# Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors

Mirela-Diana Ilie, Álvaro Flores-Martínez, Marie Chanal, Maxime Lepetit, Benoit Samson, Ayoub Lehiani, Alexandre Vasiljevic, Laura Chinezu, Emmanuel Jouanneau, David Bernard, et al.

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Mirela-Diana Ilie, Álvaro Flores-Martínez, Marie Chanal, Maxime Lepetit, Benoit Samson, et al. Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors. Neuro-Oncology, 2025, 10.1093/neuonc/noaf241. hal-05328726

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1 Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors

2

3 Mirela-Diana Ilie¹,², Álvaro Flores-Martínez¹*, Marie Chanal¹*, Maxime Lepetit³, Benoit Samson¹,³,

4 Ayoub Lehiani¹,⁵, Alexandre Vasiljevic¹,⁴, Laura Chinezu⁶,⁷, Emmanuel Jouanneau¹,⁵, David Bernard¹,

5 Olivier Gandrillon³,⁸, Franck Picard³, Gérald Raverot¹,⁹, Philippe Bertolino¹

6

7 1. Cancer Research Center of Lyon, Inserm U1052, CNRS UMR5286, Lyon 1 University, Lyon,

8 France (MDI, BS, AL, AV, MC, AFM, EJ, DB, GR, PB)

9 2. Endocrinology Department, “C.I.Parhon” National Institute of Endocrinology, Bucharest,

10 Romania (MDI)

11 3. Laboratory of Biology and Modelling of the Cell, CNRS UMR 5239, ENS de Lyon, Lyon, France (BS,

12 ML, OG, FP)

13 4. Pathology Department, Reference Center for Rare Pituitary Diseases HYPO, “Groupement

14 Hospitalier Est” Hospices Civils de Lyon, Bron, France (AV)

15 5. Neurosurgery Department, Reference Center for Rare Pituitary Diseases HYPO, “Groupement

16 Hospitalier Est” Hospices Civils de Lyon, Bron, France (AL, EJ)

17 6. Department of Histology, George Emil Palade University of Medicine, Pharmacy, Sciences and

18 Technology of Targu Mures, 540142 Targu Mures, Romania (LC)

19 7. Pathology Department, Institute of Forensic Medicine, Targu Mures, 540142 (LC)

20 8. Inria Center Grenoble Rhone-Alpes, Villeurbanne, France (OG)

21 9. Endocrinology Department, Reference Center for Rare Pituitary Diseases HYPO, “Groupement

22 Hospitalier Est” Hospices Civils de Lyon, Bron, France (GR)

23 * : These authors contributed equally

24

25 Running title: Mast cells are pro-angiogenic in gonadotroph tumors

26
27 **Corresponding author:**

28 P. Bertolino

29 Cancer Research Centre of Lyon (CRCL)

30 28 Rue Laennec, 69373 LYON CEDEX 08, France

31 Tel: +33 4 78 78 51 60

32 Email: philippe.bertolino@inserm.fr

33

34 **Word count:** 5516 words

35
36 **Abstract:**

**Background:** The tumor microenvironment (TME) represents a promising avenue to understand gonadotroph tumors and develop therapeutic tools. Here, we aimed to gain insight into the tumorigenesis mechanisms driven by the gonadotroph TME. **Methods:** Single-cell and spatial-omics were combined with histological analysis. Mice engrafted with tumor cells were used for functional validation. **Results:** using single-cell and spatial transcriptomic data from gonadotroph tumors and normal tissues, we identified mast cells in the microenvironment of gonadotroph tumors and confirmed their physical and functional interaction with endothelial cells. Quantification of mast cells in 40 patients suggested their pro-tumoral role as tumors relapsing after surgery harbored more mast cells. More interestingly, the distribution of mast cells was associated with the presence of a higher number of blood vessels, with an increased microvessel density (MVD), and with blood vessels with thicker walls. Ligand-receptor network analysis highlighted VEGFA as a modulator of mast/endothelial cell communication, a result confirmed by the identification of intratumoral mast cells expressing VEGFA in mouse and human gonadotroph tumors. Finally, using mice engrafted with gonadotroph tumor cells, we demonstrated that the depletion of mast cells reduces tumor volume through increased apoptosis. These observations were associated with increased hemorrhagic areas and a significant reduction of the number of blood vessels and MVD as evidenced in human gonadotroph tumors. **Conclusion:** we demonstrate that mast cells represent a new actor of the gonadotroph TME, and highlight their pro-angiogenic and pro-tumorigenic roles as potential targets for the therapeutic treatment of gonadotroph tumors.

