Whole exome sequencing reveals novel risk genes of pituitary neuroendocrine tumors.

Somatic genetic alterations in pituitary neuroendocrine tumors (PitNET) tissues have been identified in several studies, but detection of overlapping somatic PitNET candidate genes is rare. We sequenced and by employing multiple data analysis methods studied the exomes of 15 PitNET patients to improve discovery of novel factors involved in PitNET development. PitNET patients were recruited to the study before PitNET removal surgery. For each patient, two samples for DNA extraction were acquired: venous blood and PitNET tissue. Exome sequencing was performed using Illumina NexSeq 500 sequencer and data analyzed using two separate workflows and variant calling algorithms: GATK and Strelka2. A combination of two data analysis pipelines discovered 144 PitNET specific somatic variants (mean = 9.6, range 0-19 per PitNET) of which all were SNVs. Also, we detected previously known GNAS PitNET mutation and identified somatic variants in 11 genes, which have contained somatic variants in previous WES and WGS studies of PitNETs. Noteworthy, this is the third study detecting somatic variants in gene RYR1 in the exomes of PitNETs. In conclusion, we have identified two novel PitNET candidate genes (AC002519.6 and AHNAK) with recurrent somatic variants in our PitNET cohort and found 13 genes overlapping from previous PitNET studies that contain somatic variants. Our study demonstrated that the use of multiple sequencing data analysis pipelines can provide more accurate identification of somatic variants in PitNETs.

Introduction

Pituitary neuroendocrine tumor (PitNET) or previously often described as clinically significant pituitary adenoma is a rare non-metastasizing endocrine neoplasm affecting approximately 1 person out of 1000 in the general population [1]. Hormone secreting PitNET can cause overproduction of growth hormone (GH), adrenocorticotropic hormone (ACTH), prolactin (PRL) or rarely other hormones leading to various systemic endocrine disorders (acromegaly, Cushing’s disease, and others). PitNETs without disruption of patient’s hormone profile (previously called non-functioning pituitary adenoma) is a subgroup of PitNETs which does not cause disbalance of pituitary hormones. They are usually diagnosed later in life than hormones producing PitNETs and usually due to symptoms related to the effects of the proliferation of PitNET cells on surrounding tissue [2, 3].

High genetic heterogeneity has been observed in pituitary PitNET tissue. Newey et al. have sequenced exomes of somatic tissue of seven PitNETs which do not secrete hormones tissues and found 24 potential somatic candidate variants with an average of 3.5 variants per tumor (range from 1 to 7 per neoplasm). They also performed targeted sequencing of potential drivers in PDGFD, NDRG4, ZAK in an independent sample group of 24 tumors, but found no somatic variants in the identified candidates [4]. A study that conducted exome sequencing of 36 somatotroph PitNET tissues discovered 132 somatic variants with average 3 variants per sample ranging from 0 to 13 in the tumor. The GNAS mutations were found in 11 tumors (31.4% from 36) and several somatic variants in genes involved in cAMP and calcium signaling were identified [5]. Genome sequencing of 12 somatotroph PitNET have also found mutations in GNAS, but no other recurrent somatic variants, and in this report authors demonstrated average 129 somatic variants per tumor genome (range 11–273) and 2.3 variants per exome [6].

Song and colleagues have performed one of the largest studies to date analyzing the genetic landscape of 125 PitNETs [7] identifying GNAS and USP8 mutations repeatedly confirming the role of these genes in PitNET development, as reported by other studies [5, 8–10]. This study also indicated variants in genes NR3C1, KIF5A, GRB10 as potential recurrent candidates, and identified MEN1 somatic variant in two patients without familial MEN syndrome. The authors also found enrichment in somatic variants in Raf/MEK/ERK, PI3K/AKT/mTOR, cAMP, and other signaling pathways [7].

Exome sequencing of eight thyropinomas have not revealed convincing recurrent somatic variants [11] in this study group, but exome analysis of larger group of 42 PitNETs indicated several mutations and insertions/ deletions in genes ATAD3B, BHLHE22, KDM2B, OR5M1, TTN, VPS13B that were encounter in more that one PitNET [12]. No recurrent somatic variants were found in the whole-exome sequencing of 12 prolactinomas [13]. Nemeth et al. have studied mitochondrial variants in 44 PitNET cases and found no significant association with clinical features of the tumor, however, they demonstrated that samples with the highest homoplasmic variant rate had the highest Ki-67 index [14].

Recent study of 134 patients demonstrated novel molecular PitNET classification with deviations from cell lineage guidelines of 2017. High amount of somatic variants per exome (range: 14–247) was reported and recurrence of USP8 and GNAS variants confirmed, as well as presence of chromosomal alterations [15].

Available literature data shows that the number of overlapping candidate genes harboring somatic variants in PitNET like GNAS and USP8 have been confirmed in several independent studies [5, 7, 9, 10], but most of the discovered somatic variants have been detected in the single tumor and have not become novel targets for deeper investigation of PitNET development. Genes that are involved in the development of inherited pituitary PitNET (AIP, MEN1, CDKN1B, PRKAR1A) have mostly shown not to contain somatic variants in sporadic cases, with rare exceptions [7, 12]. Additional studies using independent sample groups and larger sample sizes are required to identify overlapping candidate gene to be further investigated for their role in PitNET development mechanisms and as potential targets for improvement of therapy and patient’s health care.

In this study, we performed exome sequencing of fifteen PitNETs and used two somatic variant detection algorithms for improved yield and reduced impact of algorithm specific biases to discover somatic variants. We discovered two recurrent somatic variants and two recurrent genes within our sample as well as 13 genes containing somatic variants which are overlapping with other independent studies. We believe that the sequencing data analysis of PitNET benefits from using multiple variant calling approaches by providing higher confidence in variants that were detected with different methods.

