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Neuro-Oncology24(7), 1126–1139, 2022 | https://doi.org/10.1093/neuonc/noac050 | Advance Access date 25 February 2022

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© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

Detection of tumor-specific DNA methylation markers in the blood of patients with pituitary neuroendocrine tumors

Grayson A. Herrgott†, Karam P. Asmaro†, Michael Wells†, Thais S. Sabedot, Tathiane M. Malta, Maritza S. Mosella, Kevin Nelson, Lisa Scarpace, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Warren R. Selman, Ana C. deCarvalho, Laila M. Poisson, Abir Mukherjee, Adam M. Robin , Ian Y. Lee , James Snyder, Tobias Walbert , Mark Rosenblum, Tom Mikkelsen, Arti Bhan, John Craig , Steven Kalkanis, Jack Rock, Houtan Noushmehr‡, , and Ana Valeria Castro‡,

Department of Neurosurgery, Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan, USA (G.A.H., K.P.A., M.W., T.S.S., T.M.M., M.S.M., K.N., L.S., A.C.C., A.M.R., I.Y.L., J.S., T.W., M.R., T.M., S.K., J.R., H.N., A.V.C.); Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA (G.A.H., K.P.A., M.W., T.S.S., T.M.M., M.S.M., J.S., H.N., A.V.C.); Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA (J.S.B.); Department of Neurological Surgery, University Hospitals of Cleveland, Cleveland, Ohio, USA (A.E.S., W.R.S.); Case Comprehensive Cancer Center, Cleveland, Ohio, USA (A.E.S.); Department of Biostatistics, Henry Ford Health System, Detroit, Michigan, USA (L.M.P.), Department of Pathology, Henry Ford Health System, Detroit, Michigan, USA (A.M.), Department of Endocrinology, Henry Ford Health System, Detroit, Michigan, USA (A.B.); Department of Otolaryngology, Skull Base, Pituitary and Endoscopy Center, Henry Ford Health System, Detroit, Michigan, USA (J.C.)

†These authors are co-first authors.‡These authors are co-senior authors.Moved to the National Cancer Institute, Bethesda, Maryland, USA (this work was completed while at Case Western Reserve University School of Medicine, Cleveland, Ohio, USA) (J.S.B.)

Corresponding Author: Ana Valeria Castro, MD, PhD, Department of Neurosurgery, Hermelin Brain Tumor Center, Omics Laboratory, Henry Ford Health System, 2799 West Grand Blvd, E&R 3096, Detroit, MI 48202, USA ([email protected]; [email protected]).

AbstractBackground. DNA methylation abnormalities are pervasive in pituitary neuroendocrine tumors (PitNETs). The fea-sibility to detect methylome alterations in circulating cell-free DNA (cfDNA) has been reported for several central nervous system (CNS) tumors but not across PitNETs. The aim of the study was to use the liquid biopsy (LB) ap-proach to detect PitNET-specific methylation signatures to differentiate these tumors from other sellar diseases.Methods. We profiled the cfDNA methylome (EPIC array) of 59 serum and 41 plasma LB specimens from pa-tients with PitNETs and other CNS diseases (sellar tumors and other pituitary non-neoplastic diseases, lower-grade gliomas, and skull-base meningiomas) or nontumor conditions, grouped as non-PitNET.Results. Our results indicated that despite quantitative and qualitative differences between serum and plasma cfDNA composition, both sources of LB showed that patients with PitNETs presented a distinct methylome land-scape compared to non-PitNETs. In addition, LB methylomes captured epigenetic features reported in PitNET tissue and provided information about cell-type composition. Using LB-derived PitNETs-specific signatures as input to develop machine-learning predictive models, we generated scores that distinguished PitNETs from non-PitNETs conditions, including sellar tumor and non-neoplastic pituitary diseases, with accuracies above ~93% in independent cohort sets.Conclusions. Our results underpin the potential application of methylation-based LB profiling as a noninvasive approach to identify clinically relevant epigenetic markers to diagnose and potentially impact the prognostication and management of patients with PitNETs.

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1127Herrgott et al. Methylation-based liquid biopsy in PitNETsN

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Key Points

• PitNETs release biologically and clinically relevant DNA methylation markers in the circulation that are detectable in serum and plasma.

• Machine-learning predictive models using serum- and plasma-derived tumor-specific methylation markers distinguish PitNETs from other pituitary and CNS diseases.

Pituitary neuroendocrine tumors (PitNETs) comprise the second most common neoplasm of the central nervous system (CNS) (~17%).1,2 Stratified by endocrine status these tumors are classified as functioning and nonfunctioning (or silent) pituitary tumor subtypes (46%-64% and 36%-54% frequency, respectively).3 The diagnosis and classification of these tumors are based on a variety of workup proced-ures, including blood hormonal profiling, imaging and immunostaining for adenohypophyseal hormones (eg, pro-lactin, growth hormone, etc.), and cell lineage-deriving tran-scription factors (eg, mainly SF1, PIT1, TPIT),4 which requires surgical removal of the tumor. In rare cases, the preopera-tive differential diagnosis of silent PitNETs from other pitu-itary diseases (OPD) may be challenging via imaging alone (eg, PitNET vs histiocytosis, supra-sellar meningiomas, low-grade gliomas or pituicytomas, etc.).5–7 Therefore, the differ-ential diagnosis of these controversial cases could benefit from other noninvasive presurgical assessment methods to assure the most appropriate management.

Liquid biopsy (LB) is a method used to detect molecular elements, such as circulating cell-free DNA (cfDNA), shed by tumors into biofluids (eg, blood, cerebrospinal fluid, etc.). Despite the shielding effect of the blood-brain bar-rier (BBB), the feasibility to detect cfDNA, particularly the tumor fraction, in the bloodstream of patients has been reported in many CNS neoplasms, including PitNETs.8–12 For instance, a recent study has demonstrated the ability to detect somatic gene variants using plasma cfDNA in PitNETs, despite the rarity of somatic mutations in these tumors.12 In contrast, genome-wide methylation abnor-malities are pervasive across PitNET tissue13–18 and yet have not been reported via the profiling of blood-derived cfDNA. Methylation patterns are tissue- and tumor-specific providing opportunity to infer the origin and differen-tiate tumor types.10,11,16,19–21 Indeed, studies involving CNS

tumor tissue and LB specimens have shown that specific methylome signatures distinguish PitNETs from other CNS tumors16 or from non-neoplastic tissue22 and are useful for diagnostic, prognostic, and predictive applications in CNS tumors.11,16,23–25 Here we aimed to differentiate PitNETs from OPD through analysis of LB specimens which has not been reported in these tumors. We found that both serum and plasma cfDNA methylomes were useful and comple-mentary in providing molecular information specific to PitNETs which also recapitulated tumor tissue findings. By combining PitNET-specific epigenetic signatures de-rived from both serum and plasma sources, we were able to develop machine-learning predictive models that differ-entiated PitNETs from other pituitary or CNS diseases and conditions with high accuracy and reliability.

Our results pave the way for the potential clinical appli-cation of liquid biopsy as a noninvasive approach to iden-tify relevant epigenetic markers and to shift paradigms in the differential diagnosis and management of PitNETs.

Methods

Patients

We conducted an analysis of methylome data from a cohort composed of archival serum or plasma samples collected from patients who underwent transsphenoidal surgery for the resection of sellar masses at: (1) the Henry Ford Health System (HFHS), Detroit, Michigan—PitNETs (n = 37); OPD (craniopharyngiomas [n = 4 serum, 4 matching plasma, 1 additional duplicate plasma], pituicytomas [n = 1 serum, 1 matching plasma], histiocytosis [n = 1 serum], cysts [n = 1 serum, 1 plasma], chordoma [n = 1 serum]) and other CNS

Importance of the Study

This study encompasses the analysis of circulating cell-free (cf) DNA methylome in the serum and plasma from patients with pituitary diseases (PitNETs and other sellar conditions), other CNS diseases or conditions, and controls. Our results contain 2 novel elements: (1) the discovery of biologically and clinically rele-vant PitNET-specific methylation signatures suitable to develop machine-learning prediction models to dis-tinguish PitNETs from other CNS tumors or conditions

with an accuracy above ~93%, and (2) both serum and plasma cfDNA methylome are useful and, possibly, complementary to providing molecular information specific to PitNETs. Finally, profiling cfDNA in liquid bi-opsy specimens could complement imaging to differ-entiate challenging cases of PitNETs, particularly the nonfunctioning subtype, from other sellar tumors and diseases, using presurgical noninvasive techniques which could, ultimately, optimize therapeutic plans.

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1128 Herrgott et al. Methylation-based liquid biopsy in PitNETs

diseases (OCD, lower-grade gliomas [n = 18], skull-base meningiomas [n = 16]; paired PitNET serum and tissue were available for 13 patients; and (2) Case Western Reserve University/University Hospitals of Cleveland, Cleveland, Ohio (CWRU/UH, plasma, n = 24, PitNETs). As controls, we profiled serum derived from patients with other CNS non-neoplastic diseases (HFHS, n = 7) and plasma from 4 healthy donors were used as controls (publicly available source, n = 4)26 (cohort described in detail in Table 1). The project was approved by the Institutional Review Board of each Institution (HFHS IRB# 10963; University Hospitals IRB # CC296 (CASE 1307)) and patients consented to have their specimens used for research purposes. For tissue, we have previously compiled methylome data from the non-neoplastic pituitary gland (n = 15) and PitNET speci-mens available in public repositories (n = 164) and gener-ated at the Hermelin Brain Tumor Center (HBTC) (n = 13), namely the Panpit cohort (n = 179)22; additionally, we pro-filed the tissue of OPD (n = 9, craniopharyngiomas, Rathke cleft cyst, rhabdoid tumor, and histiocytosis). We also har-nessed tissue methylome data of lower-grade gliomas (n = 100) and skull-base meningiomas (n = 65) from pub-licly available data (https://www.cancer.gov/tcga) or gener-ated in house9,22 and paired methylome and transcriptome data from PitNET, namely the Neou cohort.18 We used TCGAbiolinks, an open access R/Bioconductor package for integrative analysis of included TCGA data.27

Serum and Plasma Collection and Processing

Peripheral blood (15 mL) was drawn from each subject at the time of surgical procedure before the tumor excision. Plasma and serum were obtained within 1 hour from the collection (details in Supplementary Methods).

