Purkinje cells of the cerebellum, although low levels are detectable in the paraventricular region of the hypothalamus and in the pons/medulla 37 . Unlike in adults, PPP1R17 is highly expressed in neural progenitor cells in the developing human cortex 63 – 65 . This expression pattern is restricted to mammals and is not seen in ferrets or mice of comparable ages 66 . In the prenatal macaque and human cortex, PPP1R17 is localized to the outer and inner subventricular zones, and colocalizes with markers of intermediate progenitor cells and dividing cells 66 . RNA-seq time-course studies also confirmed that PPP1R17 expression is restricted to neural progenitor cells during fetal development in humans 36 , therefore its overexpression in CD likely reflects epigenetic reactivation through coordinated increase in chromatin accessibility and DNA hypomethyation. Other studies have recently described similar epigenetic reactivation of developmental genes in the pathogenesis of pulmonary hypertension 67 , however this reactivation has not previously been described in pituitary tumors. The proximate mechanism of PPP1R17 reactivation in CD adenomas is still not fully understood. Whereas our results suggest underlying epigenetic dysregulation via promoter hypomethylation, increased chromatin accessibility and RAS signaling, the underlying index event is still unclear. A response to PP2A upregulation in the adenoma is possible, although we did not identify transcriptional upregulation of other PP2A agonists CIP2A , PME1 and SET in CD adenomas. It is notable that the pituitary gland resides outside the blood brain barrier adjacent to the median eminence with its leaky vasculature, which allows for prompt homeostatic responses to environmental stressors. This architecture also allows transmission of blood-borne pathogens and other environmental agents which trigger hormone responses from the hypothalamus and pituitary gland. Given that epigenomic changes frequently occur in response to environmental cues, we speculate that cumulative exposure to environmental insults or infectious agents could create an epigenomic milieu favoring tumorigenesis via PPP1R17 dysregulation. Additional studies are needed to investigate this hypothesis. PPP1R17 overactivity induces widespread phosphoproteomic changes Overexpression of PP2A inhibitors has been identified as an oncogenic mechanism in multiple human cancers, including SET overexpression in CML, AML, T-cell ALL, B-cell CLL, non-small cell lung cancer and Wilms tumors 44 , 45 , 68 – 71 , and CIP2A overexpression in hepatocellular, breast, colorectal, ovarian, cervical, prostate, lung, head and neck cancer 46 , 72 – 76 . Small molecules that activate the phosphatase PP2A (SMAPs) are an emerging class of antitumor agents with therapeutic potential as combinatorial agents in lung cancer models 77 , 78 , as well as in multiple myeloma, leukemia and colorectal cancer 57 . Our results show the therapeutic potential of PP2A agonism using fingolimod in CD both in-vitro and in-vivo. Interestingly, PPP1R17 overexpression in mouse cortical neurospheres inhibits the G1-S transition and results in decreased proliferation 66 . In our study, PPP1R17 overexpression in mature corticotrophs (mCort) similarly resulted in prolonged G0/G1 but led to accelerated S phase and increased proliferation in mCort PPP1R 17 cells, indicating cellular context-specific effects. In addition to its role in protein dephosphorylation, PP2A was recently shown to exert direct transcriptional control by dephosphorylating RNA PolII via an integrator complex 53 , 57 , 79 . We noted phosphorylation of RNA PolII in mCort PPP1R 17 cells, with resultant differential expression of several genes. Furthermore, we noted significant overlap between the gene expression profile of mCort PPP1R 17 cells and CD adenomas, indicating that several transcriptomic changes in CD likely result from PPP1R17-mediated PP2A inhibition. Future studies will investigate whether PPP1R17 may be partly mediating kinase-phosphatase imbalance through its interactions with PRKACA, which plays a role in the formation of adrenal tumors 80 . Our study is limited by the small number of patient samples included, partly due to limited availability of en-route adult human pituitary tissues in the tumor margin. Fingolimod also possesses some non-specific effects which may limit its therapeutic window for patients with CD. Future studies will develop and test more specific PPP1R17 inhibitors for use in humans. Conclusions Our study highlights the role of kinase-phosphatase disequilibrium in the pathogenesis of benign tumors, specifically in adrenocorticotropin secreting pituitary adenomas that cause CD. We identified epigenetic reactivation of PPP1R17 , an endogenous inhibitor of PP2A, as a central driver of CD tumorigenesis irrespective of the somatic mutation status. The therapeutic potential of targeting this imbalance is underscored by the efficacy of PP2A agonism in reversing the tumorigenic effects of PPP1R17 overexpression in-vitro and in-vivo. This work advances our understanding of pituitary adenoma formation and suggests a promising avenue for therapeutic intervention in benign tumors. Supplemental Figure Legends Download figure Open in new tab Supplemental Figure S1. UMAPs of canonical cell types in each individual tumor sample. Refer to Fig 1d for aggregate figure. Download figure Open in new tab Supplemental Figure S2. Tumor corticotroph lineage tracing analysis. A. CellRank and scVelo analysis of patient sample P2. i. scVelo velocities projected onto UMAP embedding of all cells in both tumor and margin compartments colored by cell type. ii. scVelo velocities projected onto UMAP embedding of tumor corticotrophs, margin corticotrophs, and folliculostellate cells (FSC). iii. UMAP of corticotrophs from tumor and margin compartments. Cells are colored by CellRank fate probabilities, which is the likelihood that a given cell will transition toward the terminal population. iv. CellRank identified initial cell states. v. CellRank identified terminal cell states. vi. Transition matrix showing the fate probabilities of each macrostate. vii. Relative expression of relevant genes in each macrostate plotted over latent time. B. CellRank and scVelo analysis of patient sample P1. scVelo velocities projected onto UMAP embedding of tumor corticotrophs, margin corticotrophs, and FSCs (left), scVelo velocities projected onto UMAP embedding of tumor and margin corticotrophs (center), scVelo velocities projected onto UMAP embedding of tumor and margin corticotrophs with cells colored by relative PPP1R17 expression (center). C. CellRank and scVelo analysis of patient sample P3. scVelo velocities projected onto UMAP embedding of tumor corticotrophs and FSCs (left), scVelo velocities projected onto UMAP embedding of only corticotrophs (center), scVelo velocities projected onto UMAP embedding of corticotrophs with cells colored by relative PPP1R17 expression (center). D. CellRank and scVelo analysis of patient sample P3. scVelo velocities projected onto UMAP embedding of tumor corticotrophs and FSCs (left), scVelo velocities projected onto UMAP embedding of only corticotrophs (center), scVelo velocities projected onto UMAP embedding of corticotrophs with cells colored by relative PPP1R17 expression (center). Download figure Open in new tab Supplemental Figure S3. PPP1R17 expression is confined to CD adenomas. A. Multiplex immunohistochemistry of CD adenoma and surrounding margin pituitary gland (i) showing PPP1R17 expression (green) compared to corticotroph-specific POMC expression (red). PP2Ac expression (purple) staining is compared to PPP1R17 staining within the adenoma (iii – v). Scale bar = 50µM. B. Representative H&E of margin pituitary gland and CD adenoma demonstrating PPP1R17 staining. CD = Cushing’s disease. H&E = hematoxylin and eosin. Download figure Open in new tab Supplemental Figure S4. A. Multiome analysis comparing snRNAseq and snATACseq in CD corticotrophs