Materials and methods

PitNET sample collection

PitNET tissue samples were obtained from transsphenoidal resection leftover material at the Pauls Stradins Clinical University Hospital (Riga, Latvia) from March 2016 to February 2018. Patients with sporadic PitNET were enrolled from 15 consecutive surgeries that provided sufficient amounts of tissue material. Before the surgery PitNET patients were recruited to the government-funded national biobank the Genome Database of Latvian Population (LGDB) and venous blood samples were collected and processed according to the protocol described in [16]. Two written informed consents have been obtained from the patients (1) broad consent for LGDB for use of biological material and medical data for human health and hereditary research, and (2) project-specific consent to the research of pituitary tumors, both studies have been approved by the Central Medical Ethics Committee of Latvia (protocol No. 22.03.07/A7 and 2/18-02-21, respectively). Immediately after surgery PitNET tissue was cut in two parts, (1) was transferred to RNAlater™ Solution (Thermo Fisher Scientific, USA) for DNA/RNA extraction, and (2) was immersed in Dulbecco’s Modified Eagle Medium (DMEM) (Thermo Fisher Scientific, USA) containing 1x penicillin/streptomycin solution (GIBCO, USA) for other research activities.

PitNets were classified based on the strongest expressed transcription factor (PIT1, NR5A1 (also known as SF1), TBX18 (also known as TPIT)) and subsequent expression of pituitary hormones (GH, PRL, ACTH, TSHb, LHb, FSHb, CGA). In cases where IHC expression profiles could not be obtained, tumours were classified based on their clinical manifestation.

Immunohistochemistry of PitNET samples

Immunohistochemical analysis of paraffin-embedded PitNET tissues was performed as an outsourcing service in the Pauls Stradins Clinical University Hospital Institute of Pathology. The following antibodies were used: mouse anti-Growth Hormone (GH) Antibody (MA5-11926), mouse anti-Prolactin (PRL) Monoclonal Antibody (MA5-11998), mouse anti-ACTH Monoclonal Antibody (MA5-13455), mouse anti-Thyroid Stimulating Hormone (TSHb) Antibody (MA5-12159), mouse anti-Luteinizing Hormone (LHb) Monoclonal Antibody (MA5-12138), mouse anti-Follicle Stimulating Hormone (FSHb) Monoclonal Antibody (MA5-12144), mouse anti-Follicle Stimulating Hormone alpha (CGA) Monoclonal Antibody (MA1-82895), rabbit polyclonal NR5a1 antibody (PA5-25030) from Thermo Fisher Scientific, USA, mouse monoclonal Anti-Pit-1 Antibody (G-2) (sc-25258) from Santa Cruz Biotechnology, USA and rabbit polyclonal anti-TBX19 antibody (HPA072686) from Atlas Antibodies, Sweden. The staining was performed on an automated Daco IHC Stain system. Protein expression was evaluated using a 0-3-mark system (1 = <30% positive cells; 2 = 30–70% positive cells; 3 = >70% positive cells). Additional notes were made regarding special characteristics of protein expression patterns (whether cells with positive staining are equally scattered across the sample or arranged in groups; proteins are expressed in the cytoplasm, nucleus, cell membrane or cytoplasmic inclusions).

DNA extraction

Germline DNA of the patients was isolated from white blood cells according to biobank protocols [16]. Collected PitNET tissue samples were stored in RNAlater™ Solution at +4°C up to 24 h from surgery, and frozen at -20°C upon the delivery to the biobank. For DNA/RNA extraction 20–30 mg of tissue samples were lysed using Lysing Matrix D and FastPrep -24 homogenizer (MP Biomedicals, USA). DNA was extracted using AllPrep DNA/RNA Mini Kit (Qiagen, Germany) following the manufacturer’s instructions. The concentration of the extracted DNA was measured with Qubit™ dsDNA HS Assay Kit and Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, USA).

Library preparation and exome sequencing

Exome sequencing was carried out as an outsourcing service to commercial sequencing provider Genera Ltd. (Latvia). DNA libraries were constructed using Illumina Nextera TruSeq Exome kit (Illumina, USA) and sequenced with Illumina NextSeq 500/550 High Output v2 kit (150 cycles) (Illumina, USA) obtaining 75bp paired-end reads on Illumina NexSeq 500 sequencer (Illumina, USA). Exome sequencing was performed in five batches (Table 1).

Table 1

Batch information of PitNET exome sequencing.

Batch # (date)	Germline DNA samples	Somatic DNA samples
1 (jul 2017)	PN02	PN01
2 (oct 2017)	PN01; PN03; PN04; PN05; PN06; PN07	PN02; PN03; PN04; PN05; PN06; PN07
3 (nov 2017)	PN08; PN09; PN10; PN11; PN12	PN08; PN09; PN10; PN11
4 (jul 2018)	PN13; PN14; PN15	PN12; PN13; PN14; PN15
5 (jan 2019)	PN15	PN15

The sequencing data has been deposited to The European Genome-phenome Archive with access ID EGAC00001001730.

Data analysis

Illumina exome target manifest TruSeq Rapid Exome TargetedRegions v1.2 (Illumina, USA) was used to define exome regions in the UCSC hg19 reference. This version of exome has a total length of 45 297 543 bp and includes 214 126 regions.

To detect PitNET somatic variants two algorithms were employed: GATK [17] as implemented in DRAGEN Somatic Pipeline (v3.4.5) and Strelka2 (v2.9.10) [18].

Human genome GRCh37/ hg19 version was used for alignment.

GATK in DRAGEN Somatic Pipeline (v3.4.5) uses a hybrid hardware/software platform to achieve a high speed of sequencing data analysis [19]. The somatic pipeline processes both germline and somatic exome simultaneously and reports variants only present in somatic DNA.

Strelka2 variant caller was run using default parameters. Variant filtering was performed in the same manner across all result files with following parameters: "PASS" in the quality filter, quality score ≥10, and alternative allele has at least four supporting reads. Then germline-somatic variant call file pairs of the same sample were compared and somatic exome unique variants identified with following requirements: germline sample at the somatic variant position has at least 10X coverage, somatic variant is not located within or adjacent to (one nucleotide distance) homopolymer (≥4 bases) sequence and variant is not located in a repeated sequence region. Somatic variants colocated with existing database SNPs were registered and filtered if the population MAF above 0.1%. Overlapping variants between both workflows were registered as true positives. Variant Effect Predictor as implemented in ensembl.org was used to predict functional consequences of variants [20]. Copy number analysis was performed using CoNIFER [21] and XHMM [22]. Variant frequency data from gnomAD v2.1.1 database was used to present results [23].