55

56 **Keywords:** mast cells, tumor microenvironment (TME), gonadotroph adenomas, single-cell transcriptomics, spatial transcriptomics

58 **Key points:**

59 • Mast cells have a pro-angiogenic role in gonadotroph tumors and are associated with a worse outcome

60

61 • Targeted ablation of mast cells reduces LβT2 tumor growth in mice

62 • Targeting mast cells represents a potential therapeutic approach for gonadotroph tumors

63
64 **Importance of the Study**

65 Here our work identifies and validates for the first time the presence of mast cells in pituitary tumors,
66 as well as their pro-angiogenic and pro-tumorigenic roles in gonadotroph tumors. In addition, our
67 study highlights the targeting of mast cells as a potential avenue for the treatment of gonadotroph
68 tumors through the remodeling of their tumor vasculature. At the same time, our study provides data
69 on intercellular communications taking place in human gonadotroph tumors, and has generated
70 original single-cell and spatial transcriptomics datasets of human gonadotroph tumors, that will
71 benefit the scientific community.
## 72 Introduction

Pituitary tumors are a group of common endocrine neoplasms that represent 10-20% of all intracranial tumors. Among the three main pituitary tumor subtypes (defined by the respective expression of PIT1, TPIT or SF1 endocrine-lineage transcription factors), the gonadotroph PITs (GoPITs) – *that derive from SF1-expressing gonadotroph cells* – represent up to 35% of pituitary tumors ¹,². While many studies have explored the genetic, genomic, and epigenetic mechanisms of PITs, the identification of existing driver mutations in GoPITs has so far been unsuccessful ³⁻⁶. From a clinical perspective, GoPITs come with major challenges, as they lack effective medical treatment options ⁷ and are usually diagnosed late, when they have reached invasive states which limit their complete surgical resection ⁸,⁹. Therefore, a better understanding of GoPIT biology is needed to identify new therapeutic targets and novel treatment options. The tumor microenvironment (TME) constitutes an appealing therapeutic tool and a promising avenue for understanding the biology of pituitary tumors. Tumor-infiltrating lymphocytes, tumor-associated macrophages, folliculostellate cells/stem cells, vascularization, tumor-associated fibroblasts, as well as the extracellular matrix and its remodeling, have complex and intricate roles in the biology of pituitary tumors, roles that appear to be dependent on the histological subtype ¹⁰. These TME components have so far been linked to the size, proliferation, invasion, functionality, progression/recurrence, and response to treatment of pituitary tumors ¹⁰. However, the knowledge on the pituitary TME remains limited ¹¹,¹². Deciphering the composition and role of the TME, as well as of functional and signaling interactions that take place in GoPITs, is essential for better understanding initiation and progression of these tumors and, ultimately, to refine the personalized care of patients.

Mast cells are immune cells implicated in the response to allergic stimuli and in the defense against pathogens ¹³. An increasing body of evidence further supports their implication as key modulators of tumor progression in cancer biology ¹⁴,¹⁵. Indeed, mast cells have been reported in various types of tumors, where they release histamine, proteases, cytokines, and growth factors which could influence
98 cancer cell proliferation, angiogenesis, immune modulation, and extracellular matrix remodeling<sup>13,15</sup>.