DNA Isolation, Quantification, Quality Control, and DNA Methylation Data Generation

Extracted cfDNA or DNA from serum/plasma and tissue samples, respectively, were profiled using an Illumina Human EPIC array (HM850K) as described in our pre-vious manuscript.9 Data quality assessment of the LB methylome data was assessed with shinyMethyl (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4176427/) (details in Supplementary Methods). Methylation array data were processed with the minfi package in R as previously de-scribed.28 Before analysis, we removed probes with missing values and any masked probes, as provided through com-prehensive characterizations conducted by Zhou et al29 (de-tails in Supplementary Methods). Prior to analysis involving methylomes profiled through 850K and 450K Illumina plat-forms, we aligned all serum/plasma and tissue methylomes from serum/plasma or tissue to identify common probes be-tween the arrays (n = 393K common probes).

DNA Methylation Exploratory Analysis

Unsupervised analysis. We generated a three-dimen-sional (3D) genome-wide principal component analysis (PCA) of the mean methylome levels across all serum and plasma samples from patients with distinct tumor types

and non-neoplastic brain diseases, using the function prcomp (version 3.6.0). We also compared the 1K most variably methylated probes across serum and plasma samples (nserum = 59 and nplasma = 41 samples). CpGs with the highest variance across multiple cohorts and PitNET similarity (tissue and LB) were used for an unsupervised clustering through t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction for visualiza-tion (details in Supplementary Methods).

Supervised analysis. In order to identify serum or plasma-derived PitNET-specific differentially methylated probes (DMPs), we performed supervised analysis be-tween PitNETs and non-PitNET methylomes and selected probes which presented significant adjusted P-values and mean methylation differences across pairwise compari-sons (Wilcoxon rank-sum test). For the development of predictive models described below, we input probes that presented less than 5% methylation differences between serum and plasma and to increase PitNET-probe speci-ficity, less than 0.5% difference between PitNET serum and tissue, namely PitNET Epigenetic Liquid Biopsy (PeLB) probes. Similar comparisons were performed to select nonfunctioning PitNET-specific DMPs (NF-PeLB). Each CpG probe was mapped to their genomic location as CpG is-lands (CGI), shores, shelves, and open sea regions as pre-viously defined.29

Machine-learning prediction modeling—Random Forest. To investigate the potential application of PeLB as a diag-nostic tool to differentiate sellar masses and other diseases, independently of the LB source (serum or plasma), we used a random forest machine-learning approach to generate a model for binary classification (PitNET and non-PitNET or NF-PitNETs and non-PitNET), using PitNET-specific methyl-ation signatures concomitantly identified in serum (n = 59) and plasma (n = 41) specimens (details in Supplementary Methods).

Identification of tissue-derived methylation markers in the serum/plasma specimens. In order to investigate whether probes that differentiate groups in tissue spe-cimens also differentiate similar groups in LB specimens (serum or plasma) we performed the following analyses: (1) supervised analysis between the following groups to detect tissue-specific DMPs: PitNETs vs non-neoplastic controls; F-PitNET vs NF-PitNET; PitNET vs other CNS tu-mors, PitNET vs OPD, using publicly available or in house generated tissue methylome data from PitNETs and pitui-tary controls and other CNS tumors (lower-grade glioma/skull-base meningioma) (Supplementary File S2); (2) align-ment of the significant tissue-derived DMPs with serum or plasma methylomes; and (3) selection of DMPs which retained differential methylations between concordant groups in serum or plasma specimens.

Integrative analysis of PitNET liquid biopsy-derived probes with respective putative target genes and as-sociated pathways. We mapped each differentially meth-ylated CpG probe derived from the different supervised

Table 1. Demographic and Clinicopathological Information for the Serum- and Plasma-Based Cohorts

Features Serum (N = 59) Plasma (N = 41)

Median (Q1, Q3) Median (Q1, Q3)

Age (years) 56 (42.0, 65.0) 51 (42.0, 61.0)

n % n %

Sex

Females 30 50.85 18 43.9

Males 27 45.76 23 56.1

NI 2 3.39 — —

Race/ethnicity

African American 11 18.64 8 19.51

Caucasian 40 67.8 27 65.85

Other 1 1.69 2 4.88

Unknown 7 11.86 4 9.76

Source/WHO Classification (2017)

PitNET 13 22.03 24 43.9

Corticotroph 1 7.69 2 8.33

Gonadotroph 4 30.77 4 16.67

Lactotroph 2 7.69 1 4.17

Mammosomatotroph — — 1 4.17

Null cell 5 38.46 4 16.67

Plurihormonal — — 4 16.67

Plurihormonal PIT1 1 7.69 2 8.33

Somatotroph — — 1 4.17

Thyrotroph — — 3 12.5

Unknown — — 3 12.5

Non-PitNET 46 77.97 17 41.46

Control (nontumor) 7 11.86 — —

Control (healthy) — — 4 9.76

Skull-base meningioma 16 27.12 — —

Lower-grade glioma 12 20.34 6 14.63

Brain metastatic carcinoma—other CNS diseases (OCD) 1 1.69 — —

Other pituitary diseases (OPD) 10 16.95 7 17.07

Craniopharyngioma 5 50 5 71.43

Colloid cyst 1 10 1 14.29

Pituicytoma 1 10 1 14.29

Histiocytosis 1 10 — —

Rhabdoid teratoma 1 10 — —

Chordoma 1 10 — —

Functioning status

Functioning 4 30.77 6 25

Nonfunctioning 9 69.23 15 62.5

Unknown — — 3 12.5

Tumor size

Giant 2 15.38 — —

Macroadenoma 10 76.92 20 83.33

Microadenoma 1 7.69 2 8.33

Unknown — — 2 8.33

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https://www.cancer.gov/tcga

http://academic.oup.com/neuro-oncology/article-lookup/doi/10.1093/neuonc/noac050#supplementary-data

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4176427/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4176427/








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Table 1. Demographic and Clinicopathological Information for the Serum- and Plasma-Based Cohorts



Age (years) 56 (42.0, 65.0) 51 (42.0, 61.0)

n % n %

Sex

Females 30 50.85 18 43.9

Males 27 45.76 23 56.1

NI 2 3.39 — —

Race/ethnicity

African American 11 18.64 8 19.51

Caucasian 40 67.8 27 65.85

Other 1 1.69 2 4.88

Unknown 7 11.86 4 9.76

Source/WHO Classification (2017)

PitNET 13 22.03 24 43.9

Corticotroph 1 7.69 2 8.33

Gonadotroph 4 30.77 4 16.67

Lactotroph 2 7.69 1 4.17

Mammosomatotroph — — 1 4.17

Null cell 5 38.46 4 16.67

Plurihormonal — — 4 16.67

Plurihormonal PIT1 1 7.69 2 8.33

Somatotroph — — 1 4.17

Thyrotroph — — 3 12.5

Unknown — — 3 12.5

Non-PitNET 46 77.97 17 41.46

Control (nontumor) 7 11.86 — —

Control (healthy) — — 4 9.76

Skull-base meningioma 16 27.12 — —

Lower-grade glioma 12 20.34 6 14.63

Brain metastatic carcinoma—other CNS diseases (OCD) 1 1.69 — —

Other pituitary diseases (OPD) 10 16.95 7 17.07

Craniopharyngioma 5 50 5 71.43

Colloid cyst 1 10 1 14.29

Pituicytoma 1 10 1 14.29

Histiocytosis 1 10 — —

Rhabdoid teratoma 1 10 — —

Chordoma 1 10 — —

Functioning status

Functioning 4 30.77 6 25

Nonfunctioning 9 69.23 15 62.5

Unknown — — 3 12.5

Tumor size

Giant 2 15.38 — —

Macroadenoma 10 76.92 20 83.33

Microadenoma 1 7.69 2 8.33

Unknown — — 2 8.33

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Table 1. Continued



Tumor invasion

Invasive 7 53.85 23 95.83

Noninvasive 6 46.15 — —

Unknown — — 1 4.17

Knosp grade

0 4 30.77 — —

1 2 15.38 — —

2 3 23.08 — —

3 2 15.38 — —

4 2 15.38 — —

NI — — 24 100

Last report status

Alive 12 92.31 14 58.33

Dead 1 7.69 4 16.67

Lost follow-up — — 6 25

Abbreviations: NI, not informed; PitNET, pituitary neuroendocrine tumors; WHO, World Health Organization.

comparisons listed above with their putative target gene using EPIC manifest (hg38) and conducted gene set enrich-ment analyses using ingenuity pathway analysis (IPA) and DAVID Functional Annotation in efforts to explore associ-ated canonical and disease-relevant processes.29–31

In silico functional validation of serum- or plasma-derived DMPs. In efforts to assess the biological relevance of serum- and plasma-derived DMP-putative target gene pairs obtained from the various PitNET comparisons, we harnessed publicly available matching methylomic and transcriptomic data from tumor tissue PitNETs (Neou et al,18 n = 82). Correlational analyses and differential ex-pression of genes were established through the ELMER tool,32 in addition to broad-scale literature searches (details in Supplementary Methods; Supplementary File S5).