Results

Characteristics of PitNET samples

We obtained whole-exome sequencing data of paired white blood cells and PitNET tissue from 15 patients. Mean depth of coverage was 50.3X (range 15.9–82.2X, SD = 17.2). Individual PitNET patient information is shown in Fig 1. Clinically nine PitNETs were defined as NFPA, three were GH secreting and two PRL secreting tumors. Immunohistochemistry analyses identified that PIT1 cell lineage marker dominates in four PitNETs, gonadotroph PitNET marker NR5A1 (SF1) is the most abundant in five tumours and corticotroph cell lineage marker TBX19 (TPIT) has highest expression in one PitNET (Fig 1 and S1 File). All PitNETs were larger than 10 mm in at least one dimension in MRI data measurement and 60% of patients were men.

Fig 1

Amount of somatic variants, clinical information of PitNET patients (PitNET clinical type, age at diagnosis, sex), % of cells expressing lineage markers and genes with recurrent somatic variants are shown for all 15 PitNET patients.

PitNETs are listed in ascending order from left to right.

Immunohistochemistry staining results of PitNET samples

Immunohistochemistry data of paraffin-embedded PitNET tissues were obtained for 11 PitNET patients using hormone antibodies (except CGA, where eight samples were analysed) and also 11 but different samples for lineage marker antibodies (S1 File). Concordance of hormone antibody immunohistochemistry with clinical diagnosis was observed in eight out of 11 patients, meanwhile nine out of 11 patients had agreement between clinical diagnosis and ICH data for PitNET lineage markers (PIT1, NR5A, TBX19). Most of NFPA clinical diagnoses actually were gonadotroph hormones expressing PitNETs. Few notable exceptions were observed: PN02 (with clinical NFPA diagnosis) was positive for GH and ACTH as antibody staining showed up to 30% of cells containing these hormones. PN04 (NFPA) had no detectable hormone expression, but PN05 (NFPA) was expressing every tested hormone, with high levels of GH, PRL and ACTH showing up in the data, although the highest expression level was for glycoprotein alpha subunit (required to produce gonadotropic hormones). PN10 (clinically PRL secreting PitNET) and PN14 (clinically GH secreting PitNET) were also expressing high levels of the glycoprotein alpha subunit.

PitNET cell lineage markers showed difference from clinical diagnosis in PN02 and PN10. PN02 is expressing corticotroph cell lineage marker in 30–70% of its cells. PN10 which clinically manifests as prolactin secreting PitNET is expressing higher amount of NR5A which is gonadotroph cell lineage marker than PIT1 associated with development of prolactin secreting PitNET.

Identification of somatic exome variants

Results of exome sequencing of 15 PitNETs show a total of 144 PitNET specific somatic variants (mean = 9.6, range 0–31 per PitNET) of which all were SNVs. We employed two somatic variant search algorithms (GATK and Strelka2) and reported only somatic variants where both of the employed algorithms agreed.

Most of the discovered variants (N = 91) are located within the exons of genes and 58 of those are changing the amino acid sequence of the protein (range per tumor 0–11, mean 3.9) while 22 are synonymous SNVs, rest being 3 prime UTR variants (N = 4), 5 prime UTR variants (N = 3) and non coding transcript exon variant (N = 4).The most common location of variants outside the coding sequence were introns (N = 44). The rest of the variants were located in untranslated gene regions, non-coding transcripts, predicted splice sites and outside genes altogether (S1 Table).

Assessment of possible functional consequences of SNVs identified eight SNVs from seven PitNETs which were rated within the top 0.1% deleterious SNVs in the human genome according to Combined Annotation Dependent Depletion (CADD) matric (CADD Phred score >30) and further 34 SNVs within the top 1% deleterious SNVs in the human genome (Phred score >20). All but two of the analyzed PitNET samples contain at least one highly deleterious SNV (Phred score >20) (S1 Table). Three PitNETs have a somatic previously known PitNET associated GNAS mutation (position R844C in transcript ENST00000371100.4). Three genes (GNAS, AHNAK and PAML2/AKAP2) have a mutation in at least two PitNETs but in different positions. While transcribed RNA pseudogene AC002519.6 contains the same genetic variant in the same position in two PitNETs (Tables 2 and 3). Additionally, we identified 13 overlapping genes with other PitNET and neoplasm genetic studies (GNAS [8], CRTC3, SMARCA4 [24], KIT [25], PLA2G6, TESK1 [5], TSC2 [4], KLHL4, RYR1 [6], POLR3B, KHDRBS2, LAMA1, TUBGCP6 [7] and AHNAK [15]).

Table 2

Somatic variants detected in more than one PitNET sample.

CHR:pos	Ref/ Alt	PitNET samples	Alt allele fraction	Gene	Consequence	CADD Phred	gnomAD freq
chr20:57484420	C/T	PN14	41.7%	GNAS	Missense variant	34	0
		PN13	41.0%
		PN08	45.4%
chr16: 31818244	C/G	PN01	10.3%	AC002519.6	Downstream gene variant, rs111534922	2.24	0
		PN07	16.7%

Chr–chromosome, pos–position, PitNET–pituitaryneuroendocrine tumor, ref–reference allele, alt–alternative allele, CADD–CombinedAnnotation Dependent Depletion, freq–frequency.

Table 3

Somatic variants found in the same gene and in more than one PitNET sample.

Chr	Position	PitNET sample	Ref/Alt	Gene	Consequence	CADD Phred	gnomAD freq
chr11	62283636	PN02	T/C	AHNAK	3 prime UTR variant	0.57	0
chr11	62289022	PN01	T/A		synonymous variant	0.008	0.003%
Chr9	112625173	PN06	G/T	PALM2/AKAP2	Intron variant	1.26	0
Chr9	112687347	PN15	T/A		Missense variant, COSV57111433	19.12	0

Chr–chromosome, PitNET–pituitary neuroendocrine tumor, ref–reference allele, alt–alternative allele, CADD–Combined Annotation Dependent Depletion.