versus adjacent margin corticotrophs. B. GSEA analysis of pathways upregulated by snRNAseq in CD corticotrophs versus adjacent margin corticotrophs. C. Left panel: overlap between genes differentially expressed (GEX) and genes differentially accessible (ATAC) in CD corticotrophs. Right panel: GSEA analysis of pathways differentially accessible by snATACseq in CD corticotrophs. D. Comparison of chromatin accessibility (left panels) and gene expression (right panels) for NF-Y target gene CALM1 in CD corticotrophs (red, tumor) versus adjacent margin corticotrophs (blue, margin). E. Differential chromatin accessibility (left panels) and gene expression (right panels) for SP1 target gene SCIN in CD corticotrophs (red, tumor) versus adjacent margin corticotrophs (blue, margin). Download figure Open in new tab Supplemental Figure S5. The novel lncRNA PPP1R17-203 is transcribed from an internal PPP1R17 site. A. Long-range interactions predicted for PPP1R17-203. B. Predicted 3D structure of PPP1R17-203. Source: https://bioinformaticslab.erc.monash.edu/linc2function Download figure Open in new tab Supplemental Figure S6. A. DNA methylation analysis from the TCGA database demonstrating PPP1R17 promoter hypomethylation in multiple solid tumors including hepatocellular carcinoma and head and neck squamous cancer. B. Endogenous PP2A agonists PPME1 and SET are not transcriptionally overexpressed in CD. UMAP projections demonstrating expression patterns of PPME1 and SET in POMC -overexpressing CD adenoma cells. AlphaFold multimer predicts PME1 and SET interaction with PP2Ac at Arg89 and share an additional interaction site at Tyr127 with PPP1R17. C. Thermofluor protein thermal shift assay using purified recombinant human PPP1R17 and PP2Ac demonstrating a concentration-dependent decrease in the PPP1R17 melting temperature, with a half-maximal effect between 1.5 and 5 µg of PP2A. UMAP = uniform manifold approximation and projection. CD = Cushing’s disease. Download figure Open in new tab Supplemental Figure S7. A . Lentiviral vector overexpressing PPP1R17 tagged to GFP . Stable transfection in mCort cells was verified by qRT-PCR and by Western immunoblotting. B. Stable PPP1R17 overexpression in mCort cells (mCort PPP1R 17 ) showed hyperproliferation compared to mCort GFP cells (n = 8/group). mCortPPP1R17 cells also showed acceleration of the cell cycle. C. GSEA analysis of protein pathways hyperphosphorylated in mCort PPP1R 17 but rescued by 48-hour fingolimod treatment. Results of TMT labeling and mass spectrometry; n = 5/group; one-way ANOVA P < 0.05). Download figure Open in new tab Supplemental Figure S8. EGFR is overexpressed in CD. Representative mIHC image of whole- mounted CD adenoma and adjacent margin (i) with corticotroph-specific POMC expression. (ii- iv) EGFR expression (green) was compared to POMC expression (red). Scale bar = 200 µM. Download figure Open in new tab Supplemental Fig S9. A . Implantation of ATT-20 cells into the flanks of nude mice induced adrenal gland hypertrophy (n = 5/group), which did not return to baseline after fingolimod treatment (n = 4). B. Nude mice bearing ATT-20 flank xenografts formed tumors after 10-12 days. After tumor establishment, intraperitoneal fingolimod 1mg/kg daily for 17 days led to decreased tumor size. C. Fingolimod treatment had no effect on skin thickness or animal body weight. Author contributions DTA study design, bioinformatics, data analysis and interpretation, paper drafting and editing. DMu data analysis, bioinformatics, figure generation, paper editing. SB data collection. DMan in-vitro experiments. DN in vitro experiments. SC bioinformatics, data analysis. DB bioinformatics. JG bioinformatics. KJ bioinformatics, data analysis and interpretation. AE bioinformatics, high throughput sequencing. ZA in vitro assays, data analysis. KA pathological assessment. DMar immunofluorescence. CQ immunofluorescence. SW immunofluorescence, data analysis and interpretation, paper editing. NKL protein assays. NSM in vitro studies. JS protein analysis, data interpretation. YL proteome/phosphoproteome analysis, data interpretation. LKN study design, data interpretation. CT study design, data interpretation. PC study design, data analysis and interpretation, study supervision, paper editing. Competing interests The authors declare no competing interests. Data and materials availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Prashant Chittiboina, MD, MPH. Tenure Track Investigator, Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Diseases and Stroke, National Institutes of Health. 10 Center Drive, Room 3D20, Bethesda, MD 20892-1414. Phone: (301) 496-5728. Fax: (301) 402-0380. Email: prashant.chittiboina{at}nih.gov . Materials availability This study did not generate new unique reagents. Data and code availability High throughput sequencing data generated from this study has been uploaded to Geo. Custom analysis toolkits and code developed for this study has been uploaded to Github. Additional methods are available upon request from the corresponding author. List of Supplemental Materials Materials and Methods Supplemental Figures S1 through S8 Supplemental Tables S1 through S17 Supplemental video Materials and methods Resource Availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Prashant Chittiboina, MD, MPH. Tenure Track Investigator, Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Diseases and Stroke, National Institutes of Health. 10 Center Drive, Room 3D20, Bethesda, MD 20892-1414. Phone: (301) 496-5728. Fax: (301) 402-0380. Email: prashant.chittiboina{at}nih.gov . Materials availability This study did not generate new unique reagents. Data and code availability: High throughput sequencing data generated from this study has been uploaded to Geo. Custom analysis toolkits and code developed for this study has been uploaded to Github. Additional methods are available upon request from the corresponding author. Experimental model and study participant details: Patients Surgical samples were obtained from non-consecutive patients with a biochemical diagnosis of CD undergoing pituitary surgery by a single surgeon (PC) between 2006 and 2023 ( Supplemental Table S1 ). Samples were chosen based on quantity and size of surgical tissues, which determined suitability for multiome studies. Small microadenomas < 5mm maximum diameter were generally unsuitable for these assays. This study was conducted at the National Institutes of Health (NIH) Clinical Center in Bethesda, MD and approved by the combined neuroscience Institutional Review Board of the National Institute of Neurological Disorders and Stroke, Bethesda, MD (clinicaltrials.gov identifier NCT00060541 ). Each patient gave written informed consent. Hypercortisolism in CD patients was diagnosed as previously described 1 based on elevated late night salivary cortisol (chemiluminescent enzyme immunoassay, Siemens Immulite 1000 analyzer, NIH Department of Laboratory Medicine, Bethesda MD; normal range <100 ng/dL), elevated 24-hour urine free cortisol (high performance liquid chromatography/tandem Mass spectrometry, NIH Department of Laboratory Medicine, Bethesda, MD; normal range: 1.4 – 20 μg/24H for 3 – 8 year-olds, 2.6 – 37 for 9 – 12 year-olds, 4.0 – 56 μg/24H for 13 – 17 year-olds and 3.5 – 45.0 μg/24H for ≥ 18 year-olds) or low-dose dexamethasone suppression testing (1mg overnight or 2mg over 48 hours) 2 , 3 . Supplementary midnight serum cortisol (normal range < 7.5 mcg/dL) and ACTH (Nichols Advantage Immunochemiluminometric assay or chemiluminescence immunoassay, Siemens Immulite 2500 analyzer, NIH Department of Laboratory Medicine, Bethesda MD, normal range 5 – 46 pg/mL) and 24-hour urinary 17-hydroxycorticosteroids (17-OHS/Cr) excretion (Quest Diagnostics Nichols Institute, Chantilly VA; normal range 3 – 10 mg/24H for males and 2 – 6 mg/24H for females) were used as needed. Pituitary ACTH production was verified using early morning ACTH levels, 8mg overnight dexamethasone suppression testing (DST) or