Deconvolution. In order to assess whether serum or plasma samples presented methylation signa-tures that are representative of circulating tumor- or non-neoplastic cell-specific and are differential across cohorts, we applied previously described DNA methylation-based methodologies to deconvolute the relative contribution of cell types to a given serum or plasma sample (MethylCIBERSORT and python-based).26,33 We included available methylation signa-tures from immune (B cells, CD4T, CD8T, natural killer cells, monocytes, neutrophils), and non-immune cells (neuron, glial, and vascular endothelial cells).26,33 We generated our own methylation signatures from non-neoplastic pituitaries obtained from cadavers and followed the steps for defining the signatures as previ-ously described by Moss et al26 and Chakravarthy et al33 (details in Supplementary Methods).

Data Availability

Data supporting the findings of this study are available within the article and Supplementary information, and from the European Genome-Phenome Archive (accession EGA#####). Remaining data are available from the authors upon request.

Statistical Analysis

All processing and statistical analyses were done in R (4.1.2). Non-parametric Kruskal-Wallis and Wilcoxon rank-sum test and multiple testing adjustments (eg, false discovery rate [FDR]) were used to identify group-specific DMPs as stated in the previous sections and across discrete variables: cfDNA concentration (ng/mL), deconvoluted cell propor-tions (%) and sample mean methylation levels (β-values). Relationships between discrete variables were explored through the utilization of Spearman’s correlation coefficient (ρ) and corresponding P-values. We utilized random forest analyses as the machine-learning method for prediction and classification of the samples as PitNET or non-PiNET. Performance and clinical utility of both models in the respec-tive independent cohorts were evaluated through Mathew’s correlation coefficient (MCC) and the clinical utility index (CUI)34,35 (details in Supplementary Methods).

Results

Characterization of Pituitary Cell-Free DNA Methylome

cfDNA quantification and methylome data quality. Our serum or plasma methylome samples met the quality

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Tumor invasion

Invasive 7 53.85 23 95.83

Noninvasive 6 46.15 — —

Unknown — — 1 4.17

Knosp grade

0 4 30.77 — —

1 2 15.38 — —

2 3 23.08 — —

3 2 15.38 — —

4 2 15.38 — —

NI — — 24 100

Last report status

Alive 12 92.31 14 58.33

Dead 1 7.69 4 16.67

Lost follow-up — — 6 25

Abbreviations: NI, not informed; PitNET, pituitary neuroendocrine tumors; WHO, World Health Organization.

control for all parameters used in the quality control as-sessment (eg, bisulfite conversion), independently of con-centration of cfDNA (Supplementary Figure S1C and D). Methylation quality was not correlated with pre-analytical features, such as date of collection. Total extracted serum cfDNA quantities in patients with PitNET were signifi-cantly higher compared to controls in serum and lower compared to OPD in both serum and plasma specimens (Supplementary Figure S1A; Supplementary File S1). The concentration of plasma cfDNA in PitNET patients was higher and not significantly different compared to their glioma counterparts (Supplementary Figure S1A; Supplementary File S1). PitNET serum or plasma cfDNA concentrations were higher in nonfunctioning compared to functioning PitNET and were not correlated with size and invasion status (Supplementary Figure S1A and B; Supplementary File S1). PitNET cfDNA mean concentra-tions were significantly lower across serum cohorts com-pared to plasma counterparts.

Methylome levels detected in serum or plasma speci-mens segregate PitNETs from non-PitNET samples—unsupervised analysis. The 3D PCA of the genome-wide mean methylation levels of either serum or plasma cfDNA samples showed separation between PitNETs and non-PitNETs, and partial segregation between functioning vs nonfunctioning PitNET more evident in serum (Figure 1A). Differential methylation patterns of the 1K most variant methylated probes across groups were more evident in plasma than serum specimens (Figure 1B).

Differential immune and non-immune cell compositions across groups were observed in serum and plasma. Using MethylCIBERSORT33 to estimate cell composition we observed that the proportion of whole pituitary meth-ylation signatures in PitNET serum or plasma were signif-icantly different compared to controls and OPD; however, the direction of estimated proportion differences was dis-cordant between both sources, that is, the proportion of this cell type in PitNETs was higher in serum, and lower in plasma specimens than comparison groups (Figure 1C). Neuron proportions were lower in PitNET in relation to OPD. Differential proportions of specific immune cell-type signatures, such as monocytes and neutrophils, were concordant with plasma results; however, for CD4 and CD8 T-cell signatures, only appreciable in plasma samples (Figure 1C). Comparisons of estimated proportions across different sources derived from the same patient with OPD (ie, serum, plasma, or tissue) showed significant and pos-itive correlations across multiple cell types, that is, CD4T, neutrophils, natural killer cells (details in Supplementary File S5). Except for glial cells that were not estimated by MethylCIBERSORT, the immune and non-immune cell-type distribution estimated through both deconvolution algo-rithms were significantly and positively correlated between the 2 deconvolution methods more frequently in plasma than in serum samples (Supplementary Figure S2B).

Tumor-specific signatures distinguish PitNETs from non-PitNET in serum or plasma samples—supervised analysis. The supervised analysis between PitNETs and

non-PitNETs yielded the identification of 110 serum (mean methylation difference [diff.mean]; −0.13 > diff.mean > 0.16, P-valueFDR < .05) and 112 plasma DMPs (−0.23 > diff.mean > 0.25, P-valueFDR < .01) (Figure 2A; Supplemental Files S3 and S4). Differential mean methylation levels of plasma-derived DMPs across most comparison groups, in-cluding PitNET vs OPD, were more evident in plasma sam-ples, while the serum DMPs only distinguished PitNETs from lower-grade gliomas (Figure 2A). We further filtered both PitNET-specific DMP sets to probes that explicitly dif-ferentiated nonfunctioning PitNET from OPD and mapped them to their putative target genes depicted in Figure 2D.

Mean methylation levels of DMPs resulting from the comparison of PitNET and OPD (nserum = 91; nplasma = 115) significantly differentiated PitNET and OPD in serum but not in plasma (Supplementary Figure S3C), while the mean methylation of DMPs derived from the comparison be-tween nonfunctioning and functioning PitNETs (nserum = 49; nplasma = 56) differentiated these groups only in plasma spe-cimens (Supplementary Figure S2B).

In all supervised analyses, the identified DMPs which distinguished the respective groups were mostly located in intergenic regions, overlapping enhancers, and mainly an-notated in open sea regions (Supplementary Files S2–S4).

PeLB and NF-PeLB scores accurately discriminate PitNETs from other groups—Random Forest Model. We developed and validated predictive models which involved DMPs derived through compounded analysis of both serum and plasma methylomes (details in Supplementary Methods). PeLB and NF-PeLB scores above 0.57 and 0.37, respectively, predicted whether a LB specimen originated from a patient with PitNET or a non-PitNET condition or disease in an independent cohort, with 100% and ~93% ac-curacies, respectively (SE: 100%/87.5%, SP: 100%/94.7%) with satisfactory reliability and clinical utility as evalu-ated via values of the MCC (100% and ~77%, respectively) and CUI+ (100% and ~82%, respectively) (Figure 2B and C; Supplementary Figures S3B and S5A and B).

PitNET liquid biopsy-derived probes mapped to genes involved in relevant pathways associated with tu-morigenesis. We compiled genes putatively targeted by DMPs derived from different supervised comparisons. Serum-, plasma-, and compounded source-derived gene sets showed enrichment for relevant pathways involved in tumor behavior, immune response, and cell metabolism, among others (details in Supplementary Figure S2A–C).

DMPs derived from tissue comparisons are present in the serum or plasma cfDNA methylome from patients with PitNETs. t-SNE depicting the 250 most variant CpG probes showed that serum and plasma cfDNA from PitNET patients clustered with PitNET and OPD and segregated from skull-base meningiomas and lower-grade gliomas (Figure 3A).

Among the tissue DMPs derived from the supervised analysis between controls vs PitNETs (n = 1544 DMP), functioning vs nonfunctioning (n = 187 DMP), PitNET vs other CNS tumors (n = 287 DMP), and PitNET vs OPD (n = 147 DMP), 133 and 250 DMPs were captured in serum

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Kruskal−Wallis, p = 0.2

0.02

0.04

0.06B–Cell


0.00

0.01

0.02

0.03

CD4 T-CellKruskal−Wallis, p = 0.37

0.000

0.001

0.002

0.003

0.004

0.005CD8 T-Cell

Kruskal−Wallis, p = 0.066**

0.1

0.2

0.3

MonocyteKruskal−Wallis, p = 0.00077

0.4

0.5

0.6Neutrophil

Kruskal−Wallis, p = 0.088 **

0.15

0.20

0.25Neuron


0.04

0.06

0.08

0.10

Natural Killer

Kruskal−Wallis, p = 0.0022** **

0.000

0.025

0.050

0.075

Whole PituitaryKruskal−Wallis, p = 0.31

0.03

0.06

0.09

Vascular Endothelial Cell

MonocyteKruskal−Wallis, p = 7e−05

0.2

0.4

0.6

0.8

NeutrophilKruskal−Wallis, p = 0.00029

0.0

0.2

0.4

0.6


0.00

0.03

0.06

0.09

0.12B–Cell


0.0

0.1

0.2


0.0

0.1

0.2

0.3

0.4

0.5CD8 T-Cell


0.05

0.10

0.15

0.20

0.25Neuron


0.04

0.08

0.12

0.16

Natural Killer


0.00

0.02

0.04

0.06Whole Pituitary


0.000

0.025

0.050

0.075

0.100


Imm

une

Cel

l Typ

es

Non

-Imm

une

Nontumor PitNET Other PituitaryDiseases



Control PitNET Other PituitaryDiseases



SERUM PLASMA

MethylCIBERSORT Estimated Cell Proportions

** ** ** ** ** **

** **

** ** ** ** ** **

All Serum - Whole Genome

PC1

PC

3

PC2

PC1

PC

3

PC2

All Plasma - Whole Genome

Pituicytoma

CraniopharyngiomaColloid Cyst

Craniopharyngioma

Pituicytoma

Craniopharyngioma

Chordoma

Rhabdoid Teratoma

Pituitary Colloid Cyst/meningioma

PituitaryHistiocytosis

A

B

1k m

ost

var

ian

t se

rum

−d

eriv

ed p

rob

es

1k m

ost

var

ian

t p

lasm

a−d

eriv

ed p

rob

es

Sample TypeHistotypeFunctional StatusSexAge Bracket

Sample TypeHistotype

Functional StatusSex

Age Bracket

Probe familyGenebodyIntergenicPromoter

EnhancerYesNo

CpG LocationCpG IslandsOpen SeasShelvesShores

Sample Type

Lower Grade GliomaMeningioma

Non−tumor

Other CNS diseases

Other Pituitary diseasesPitNET

HistotypeAbscessAdamantinomatous CraniopharyngiomaAstrocytomaAnaplastic Astrocytoma