Copy number analysis was performed using CoNIFER [21] and XHMM [22] algorithms did not reveal presence of copy number alterations in analysed samples.

Discussion

This is the first study demonstrating the advantages of exome sequencing analysis using multiple data analysis workflows in the research of PitNET genetic composition. Despite the existing knowledge that in the exome and genome sequencing variant calling results from the single pipeline are incomplete [19, 26], no current literature data is examining the consequences of this for the discovery of PitNET somatic variants. It has been demonstrated that non-overlapping variants from different pipelines mostly are true positive nucleotide changes [19] yet most raw non-overlapping calls of both GATK and Strelka2 in our data showed low alternate allele frequency and other variant calling issues. Therefore, our results provide a more comprehensive approach to identify the genetic composition of PitNET. Exome analysis of blood–tumor pairs from 15 PitNET patients using two distinct data analysis workflow showed increased mean number of somatic variants per PitNET (9.6) compared to previous publications (range 2.3–3.5) [4, 6, 7] and is supported by two of the latest reports [12, 15] showing number of somatic variants often above 10 per tumor. Such an improved approach can lead to the identification of novel mechanisms of PitNET development. Although studies published in literature already encompass several hundred exomes and genomes, recurrent somatic variants beyond established risk factors (GNAS, USP8) are virtually non-existent in sporadic PitNET cases. This could be explained that PitNETs form according to Knudson’s two hit hypothesis [27] and large amounts of nucleotide change combinations can result in a PitNET. Also it is possible that constraints of current exome and genome sequencing technology and analysis algorithms are unable to find certain amounts of somatic variants, therefore important findings are missing from literature. The third reason for low overlap in results of the published PitNET genomic studies could be bias introduced by sampling as surgery materials are obtained mostly from patients with PitNETs which are unresponsive to drug treatment.

Regarding IHC results confirmed that most of the PitNET presented clinical phenotype according to their expressed lineage markers and hormones. Only one of the IHC tested clinically non-functional PitNET (PN02) differed from the rest by having TPIT cell lineage marker ekspressed in 30%– 70% of observed cells rather than NR5A1. On the other hand, PN10 was presenting hyperprolactinemia phenotype, but the highest proportion of cells (>70%) was expressing CGA and NR5A1 indicating gonadotroph PitNET [28], nevertheless, PN10 was also expressing POU1F1 cell lineage marker and PRL in up to 30% of cells, probably causing more noticeable hyperprolactinemia induced clinical phenotype. Also we observed that four out of five PRL or GH secreting PitNETs were expressing more than one cell lineage marker while the same was true in two out of six PitNETs which were designated as non-functioning PitNET. Similar differences between secretion subtype and cell lineage markers has been observed in other studies [15] and they are of low concern regarding detection of somatic tumor variants.

We have, for the first time, detected recurrent somatic variant located in exactly the same position in an AC002519.6 of the two PitNETs (PN01 and PN07) (Table 2). The variant is listed in dbSNP with identifier rs111534922, but has 0 MAF across all populations. As a transcribed RNA pseudogene, there is little information about it in literature [29].

Total transition to transversion ratio was 1.3. This is in line with literature data where variability of transition/transversion ratio across PitNET is high, although the majority have it between 1.2 and 3 [6].

It is fairly common to observe somatic mutations in GNAS, USP8 and MEN1 when performing WES or WGS in PitNET patients [6, 7, 10]. We also detected common GNAS somatic mutation at chr20:57484420, rs11554273 (also known as GNAS 201 R>C) in three PitNET patients (all expressing PIT) (Table 3). GNAS rs11554273 has virtually zero frequency of a minor allele in the general population sample, but is repeatedly found in PitNET tissues [6, 7] and it is heterogeneously impacting clinical properties of somatotroph PitNET [30].

The only other recurrent gene with somatic variants in more than one tumor was AHNAK (PN01 and PN02), but impact on gene function for both variants is hard to pinpoint due their supposedly benign consequence (synonymous variant and 3’ untranslated region variant) and low predicted consequence phred score by CADD (0.009 and 0.59 respectively). Interesting that somatic variants in AHNAK have been reported before in PitNET [15]. AHNAK previously has been implicated in both pro-tumorigenic [31-33] and anti-tumor [34] roles of which the most interesting in PitNETs would be its involvement in epithelial-mesenchymal transition [35]. Nevertheless, its role in PitNETs should be defined in additional, more focused studies. Another way to perform the research of somatic variants of PitNET is to evaluate their presence in the analyzed sample as an allelic fraction. Although limitations of this approach are clear (random distribution of allelic fraction, higher false discovery rate, allele-specific library amplification and PitNET tissue sample pollution with non-tumor cells), we speculate that at least part of somatic variants with allelic fraction below 20% (designated as “subclonal”) arose as a result of increased rate of cell division and diminished capability to repair DNA replication errors in an already established PitNET. This may indicate that the majority of true subclonal variants are present only in part of PitNET cells and promotes a question if these genetically heterogeneous cell groups have an impact on overall PitNET properties and clinical characteristics. Fifty-eight (40.35%) of detected somatic variants in this study were subclonal, showing that multiple variation detection algorithms identify such variants and that they are common in PitNETs.

Several genes that have been reported with somatic variants (without dbSNP identifier and gnomAD frequency) in previous PitNET exome and genome sequencing studies, also harbor somatic variants in tumors of our PitNET patients. These genes are (GNAS [8], TSC2 [4], KLHL4 [6], POLR3B, KHDRBS2, LAMA1, TUBGCP6 [7], RYR1 [7, 6], PLA2G6, TESK1 [5], AHNAK [15] but their somatic variants are all novel and in all cases distant enough to exclude variant hotspots or disruption of certain part of the protein. These genes could be potential candidates for further studies as functional pathways impacted by these genes lead to the formation of PitNETs. RYR1 has been reported in previous PitNET studies at least twice [6, 7] and had not been highlighted as PitNET associated. RYR1 is not expressed in rat pituitary [36], no data about this gene expression in human pituitary have been reported, if indeed RYR1 is related to PitNET development this could also be due to an indirect effect through gene-gene interaction or linkage disequilibrium with another causative factor. RYR1 functions as a regulator of inner cell Ca2+ flow, the dysfunctions of this factor have been implicated in heart conditions, myopathies and neurodegenerative disease [37]. It has been shown that somatic variation in calcium regulation related genes are common in PitNETs [5], therefore this is an interesting target for potential functional studies in PitNET development.