99 Depending on the type of tumor, the state of tumor progression and the microenvironmental cues, 100 mast cells have been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Pro- 101 tumorigenic actions of mast cells are associated with their production of cytokines (VEGFs, Adenosine, 102 IL-13, ...) that favor angiogenesis, lymphangiogenesis and immunosuppression, while their protective 103 roles are related to their production of molecules such as TNF-α or IL-9 that respectively promote 104 cytotoxicity or growth inhibition<sup>15</sup>. Interestingly, a pan-cancer single-cell transcriptional atlas including 105 15 human cancer types recently linked the prevailing pro-angiogenic role of mast cells to their pro- 106 tumorigenic effect<sup>16</sup>. While the presence of mast cells in pituitary tumors has been suggested by 107 bioinformatics analyses and deconvolution approaches of bulk RNA transcriptomes<sup>17,18</sup>, the presence 108 of those cells remains to be confirmed. Moreover, deciphering their pro- and anti-tumorigenic roles 109 may offer promising avenues for novel therapeutic strategies to target GoPITs and pituitary tumors. 110

110

111 Here, using single-cell and spatial transcriptomics we identify mast cells as a new player in the 112 gonadotroph tumor microenvironment. We show through histological analysis of mast cells in a cohort 113 of patients that they are more abundant in GoPITs relapsing more rapidly following surgical resection. 114 This pro-tumorigenic role was further supported by the identification of their pro-angiogenic role and 115 the beneficial effect of their depletion in a mouse model engrafted with gonadotroph tumor cells. Our 116 results further suggest that targeting mast cells in patients may provide a promising strategy to impede 117 tumor relapse. 118

118

119

120 **Material and methods**

121 **Human tumor samples and clinicopathological data**

122 All gonadotroph tumors were collected in Lyon, France, by the same neurosurgeon (EJ), and 123 subsequently diagnosed by a single pathologist (AV), according to the Trouillas classification<sup>19</sup>.
124 Clinicopathological data were retrospectively collected from the medical files. Radiological cavernous
125 sinus invasion was evaluated on magnetic resonance imaging using the revised Knosp classification 20.
126 Tumor proliferative status and grading were based on the five-tiered classification 21. Control pituitary
127 tissues were obtained from peritumoral pituitary tissues lacking histological tumor lesions and from
128 post-mortem autopsies. Samples are listed in **Suppl Table1**. All the analyses were conducted in
129 accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later
130 amendments and were approved by the Ethics Committee of Hospices Civils de Lyon (approval number
131 21_439).

132

133 **Immunohistological staining**

134 Immunohistochemistry (IHC) and double immunofluorescence (IF) were performed as detailed
135 previously 22. For IHC, staining was performed on a BenchMark® ULTRA immunostainer (Ventana
136 Medical system Inc, Roche Diagnostics, Tucson Arizona, USA) with the following antidodies: Tryptase
137 (clone AA1, Dako Agilent Technologies, Santa Clara, CA, USA, cat#M7052, 1:500), CD34 (clone QBEnd-
138 10, Dako Agilent Technologies, cat#GA632, 1:40). For IF staining, antibodies were: TPSAB1 (clone G3,
139 Merck Millipore, Molsheim, France, cat#MAB1222, 1:25000 for IHC, 1:10000 for IF), VEGFA (polyclonal,
140 Abcam, Cambridge, UK, cat#ab46154, 1:1000 for IF), CD34 (clone EP373Y, Abcam, Cambridge, UK,
141 cat#ab81289, 1:10000 for IHC, 1:2000 for IF), SV40T-Antigen (v-300) (polyclonal, Santa Cruz
142 Biotechnology, Dallas, Texas, USA, cat #sc-20800, 1:2500), Cleaved Caspase-3 (polyclonal, Cell
143 Signaling Technology, Danvers, MA, USA, cat#9661, 1:1000). Images were acquired on a Zeiss Axio
144 Imager M2 microscope (Carl Zeiss Microscopy GmbH, Jena, Germany) or a Nikon Ni-E microscope
145 (Nikon Corporation, Tokyo, Japan).