Chordoma (Pituitary)Craniopharyngioma, NOS

Epilepsy

Meningioma, atypicalMeningioma, fibrousMeningioma, meningothelialMeningioma, NOSMeningioma, psammomatousMeningioma, rhabdoid

Non−specific/reactiveOligodendrogliomaPapillary CraniopharyngiomaPitNETPituicytomaPituitary histiocytosisPituitary Rhabdoid TumorPrimary vasculopathy (collagenized)Vasculopathy

Anaplastic OligoastrocytomaBrain Metastatic Adenocarcinoma

Colloid Cyst (Pituitary)

Healthy Control

Functional StatusFunctioningNonfunctioning

SexFemaleMaleNI

Age Bracket>8020−4040−6060−80NI

Methylation β−values

0

0.5

1

Color Scaling/Row Annotations:

Column Annotations:

C

Pituitary TumorNontumorHealthy ControlLower Grade GliomaSkull-base MeningiomaOther Pituitary DiseasesOther CNS Diseases

Sample type:

Nonfunctioning PitNETUnkown PitNET

Functioning PitNETFunctional status:

SERUM PLASMA

Fig. 1 Exploratory analysis of the liquid biopsy-derived cfDNA methylome. (A) Principal component analysis of the genome-wide mean meth-ylation of serum (n = 59) or plasma (n = 41) cfDNA cohorts; (B) Heatmap of the methylation levels (β-values) of the 1K most variably methyl-ated probes across liquid biopsy-based sample cohorts; (C) Boxplots depicting the estimated cell proportions of liquid biopsy specimens using MethylCIBERSORT. Comparisons are provided across immune and non-immune cell types between PitNETs, other pituitary diseases, and control specimens (Kruskal-Wallis and Wilcoxon rank-sum means; **Wilcoxon P-value < .05). Abbreviations: cfDNA, cell-free DNA; PitNETs, pituitary neuroendocrine tumors.

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0.02

0.04

0.06B–Cell


0.00

0.01

0.02

0.03


0.000

0.001

0.002

0.003

0.004

0.005CD8 T-Cell

Kruskal−Wallis, p = 0.066**

0.1

0.2

0.3

MonocyteKruskal−Wallis, p = 0.00077

0.4

0.5

0.6Neutrophil


0.15

0.20

0.25Neuron


0.04

0.06

0.08

0.10

Natural Killer

Kruskal−Wallis, p = 0.0022** **

0.000

0.025

0.050

0.075

Whole PituitaryKruskal−Wallis, p = 0.31

0.03

0.06

0.09


MonocyteKruskal−Wallis, p = 7e−05

0.2

0.4

0.6

0.8

NeutrophilKruskal−Wallis, p = 0.00029

0.0

0.2

0.4

0.6


0.00

0.03

0.06

0.09

0.12B–Cell


0.0

0.1

0.2


0.0

0.1

0.2

0.3

0.4

0.5CD8 T-Cell


0.05

0.10

0.15

0.20

0.25Neuron


0.04

0.08

0.12

0.16

Natural Killer


0.00

0.02

0.04

0.06Whole Pituitary


0.000

0.025

0.050

0.075

0.100


Imm

une

Cel

l Typ

es

Non

-Imm

une







SERUM PLASMA

MethylCIBERSORT Estimated Cell Proportions

** ** ** ** ** **

** **

** ** ** ** ** **

All Serum - Whole Genome

PC1

PC

3

PC2

PC1

PC

3

PC2

All Plasma - Whole Genome

Pituicytoma

CraniopharyngiomaColloid Cyst

Craniopharyngioma

Pituicytoma

Craniopharyngioma

Chordoma

Rhabdoid Teratoma

Pituitary Colloid Cyst/meningioma

PituitaryHistiocytosis

A

B

1k m

ost

var

ian

t se

rum

−d

eriv

ed p

rob

es

1k m

ost

var

ian

t p

lasm

a−d

eriv

ed p

rob

es

Sample TypeHistotypeFunctional StatusSexAge Bracket


Functional StatusSex

Age Bracket

Probe familyGenebodyIntergenicPromoter

EnhancerYesNo

CpG LocationCpG IslandsOpen SeasShelvesShores

Sample Type

Lower Grade GliomaMeningioma

Non−tumor

Other CNS diseases

Other Pituitary diseasesPitNET

HistotypeAbscessAdamantinomatous CraniopharyngiomaAstrocytomaAnaplastic Astrocytoma

Chordoma (Pituitary)Craniopharyngioma, NOS

Epilepsy

Meningioma, atypicalMeningioma, fibrousMeningioma, meningothelialMeningioma, NOSMeningioma, psammomatousMeningioma, rhabdoid

Non−specific/reactiveOligodendrogliomaPapillary CraniopharyngiomaPitNETPituicytomaPituitary histiocytosisPituitary Rhabdoid TumorPrimary vasculopathy (collagenized)Vasculopathy

Anaplastic OligoastrocytomaBrain Metastatic Adenocarcinoma

Colloid Cyst (Pituitary)

Healthy Control

Functional StatusFunctioningNonfunctioning

SexFemaleMaleNI

Age Bracket>8020−4040−6060−80NI

Methylation β−values

0

0.5

1

Color Scaling/Row Annotations:

Column Annotations:

C

Pituitary TumorNontumorHealthy ControlLower Grade GliomaSkull-base MeningiomaOther Pituitary DiseasesOther CNS Diseases

Sample type:

Nonfunctioning PitNETUnkown PitNET

Functioning PitNETFunctional status:

SERUM PLASMA

Fig. 1 Exploratory analysis of the liquid biopsy-derived cfDNA methylome. (A) Principal component analysis of the genome-wide mean meth-ylation of serum (n = 59) or plasma (n = 41) cfDNA cohorts; (B) Heatmap of the methylation levels (β-values) of the 1K most variably methyl-ated probes across liquid biopsy-based sample cohorts; (C) Boxplots depicting the estimated cell proportions of liquid biopsy specimens using MethylCIBERSORT. Comparisons are provided across immune and non-immune cell types between PitNETs, other pituitary diseases, and control specimens (Kruskal-Wallis and Wilcoxon rank-sum means; **Wilcoxon P-value < .05). Abbreviations: cfDNA, cell-free DNA; PitNETs, pituitary neuroendocrine tumors.

A

B

C D

Kruskal–Wallis, p = 0.016

Met

hyla

tio

n v

alu

es (

mea

n, ββ

-val

ues

)P

itu

itar

y–eL

B S

core

(P

eLB

Sco

re)

Pit

NE

TO

ther

32 s

eru

m–r

elev

ant

PG

Ps

35 P

lasm

a–re

leva

nt

PG

Ps

SERUM PLASMASupervised Analysis: PitNET vs Non-PitNET

Kruskal–Wallis, p = 4.3e–05

**

***

****

0.60

0.50

0.60

0.55

0.50

Nontumor/Control

MTS Cases(n = 13)

Tissue SimilarCpGs (n = 42k)

Plasma SimilarCpGs (n = 23k)

MTS mean diff.

mean methylation diff < 0.005mean.diff = /µtissue - µserum/

MTS mean diff.

RBC’sbloodclot

Plasma

SerumWBC’s &platelets FD

R ad

j. p-v

alue

group mean diff.

mean methylation diff < 0.005mean.diff = /µtissue - µserum/

Dimensionality reduction to define parent featureset using sample source similarity

Machine-driven randomization ofsamples into Training (66.6%) & Model

selection (33.4%) sets.

Storage of PeLB score and ROCfrom Step #5 & repeat steps #2–5 Application of predictive model

(step #4) to Model Selection setSelection of the Pituitary-eLB

score cutoff & model (Step #5) whichcorrectly classifies (Model Selection set)

PitNET & Non-PitNET with100% accuracy

PeLB Scores across Independent & Model Selection Cohorts (n = 45)

Validation of the chosen model onan Independent Testing Set

Accuracy: 100%Sens: 100%Spec: 100%

Accuracy: 100%MCC: 100%

CUI (+Ve): 100%CUI (–Ve): 100%

Sensitivity: 100%PPV: 100%

Specificity: 100%NPV: 100%

SerumPlasma

Sample Source

PitNET (n = 7) vs.Non-PitNET (11)

1.00

0.75

SERUM PLASMA

0.50

0.25

0.00

PitNET (n = 10) vs.

Non-PitNET (n = 17)(30 hypo- and hyper-FDR DMPs)

Machine-driven supervisedanalysis of training samples

to define signature set (DMPs)

Generation of predictiveStep #1.

Step #2.

Discovery Set

Training

Model SelectionPitNET (n = 10) vs.Non-PitNET (n = 17)

PitNET (n = 20) vs.

Serum (n = 48) &Plasma (n = 34);

23k Pit-specific CpGs Non-PitNET (n = 35)

Step #3.Step #4.

Step #5.

Signature Set

PitNET (n = 20) vs.

Non-PitNET (n = 35)(30 hypo- and hyper-FDR DMPs)

Signature Set

ML

Step #6.

Step #7.