ovine corticotropin-releasing hormone (CRH) stimulation testing. Average post-CRH or post-DDAVP stimulated levels were determined preoperatively in all CD patients by averaging serum cortisol at 30 and 45 minutes after stimulation, and for ACTH by averaging serum levels at 15 and 30 minutes after stimulation. All patients underwent a sub-labial trans-sphenoidal approach for microscope-assisted selective adenectomy. Adenomas were identified pre-operatively using high-resolution magnetic resonance imaging (MRIs) with Pituitary protocol, and their locations were verified intraoperatively by visual inspection. Adenomas were removed by micro-surgical dissection along a circumferential pseudo-capsular plane. Pituitary adenoma was removed en-bloc within its pseudocapsule wherever possible 4 and a portion was sent for histopathological examination following formalin fixation and paraffin embedding (FFPE). The en-route or adjacent non-viable adjacent normal pituitary gland was removed separately and annotated for analysis. Research specimens were labeled separately and fresh-frozen for further study. Animal studies All procedures were approved by National Institute of Neurological Disorders and Stroke, Animal Care and Use Committee and performed in accordance with National Institutes of Health guidelines. Adult pregnant NIH Swiss female mice were euthanized with CO2 at two gestation ages, embryonic day (E) 14.5 and E18.5. Embryos and pituitaries of the adult dams were collected, fixed in 4% formaldehyde (Sigma)/0.1M phosphate buffer saline (PBS, 24h), transferred to 30% sucrose (48hr, 4°C), embedded in OCT (Sakura Tissue-Tek) and stored at - 80°C until sectioning. Sections (pituitary, 12µm; E14.5 and E18.5, 16µm) were cut on a cryostat (Leica Biosystem CM 3050S). For in-vivo tumor injections, 4-5 week-old female athymic nude mice (NU/NU) were purchased from Charles River Laboratories and housed under standard conditions in a pathogen-free facility with ad libitum access to food and water. Mice were grouped into untreated mice, mice injected with cells, and mice injected with cells and treated with fingolimod. Number of mice per group were determined based on power calculations assuming effect size of 30% based on in-vitro data. Confluent ATT-20 cells were harvested by trypsinization and counted. Cell suspensions of 7 x 10 6 cells in 100mL PBS were injected into the flank subcutaneously using 25G needles. After allowing 10-12 days for tumor establishment, tumor-bearing mice were randomized into DMSO versus fingolimod treatment groups. Fingolimod was dissolved in normal saline and injected intraperitoneally at a daily concentration of 1mg/Kg body weight. Tumors were measured twice a week using digital calipers, and tumor volume was estimated using the formula 0.5 x A x B 2 , where A was the long diameter and B the short diameter. Mice were monitored daily for clinical signs of distress by dedicated animal handlers at the NIH Animal Research Facility in keeping with NIH IACUC guidelines. A licensed veterinarian from the NIH was on call 24 hours for any animal emergencies. At the end of the treatment period, surviving mice were euthanized humanely using carbon dioxide according to AVMA Guidelines for the Euthanasia of Animals and NIH IACUC guidelines. Tumors were harvested and weighed. Adrenal glands were also harvested and measured. Corticotroph cells and constructs Corticotroph cells (mCort) were harvested as previously described 5 from female BALB/c mice aged 6-8 weeks (Taconic Biosciences, USA). Cells were digested and homogenized in 1 mg/ml collagenase (Sigma-Aldrich) for 15 minutes and cultured in DMEM (Thermo Fisher Scientific) with 10% FBS (Thermo Fisher Scientific) and 1% penicillin-streptomycin (Gibco) in 5% CO2 at 37°C. Following dissociation and culture of pooled mouse (n = 10) pituitary cells, corticotrophs were isolated by flow cytometry using anti corticotropin-releasing hormone receptor 1 (anti-CRHR1) antibody (Invitrogen) followed by Alexa Fluor-555 conjugated antibody (Thermo Fisher) as previously described 5 . Cell sorting and analysis were carried out using a MoFlo Astrios cell sorter and Summit acquisition and analysis software (Beckman Coulter) to identify mouse corticotroph cells only (mCort). The gene expression profile of mCort cells was confirmed with microarray analysis (Clariom S Mouse, Thermo Fisher) and analyzed with R packages (affy, limma and pheatmap). Corticotroph phenotype preservation was confirmed by the presence of secreted ACTH at each generation in the cell culture media by enzyme-linked immunosorbent assay (ELISA), using the ACTH ELISA kit (MDBio products) according to the manufacturer’s instructions. Lentiviral vectors encoding PPP1R17-GFP or GFP -only were obtained from Origene (Cat# RC207007L4V and PS100093). Lentiviral particles were packaged using Lenti-X Packaging Single Shots (Takara Bio # 631278) and high-titer virus was generated using HEK293T cells (ATCC #CRL-3216). After 48 hours, viral supernatants were collected, passed through a 0.45 μm filter (Starstedt #83.1826) and quantified using Lenti-X GoStix Plus (Takara Bio #631280). 10 IFUs/5x10 6 cells were applied in normal media to mCORT or ATT-20 cells for 48 hours, followed by antibiotic selection for 2 weeks using 1mM Puromycin (Thermo Fisher #A1113802). GFP positivity was confirmed in surviving cells, and PPP1R17 expression was verified using qRT-PCR and Western immunoblotting. Single Nucleus Multiome analysis CD adenomas and adjacent normal pituitary glands were embedded in OCT and stored at -80°C. A clean razor blade was used to cut away OCT fragments. Nuclei were isolated from the frozen tissues using an adaptation of a previously established protocol 6 . Samples were placed on a clean petri dish and washed with 800μL of Detergent-Lysis Buffer (0.1% Triton-X). Tissues were chopped into small pieces, homogenized in a 7mL Dounce Homogenizer on ice (Fisher Scientific # 501945204), and passed through a 70 mm MACS strainer (Miltenyi Biotech # 130-098-462). Nuclei were spun down at 3200 xg for 5 minutes at 4°C and processed using the Chromium nuclei Isolation Kit (10x Genomics #1000494). Briefly, nuclei were resuspended in 1mL of low sucrose buffer and gently layered above 4 mL of high sucrose buffer taking care not to disrupt the density gradient. The gradient was spun down at 3200 xg for 20 minutes at 4°C, then resuspended in Nuclei Resuspension Buffer. Aliquots of resuspended nuclei were stained with Acridine Orange dye (Logos Biosystems # F23001) and counted using an automated fluorescence cell counter (Logos Biosystems # L20001). Nuclei were then immediately processed using a Single Cell Multiome ATAC + Gene Expression Assay (10X Genomics # 1000285) following manufacturer recommendations. Nuclei were loaded onto a Next GEM Chip J (10X Genomics # 1000230), targeting a yield of 3,000 – 6000 nuclei. Library preparation was performed according to the manufacturer’s recommendations. Libraries were pooled and sequenced on an Illumina NextSeq 500 using a pair of High-Output Reagent Kits v2.5 with 150 cycles (Illumina # 20024907). Sequencing reads were demultiplexed and aligned to the hg38 reference genome using the 10X Genomics CellRanger software (arc v2.0.0) mkfastq function with default settings, and counts were generated using the CellRanger-arc count function. Filtered .h5 files were processed in R using Seurat v4.3 14. Cell Free RNA contamination was estimated and removed using the SoupX R package. Nuclei with high mitochondrial RNA contamination (>25%), low number of unique genes (nFeature_RNA > 300), and low total reads (nCount_RNA > 500) were removed, and doublets were identified and removed using the scDoubletFinder package. The data was log normalized using NormalizeData(), and variable features were identified using FindVariableFeatures(). The data was scaled using ScaleData() before principal component analysis using RunPCA(). Single nuclei underwent marker-based classification using scSorter 7 