146

147 **Image Analysis**

148 Whole slide image scans were acquired with a Zeiss Axioscan 7 (Carl Zeiss Microscopy GmbH, Jena,
149 Germany). Visualization, image analysis, and whole slide quantification were performed with QuPath
v0.4.3<sup>23</sup>. Prior to their quantification, all human tumor histological sections were manually annotated and regions with staining artefacts were excluded by our pathologist. For all whole-slide cell quantifications, a single-cell segmentation was performed in QuPath, and the percentage of positive cells compared to the total number of cells or per mm² was quantified. Hemorrhagic areas were quantified from hematoxylin and eosin on scanned sections using a QuPath pixel classifier. For microvessel density (MVD) quantification, a supervised machine learning-based workflow in QuPath was used. Briefly, an artificial neural network pixel classifier (ANN_MLP) was trained on representative regions of CD34-immunostained human and mouse tumor sections to identify vascular structures. Once trained, the model was applied to whole-slide images, and the number of CD34-positive vessels per mm² was automatically quantified. All segmentations and vessel counts were independently and blindly validated by two researchers. For morphometric analysis, a fully automated segmentation and morphological analysis algorithm, previously validated for histological sections<sup>24</sup>, was employed. Serial sections were first co-registered using ImageJ/FIJI<sup>25</sup>, followed by rigid alignment using the *Warpy* extension in QuPath. Mast cell-rich (“dense”) and mast cell-poor (“sparse”) areas were defined based on the spatial overlap of density maps generated in QuPath from whole-slide positive cell quantifications obtained from consecutive tissue sections stained for Tryptase and CD34. For each sample, at least three CD34-stained low-magnification fields were randomly selected within both dense and sparse regions. These selected fields were processed using the segmentation algorithm, and morphometric descriptors of tumor vasculature were extracted. Parameters such as vessel wall thickness or the number of solid microvessels (i.e., CD34-positive vascular objects lacking a visible lumen in two-dimensional sections) were computed.

171

## Single-cell and spatial transcriptomics

The single-cell preparation and sequencing of six freshly resected gonadotroph tumors was conducted as detailed previously<sup>26</sup>, using a Chromium Next GEM Single Cell 3' Kit v3.1 (10x Genomics, Pleasanton, CA, USA). Raw sequencing data were trimmed with fastp version 0.23.2 and processed with Cell Ranger
176 version 6.0.2, implemented in scAN1.0 26, using the GRCh38 Ensembl human genome as reference.
177 Raw single cell sequencing data (fastq) of 4 peritumoral pituitary tissues and 3 post-mortem pituitaries
178 were downloaded from the National Genomic Data Centre (NGDC accession #HRA003483 27 and
179 #HRA003483 28). Fastq files were processed with STARsolo 2.7.10 accessed via the Galaxy webserver
180 (usegalaxy.eu), using the cell ranger pipeline and the GRCh38 Ensembl human genome. Downstream
181 quality control (QC) and analysis were performed as mentioned below. Spatial transcriptomic analysis
182 was done on 10 µM cryo-sections of 2 collected tumors using a Visium Kit (10x Genomics, Pleasanton,
183 CA, USA). Sequenced data were processed through spaceranger-2.1.0 using the GRCh38 Ensembl
184 genome as reference, prior to subsequent bioinformatics analysis