ROC Curves (N = 500)

Pituitary-eLB (PeLB)Step #8.

PitNET Other PituitaryDiseases

Lower GradeGlioma

Nontumor/Control


Lower GradeGlioma

Meningioma Other DNSDiseases

Nontumor/Control


Lower GradeGlioma

Meningioma

Random Forest model


Functional Status

SexAge Bracket

RP11–8P11.4FXR1TBCDCYB5D2ANKY1Y_RBASFT2D1HNRNPA1P49LDLRAP1RUNX3AGAP2AGA2–AS1RUNX3ARHGAP22C1QTNF4LINC00982CAPN14DCP1ARUFY1MAPK14BLKAGAP1HS1BP3TMEM177NCOR2RP13–20L14.4OGFOD3

TLE2NUP62CTC–326K19.6IL4l1ECE1CARS2SLC25A52IGSF21CSGALNACT1RP11–1105O14.1MYCBPAPRP11–94C24.6OPCMLRP11–196E1.3LA16c–306E5.2NAA60ANF597ILDR1HLA-DQB2PAHFAM221AAC006026.13IGSF8KCNAB2NPHP4RP11–190C22.8TIMMDC1CACNB1

SEC14L1RAD51BRP1–225E12.3HECARP1–225E12.2CD247AKR1D1P1SCMH1

EIF3EAP006621.5FAM101BTPPP

Color Scaling/Row Annotations:Methylation β-values

10.8

Yes Functioning Adamantinomatous CraniopharyngiomaChordoma (Pituitary)Craniopharyngioma, NOSPapillary CraniopharyngiomaPitNETPituicytomaPituitary Colloid Cyst/meningiomaPituitary histiocytosisPituitary Rhabdoid Tumor

Nonfunctioning

PitNETOther Pituitary Diseases

FemaleMale

20–4040–6060–80

No

GenebodyIntergenicPromoter

0.60.40.2

Both

PitNET vs Non-PitNETPitNET vs OPD

0

0.05FDR

0.040.030.020.010

Enhancer p–value

Comparison Groups

Probe family Sample Type

Functional Status Histotype Sex

Age Bracket

Column Annotations

Fig. 2 Supervised analysis across liquid biopsy samples. (A) Mean methylation levels across DMPs resulting from comparison of PitNET and non-PitNET liquid biopsy samples (DMP: nserum = 110; nplasma = 112; Wilcoxon rank-sum test and Kruskal-Wallis; *P-value < .05, **P-value < .01, ***P-value < .001); (B) Schematic outline of steps to developing a machine-learning-based model to differentiate across PitNET from other sellar and CNS diseases, using liquid biopsy specimens—the PitNET epigenetic liquid biopsy (PeLB) model; (C) PeLB score distributions across model

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or plasma, respectively, and also discriminated the corre-sponding comparison groups.

These DMPs were associated with genes reportedly relevant to pituitary diseases and involved in cell growth and hormonal signaling (eg, Notch pathway, protein ki-nase A, and HOTAIR signaling), cell metabolism, and tumor progression (eg, PFKFB4), among others (Figure 3D; Supplementary Figure S4D).

PitNET-specific DMP detected in serum or plasma spe-cimens are functionally relevant in PitNET tissue. In overlapping serum- or plasma-derived DMPs obtained from several PitNET-focused comparisons with the Neou PitNET tissue paired transcriptome and methylation datasets, we were able to identify multiple putative target genes whose expression levels were significantly and negatively correlated with the methylation levels of CpG probes located in gene regulatory regions (enhancers or promoters) (Figure 4A and B). Additionally, we showed that putative target genes associated with serum/plasma DMPs derived from the comparison between functioning and nonfunctioning presented differential expression and meth-ylation between these groups in PitNET tissue (Figure 4C). We also observed that in functioning PitNET, probes asso-ciated with HDAC4, TRIM5, and CAMK2N1 genes, involved in transcription factor and protein kinase binding activities, presented lower methylation levels and higher gene ex-pression compared to nonfunctioning PitNET (Figure 4C). Literature reports on the biological or clinical importance of these probe-gene pairs for tumorigenesis are displayed in Supplementary File S5. Briefly, they include genes re-lated to pituitary gland development, cell proliferation, gene expression, metal ion binding, and others.

Discussion

Methylation profiling of cfDNA circulating in biofluids, such as blood (serum or plasma), has been useful for the early detection, prognostication, and surveillance of intra- and extracranial neoplasms, as shown by our group and others.10,11,36,37 Herein, our results suggest that similar to other CNS tumors, PitNETs release tumor-related informa-tion in the blood that allows the identification of clinically relevant methylation signatures specific to patients with PitNETs (Figure 1B; Supplementary Figure S3C).

Standard approaches, including clinical features, hor-monal assessment using blood/urine, imaging of the pi-tuitary gland, and pathological assessment obtained by surgery are, in most cases, sufficient to the diagnosis and classification of PitNETs.38 However, there are challenging sellar disease cases, such as rare primary or secondary sellar tumors or non-neoplastic diseases that may be misdiagnosed as PitNETs, in particular as NF-PitNETs,5–7

that could benefit from a presurgical and noninvasive di-agnostic approach to better guide the appropriate man-agement. Here, the unsupervised analysis of methylome in serum or plasma specimens shows that distinct mean genome-wide methylation levels separate PitNET from non-neoplastic specimens, other CNS tumors and pituitary diseases, and PitNET functional subtypes; differences that are more appreciable in plasma-derived samples (Figure 1A and B). We also observed that serum- or plasma-derived probes clustered together with PitNET/OPD but not with lower-grade glioma and meningioma tissue (t-SNE) (Figure 3A). Altogether, these findings suggest that serum or plasma cfDNA from patients with PitNETs contain meth-ylation fingerprints specifically related to these tumors. Capitalizing on these observations, we developed predic-tion models using a combination of serum- and plasma-derived PitNET or NF-PitNET-specific DMPs that accurately classified independent CNS cohorts into their respective memberships with 100% and ~93% accuracy, respectively, alongside commendable measurements of reliability and clinical utility (Figure 2B and C; Supplementary Figure S5A and B). Although compelling, these results warrant valida-tion in a larger cohort of LB samples, particularly among conditions that mimic NF-PitNET.5–7

To complement the evidence of the presence of tumor-specific features in the LB samples, we also investigated the cell composition of serum and plasma samples in our cohort. In the absence of standard methods, such as flow cytometry, we applied and contrasted 2 DNA methylation-based deconvolution methods to estimate cell-type com-position in serum and plasma samples.26,33 These methods have shown to reliably deconvolute cell types in tissue or plasma samples of different CNS tumors or other condi-tions.26,33 Through MethylCIBERSORT deconvolution,33 we observed differential immune and non-immune cell-type proportions across patients with PitNETs compared to con-trols and OPD (eg, whole pituitary, vascular endothelial, and immune cells) which was more apparent in plasma specimens compared to serum counterparts (Figure 1C). Notably, these results were highly correlated with the python-based method26 (Supplementary Figure S2B), sug-gesting the consistency in results of DNA methylation-based deconvolution methods. Interestingly, some immune signatures detected in serum or plasma recapitu-lated the findings from tissue deconvolution in a subset of our cohort (Supplementary File S5); but these results war-rant further confirmation through standard methods, such as flow cytometry.

We also showed that PitNET methylation signatures identified in tissue could be captured in serum or plasma cfDNA and distinguished similar PitNET groups (eg, PitNET vs non-PitNETs, vs control, functioning vs nonfunctioning). We attempted to explore the biological and functional roles of the putative target genes by aligning DMP derived from different group comparisons in serum or plasma

selection and independent cohorts, with performance parameters (y-axis: PeLB score; PeLB score ≥0.57 = PitNET; <0.57 = non-PitNET); (D) Heatmap displaying the methylation levels of DMPs resulting from the comparison of PitNETs and other pituitary diseases across serum (n = 23) or plasma (n = 31) specimens and their putative target genes; sorted by sample type. Abbreviations: DMPs, differentially methylated probes; PitNETs, pituitary neuroendocrine tumors.

Fig. 2 Continued

Step #3. Serum & plasma DMPset selection

Visualization

FD

R a

dj. p

-val

ue

Mean methyl diff.

[FDR adj pvalue & diff.mean]

Legend:

NFPTvFPTPTvOCTPTvNTPTvOPDMultiple

D

D

A

−50

−25

0

25

t−S

NE

2

Sample medium:Plasma (n = 34)

Serum (n = 23)

Tissue (n = 752)

Sample type:IDHmut LGG (n = 419)

IDHWT LGG (n = 94)

Meningioma (n = 65)

Functioning PitNET (n = 93)

Nonfunctioning PitNET (n = 105)

Unknown PitNET (n = 3)

Legend:

Other Pituitary Diseases (n = 30)

B

C

Sample type:

Glioma

Meningioma

PitNET

Sample source:

Serum

Tissue

Plasma

Nontumor/control

Functioning PitNETNonfunctioning PitNET

Other pituitary diseases

PC1 (25.27%)

PitNET vs. Nontumor/Control

PitNET vs Other Pituitary Diseases

−6

−3

0

3

6

−10 100

PC1 (7.4%)

PC

2 (4

.55%

)

PC1 (17.85%)

−2

−1

0

1

2

−2 −1 210

PC

2 (1

3.2%

)

−2

−1

0

1

2

−2 −1 210

PC1 ( 22.97%)

PC

2 (1

0.39

%)

PC

2 (4

.98%

)

PC1 (12.94%)

–2.0

–1.0

0.0

1.0

2.0

0.90.60.30

PC1 (65.42%)

−0.2

−0.1

0.0

0.1

0.2

− 2.00.02.0

PC

2 (1

5.58

%)

PC

2 (9

.66%

)

0.15.0–0.5–1.0

–1.0

–0.5

0.0

0.5

1.0

0.0

t SNE1PitNET vs. Other CNS tumors

−2.5

0.0

2.5

5.0

−10 − 5101505

PC

2 (4

.42%

)