LAMA1 [38, 39] and AHNAK [35] has been implicated in tumor metastasis while TSC2 is considered tumor suppressor (reviewed in [40]). Relation of other overlapping genes with PitNET is more obscure.

Tumor-related gene KIT [25], was also detected to have a somatic variant. (synonymous and splice site variant) was found in PN02. This somatic variant was estimated to be relatively benign.

Several limitations can be identified for our study. The deviation of PitNET type distribution in our sample from general distribution of clinical PitNET patient type in our population and literature [41] could be explained with relatively small sample size and/ or overrepresentation of large PitNET (over 10 mm in largest dimension)which are more likely to be treated with surgery and therefore providing tumor cells for genetic research. The unusual gender disbalance also is most likely attributed to chance as the material from surgeries from March 2016 to February 2018 were collected. And finally, chromosomal aberrations in PitNET are often investigated by using array genotyping which could add information on copy number variations across PitNET genomes. We did not detect high confidence copy number variations in our samples confirming problems in such analysis from exome data mentioned in M. Fromer and S. M. Purcell, 2015 [22].

In conclusion, besides GNAS and AHNAK, we have identified PitNET 11 more genes overlapping from previous PitNET studies which contain somatic variants of which several (RYR1, LAMA1, AHNAK, TSC2) could be highlighted as potential candidates for further PitNET research. The role of these genes and somatic variants in PitNET development should be investigated in further studies. We propose the use of multiple sequencing data analysis pipelines as they can provide more accurate identification of somatic variants in PitNETs.

Description of all somatic variants of PitNET exomes.

(XLSX)

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Example photographs of immunohistochemistry detection of PIT1 cell lineage marker, corticotroph cell lineage marker TBX19 (TPIT) and gonadotroph PitNET cell lineage marker NR5A1 (SF1).

(ZIP)

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19 Mar 2021

Submitted filename: Response to Reviewers_V2.doc

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29 Jun 2021

PONE-D-20-40370

Whole exome sequencing reveals novel risk genes of pituitary neuroendocrine tumors

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this manuscript, the authors described their findings of risk genes from patients with pituitary neuroendocrine tumors with whole exome sequencing data. The authors have improved their analysis methods and changed the manuscripts according to the suggestions from previous reviewers. This work could be helpful for future research and treatment of this disease. Thus, I suggest accepting this manuscript if the authors can go through the manuscript carefully and made improvements seriously.

1. For methods, include the details about how genes and variants were annotated. Which version of gnomAD data is used? Some brief explanation about columns in table S1 is necessary.

2. Improve the language. “Is the manuscript presented in an intelligible fashion and written in standard English?” The authors replied that “Thank you for this comment we thoroughlychecked the manuscript for…” It is unexpected that “thoroughlychecked” should be “thoroughly checked”.

I will list some examples:

In abstract: “We sequenced and employing multiple data analysis methods studied the exomes of 15 PitNET patients to improve discovery of novel factors involved in PitNET development.”

Line 96: “Extended studies using independent sample groups and larger sample sizes are required to reveal overlapping candidates to be further investigated for their role in PitNET development mechanisms and potential improvement of therapy and patient's health care.” Hard to understand.

Line 102: “We discovered two recurrent somatic variants within our sample and two recurrent genes within our sample as well as 13 genes compared to other independent studies.” It should be “our samples”? It is not necessary to use “our sample” twice. “samples”?

Line 299: “but both variants are unlikely to impact gene function both due their consequence (synonymous variant and 3’ untranslated region variant) and consequence prediction phred score by CADD (0.009 and 0.59 respectively).”

Line 354: “In conclusion we have identified PitNET 11 more genes overlapping from previous PitNET studies which contain somatic variants of which several (RYR1, LAMA1, AHNAK, TSC2) could be highlighted as potential candidates for further PitNET research.” Line 36: “In conclusion, we have identified two novel PitNET candidate genes (AC002519.6 and AHNAK) with recurrent somatic variants in our PitNET cohort and found 13 more genes overlapping from previous PitNET studies that contain somatic variants.” The description seems wrong. 11 or 13? “more genes overlapping from previous studies”? My understanding is overlapping means recurrent detection of these genes, and it is hard to know what “more” means here.

3. Please use a table to describe the sequenced runs for each sample. Current description in “Library preparation and exome sequencing” include partial details. If there is no batch effect, the authors may simplify the descriptions. Current description only included partial details about how samples were sequenced.

4. “Most of the discovered variants (N = 93) are located within the coding sequence of genes and 58 of those are changing the amino acid sequence of the protein (range per tumor 0 – 11 , mean 3.9) while 22 are synonymous SNVs.” 58 + 22 = 80. What are the rest 13 variants?

5. No summary for “Immunohistochemistry of PitNET samples”.

6. Line 104: “We show that the sequencing data analysis of PitNET benefits from using multiple variant calling approaches”. Past tense should be used. Also, current evidence does not support the conclusion that “multiple variant calling approaches” benefit “data analysis of PitNET”.

Minor:

Line 27: change “Two samples” to “For each patient, two samples”.

Line 345: change “largePitNET” to “large PitNET”?

Reviewer #2: The manuscript PONE-D-20-40370 entitled ‘Whole exome sequencing reveals novel risk genes of pituitary neuroendocrine tumors’ identify novel mutations in pituitary tumors. The study is really interesting from the clinical point of view, since it validates some of previously described mutations and provides evidence of new candidate genes. It is not a large cohort, but it is still informative work. Nevertheless, there are some issues that may be addressed.

1. The pituitary tumor subtype is a key element of these kind of studies, and it must be indicated in the abstract and introduction. Although it may be clear in the figure, a more detailed characterization including IHC should be included, perhaps a non-gonadotroph but ACTH- or GH-silent adenomas were included, which may be interesting.