referencing canonical markers of pituitary cell types 5 , 8 . Cell-based classification was validated by manually comparing predictions from scSorter with clusters identified by uniform manifold approximation and projection (UMAP) and unsupervised clustering functions 14: RunUMAP (dims=1:30), FindNeighbors (dims = 1:30), FindClusters (resolution = 0.5). Differentially expressed genes (DEGs) were identified for each cell class using the Seurat FindAllMarkers() function with the default settings (Log2-fold change >0.25, and gene must be expressed in >25% of cells in that cluster). Individual datasets were integrated using BBKNN 9 in Python to remove batch effects while preserving biological variation. Gene signatures across canonical cell types in the integrated object were identified by detecting DEGs with the scanpy rank_genes_groups() function in Python. DEGs were filtered based on a log2 fold-change greater than 3 and expression in more than 25% of cells within the corresponding cell type. Additionally, DEGs between tumor core and adjacent margin corticotrophs were identified across both integrated and syngeneic samples using the scanpy rank_genes_groups() function. sn-RNAseq data was visualized using the scanpy python package. For RNA velocity and lineage tracing analysis, the python package scvelo 10 was used to calculate ratios of spliced to unspliced RNA and RNA velocities using the unfiltered expression matrix from 10x CellRanger software, and ratios of spliced to unspliced mRNA calculated from velocyto. The Cellrank 11 python package was used to compute a transition matrix, initial and terminal states, cell lineage fate probabilities, and lineage specific gene drivers. For differential chromatin accessibility analysis, ATACseq reads and fragments associated with corticotrophs were extracted from each sample’s .bam and fragments file. Peaks unique to corticotrophs were identified by MACS3 12 . Count matrices of peaks and gene regions (TSS-5000bp to TES+5000bp) were generated from each sample’s fragment file using a combination of standard python data processing libraries and the Bedtools Tabix function 13 . The python implementation of DESeq2 14 , 15 was used to identify differentially accessible peaks and gene regions, and comparisons were made between integrated tumor core versus adjacent margin samples. Homer was used to identify enriched transcription factor binding motifs. DeepTools 16 and pyCircos (v0.3.0) were used for visualization. All code used for analysis and visualization is available on github. DNA methylation 5 μm sections were cut from formalin-fixed paraffin-embedded surgically derived specimens from Cushing’s disease adenomas. Autopsy-derived human pituitary gland sections were used as controls as previously published 5 . DNA was isolated and purified (Qiagen DNeasy #69556). After sodium bisulfite conversion using the Zymogen EZ DNA Methylation kit (#D5001), 1ug each of methylated and unmethylated DNA was analyzed using the Illumina Infinium MethylationEPIC chip (#20087706). Samples were randomly assigned to plates. Signal intensities were retrieved, and poorly performing probes were removed (P value > 0.01 for > 1% CpGs, ch probes, normal rs probes, CpGs on sex chromosomes, SNP probes, cross-reactive probes, invariable probes). Data was processed and analyzed using the MissMethyl R package. Beta values were calculated for each probe and adjusted for cell type composition 17 . Probes associated with genes of interest were identified and compared across samples. P values were adjusted for multiple comparisons using false discovery rate analysis with Benjamini-Hockberg statistics. Promoter-specific DNA methylation was assessed after bisulfite conversion using custom primers (MethPrimer.com) specific to methylated versus unmethylated PPP1R17 promoter. Samples used were a subset of the samples used for the DNA methylation assay. For the PPP1R17 promoter, 2uL of eluted DNA was used for each PCR reaction, with methylated primer forward sequence 5’ – 3’ TACGTTTTTTTTGTTTTTCGTTTTC, reverse ATAACTTTTTCTACTATCCACTCGAC. The unmethylated primer forward sequence was TGTTTTTTTTGTTTTTTGTTTTTGT, reverse ATAACTTTTTCTACTATCCACTCAAC. qRT-PCR was performed using the Sso Universal IT SYBR green supermix (BioRad #1725271). Beta values were quantified using the ratio of methylated / (methylated + unmethylated) expression. Bulk RNAseq Total RNA was extracted from stable cell lines using the RNeasy Mini Kit (Qiagen, #74004). RNA quantity and quality were measured by NanoDrop ND-1000, and integrity assessed by agarose gel electrophoresis. PolyA+ mRNA isolation, size fragmentation, cDNA synthesis, size selection, and next gen sequencing were performed at the National Intramural Sequencing Center, Bethesda, MD according to their standard protocols, as described previously 5 . RNA-Seq libraries were constructed from 1 μg total RNA after rRNA depletion using Ribo-Zero GOLD (Illumina). The Illumina TruSeq RNA Sample Prep V2 Kit was used according to manufacturer’s instructions. Paired-end sequence files (.fastq) per sample were quality inspected using the FastQC tool 0.11.8 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) then adaptor clipped (TruSeq3-PE-2.fa:2:30:10) and trimmed to remove 5’ nucleotide bias (HEADCROP:12) and low quality calls (TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:15) using the Trimmomatic tool 0.39 ( http://www.usadellab.org/cms/?page=trimmomatic ). Surviving intact pairs of reads per sample were then imported into the CLCbio Genomics Workbench v11 ( https://www.qiagenbioinformatics.com/ ) and reference mapped by sample in stranded fashion against GRCh38 using the "RNA-Seq Analysis" tool supported therein under default parameters. Expression in counts per known annotated gene (Homo_sapiens.GRCh38.96.chr.gff3) were then exported from the tool. Analysis of bulk RNA sequencing data was performed using the EdgeR package on total exon counts per gene. A paired analysis was performed with GLM-based approach with design matrix including sample type. Genes were filtered by expression with filterByExpr, dispersion estimated with estimateDisp, and the GLM fit with glmFit. Then, the log ratio test was used to detect differences between core and margin using glmLRT. Reverse chromatin immunoprecipitation (rChIP) rChIP was performed by modifying a previously published protocol 18 . Surgical tissues were fragmented into 2-4 mm 3 pieces and cross-linked using 1% paraformaldehyde (Sigma #F8775) for 10 minutes, followed by glycine quench for 5 minutes at room temperature. ChIP was performed using an EZ-Magna ChIP kit (Millipore Sigma #17-10086) following manufacturer’s instructions. Sonication was optimized for each cell or tissue type, and fragment sizes ∼ 200kb were verified using gel electrophoresis. Both cells and fragmented surgical tissues were sonicated on wet ice using 10 cycles of 10 seconds on, 50 seconds off using 75% power (Qsonica #Q125-110). Immunoprecipitation was performed using dCas9-3XFLAG-Biotin Protein (Millipore Sigma #DCAS9PROT-50UG) and synthetic guide RNAs specific to the PPP1R17 promoter (Invitrogen TrueGuide #A35533) overnight at 4 degrees on a rotating rocker using Anti-FLAG M2 Magnetic Beads (Millipore Sigma #M8823). Negative controls included the dCas9-3XFLAG-Biotin Protein, however the synthetic guide RNAs for promoter-specific targeting were excluded. Purification and reversal of cross linking was performed using an EZ-Magna ChIP kit (Millipore Sigma #17-10086). To validate locus-specific pulldown, an aliquot of the eluent was treated with proteinase K (Millipore #20-298) and PCR was run using primers specific to the PPP1R17 promoter. Protein fragments were then purified and treated with RNAse A (Millipore #20-297) and quantified using Mass spectrometry (SimulTOF 300). Proteomic and phospho-proteomic profiling Human tumors and their adjacent normal pituitary glands were fragmented into 2-4 mm 3 pieces. Tumor samples or cultured cells were lysed using 1x Ripa buffer with 5% SDS and protease inhibitor (Thermo Fisher #89900) on ice for 10 minutes with frequent agitation, followed by centrifugation at 14,000 rpm for 5 minutes. Protein concentration was measured from the supernatant using a Pierce BCA Protein Assay Kit (Thermo Fisher #23225). Multiple batches of samples were analyzed. Samples were alkylated with NEM, digested with trypsin and labeled with TMT 6plex, TMT 11plex or TMTpro reagents. For each set of experiments, samples were combined together after labeling and quenching. 