PC1 (17.35%)

PC1 (37.25%)

−0.8

−0.4

0.0

0.4

0.8

−0.8 − 8.04.00.04.0

PC1 (32.78%)

PC

2 (1

6.88

%)

Nonfunctioning vs. Functioning PitNET

PC

2 (3

.01%

)

PC1 (21.39%)

–2.0

–1.0

0.0

1.0

2.0

0.0 5.25.2– 5.0–5.0

PC1 (31.43%)

PC

2 (2

1.19

%)

0.15.0–0.5–1.0

–1.0

–0.5

0.0

0.5

1.0

0.0

−0.5

–0.25

0.00

0.25

− 5.00.05.0

PC

2 (7

.53%

)

PC1 (20.93%)−1.0 1.0

−0.8

−0.4

0.0

0.4

0.8

−0.8 − 8.04.00.04.0

PC

2 (1

0.34

%)

TIS

SU

ES

ER

UM

PL

AS

MA

TIS

SU

ES

ER

UM

PLA

SM

A

TIS

SU

ES

ER

UM

PL

AS

MA

TIS

SU

ES

ER

UM

PL

AS

MA

E

hyper

hypo

hyper

hypo

AEBP1†

NCAM1†

CDH23†

BRINP3 †

PADI2†

KCNQ1†*

PADI2†

DNAAF1†

ARHGEF2 †

CDH23†

ESR1 †

FOXO3 †

MMP9 †

PituitaryAdenoma

FamilialPitNET

Acromegaly

SporadicPitNET

InvasivePitNET

NFPitNET

InvasiveNF-PitNET

PlurihormonalPitNET

Prolactinoma

Thyrotrophinoma

Gonadotrophinoma

GH/PRLSecreting

PitNET

Sporadic InvasivePitNET

Tumorigenesis

PitNET Development

ABL1†

HMGA2 †

THRB †

MMP14*

TGFBR2 †

KCNQ1 †*

TGFBR2 †

ABCA4 †

RET †

KCNQ1*

hyper

hypo

hyper

hypo

Disease/Function:IPA - Disease

IPA - Function

Gene symbol:Expected Mutation

Expected over-expression

Expected under-expression

Variable Expression

Enhancer-linked†Promoter-linked*

PitNET vs. Other CNS Tumors

PitNET vs. Other CNS

Tumors

PitNET vs. Other PituitaryDiseases

PitNET vs. Other Pituitary

Diseases

SERUM PLASMADisease/function:

Regulated Genes: Regulated Genes:Methylation levels

(DNA regulatory CpGs):Methylation Levels(DNA regulatory CpGs):

Step #2. LB Supervised analysesPitNET

Other PituitaryDiseases

vs.

Other CNSTumors

Step #1. Gene mapping to regulatory CpGs

PitNET Relevant genes

[Genehancer & Gencode]

Enhancers/Promoters

Aim : Detection of differentiallymethylated regulatory elements associated with reported PitNETspecific genes in liquid biopsy.

Figure 3E

Visualization(Figure 3C/D)

Step #3. Tissue & LBcommon DMP set selection

FD

R a

dj. p

-val

ue

Mean methyl diff.



vs.F-PitNETvs.Other CNS

Tumors

Nontumor/Control

Step #1. Tissue methylome supervised analyses

Tissue DMP Set selectionsp-valueFDR & mean methylation

Step #2. Concordant LB sample type comparisons


NF-PitNETvs.

F-PitNETvs.Other CNS

Tumors

Tissue-relevant DMPs

Overlap w/LB methylomeMapped Genes(EPIC Manifest hg38)

Ingenuity Pathway Analysis

(Figure 3D)

Step #4. Gene mapping & pathway analysis

Aim: Identification of tissue-based markers relevant in

liquid biopsy samples.

Figure 3C/D

Nontumor/Control

NF-PitNETPitNET

PitNET TUMORFUNCTION

Fig. 3. Relationship between tissue and liquid biopsy methylome. (A) t-Distributed Stochastic Neighbor Embedding (t-SNE) using the 250 most variably methylated and PitNET-specific CpG sites across multiple cohorts; (B) Schematic detailing the analysis aims, comparison groups, and sources associated with the following: (C) Principal component analyses of tissue-derived DMPs which retained significance in concordant liquid biopsy comparisons; (D) A circos plot (ShinyCirco) depicting molecular and biological features associated with the aforementioned DMPs: supervised group assignment; chromosomal location, target genes, pathways output from the ingenuity pathway analysis (IPA), and significances (−log10 P-value; y-axis: difference in mean methylation [diff.mean]); (E) A river plot depicting disease-related genes, mapped to regulatory DMPs (enhancer/promoter) derived from the liquid biopsy comparisons of PitNET vs non-PitNET or PitNET vs other pituitary diseases. Abbreviations: DMPs, differentially methylated probes; PitNETs, pituitary neuroendocrine tumors.

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Step #3. Serum & plasma DMPset selection

Visualization

FD

R a

dj. p

-val

ue

Mean methyl diff.


Legend:

NFPTvFPTPTvOCTPTvNTPTvOPDMultiple

D

D

A

−50

−25

0

25

t−S

NE

2

Sample medium:Plasma (n = 34)

Serum (n = 23)

Tissue (n = 752)

Sample type:IDHmut LGG (n = 419)

IDHWT LGG (n = 94)

Meningioma (n = 65)

Functioning PitNET (n = 93)

Nonfunctioning PitNET (n = 105)

Unknown PitNET (n = 3)

Legend:

Other Pituitary Diseases (n = 30)

B

C

Sample type:

Glioma

Meningioma

PitNET

Sample source:

Serum

Tissue

Plasma

Nontumor/control

Functioning PitNETNonfunctioning PitNET

Other pituitary diseases

PC1 (25.27%)

PitNET vs. Nontumor/Control

PitNET vs Other Pituitary Diseases

−6

−3

0

3

6

−10 100

PC1 (7.4%)

PC

2 (4

.55%

)PC1 (17.85%)

−2

−1

0

1

2

−2 −1 210P

C2

(13.

2%)

−2

−1

0

1

2

−2 −1 210

PC1 ( 22.97%)

PC

2 (1

0.39

%)

PC

2 (4

.98%

)

PC1 (12.94%)

–2.0

–1.0

0.0

1.0

2.0

0.90.60.30

PC1 (65.42%)

−0.2

−0.1

0.0

0.1

0.2

− 2.00.02.0

PC

2 (1

5.58

%)

PC

2 (9

.66%

)

0.15.0–0.5–1.0

–1.0

–0.5

0.0

0.5

1.0

0.0

t SNE1PitNET vs. Other CNS tumors

−2.5

0.0

2.5

5.0

−10 − 5101505

PC

2 (4

.42%

)

PC1 (17.35%)

PC1 (37.25%)

−0.8

−0.4

0.0

0.4

0.8

−0.8 − 8.04.00.04.0

PC1 (32.78%)

PC

2 (1

6.88

%)

Nonfunctioning vs. Functioning PitNET

PC

2 (3

.01%

)

PC1 (21.39%)

–2.0

–1.0

0.0

1.0

2.0

0.0 5.25.2– 5.0–5.0

PC1 (31.43%)

PC

2 (2

1.19

%)

0.15.0–0.5–1.0

–1.0

–0.5

0.0

0.5

1.0

0.0

−0.5

–0.25

0.00

0.25

− 5.00.05.0

PC

2 (7

.53%

)

PC1 (20.93%)−1.0 1.0

−0.8

−0.4

0.0

0.4

0.8

−0.8 − 8.04.00.04.0

PC

2 (1

0.34

%)

TIS

SU

ES

ER

UM

PL

AS

MA

TIS

SU

ES

ER

UM

PLA

SM

A

TIS

SU

ES

ER

UM

PL

AS

MA

TIS

SU

ES

ER

UM

PL

AS

MA

E

hyper

hypo

hyper

hypo

AEBP1†

NCAM1†

CDH23†

BRINP3 †

PADI2†

KCNQ1†*

PADI2†

DNAAF1†

ARHGEF2 †

CDH23†

ESR1 †

FOXO3 †

MMP9 †

PituitaryAdenoma

FamilialPitNET

Acromegaly

SporadicPitNET

InvasivePitNET

NFPitNET

InvasiveNF-PitNET

PlurihormonalPitNET

Prolactinoma

Thyrotrophinoma

Gonadotrophinoma

GH/PRLSecreting

PitNET

Sporadic InvasivePitNET

Tumorigenesis

PitNET Development

ABL1†

HMGA2 †

THRB †

MMP14*

TGFBR2 †

KCNQ1 †*

TGFBR2 †

ABCA4 †

RET †

KCNQ1*

hyper

hypo

hyper

hypo

Disease/Function:IPA - Disease

IPA - Function

Gene symbol:Expected Mutation

Expected over-expression

Expected under-expression

Variable Expression

Enhancer-linked†Promoter-linked*

PitNET vs. Other CNS Tumors

PitNET vs. Other CNS

Tumors

PitNET vs. Other PituitaryDiseases

PitNET vs. Other Pituitary

Diseases

SERUM PLASMADisease/function:

Regulated Genes: Regulated Genes:Methylation levels

(DNA regulatory CpGs):Methylation Levels(DNA regulatory CpGs):

Step #2. LB Supervised analysesPitNET


vs.

Other CNSTumors

Step #1. Gene mapping to regulatory CpGs

PitNET Relevant genes

[Genehancer & Gencode]

Enhancers/Promoters

Aim : Detection of differentiallymethylated regulatory elements associated with reported PitNETspecific genes in liquid biopsy.