2. Since there is a vast range of mutation per sample, from 0 to 31, is there any correlation with clinical or molecular characteristics?

3. In the discussion, the authors indicate that the use of different workflows is advantageous, nevertheless, they do not really compare the methods, since they combine the results of the different workflows used.

Reviewer #3: This article reported results for the identification of two novel PitNET candidate genes ( AC002519.6 and AHNAK) with recurrent somatic variants in a PitNET cohort and found 13 more genes overlapping from previous PitNET studies containing somatic variants. This article does not show relevant and robust results in the field of PitNETs. Thus, there are major aspects that could be review aiming to improve the manuscript:

- The authors should considerably improve the result section. In general, the results are insufficient since the authors could acquire more results from the methodology used and data analysis. Then, we encouraged the author to deeply analyzed the data obtained from both methodologies used (GATK and Strelka2).

- The sample cohort is only for 15 samples, being only three GH secreting and two PRL secreting tumors. Thus, the authors should complete the cohort with more samples for each subtype to avoid bias in the data analysis or reanalyzed other cohorts with the same algorithm in order to corroborate the data of your cohort.

As a minor comment, I would like to suggest that the expression level in figure 1 should be represented as a log2(FC) and not as a percentage because they could be ambiguous.

In conclusion, this article needs to improve major/minor aspects to achieve an appropriate relevance.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Antonio C. Fuentes-Fayos

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4 Jan 2022

Response to Reviewers

We thank editor and reviewers for considering this work and involvement to improve quality, relevance and readability of this research article. We looked into comments made by reviewers and did our best to improve the manuscript according to reviewers' suggestions.

Specific answers to all the issues raised by reviewers are listed below:

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

Author answer: We have changed multiple instances in the manuscript according to further reviewer comments including additional information on immunohistochemistry results, sequencing batches, improved data and legend if Fig 1, added legend to S1 and expanded discussion. Also we aligned conclusions more closely to the data.

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: I Don't Know

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Author answer: We thank for careful check of the manuscript requirements

Reviewer #1: In this manuscript, the authors described their findings of risk genes from patients with pituitary neuroendocrine tumors with whole exome sequencing data. The authors have improved their analysis methods and changed the manuscripts according to the suggestions from previous reviewers. This work could be helpful for future research and treatment of this disease. Thus, I suggest accepting this manuscript if the authors can go through the manuscript carefully and made improvements seriously.

1. For methods, include the details about how genes and variants were annotated. Which version of gnomAD data is used? Some brief explanation about columns in table S1 is necessary.

Author answer: Thank you for your time and effort to help us improve the manuscript! We used gnomAD v2.1.1 for variant frequency data. We have included this information in the manuscript line 224: "Variant frequency data from gnomAD v2.1.1 database was used to present results." We have added additional tab "Legend" to the S1 table, explaining all the column names and abbreviations within them.

2. Improve the language. “Is the manuscript presented in an intelligible fashion and written in standard English?” The authors replied that “Thank you for this comment we thoroughlychecked the manuscript for…” It is unexpected that “thoroughlychecked” should be “thoroughly checked”.

Author answer: Thank you for careful reviewing. This is unfortunate technical issue of dropping "space" between words is caused by moving manuscript between authors who are using different versions of MS Office. It is caused during the saving procedure, therefore, impossible to prevent if using affected version. We will correct the spelling mistakes and use newest MSOffice version to ensure these mistakes are not repeated.

I will list some examples:

In abstract: “We sequenced and employing multiple data analysis methods studied the exomes of 15 PitNET patients to improve discovery of novel factors involved in PitNET development.”

Author answer: Changed to: “We sequenced and by employing multiple data analysis methods studied the exomes of 15 PitNET patients to improve discovery of novel factors involved in PitNET development.”

Line 96: “Extended studies using independent sample groups and larger sample sizes are required to reveal overlapping candidates to be further investigated for their role in PitNET development mechanisms and potential improvement of therapy and patient's health care.” Hard to understand.

Author answer: We rephrased the sentence to convey our message clearer: "Additional studies using independent sample groups and larger sample sizes are required to identify overlapping candidate gene to be further investigated for their role in PitNET development mechanisms and as potential targets for improvement of therapy and patient's health care."

Line 102: “We discovered two recurrent somatic variants within our sample and two recurrent genes within our sample as well as 13 genes compared to other independent studies.” It should be “our samples”? It is not necessary to use “our sample” twice. “samples”?

Author answer: We agree to the reviewer and changed sentence to: “We discovered two recurrent somatic variants and two recurrent genes within our sample as well as 13 genes containing somatic variants which are overlapping with other independent studies.”

Line 299: “but both variants are unlikely to impact gene function both due their consequence (synonymous variant and 3’ untranslated region variant) and consequence prediction phred score by CADD (0.009 and 0.59 respectively).”

Author answer: Indeed, this part of the sentence is confusing. We have edit that to: "but impact on gene function for both variants is hard to pinpoint due their supposedly benign consequence (synonymous variant and 3’ untranslated region variant) and low predicted consequence phred score by CADD (0.009 and 0.59 respectively)."

Line 354: “In conclusion we have identified PitNET 11 more genes overlapping from previous PitNET studies which contain somatic variants of which several (RYR1, LAMA1, AHNAK, TSC2) could be highlighted as potential candidates for further PitNET research.” Line 36: “In conclusion, we have identified two novel PitNET candidate genes (AC002519.6 and AHNAK) with recurrent somatic variants in our PitNET cohort and found 13 more genes overlapping from previous PitNET studies that contain somatic variants.” The description seems wrong. 11 or 13? “more genes overlapping from previous studies”? My understanding is overlapping means recurrent detection of these genes, and it is hard to know what “more” means here.

Author answer: The confusion probably arises because we have incorrectly used "more" in both places. In discussion we already extended description about GNAS and AHNAK and then there are "11 more" (additional) overlapping genes (11+2=13) as you correctly deduced. We have dropped "more" from line 38 [36] and edited line 391 [354] to: "In conclusion, besides GNAS and AHNAK, we have identified PitNET 11 more genes overlapping from previous PitNET studies which contain somatic variants of which several (RYR1, LAMA1, AHNAK, TSC2) could be highlighted as potential candidates for further PitNET research."