185

186 Bioinformatics analysis

187 For downstream single-cell analysis, Seurat package version 4.3.0 29 was used in R version 4.3.1 via
188 RStudio 2023.06.1+524. QC was done by removing: 1) genes expressed in less than 5 cells, 2) cells
189 expressing less than 500 genes, and 3) cells having a number of genes, a number of transcripts, or a
190 percentage of mitochondrial genes above four times the median absolute deviation. For subsequent
191 analysis, Single-cell data were integrated using Harmony version 0.1.1 30 and cell-clusters were
192 annotated using well-recognized canonical markers from the literature. Ligand-receptor networks
193 were inferred using CellPhoneDB v3.0.0 31 implemented in Python v3.7 (parameters used included:
194 expression of the ligand/receptor in at least 10% of cells from a specific cluster, 100,000 iterations for
195 the statistical analysis, and a p-value < 0.05) and CellChat 1.6.1 32 (the parameter used consisted of a
196 minimum of 10 cells from a specific cluster to express the ligand/receptor). Chord diagrams were
197 generated in R using ktk plots 1.2.5 33. NicheNet (nichenetr 1.1.1) 34 was used to predict ligand-receptor
198 interactions that might drive vascular endothelial growth factor A (VEGFA) gene expression in mast
199 cells. For spatial transcriptomic analysis of Visium sequencing, we perform QC, filtering and clustering
200 through Seurat 4.3.0. Cell type deconvolution in each spot was performed through Semla1.1.6 35 using
our 6 integrated gonadotroph tumors as a reference. Pathway gene ontology analysis and representation were obtained through Spata2.0.4 ³⁶.

203

## Animal model and in vivo experimentation

Young and old pituitary glands were collected from 5-month-old and 22-month-old C57bl/6 mice.

Pituitaries were fixed in 4%-buffered formalin prior to their embedding in paraffin. The presence of pituitary tumors was detected following histological examination of hematoxylin and eosin-stained sections. Six animals per age were analyzed; spontaneous tumoral lesions were found in 2 out of 6 aged mice. For tumor grafts, 1.10e6 LβT2 cells (kindly provided by Professor Pamela Mellon ³⁷ were injected subcutaneously in a 2:1 mixture of phosphate-buffered saline/Corning™ Matrigel™ Matrix HC (cat#354248, Corning, Fisher Scientific, Hampton, NH, USA) on the right flank of 3- to 4-month-old male Rag2KO mice (B6(Cg)-Rag2tm1.1Cgn/J, cat# JAX:008449, The Jackson Laboratory, Farmington, CT, USA). At day 7 following the graft, animals were split into 2 groups, treated with an anti-mouse purified CD117 (c-kit) antibody (clone ACK2, cat#135132, BioLegend, San Diego, CA, USA) or an IgG2b isotype control antibody (clone RTK4530, cat#400672, BioLegend, San Diego, CA, USA), respectively. Treatments were performed through intraperitoneal injection of 3.33 mg/kg of antibody twice a week. Tumors were collected, measured and weighed at day 21 and then fixed in 4% buffered formalin prior to their paraffin embedding and histological analysis. All animal experiments were approved by the ethics committee number CEEA-055 and CE2A-015, and French Ministry of Research and Innovation in accordance with European Union Guidelines (ethical permits accepted under reference APAFIS#12166, APAFIS#14068 and APAFIS #52396).

222

## Statistical analysis

Statistical analyses and graph generation were performed in GraphPad Prism 9 (GraphPad, California, USA). Mann-Whitney test, Kruskal-Wallis test, Wilcoxon matched paired test, Fischer's exact test, and Spearman's correlation were applied as indicated. A p-value < 0.05 was considered significant.