Figure 3E

Visualization(Figure 3C/D)

Step #3. Tissue & LBcommon DMP set selection

FD

R a

dj. p

-val

ue

Mean methyl diff.



vs.F-PitNETvs.Other CNS

Tumors

Nontumor/Control

Step #1. Tissue methylome supervised analyses

Tissue DMP Set selectionsp-valueFDR & mean methylation

Step #2. Concordant LB sample type comparisons


NF-PitNETvs.

F-PitNETvs.Other CNS

Tumors

Tissue-relevant DMPs

Overlap w/LB methylomeMapped Genes(EPIC Manifest hg38)

Ingenuity Pathway Analysis

(Figure 3D)

Step #4. Gene mapping & pathway analysis

Aim: Identification of tissue-based markers relevant in

liquid biopsy samples.

Figure 3C/D

Nontumor/Control

NF-PitNETPitNET

PitNET TUMORFUNCTION

Fig. 3. Relationship between tissue and liquid biopsy methylome. (A) t-Distributed Stochastic Neighbor Embedding (t-SNE) using the 250 most variably methylated and PitNET-specific CpG sites across multiple cohorts; (B) Schematic detailing the analysis aims, comparison groups, and sources associated with the following: (C) Principal component analyses of tissue-derived DMPs which retained significance in concordant liquid biopsy comparisons; (D) A circos plot (ShinyCirco) depicting molecular and biological features associated with the aforementioned DMPs: supervised group assignment; chromosomal location, target genes, pathways output from the ingenuity pathway analysis (IPA), and significances (−log10 P-value; y-axis: difference in mean methylation [diff.mean]); (E) A river plot depicting disease-related genes, mapped to regulatory DMPs (enhancer/promoter) derived from the liquid biopsy comparisons of PitNET vs non-PitNET or PitNET vs other pituitary diseases. Abbreviations: DMPs, differentially methylated probes; PitNETs, pituitary neuroendocrine tumors.

Dow




A B

BACH2

FOXP1

TIMP2

PRKCZ

GALNS

ALDH1A2

ATP2C1

HECA

SHMT2

CUL3

ATXN1

KLF6

CYB5D2

CALR

CDK5R1

LCN2

TRAF6

GLI2

Pituitary glanddevelopment

Positive regulationof cell proliferation

Positive regulationof neuronal

differentiation

Protein KinaseC Activity

Negative regulationof mitotic cell cycle

Metal ionbinding

Positive regulationof gene expression

Negative regulation of transcription

(DNA-templated)


(from RNA polymeraseII promoter)

µ met

hyla

tion

µ expr

essi

on

8

10

12

14µexpression

0.2

0.4

0.6

µmethylation

Legend:

GO TermGenes

0.0

0.1

0.2

0.3

0.4

0.5

cg03

3112

74 (

β -va

lues

-pla

sma)

0.2

0.3

0.4

0.5

0.6

0.7

cg14

1749

12 (β

-val

ues-

plas

ma)

0.93

0.95

0.97

0.99

cg18

4731

37 (

β -va

lues

-ser

um)

OPD PitNET

*

PRKCA

D

8

9

10

cg27612889 // HDAC4ρ = –0.64; p-value = 7.6e-11

8

9

10

11cg06874896 // FOXP1

ρ = –0.62; p-value = 3.6e-10

5

6

7

8

9

10

cg00172803 // SLC38A8

ρ = –0.57; p-value = 1.6e-08

6

7

8

9

cg02834755 // TRIM5ρ = –0.56; p-value = 4.56e-08

6

7

cg23670353 // PM20D1ρ = –0.76; p-value < 2.2e-16

6

8

10

cg18321085 // KCNT2ρ = –0.75; p-value < 2.2e-16

8

9

cg16220260 // C1orf35ρ = –0.55; p-value = 6.31e-08

8

9

10

11

12

13

cg23752012 // FCRL6ρ = –0.53; p-value = 3.62e-06

8

9

10

11

12

13

cg24894107 // CAMK2N1ρ = –0.46; p-value = 1.38e-05

6

7

8

9

10

cg01081096 // ATG9Bρ = –0.41; p-value = 1.56e-04

6

7

cg01081096 // LSMEM1ρ = –0.36; p-value = 8.31e-04

6

7

8

9

cg00549331 // HMCN1ρ = –0.32; p-value = 0.0035

8

9

10

11

cg00729136 // FRA10AC1

ρ = –0.28; p-value = 0.012

5

6

7

8

9

cg01678321 // NMUR2

ρ = –0.18; p-value = 0.109

9.0

9.5

10.0

10.5

11.0

0.25 0.50 0.75 0.2 0.4 0.6 0.8 0.25 0.50 0.75

0.25 0.50 0.75

0.25 0.50 0.75 0.25 0.50 0.75 0.25 0.50 0.75

0.25 0.50 0.75 0.25 0.50 0.75 0.25 0.50 0.75 0.25 0.50 0.750.25 0.50 0.75

0.2 0.4 0.6 0.8 0.25 0.50 0.750.25 0.50 0.75

cg05084299 // RPS6KC1ρ = –0.11; p-value = 0.31

Promoter Probe-Gene PairsEnhancer Probe-Gene Pairs

FunctioningNonfunctioning

Functional Status:

–lo

g2 (

Gen

e ex

pre

ssio

n)

DNA Methylation (β -values)

–lo

g2 (

Gen

e ex

pre

ssio

n)

DNA Methylation (β-values)

2.5

5.0

7.5

10.0

12.5ρ = –0.5; p = 1.69e-06

cg03311274 // ALDH1A2(Enhancer)

11.0

11.5

12.0

12.5

ρ = –0.49; p = 3.32e-06

cg02082588 // ATP2C1(Promoter)

8

10

12

14

ρ = –0.38; p = 4.21e-04

cg01190276 // THRB(Enhancer)

6

7

8ρ = –0.34; p = 1.65e-03

cg12480903 // CD247(Enhancer)

6

7

8

9

0.2 0.4 0.6 0.25 0.50 0.75

0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6

0.25 0.50 0.75

ρ = –0.22; p = 0.043

cg13713922 // GLI2(Enhancer)

PitNET vs Other Sample Types: Probe-Gene PairsPitNET vs Other Sample Types: DAVID

C

NF-PitNET vs F-PitNET: DAVID

HDAC4

TRIM5

FOXP1

CAMK2N1

Positive regulation of sequence-specific DNA

binding TF activity

Positive regulation of smooth muscle cell proliferation

Protein Kinasebinding

GO Term

Genes

HD

AC

4 (–

log 2

(exp

r)-t

issu

e)

8

9

10

11

6

7

8

9

TR

IM5

(–lo

g 2(e

xpr)

-tis

sue)

8

10

12

CA

MK

2N1

(–lo

g 2(e

xpr)

-tis

sue)

0.25

0.50

0.75

0.25

0.50

0.75

0.25

0.50

0.75

cg27

6128

89 (

β-v

alue

s-tis

sue)

cg02

8347

55 (

β-v

alue

s-tis

sue)

cg24

8941

07 (

β-v

alue

s-tis

sue)

0.75

0.80

0.85

0.6

0.8

1.0

0.25

0.50

0.75

cg27

6128

89 (

β-v

alue

s-se

rum

)cg

1390

2645

(β

-val

ues-

plas

ma)

cg22

9427

04 (

β-v

alue

s-se

rum

)

Functioning Nonfunctioning Functioning Nonfunctioning Functioning Nonfunctioning

***

**

*** *** *

** *** *

****** *

Fig. 4 In silico validation of probe and putative target gene pairs and exploration of gene ontologies. (A) Negatively correlated methylation and expression levels of PitNET relevant probe-gene pairs, with associated gene ontologies (DAVID); (B) PitNET tissue-methylation and -expression levels18of negatively correlated and potentially PitNET relevant probe-gene pairs derived from multiple supervised analyses in liquid biopsy speci-mens; (C) PitNET tissue methylation and gene expression levels of negatively correlated probe-gene pairs annotated in regulatory regions of the genes (ELMER); (D) Negatively correlated methylation and expression levels of nonfunctioning PitNET-specific probe-gene pairs showing differ-ential methylation and expression levels between nonfunctioning and functioning PitNETs across liquid biopsy and tissue specimens (box plots), and associated DAVID results. Abbreviation: PitNETs, pituitary neuroendocrine tumors.

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euro-

On

cology

A B

BACH2

FOXP1

TIMP2

PRKCZ

GALNS

ALDH1A2

ATP2C1

HECA

SHMT2

CUL3

ATXN1

KLF6

CYB5D2

CALR

CDK5R1

LCN2

TRAF6

GLI2

Pituitary glanddevelopment

Positive regulationof cell proliferation

Positive regulationof neuronal

differentiation

Protein KinaseC Activity

Negative regulationof mitotic cell cycle

Metal ionbinding

Positive regulationof gene expression


(DNA-templated)


(from RNA polymeraseII promoter)

µ met

hyla

tion

µ expr

essi

on

8

10

12

14µexpression

0.2

0.4

0.6

µmethylation

Legend:

GO TermGenes

0.0

0.1

0.2

0.3

0.4

0.5

cg03

3112

74 (

β -va

lues

-pla

sma)

0.2

0.3

0.4

0.5

0.6

0.7

cg14

1749

12 (β

-val

ues-

plas

ma)

0.93

0.95

0.97

0.99

cg18

4731

37 (

β -va

lues

-ser

um)