3. Please use a table to describe the sequenced runs for each sample. Current description in “Library preparation and exome sequencing” include partial details. If there is no batch effect, the authors may simplify the descriptions. Current description only included partial details about how samples were sequenced.

Author answer: As suggested by reviewer we have introduced additional table in section "Library preparation and exome sequencing" and edited text starting from line 167: "Exome sequencing was performed in five batches (Table 1)." Subsequently, table numbering was adjusted in the rest of manuscript.

4. “Most of the discovered variants (N = 93) are located within the coding sequence of genes and 58 of those are changing the amino acid sequence of the protein (range per tumor 0 – 11 , mean 3.9) while 22 are synonymous SNVs.” 58 + 22 = 80. What are the rest 13 variants?

Author answer: Thank you for pointing out this issue. Counting of somatic variants in our study is indeed so not clear and two different variant properties contribute to that: 1) in two cases somatic variants are located on the genomic position which belongs to two genes located on forward and reverse strands, therefore consequences are more than somatic variants and this is compounded by the fact that two variants are found in more than one sample making unique somatic variant count even more different; 2) there are somatic variants with multiple predicted consequences: five variants with two, one variant with three (S1 "Consequence" column). Taken together this causes challenging environment for counting variant consequences and describing them in clear, unambiguous way. To answer the question about rest 13 variants: there are four 3_prime_UTR_variants, three 5_prime_UTR_variants, four non_coding_transcript_exon_variants (S1), total 11 and the two missing are incorrectly counted double consequence variants: missense_variant, splice_region_variant. To make this sentence clearer we have edited this sentence and corrected the error counting double consequence variants twice: "Most of the discovered variants (N = 91) are located within the exons of genes and 58 of those are changing the amino acid sequence of the protein (range per tumor 0 – 11, mean 3.9) while 22 are synonymous SNVs, rest being 3 prime UTR variants (N = 4), 5 prime UTR variants (N = 3) and non-coding transcript exon variant (N = 4)."

5. No summary for “Immunohistochemistry of PitNET samples”

Author answer: Thank you for pointing this out, we have added more elaborate description in "Discussion" section about immunohistochemistry results: "Regarding IHC results confirmed that most of the PitNET presented clinical phenotype according to their expressed lineage markers and hormones. Only one of the IHC tested clinically non-functional PitNET (PN02) differed from the rest by having TPIT cell lineage marker ekspressed in 30% – 70 % of observed cells rather than NR5A1. On the other hand, PN10 was presenting hyperprolactinemia phenotype, but the highest proportion of cells (>70%) was expressing CGA and NR5A1 indicating gonadotroph PitNET [28], nevertheless, PN10 was also expressing POU1F1 cell lineage marker and PRL in up to 30% of cells, probably causing more noticeable hyperprolactinemia induced clinical phenotype. Also we observed that four out of five PRL or GH secreting PitNETs were expressing more than one cell lineage marker while the same was true in two out of six PitNETs which were designated as non-functioning PitNET. Similar differences between secretion subtype and cell lineage markers has been observed in other studies [15] and they are of low concern regarding detection of somatic tumor variants."

6. Line 104: “We show that the sequencing data analysis of PitNET benefits from using multiple variant calling approaches”. Past tense should be used. Also, current evidence does not support the conclusion that “multiple variant calling approaches” benefit “data analysis of PitNET”.

Author answer: We have changed the wording to make the statement more precise: "We believe that the sequencing data analysis of PitNET benefits from using multiple variant calling approaches by providing higher confidence in variants that were detected with different methods".

Minor:

Line 27: change “Two samples” to “For each patient, two samples”.

Line 345: change “largePitNET” to “large PitNET”?

Author answer: Thank you this, indeed, improves clarity. We have edited accordingly.

Reviewer #2: The manuscript PONE-D-20-40370 entitled ‘Whole exome sequencing reveals novel risk genes of pituitary neuroendocrine tumors’ identify novel mutations in pituitary tumors. The study is really interesting from the clinical point of view, since it validates some of previously described mutations and provides evidence of new candidate genes. It is not a large cohort, but it is still informative work. Nevertheless, there are some issues that may be addressed.

Author answer: Thank you for reviewing and considering our research manuscript.

1. The pituitary tumor subtype is a key element of these kind of studies, and it must be indicated in the abstract and introduction. Although it may be clear in the figure, a more detailed characterization including IHC should be included, perhaps a non-gonadotroph but ACTH- or GH-silent adenomas were included, which may be interesting.

Author answer: We agree that IHC information was somewhat limited and didn't provide full overview about the sequenced samples. Therefore, we have added subsection in Results: "Immunohistochemistry staining results of PitNET samples" with a text: "Immunohistochemistry data of paraffin-embedded PitNET tissues were obtained for 11 PitNET patients using hormone antibodies (except CGA, where eight samples were analysed) and also 11 but different samples for lineage marker antibodies (S2). Concordance of hormone antibody immunohistochemistry with clinical diagnosis was observed in eight out of 11 patients, meanwhile nine out of 11 patients had agreement between clinical diagnosis and ICH data for PitNET lineage markers (PIT1, NR5A, TBX19). Most of NFPA clinical diagnoses actually were gonadotroph hormones expressing PitNETs. Few notable exceptions were observed: PN02 (with clinical NFPA diagnosis) was positive for GH and ACTH as antibody staining showed up to 30% of cells containing these hormones. PN04 (NFPA) had no detectable hormone expression, but PN05 (NFPA) was expressing every tested hormone, with high levels of GH, PRL and ACTH showing up in the data, although the highest expression level was for glycoprotein alpha subunit (required to produce gonadotropic hormones). PN10 (clinically PRL secreting PitNET) and PN14 (clinically GH secreting PitNET) were also expressing high levels of the glycoprotein alpha subunit.