95% of the combined sample was used for phosphopeptide enrichment using High-Select™ TiO2 Phosphopeptide Enrichment Kit and High-Select™ Fe-NTA Phosphopeptide Enrichment Kit (Thermo Fisher). 5% of the combined sample was fractionated using Pierce high pH reverse phase cartridge. 8 to 10 fractions were collected for each set. LC-MS/MS data acquisition was performed on a Thermo Scientific Orbitrap Lumos mass spectrometer which was coupled to a Thermo Scientific Ultimate 3000 HPLC. For each run, peptides were separated on an ES902 nano-column over 180 min at a flow rate of 300 n/min. TMT MS2 method was used. Both MS1 and MS2 scans were performed in orbitrap. The resolution for MS1 and MS2 scans were 120 K and 50 K, respectively. Peptides were fragmented using the HCD method with collision energy fixed at 38% for samples labeled with TMTpro and 33% for samples labeled with TMT 6plex and 11plex reagents. The precursor isolation window was 1.2 Da. Proteome Discoverer 2.4 was used for database search and TMT quantification. Raw abundance values of each sample were generated and analyzed, then compared using paired T-tests for CD samples and two-sample T-tests for mCort samples, with FDR correction for multiple testing as appropriate. Pairwise analysis of total proteins and phosphopeptide/total protein ratios were computed for each tumor-normal pair and compared using two-sample tests of proportions with FDR corrections for multiple testing as appropriate. Proteins hyperphosphorylated after stable PPP1R17 overexpression and rescued using fingolimod were identified using one-way ANOVA. All code used for analysis and visualization is available on github. Phosphoprotein motif analysis was performed using the MEME suite motif-based sequence analysis tool 19 . Statistically significant post-translational modification (PTM) motifs were identified using the MoMo suite to implement the most widely used PTM algorithms motif-x and MoDL. MoMo was implemented using the web server ( http://meme-suite.org ) with n = 2, eval = 0.05. Multiplex immunocytochemistry 5 µm-thick paraffin-embedded formalin-fixed human sections were deparaffinized and treated using a standard antigen unmasking step in 10 mM Tris/EDTA buffer pH 9.0. Sections were then blocked with Human BD Fc Blocking solution (BD Biosciences #564219) and treated with TrueBlack Reagent (Biotium #23014-T) to quench intrinsic tissue autofluorescence. The sections were then immunoreacted for 1 hour at RT using 1 µg/ml cocktail mixture of immunocompatible antibodies. Primary antibodies were either directly conjugated or indirectly labelled with secondary antibodies using the following spectrally compatible fluorophores: Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 546, Alexa Fluor 594, Alexa Fluor 647, IRDye 800CW. After washing off excess antibodies, sections were counterstained using 1 µg/ml DAPI (Thermo Fisher Scientific #D1306) for visualization of cell nuclei. Slides were then mounted using Immu-Mount medium (Thermo Fisher Scientific #FIS9990402) and imaged using an Axio Imager.Z2 slide scanning epifluorescence microscope (Zeiss) equipped with a 20X/0.8 Plan-Apochromat (Phase-2) non-immersion objective (Zeiss), a high-resolution ORCA-Flash4.0 sCMOS digital camera (Hamamatsu), a 200W X-Cite 200DC broad band lamp source (Excelitas Technologies), and 7 customized filter sets (Semrock). Image tiles (600 x 600 µm viewing area) were individually captured at 0.325 micron/pixel spatial resolution, and tiles were seamlessly stitched into whole specimen images using the ZEN 2 image acquisition and analysis software program (Zeiss). Pseudocolored stitched images were exported to Adobe Photoshop and overlaid as individual layers to create multicolored merged composites. Immunohistochemistry Surgically derived tissue human specimens were fixed in formalin and embedded in paraffin. Tissue blocks were sectioned (5 μm) and stained with an automated immunostainer (Bond-Max, Leica). Tissues were probed using PPP1R17 antibodies (Novus # NBP2-13800) after being validated with positive control tissue samples and a high pH (EDTA) epitope retrieval. Digital IHC images were converted to grayscale and the auto-threshold function was used to delineate antibody positive tissue. For mouse tissues, chromogen staining was performed for avidin–biotin horseradish peroxidase (HRP)/3,3’-diaminobenzidine (DAB) detection using standard procedures. Sections were postfixed in 4% formaldehyde (15min), rinsed in PBS and treated with 0.3% H2O2/30% methanol for endogenous peroxidase suppression (10 min). Antigen retrieval was performed by incubating sections in citrate buffer at 90°C (water bath, 1h). Sections were then washed in PBS, blocked with 10% normal horse serum/0.3% triton X-100, washed (PBS) and incubated in PPP1R17 rabbit antibody (24hr, 4°C; 1:500, LSBio LS-C817026). The following day, sections were washed (PBS), incubated in donkey anti-rabbit biotinylated secondary (1hr, 1:500 Jackson ImmunoResearch #AB-2340595), washed in PBS, incubated in avidin-biotin-HRP complex (1h, Vector Laboratory kit #PK-6100) and reacted with Nickel DAB/glucose oxidase in 0.175M Sodium Acetate buffer. After washing and air-drying, slides were coverslipped with permount (Fisher Scientific #SP15-100). Mouse slides were also treated as described above until incubation in Alexa 555 secondary donkey anti-rabbit (1h, 1:000; Invitrogen #A-31572). After secondary, the slides were washed (PBS), fixed (10min, 4% formadehyde), washed and then incubated with goat proopiomelanocortin (POMC) antibody (1:2000, 24hr, 4°C; Abcam #ab32893), and detected using Alexa Fluour 488 secondary donkey anti-goat (Invitrogen 1:1000 #A-11055). Slides were coverslipped with DAPI-containing Vectashield (Vector Laboratories #H-1000-10). Control sections in which one of the primary antibodies was replaced with bovine serum albumin (BSA) showed no detectable signal at that wavelength. Sections were imaged on a Nikon Eclipse Ni microscope (Nikon) equipped with a Retiga EXi Fast1394 camera (QImaging) or with a Nikon Eclipse TE200 spinning disk microscope (Nikon) equipped with an EMCCD ImageM digital camera (Hamamatsu), using the iVision software (BioVision). RNA extraction and quantitative real time PCR (qRT-PCR) RNA was extracted from both human and mouse cells using an RNeasy Mini Kit (Qiagen #74004) according to the manufacturer’s instructions. Complementary DNA (cDNA) libraries were constructed using SuperScript III for qRT-PCR (Invitrogen Life Technologies). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the Sso Universal IT SYBR green supermix (BioRad #1725271). Gene expression in mouse corticotroph cells was determined using primers designed for PPP1R17 : forward AGACCAGTTCATTAAGGA, reverse TGATCTGACTCAACATTC. Relative gene expression was calculated using the DDCt method with beta actin or GAPDH as housekeeping genes. Western immunoblotting Cells were washed with ice-cold PBS and whole-cell lysates were collected on ice using a radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo Scientific #89900) containing a protease inhibitor cocktail (Halt protease inhibitor, Sigma-Aldrich #539137). Whole-cell lysates were quantified using the bicinchoninic acid (BCA) protein assay (Thermo Scientific #23227). Proteins were electrophoretically separated on 4%–12% NuPAGE Bis-Tris gels (Invitrogen #NP0321BOX) and electroblotted onto polyvinylidene difluoride (PVDF) membranes with the Trans-Blot Transfer Turbo System (Bio-Rad). After blocking membranes for 1 hour in 5% bovine serum albumin (BSA) in 0.05% TBS-Tween at room temperature, they were incubated overnight at 4° C with primary antibodies. Membranes were washed with TBS-Tween and incubated with peroxidase-conjugated secondary antibody for 1 hour at room temperature while rocking. Membranes were developed with ECL (Super Signal West Pico PLUS, Thermo Fisher #34579). Immunoreactive signal was detected and imaged using the ChemiDoc MP Imaging System (Bio-Rad) with subsequent densitometric quantification performed using ImageJ v1.53e. Cell cycle analysis Cell cycle was analyzed using a Click-iT Plus EdU Alexa Fluor 647 Flow Cytometry assay kit (Thermo Fisher #C10635) following manufacturer’s instructions. Briefly, 10 mM of EdU was added to cell cultures for 2 hours. Cells were harvested using 0.25% Trypsin-EDTA (Gibco #25200-056), washed with 1% BSA in PBS and fixed and permeabilized using the Click-iT kit reagents. After further washes, the cells were incubated for 30 minutes at room temperature in 500 mL of the Click-iT Plus reaction cocktail containing Alexa Fluor 647 picolyl azide fluorescent dye, then counterstained with 1 µg/ml DAPI (Thermo Fisher Scientific #D1306). Cells were analyzed using a flow cytometer (Beckman Coulter) with 633/635 nm excitation and a red emission filter (660/20 nm) for Alexa Fluor 647, and 358 nm excitation and blue emission filter (425/475 nm) for DAPI. Proportions of cells in the S-phase were compared between samples using two-sample tests of proportions, with P < 0.05 considered statistically significant. Cell viability assays Cell cultures were seeded in 96-well plates at a density of 5,000 – 10,000 cells per well in 100uL of media and allowed to grow for 24 hours prior to treatment. Cells were treated with increasing concentrations (5μm, 10μm, 20μm, 50μm) of Fingolimod, DT-061 and ABL127 for 48 hours prior to viability assay. Prior to analysis, plates were equilibrated to room temperature and cell viability assay performed using CellTiterGlo (Promega #G7571) according to the manufacturer’s instructions. Plates were incubated for 30 minutes at room temperature prior to recording luminescence on a Synergy Neo2 microplate reader (BioTek). Proximity ligation assay (PLA) Proximity between PPP1R17 and PP2Ac was determined using a Duolink Proximity Ligation Assay (PLA) Red Mouse/Rabbit kit (Sigma #DUO92101) following the manufacturer’s instructions. 5 μm human CD adenoma sections on glass slides were rinsed with PBS, then deparaffinized using Xylene and slowly rehydrated using decreasing concentrations of 5-minute Xylene washes (100%, 95%, 70%). Slides were then permeabilized for 15 minutes using 0.1% TritonX-100 in PBS, then blocked for 1 hour in Duolink blocking solution using a 37°C humidity chamber. Slides were incubated overnight at 4°C in antibodies for PP2Ac (Millipore #MABE1783) and PPP1R17 (Novus # NBP2-13800). PLA PLUS and MINUS probes were added, then ligation was performed using the Duolink Ligase for 30 minutes in a 37°C humidity chamber. After several washes, Duolink Polymerase was added to the slides in 1x Amplification buffer and incubated for 100 minutes in a 37°C humidity chamber. Slides were mounted using Duolink in-situ mounting media with DAPI and imaged. PP2A activity assay Human PPP1R17 and PP2Ac were cloned into pET FLAG vectors, transformed using BL21 gold competent bacterial cells (Sigma) and amplified. After amplification, FLAG-tagged PPP1R17 and PP2Ac proteins were purified using a HiTrap nickel column (Sigma). PP2A activity was assessed using a Serine/Threonine Phosphatase Assay (Promega #V2460) following manufacturer’s instructions. Plates were incubated for 30 minutes at room temperature prior to quantifying absorbance of the molybdate:malachite green:phosphate complex at 630nm on a Synergy Neo2 microplate reader (BioTek). Thermal shift assay Protein thermal shift assays were completed using an Applied Biosystems QuantStudio 6 Flex real time PCR instrument with 384-well plate or a QantaBio qPCR instrument. The assay plates contained a final volume of 20 μL with PBS (10mM phosphate and 150 mM NaCl [pH 7.4]) and protein samples (purified PP2Ac) plus the binding partner PPP1R17 and protein thermal shift Dye kit (Cat# 4461146, diluted 1:125 in kit diluent). The plate was sealed and mixed briefly, followed by plate centrifugation at 1,000 RPM for 2 minutes. After a 30-minute incubation at 25°C, the plate was subjected to thermal shift by ramping the temperature from 25°C to 99°C at 0.05°C increments per second. The relative fluorescence emitted by the thermal shift dye was recorded during the temperature ramp phase and plotted versus temperature. The derivative of these thermal melt curves was determined, and Tm calculated from these data. The Tm values were plotted as a function of TDP43 concentration in GraphPad Prism, and the inflexion point of the curve determined the binding affinity of ligand to binding protein. The experiments were completed with n = 4 replicate samples per treatment. Each experiment was repeated 3 times. AlphaFold multimer AlphaFold multimer was designed specifically to predict the structures of protein complexes more accurately than AlphaFold and AlphaFold2 20 . We generated protein structure and interaction inference using the AlphaFold2 algorithm 21 . Briefly, FASTA files with the canonical protein sequence (PP2A, PPP1R17, and PME1) were analyzed on the NIH HPC cluster (BioWulf). The AlphaFold2 algorithm preferentially used existing experimental PDB structures where available. Multiple sequence alignment and model predictions were then generated for protein interaction inference and multimer models. Multimer model prediction alignment error rates, and pLDDT values were further analyzed with the alphapickle package (M. J. Arnold. 2021. AlphaPickle. doi.org/10.5281/zenodo.5708709). The top ranked multimer models were used to analyze protein interaction sites, and to generate the figure panels. Statistical analysis Means were compared using two-sample T tests after testing for variance equality and using Welch’s approximation for degrees of freedom 22 . P values < 0.05 two-tailed were considered statistically significant. Statistical analyses were performed using STATA 14/IC (StataCorp LP, College Station, Texas) or GraphPad Prism 9.0 (GraphPad Software, La Jolla, California). View this table: View inline View popup Key resources table Acknowledgements This study was supported by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke, Eunice Kennedy Shriver National Institute for Child Health, Human Development, and the National Institute of Diabetes and Digestive Kidney Diseases and National Human Genome Research Institute, Bethesda, MD, USA. References 1. ↵ van Wageningen , S. et al. Functional overlap and regulatory links shape genetic interactions between signaling pathways . Cell 143 , 991 – 1004 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 2. ↵ Smoly , I. , Shemesh , N. , Ziv-Ukelson , M. , Ben-Zvi , A. & Yeger-Lotem , E . An Asymmetrically Balanced Organization of Kinases versus Phosphatases across Eukaryotes Determines Their Distinct Impacts . PLoS Comput. Biol . 13 , e1005221 ( 2017 ). OpenUrl CrossRef PubMed 3. ↵ Qin , S. , Kitty , I. , Hao , Y. , Zhao , F. & Kim , W . Maintaining Genome Integrity: Protein Kinases and Phosphatases Orchestrate the Balancing Act of DNA Double-Strand Breaks Repair in Cancer . Int. J. Mol. Sci . 24 , 10212 ( 2023 ). 4. ↵ Boni , C. & Sorio , C . Current Views on the Interplay between Tyrosine Kinases and Phosphatases in Chronic Myeloid Leukemia . Cancers (Basel ). 13 , 2311 ( 2021 ). OpenUrl PubMed 5. ↵ Parada , C. A. et al. Kinome and phosphoproteome of high-grade meningiomas reveal AKAP12 as a central regulator of aggressiveness and its possible role in progression . Sci. Rep . 8 , 2098 ( 2018 ). OpenUrl CrossRef PubMed 6. ↵ Janssens , V . & Goris , J . Protein phosphatase 2A: a highly regulated family of serine/threonine phosphatases implicated in cell growth and signalling . Biochem. J . 