OPD PitNET

*

PRKCA

D

8

9

10

cg27612889 // HDAC4ρ = –0.64; p-value = 7.6e-11

8

9

10

11cg06874896 // FOXP1

ρ = –0.62; p-value = 3.6e-10

5

6

7

8

9

10

cg00172803 // SLC38A8

ρ = –0.57; p-value = 1.6e-08

6

7

8

9

cg02834755 // TRIM5ρ = –0.56; p-value = 4.56e-08

6

7

cg23670353 // PM20D1ρ = –0.76; p-value < 2.2e-16

6

8

10

cg18321085 // KCNT2ρ = –0.75; p-value < 2.2e-16

8

9

cg16220260 // C1orf35ρ = –0.55; p-value = 6.31e-08

8

9

10

11

12

13

cg23752012 // FCRL6ρ = –0.53; p-value = 3.62e-06

8

9

10

11

12

13

cg24894107 // CAMK2N1ρ = –0.46; p-value = 1.38e-05

6

7

8

9

10

cg01081096 // ATG9Bρ = –0.41; p-value = 1.56e-04

6

7

cg01081096 // LSMEM1ρ = –0.36; p-value = 8.31e-04

6

7

8

9

cg00549331 // HMCN1ρ = –0.32; p-value = 0.0035

8

9

10

11

cg00729136 // FRA10AC1

ρ = –0.28; p-value = 0.012

5

6

7

8

9

cg01678321 // NMUR2

ρ = –0.18; p-value = 0.109

9.0

9.5

10.0

10.5

11.0

0.25 0.50 0.75 0.2 0.4 0.6 0.8 0.25 0.50 0.75

0.25 0.50 0.75

0.25 0.50 0.75 0.25 0.50 0.75 0.25 0.50 0.75

0.25 0.50 0.75 0.25 0.50 0.75 0.25 0.50 0.75 0.25 0.50 0.750.25 0.50 0.75

0.2 0.4 0.6 0.8 0.25 0.50 0.750.25 0.50 0.75

cg05084299 // RPS6KC1ρ = –0.11; p-value = 0.31

Promoter Probe-Gene PairsEnhancer Probe-Gene Pairs

FunctioningNonfunctioning

Functional Status:

–lo

g2 (

Gen

e ex

pre

ssio

n)

DNA Methylation (β -values)

–lo

g2 (

Gen

e ex

pre

ssio

n)

DNA Methylation (β-values)

2.5

5.0

7.5

10.0

12.5ρ = –0.5; p = 1.69e-06

cg03311274 // ALDH1A2(Enhancer)

11.0

11.5

12.0

12.5

ρ = –0.49; p = 3.32e-06

cg02082588 // ATP2C1(Promoter)

8

10

12

14

ρ = –0.38; p = 4.21e-04

cg01190276 // THRB(Enhancer)

6

7

8ρ = –0.34; p = 1.65e-03

cg12480903 // CD247(Enhancer)

6

7

8

9

0.2 0.4 0.6 0.25 0.50 0.75

0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6

0.25 0.50 0.75

ρ = –0.22; p = 0.043

cg13713922 // GLI2(Enhancer)

PitNET vs Other Sample Types: Probe-Gene PairsPitNET vs Other Sample Types: DAVID

C

NF-PitNET vs F-PitNET: DAVID

HDAC4

TRIM5

FOXP1

CAMK2N1

Positive regulation of sequence-specific DNA

binding TF activity

Positive regulation of smooth muscle cell proliferation

Protein Kinasebinding

GO Term

Genes

HD

AC

4 (–

log 2

(exp

r)-t

issu

e)

8

9

10

11

6

7

8

9

TR

IM5

(–lo

g 2(e

xpr)

-tis

sue)

8

10

12

CA

MK

2N1

(–lo

g 2(e

xpr)

-tis

sue)

0.25

0.50

0.75

0.25

0.50

0.75

0.25

0.50

0.75

cg27

6128

89 (

β-v

alue

s-tis

sue)

cg02

8347

55 (

β-v

alue

s-tis

sue)

cg24

8941

07 (

β-v

alue

s-tis

sue)

0.75

0.80

0.85

0.6

0.8

1.0

0.25

0.50

0.75

cg27

6128

89 (

β-v

alue

s-se

rum

)cg

1390

2645

(β

-val

ues-

plas

ma)

cg22

9427

04 (

β-v

alue

s-se

rum

)

Functioning Nonfunctioning Functioning Nonfunctioning Functioning Nonfunctioning

***

**

*** *** *

** *** *

****** *

Fig. 4 In silico validation of probe and putative target gene pairs and exploration of gene ontologies. (A) Negatively correlated methylation and expression levels of PitNET relevant probe-gene pairs, with associated gene ontologies (DAVID); (B) PitNET tissue-methylation and -expression levels18of negatively correlated and potentially PitNET relevant probe-gene pairs derived from multiple supervised analyses in liquid biopsy speci-mens; (C) PitNET tissue methylation and gene expression levels of negatively correlated probe-gene pairs annotated in regulatory regions of the genes (ELMER); (D) Negatively correlated methylation and expression levels of nonfunctioning PitNET-specific probe-gene pairs showing differ-ential methylation and expression levels between nonfunctioning and functioning PitNETs across liquid biopsy and tissue specimens (box plots), and associated DAVID results. Abbreviation: PitNETs, pituitary neuroendocrine tumors.

with publicly available PitNET tissue methylation and ex-pression datasets.18 We identified significant negative cor-relations between the methylation of CpG located in gene regulatory regions and the expression levels of their puta-tive target genes suggesting that these genes are epigenet-ically regulated by these DMPs (Figure 4B; Supplementary File S5). This hypothesis was corroborated by the finding that genes mapped to DMPs derived from the compar-ison between functioning and nonfunctioning in serum/plasma were differentially expressed in PitNET tissue. Overall, these target genes were involved in pathways related to pituitary development, function, tumorigen-esis, and behavior, as well as innate immune responses, cellular signaling and cell cycle (proliferation and apop-tosis), etc. and some were potential prognostic markers (TXNRD1) in other tumors or reported as therapeutic tar-gets in prolactinomas (ERBB2) (Figures 3D and 4A and D; Supplementary Figure S4A and D; Supplementary Files S2–S5).14,17,39–41 These results suggest that serum and plasma are resourceful for the identification of biologically and clinically relevant methylation markers and lay the groundwork for future functional and clinical studies.42,43

Most reported LB studies have used plasma instead of serum as a source of cfDNA to perform omics analysis. Although certain molecular results could be impacted by the use of serum profiling, for example, detection of so-matic mutations due to possible dilution or contamina-tion with genomic DNA derived from blood and other cells during the coagulation process,10,12,44 it does not seem to interfere with the detection of cell-specific methyla-tion markers as shown in this study. However, no formal head-to-head comparisons of molecular results between both sources have been previously reported. Here, we per-formed a head-to-head comparison between methylome analyses results derived from serum and plasma cfDNA and observed that these 2 sources of DNA are different in several aspects, such as cell composition estimations and genome-wide and supervised mean methylation levels; however, they both provided unique or common PitNET-specific markers that were able to differentiate PitNETs from other tumors, suggesting that the use of either blood element is useful, and possibly complementary, as a nonin-vasive methylation-based diagnostic tool (Supplementary Figure S2C; Supplementary File S5).45,46

Although grounded on robust bioinformatic analysis, our results are limited by the relatively small cohort of PitNETs, specifically NF-PitNETs and OPD, the inclusion of controls with known epigenetic abnormalities in blood samples (eg, epilepsy); unavailability of paired methylation and expres-sion across OPD and validation with functional studies.

Altogether, our results provide evidence that PitNETs re-lease DNA methylation markers in the serum/plasma and that blood-based LB constitutes a reliable approach for the noninvasive detection of clinically relevant epigenetic signa-tures specific to PitNETs. Specifically, using serum or plasma specimens, it is feasible to generate methylation-based pre-diction models that successfully distinguish PitNET and NF-PitNET from OPD. These results lay the groundwork for the potential application of these models to complement im-aging to differentiate challenging cases of sellar diseases that mimic PitNETs and ultimately optimize diagnostic and thera-peutic management using noninvasive techniques.

Supplementary Material

Supplementary material is available at Neuro-Oncology online.

Keywords

liquid biopsy | methylation | PitNET | plasma | serum

Funding

This work was supported by the Department of Neurosurgery, HBTC and HFHS; A.V.C. and K.P.A. are supported by the Henry Ford Hospital (A30935, A30957; GME Research Grants 202199); A.E.S. is supported by the Peter D. Cristal Chair of Neurosurgical Oncology and the Kimble Family Foundation.

Acknowledgments

The authors are grateful to the HFHS patients and UH patients who consented to the usage of PitNETs for research purposes. We thank Nancy Takacs, Nicolette Lineberry, and Heather Mengel for their administrative support; Simona L. Cazacu and Bartow Thomas for the collection, handling, and mainte-nance of the tumor bank at the Hermelin Brain Tumor Center (HBTC); Andrea Transou for tumor pathology processing; Laura A. Hasselbach for DNA extraction; Daniel Weisenberger and team at USC Epigenome Center for assistance with DNA methylation profiling; CWRU/UH team: Penelope Miron, Kristy Miskimen, Kristin Waite, Gino Cioffi, Tuesday Gibson for plasma sample selection, cfDNA extraction and shipping and clinicopathological curation; Susan MacPhee for proofreading the manuscript.

Conflict of interest statement. The authors declare to have no conflict of interest.

Authorship statement. Overall concept and coordination of the study: A.V.C., J.R., H.N., G.A.H., and K.P.A.; retrieval of publicly available molecular and clinical data: G.A.H., K.P.A., M.W., and A.V.C.; bioinformatic and statistical analyses: G.A.H., M.W., T.S.S., T.M.M., M.S.M., H.N., A.V.C., and input from L.M.P.; pa-thology review (HFHS): A.M., and J.S.B.; serum and tissue col-lection, tumor bank storing, and maintenance: K.N., A.C.C., and L.S.; molecular data generation: T.M.M. and A.C.C.; procurement of biospecimens/clinical data (CWRU/UH): J.S.B., A.E.S., W.R.S., and J.C.; the manuscript was written by A.V.C., G.A.H., H.N., and M.W.; intellectual contribution: K.P.A., J.S., J.C., T.M.M., S.K., T.W., J.S.B., M.R., T.M., A.B., and S.K. All authors contributed to the revision of the manuscript.

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