PitNET cell lineage markers showed difference from clinical diagnosis in PN02 and PN10. PN02 is expressing corticotroph cell lineage marker in 30 – 70% of its cells. PN10 which clinically manifests as prolactin secreting PitNET is expressing higher amount of NR5A which is gonadotroph cell lineage marker than PIT1 associated with development of prolactin secreting PitNET. " And added paragraph in Discussion: "Regarding IHC results confirmed that most of the PitNET presented clinical phenotype according to their expressed lineage markers and hormones. Only one of the IHC tested clinically non-functional PitNET (PN02) differed from the rest by having TPIT cell lineage marker ekspressed in 30% – 70 % of observed cells rather than NR5A1. On the other hand, PN10 was presenting hyperprolactinemia phenotype, but the highest proportion of cells (>70%) was expressing CGA and NR5A1 indicating gonadotroph PitNET [28], nevertheless, PN10 was also expressing POU1F1 cell lineage marker and PRL in up to 30% of cells, probably causing more noticeable hyperprolactinemia induced clinical phenotype. Also we observed that four out of five PRL or GH secreting PitNETs were expressing more than one cell lineage marker while the same was true in two out of six PitNETs which were designated as non-functioning PitNET. Similar differences between secretion subtype and cell lineage markers has been observed in other studies [15] and they are of low concern regarding detection of somatic tumor variants."

2. Since there is a vast range of mutation per sample, from 0 to 31, is there any correlation with clinical or molecular characteristics?

Author answer: This indeed is interesting point, we did several correlation analyses between mutation count and available clinical characteristics and did not observe any significant correlation.

3. In the discussion, the authors indicate that the use of different workflows is advantageous, nevertheless, they do not really compare the methods, since they combine the results of the different workflows used.

Author answer: Thank you for this important point indeed we wanted to look at different algorithms that are using different methods to distinguish between true and false signal in the NGS data to understand more reliable data in our case. During the procedure of the analysis we have come to the conclusion that the somatic variants that are detected by one algorithm but not by other are not necessarily false positives, but more likely lack confidence in certain areas of variant detection. We decided to select variants with the highest confidence with the knowledge that they do not represent 100% of possible finds. We have included this information at the beginning of the discussion and provided references (such as 19 Miller et al.) where readers can explore this field further. Therefore, at the end of the analysis we reframed our aim not to compare the workflows but to obtain the most reliable somatic mutation data describing our PitNet samples.

Reviewer #3: This article reported results for the identification of two novel PitNET candidate genes ( AC002519.6 and AHNAK) with recurrent somatic variants in a PitNET cohort and found 13 more genes overlapping from previous PitNET studies containing somatic variants. This article does not show relevant and robust results in the field of PitNETs. Thus, there are major aspects that could be review aiming to improve the manuscript:

Author answer: Thank you for careful and constructive review of our manuscript and showing additional points how we could improve the study.

- The authors should considerably improve the result section. In general, the results are insufficient since the authors could acquire more results from the methodology used and data analysis. Then, we encouraged the author to deeply analyzed the data obtained from both methodologies used (GATK and Strelka2).

Author answer: Thank you for this important comment we have edited the results section to improve result representation, including description of immunohistochemstry results, description of main somatic variant findings, added S2 table legend, added and clarified data to Fig1. Indeed, we have considered to more deeply compare the results provided by both methodologies used (GATK and Strelka2) as different algorithms that are using different methods to distinguish between the true and false signal in the NGS data and to understand more reliable data in our study. During the data analysis we have come to the conclusion that the somatic variants that are detected by one algorithm but not by other are not necessarily false positives, but more likely lack confidence in certain areas of variant detection. To more properly represent the data of PitNET somatic variant we decided to select variants with the highest confidence with the knowledge that they do not represent 100% of possible finds. We have included this information at the beginning of the discussion and provided references (such as 19 Miller et al.) where readers can explore this field further. Therefore, at the end of the analysis we reframed our aim not to compare the workflows but to obtain the most reliable somatic mutation data describing our PitNet samples and concentrate on representing the somatic variation landscape of PitNETs.

- The sample cohort is only for 15 samples, being only three GH secreting and two PRL secreting tumors. Thus, the authors should complete the cohort with more samples for each subtype to avoid bias in the data analysis or reanalyzed other cohorts with the same algorithm in order to corroborate the data of your cohort.

Author answer: The recruitment of patients and sequencing of exomes has been completed in year 2018, as well as funding for this project. We have since moved to sequence genomes using different sequencing platform (MGI Sequencers) and therefore we will be unable to increase sample size using the same methods in all stages. Nevertheless, we think that our findings contribute positively towards field studying exomes of PitNETs and provide addional insight into genetic variation and makes such sequencing data available for possible future reasearch. We have identified recurring somatic variant in pseudogene (AC002519.6) and recurrent genes AHNAK, PALM2/AKAP2) in our sample. As well as potential candidates across PitNET studies (GNAS, TSC2, KLHL4, POLR3B, KHDRBS2, LAMA1, TUBGCP6, RYR1, PLA2G6, TESK1, AHNAK). Therefore, we think these data could serve as leads for future genetic and functional studies.

As a minor comment, I would like to suggest that the expression level in figure 1 should be represented as a log2(FC) and not as a percentage because they could be ambiguous.

Author answer: Thank you, here it is our problem with imprecise caption "Expression level". We have changed this caption to "% of cells expressing lineage marker" in legend and also replaced text in figure describing PitNET characteristics: "Expression level of PA lineage markers" with "% of cells expressing PitNET lineage marker"

In conclusion, this article needs to improve major/minor aspects to achieve an appropriate relevance.

Author answer: Thank you once again for insightful review and pointing out issues with the manuscript, we tried to take into account and join suggestions of all three reviewers.

Submitted filename: Peculis_et_al_Response_to_reviewers.docx

Click here for additional data file.

1 Mar 2022

Whole exome sequencing reveals novel risk genes of pituitary neuroendocrine tumors

PONE-D-20-40370R1

Dear Dr. Peculis,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: All comments have been addressed. No further comments.

Reviewer #3: (No Response)

**********

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Reviewer #2: No

Reviewer #3: No

29 Mar 2022

PONE-D-20-40370R1

Whole exome sequencing reveals novel risk genes of pituitary neuroendocrine tumors

Dear Dr. Peculis:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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