353 , 417 ( 2001 ). 7. ↵ Detre , J. A. , Nairn , A. C. , Aswad , D. W. & Greengard , P. Localization in mammalian brain of G-substrate, a specific substrate for guanosine 3’,5’-cyclic monophosphate-dependent protein kinase . J. Neurosci . 4 , 2843 – 9 ( 1984 ). OpenUrl Abstract / FREE Full Text 8. ↵ Hall , K. U. et al. Phosphorylation-dependent inhibition of protein phosphatase-1 by G- substrate. A Purkinje cell substrate of the cyclic GMP-dependent protein kinase . J. Biol. Chem . 274 , 3485 – 95 ( 1999 ). OpenUrl Abstract / FREE Full Text 9. ↵ Kremmer , E. , Ohst , K. , Kiefer , J. , Brewis , N. & Walter , G . Separation of PP2A core enzyme and holoenzyme with monoclonal antibodies against the regulatory A subunit: abundant expression of both forms in cells . Mol. Cell. Biol . 17 , 1692 – 701 ( 1997 ). OpenUrl Abstract / FREE Full Text 10. ↵ Green , D. D. , Yang , S. I. & Mumby , M. C . Molecular cloning and sequence analysis of the catalytic subunit of bovine type 2A protein phosphatase . Proc. Natl. Acad. Sci. U. S. A . 84 , 4880 – 4 ( 1987 ). OpenUrl Abstract / FREE Full Text 11. ↵ Haesen , D. , Sents , W. , Lemaire , K. , Hoorne , Y. & Janssens , V . The basic biology of PP2A in hematologic cells and malignancies . Front. Oncol . 4 , 1 – 11 ( 2014 ). OpenUrl CrossRef PubMed 12. ↵ Hall , W. A. , Luciano , M. G. , Doppman , J. L. , Patronas , N. J. & Oldfield , E. H . Pituitary magnetic resonance imaging in normal human volunteers: occult adenomas in the general population . Ann. Intern. Med . 120 , 817 – 20 ( 1994 ). OpenUrl CrossRef PubMed Web of Science 13. ↵ Ezzat , S. et al. The prevalence of pituitary adenomas: A systematic review . Cancer 101 , 613 – 619 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 14. ↵ Ostrom , Q. T. et al. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017 . Neuro. Oncol . 22 , IV1–IV96 ( 2020 ). 15. ↵ Theodoropoulou , M. & Reincke , M . Predicting long-term outcomes in Cushing’s disease: so close and yet so far . J. Clin. Endocrinol. Metab . ( 2025 ) doi: 10.1210/clinem/dgaf429 . OpenUrl CrossRef 16. ↵ Oldfield , E. H . Surgical management of Cushing’s disease: a personal perspective . Clin. Neurosurg . 58 , 13 – 26 ( 2011 ). OpenUrl PubMed 17. ↵ Dallapiazza , R. F. , Oldfield , E. H. & Jane , J. A . Surgical management of Cushing’s disease . Pituitary 18 , 211 – 6 ( 2015 ). OpenUrl PubMed 18. ↵ Lonser , R. R. , Nieman , L. & Oldfield , E. H . Cushing’s disease: pathobiology, diagnosis, and management . J. Neurosurg . 126 , 404 – 417 ( 2017 ). OpenUrl CrossRef PubMed 19. ↵ Neou , M. et al. Pangenomic Classification of Pituitary Neuroendocrine Tumors . Cancer Cell 37 , 123 – 134 .e5 ( 2020 ). OpenUrl CrossRef PubMed 20. ↵ Reincke , M. et al. Mutations in the deubiquitinase gene USP8 cause Cushing’s disease . Nat. Genet . 47 , 31 – 38 ( 2014 ). OpenUrl PubMed 21. ↵ Ma , Z.-Y. et al. Recurrent gain-of-function USP8 mutations in Cushing’s disease . Cell Res . 25 , 306 – 17 ( 2015 ). OpenUrl CrossRef PubMed 22. ↵ Chen , J. et al. Identification of recurrent USP48 and BRAF mutations in Cushing’s disease . Nat. Commun . 9 , 1 – 9 ( 2018 ). OpenUrl CrossRef PubMed 23. ↵ Salomon , M. P. et al. The Epigenomic Landscape of Pituitary Adenomas Reveals Specific Alterations and Differentiates Among Acromegaly, Cushing’s Disease and Endocrine- Inactive Subtypes . Clin. Cancer Res . 24 , 4126 – 4136 ( 2018 ). OpenUrl Abstract / FREE Full Text 24. ↵ García-Martínez , A. et al. DNA Methylation of Tumor Suppressor Genes in Pituitary Neuroendocrine Tumors . J. Clin. Endocrinol. Metab . 104 , 1272 – 1282 ( 2019 ). OpenUrl PubMed 25. ↵ Amaral , F. C. et al. MicroRNAs differentially expressed in ACTH-secreting pituitary tumors . J. Clin. Endocrinol. Metab . 94 , 320 – 323 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 26. ↵ Gentilin , E. et al. MiR-26a plays an important role in cell cycle regulation in ACTH- secreting pituitary adenomas by modulating protein kinase Cλ . Endocrinology 154 , 1690 – 1700 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 27. ↵ Bilodeau , S. et al. Role of Brg1 and HDAC2 in GR trans-repression of the pituitary POMC gene and misexpression in Cushing disease . Genes Dev . 20 , 2871 – 86 ( 2006 ). OpenUrl Abstract / FREE Full Text 28. ↵ Asuzu , D. T. et al. Pituitary adenomas evade apoptosis via noxa deregulation in Cushing’s disease . Cell Rep . 40 , 111223 ( 2022 ). 29. ↵ Guo , H. & Li , J . scSorter: assigning cells to known cell types according to marker genes . Genome Biol . 22 , ( 2021 ). 30. ↵ Hao , Y. et al. Integrated analysis of multimodal single-cell data . Cell 184 , 3573 – 3587 .e29 ( 2021 ). OpenUrl CrossRef PubMed 31. ↵ Subramanian , A. & Schwartz , R . Reference-free inference of tumor phylogenies from single-cell sequencing data . BMC Genomics 16 Suppl 1 , S7 ( 2015 ). 32. ↵ Perez , R. K. et al. Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus . Science (80-.). 376 , ( 2022 ). 33. ↵ Lange , M. et al. CellRank for directed single-cell fate mapping . Nat. Methods 19 , 159 – 170 ( 2022 ). OpenUrl CrossRef PubMed 34. ↵ Endo , S. G-substrate: The cerebellum and beyond . Progress in Molecular Biology and Translational Science vol. 106 (Elsevier Inc., 2012 ). 35. ↵ O’Connor , C. M. , Perl , A. , Leonard , D. , Sangodkar , J. & Narla , G . Therapeutic targeting of PP2A . Int. J. Biochem. Cell Biol . 96 , 182 – 193 ( 2018 ). OpenUrl CrossRef PubMed 36. ↵ Zhu , Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development . Science (80-.). 362 , ( 2018 ). 37. ↵ Hall , K. U. et al. Phosphorylation-dependent inhibition of protein phosphatase-1 by G- substrate: A purkinje cell substrate of the cyclic GMP-dependent protein kinase . J. Biol. Chem . 274 , 3485 – 3495 ( 1999 ). OpenUrl Abstract / FREE Full Text 38. ↵ Oldfield , A. J. et al. NF-Y controls fidelity of transcription initiation at gene promoters through maintenance of the nucleosome-depleted region . Nat. Commun . 10 , 3072 ( 2019 ). OpenUrl CrossRef PubMed 39. ↵ Oldfield , A. J. et al. Histone-Fold Domain Protein NF-Y Promotes Chromatin Accessibility for Cell Type-Specific Master Transcription Factors . Mol. Cell 55 , 708 – 722 ( 2014 ). OpenUrl CrossRef PubMed 40. ↵ Deniaud , E. et al. Overexpression of Transcription Factor Sp1 Leads to Gene Expression Perturbations and Cell Cycle Inhibition . PLoS One 4 , e7035 ( 2009 ). OpenUrl CrossRef PubMed 41. ↵ Zhang , R. et al. MAP7 interacts with RC3H1 and cooperatively regulate cell-cycle progression of cervical cancer cells via activating the NF-κB signaling . Biochem. Biophys. Res. Commun . 527 , 56 – 63 ( 2020 ). OpenUrl CrossRef PubMed 42. ↵ Liu , X. et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9 . Cell 170 , 1028 – 1043 .e19 ( 2017 ). OpenUrl CrossRef PubMed 43. ↵ Sacco , F. et al. A subset of RAB proteins modulates PP2A phosphatase activity . Sci. Rep . 6 , 32857 ( 2016 ). 44. ↵ Li , M. , Makkinje , A. & Damuni , Z . The Myeloid Leukemia-associated Protein SET Is a Potent Inhibitor of Protein Phosphatase 2A . J. Biol. Chem . 271 , 11059 – 11062 ( 1996 ). OpenUrl Abstract / FREE Full Text 45. ↵ Perrotti , D. & Neviani , P . Protein phosphatase 2A: A target for anticancer therapy . Lancet Oncol . 14 , e229 – e238 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 46. ↵ Junttila , M. R. et al. CIP2A Inhibits PP2A in Human Malignancies . Cell 130 , 51 – 62 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 47. ↵ Teufel , F. et al. Deorphanizing Peptides Using Structure Prediction . J. Chem. Inf. Model . 63 , 2651 – 2655 ( 2023 ). OpenUrl CrossRef PubMed 48. ↵ Gordon , C. H. , Hendrix , E. , He , Y. & Walker , M. C . AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes . Biomolecules 13 , 1243 ( 2023 ). OpenUrl PubMed 49. ↵ Cheng , A. , Grant , C. E. , Noble , W. S. & Bailey , T. L . MoMo: discovery of statistically significant post-translational modification motifs . Bioinformatics 35 , 2774 – 2782 ( 2019 ). OpenUrl CrossRef PubMed 50. ↵ Bailey , T. L. , Johnson , J. , Grant , C. E. & Noble , W. S. The MEME Suite . Nucleic Acids Res . 43 , W39 – 49 ( 2015 ). OpenUrl CrossRef PubMed 51. ↵ Furth , J ., Gadsen , E. L. & Upton , A. C. ACTH secreting transplantable pituitary tumors . Proc. Soc. Exp. Biol. Med . 84 , 253 – 4 ( 1953 ). OpenUrl CrossRef PubMed 52. ↵ Buonassisi , V ., Sato , G . & Cohen , A . I. Hormone-producing cultures of adrenal and pituitary tumor origin . Proc. Natl. Acad. Sci. U. S. A . 48 , 1184 – 90 ( 1962 ). OpenUrl FREE Full Text 53. ↵ Zheng , H. et al. Identification of Integrator-PP2A complex (INTAC), an RNA polymerase II phosphatase . Science (80-.). 370 , eabb5872 ( 2020 ). 54. ↵ Abusdal , M. et al. PCSK1N as a tumor size marker and an ER stress response protein in corticotroph pituitary adenomas . J. Clin. Endocrinol. Metab . ( 2024 ) doi: 10.1210/clinem/dgae643 . OpenUrl CrossRef 55. ↵ Cristóbal , I. et al. PP2A inhibition is a common event in colorectal cancer and its restoration using FTY720 shows promising therapeutic potential . Mol. Cancer Ther . 13 , 938 – 47 ( 2014 ). OpenUrl Abstract / FREE Full Text 56. Liu , C.-Y. et al. Targeting SET to restore PP2A activity disrupts an oncogenic CIP2A- feedforward loop and impairs triple negative breast cancer progression . EBioMedicine 40 , 263 – 275 ( 2019 ). OpenUrl PubMed 57. ↵ Vervoort , S. J. et al. The PP2A-Integrator-CDK9 axis fine-tunes transcription and can be targeted therapeutically in cancer . Cell 184 , 3143 – 3162 .e32 ( 2021 ). OpenUrl CrossRef PubMed 58. ↵ Groussin , L. et al. Mutations of the PRKAR1A gene in Cushing’s syndrome due to sporadic primary pigmented nodular adrenocortical disease . J. Clin. Endocrinol. Metab . 87 , 4324 – 9 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 59. ↵ Butera , A. , Melino , G. & Amelio , I . Epigenetic “Drivers” of Cancer . J. Mol. Biol . 433 , 167094 ( 2021 ). 60. ↵ Reincke , M. et al. Mutations in the deubiquitinase gene USP8 cause Cushing’s disease . Nat. Genet . 47 , 31 – 38 ( 2015 ). OpenUrl CrossRef PubMed 61. ↵ Tian , C. et al. Impaired histone inheritance promotes tumor progression . Nat. Commun . 14 , 3429 ( 2023 ). OpenUrl CrossRef PubMed 62. ↵ Endo , S. et al. Molecular identification of human G-substrate, a possible downstream component of the cGMP-dependent protein kinase cascade in cerebellar Purkinje cells . Proc. Natl. Acad. Sci. U. S. A . 96 , 2467 – 2472 ( 1999 ). OpenUrl Abstract / FREE Full Text 63. ↵ Noctor , S. C. , Martínez-Cerdeño , V. , Ivic , L. & Kriegstein , A. R . Cortical neurons arise in symmetric and asymmetric division zones and migrate through specific phases . Nat. Neurosci . 7 , 136 – 144 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 64. Pollen , A. A. et al. Molecular Identity of Human Outer Radial Glia during Cortical Development . Cell 163 , 55 – 67 ( 2015 ). OpenUrl CrossRef PubMed 65. ↵ Zhong , S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex . Nature 555 , 524 – 528 ( 2018 ). OpenUrl CrossRef PubMed 66. ↵ Girskis , K. M. et al. Rewiring of human neurodevelopmental gene regulatory programs by human accelerated regions . Neuron 109 , 3239 – 3251 .e7 ( 2021 ). OpenUrl CrossRef PubMed 67. ↵ Chelladurai , P. et al. Epigenetic reactivation of transcriptional programs orchestrating fetal lung development in human pulmonary hypertension . Sci. Transl. Med . 14 , ( 2022 ). 68. ↵ Neviani , P. et al. The tumor suppressor PP2A is functionally inactivated in blast crisis CML through the inhibitory activity of the BCR/ABL-regulated SET protein . Cancer Cell 8 , 355 – 368 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 69. Liu , H. et al. Overexpression of PP2A inhibitor SET oncoprotein is associated with tumor progression and poor prognosis in human non-small cell lung cancer . Oncotarget 6 , 14913 – 14925 ( 2015 ). OpenUrl CrossRef PubMed 70. Cristóbal , I. et al. Overexpression of SET is a recurrent event associated with poor outcome and contributes to protein phosphatase 2A inhibition in acute myeloid leukemia . Haematologica 97 , 543 – 550 ( 2012 ). OpenUrl Abstract / FREE Full Text 71. ↵ Christensen , D. J. et al. SET oncoprotein overexpression in B-cell chronic lymphocytic leukemia and non-Hodgkin lymphoma: a predictor of aggressive disease and a new treatment target . Blood 118 , 4150 – 4158 ( 2011 ). OpenUrl Abstract / FREE Full Text 72. ↵ Böckelman , C. et al. CIP2A overexpression is associated with c-Myc expression in colorectal cancer . Cancer Biol. Ther . 13 , 289 – 295 ( 2012 ). OpenUrl CrossRef PubMed 73. Böckelman , C. et al. Prognostic role of CIP2A expression in serous ovarian cancer . Br. J. Cancer 105 , 989 – 995 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 74. Xu , P. , Xu , X.-L. , Huang , Q. , Zhang , Z.-H. & Zhang , Y.-B . CIP2A with survivin protein expressions in human non-small-cell lung cancer correlates with prognosis . Med. Oncol . 29 , 1643 – 1647 ( 2012 ). OpenUrl CrossRef PubMed 75. Lucas , C. M. et al. Cancerous inhibitor of PP2A (CIP2A) at diagnosis of chronic myeloid leukemia is a critical determinant of disease progression . Blood 117 , 6660 – 6668 ( 2011 ). OpenUrl Abstract / FREE Full Text 76. ↵ Lin , Y.-C. , Chen , K.-C. , Chen , C.-C. , Cheng , A.-L. & Chen , K.-F . CIP2A-mediated Akt activation plays a role in bortezomib-induced apoptosis in head and neck squamous cell carcinoma cells . Oral Oncol . 48 , 585 – 593 ( 2012 ). OpenUrl CrossRef PubMed 77. ↵ Kauko , O. et al. PP2A inhibition is a druggable MEK inhibitor resistance mechanism in KRAS-mutant lung cancer cells . Sci. Transl. Med . 10 , ( 2018 ). 78. ↵ Leonard , D. et al. Selective PP2A Enhancement through Biased Heterotrimer Stabilization . Cell 181 , 688 – 701 .e16 ( 2020 ). OpenUrl CrossRef PubMed 79. ↵ Huang , K.-L. et al. Integrator Recruits Protein Phosphatase 2A to Prevent Pause Release and Facilitate Transcription Termination . Mol. Cell 80 , 345 – 358 .e9 ( 2020 ). OpenUrl CrossRef PubMed 80. ↵ Beuschlein , F. et al. Constitutive activation of PKA catalytic subunit in adrenal Cushing’s syndrome . N. Engl. J. Med. 370 , 1019 – 28 ( 2014 ). OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted September 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. You are going to email the following Phosphoproteomic dysregulation drives tumor proliferation in Cushing’s disease Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Phosphoproteomic dysregulation drives tumor proliferation in Cushing’s disease David T. Asuzu , Dustin Mullaney , Debjani Mandal , Diana Nwokoye , Sheelu Varghese , Daniela Tortoza Lopez , Dhruval Bhatt , Kory Johnson , Abdel Elkahloun , Zied Abdullaev , Kenneth Aldape , Dragan Maric , Clarisse Quignon , Nasir S. Malik , Joseph P. Steiner , Yan Li , Susan Wray , Lynnette K Nieman , Christina Tatsi , Prashant Chittiboina bioRxiv 2025.09.28.679056; doi: https://doi.org/10.1101/2025.09.28.679056 Share This Article: Copy Citation Tools Phosphoproteomic dysregulation drives tumor proliferation in Cushing’s disease David T. Asuzu , Dustin Mullaney , Debjani Mandal , Diana Nwokoye , Sheelu Varghese , Daniela Tortoza Lopez , Dhruval Bhatt , Kory Johnson , Abdel Elkahloun , Zied Abdullaev , Kenneth Aldape , Dragan Maric , Clarisse Quignon , Nasir S. Malik , Joseph P. Steiner , Yan Li , Susan Wray , Lynnette K Nieman , Christina Tatsi , Prashant Chittiboina bioRxiv 2025.09.28.679056; doi: https://doi.org/10.1101/2025.09.28.679056 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 Cancer Biology Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17637) Bioengineering (13864) Bioinformatics (41853) Biophysics (21403) Cancer Biology (18540) Cell Biology (25429) Clinical Trials (138) Developmental Biology (13356) Ecology (19862) Epidemiology (2067) Evolutionary Biology (24287) Genetics (15585) Genomics (22464) Immunology (17701) Microbiology (40300) Molecular Biology (17142) Neuroscience (88440) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4814) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9809) Zoology (2268)