224

225

226
<table><tr><td>227</td><td></td></tr><tr><td>228</td><td></td></tr><tr><td>229</td><td>Results</td></tr><tr><td>230</td><td>Identification and characterization of mast cells from single-cell sequencing of gonadotroph tumors</td></tr><tr><td>231</td><td>We addressed the cell composition of non-functioning gonadotroph tumors through the single-cell (sc)</td></tr><tr><td>232</td><td>sequencing of freshly resected tumors from 3 male and 3 female patients (Suppl. Table1a). Following</td></tr><tr><td>233</td><td>QC-filtration, a total number of 24,471 sequenced-cells were integrated using harmony <sup>30</sup> and</td></tr><tr><td>234</td><td>subsequently clustered and annotated (Fig.1A,B). As expected, tumor cells were the most abundant</td></tr><tr><td>235</td><td>cell type (64.3%) with a moderate heterogeneity between samples (Fig.1C, Suppl.Fig.1). A small</td></tr><tr><td>236</td><td>number of normal anterior pituitary cells (0.20%) was present, supporting the absence of peritumoral</td></tr><tr><td>237</td><td>or normal tissue contamination in the samples. Overall, we noted that the level of FSH and LH gene</td></tr><tr><td>238</td><td>expression of each tumor closely matched the percentage of FSH and LH immune reactivity assessed</td></tr><tr><td>239</td><td>at the histological diagnosis of the sequenced tumors (Suppl.Fig.1B). Interestingly, while most of the</td></tr><tr><td>240</td><td>major TME cell populations reported in pituitary tumors were present, including stromal cells (13.3%),</td></tr><tr><td>241</td><td>monocytes/macrophages (MoMacs) (12.2%), endothelial cells (6.7%), T cells (1.2%) and stem cells</td></tr><tr><td>242</td><td>(0.40%), our clustering revealed the presence of a rare population of mast cells, which represented</td></tr><tr><td>243</td><td>0.5% of the total sequenced cells (Fig.1C). Although very scarce (i.e., 133 cells), these cells were present</td></tr><tr><td>244</td><td>in five of the six sequenced tumors (median 0.39%, range 0-1.28%) (Fig.1D). They also displayed a well-</td></tr><tr><td>245</td><td>defined gene signature (Suppl. Table2) with the 10 most expressed genes having previously been</td></tr><tr><td>246</td><td>reported as functional markers of mast cells. Indeed, TPSAB1/TPSB2, and CPA3 that encode Tryptase</td></tr><tr><td>247</td><td>and carboxypeptidase A3, 2 abundant proteases in mast cell secretory granules <sup>38,39</sup>, were the most</td></tr><tr><td>248</td><td>enriched genes in the mast cell cluster (Fig.1E). AREG encoding amphiregulin, an epidermal growth</td></tr><tr><td>249</td><td>factor-like molecule <sup>40</sup>; MS4A2 encoding the beta subunit of the high affinity IgE receptor; HPGDS</td></tr><tr><td>250</td><td>encoding prostaglandin-D synthase, an enzyme producing prostaglandin D2; and KIT encoding the</td></tr><tr><td>251</td><td>tyrosine kinase receptor KIT, which is a receptor implicated in the recruitment, differentiation,</td></tr><tr><td>252</td><td>proliferation, survival, and activation of mast cells <sup>41</sup>, were also highly enriched in mast cells (Fig.1E).</td></tr></table>
Although the expression level of several genes varied considerably between individual mast cells, no distinct subtypes arose from their sub-clustering (Fig.1F).

255

## The detection of mast cells is driven by a gonadotroph tumor context rather than an aging process

Since pituitary tumors are more prone to develop in older individuals ⁴², we wondered whether mast cells could be detected in aged pituitary tissues and whether their presence was related to an aging mechanism rather than a tumorigenic process. We addressed this by integrating 7 published single-cell datasets obtained from 4 peritumoral pituitary samples ²⁷ and 3 control pituitary glands collected *post-mortem* ²⁸ with our gonadotroph tumors (Fig.2A, Suppl Table1b). Through this approach, we found that, while normal endocrine cells (SF1-, TPIT- and PIT1.GH- and PIT1.PRL-cells) can be found in gonadotroph tumors, tumor cells were, as expected, absent in normal and peritumoral pituitary samples (Fig.2B). These analyses further revealed that nearly no mast cells (0.8% of the total number of mast cells, n=1/128) were found in control pituitary tissues (Fig.2B). More importantly, there were no mast cells in any normal sample (all older than 76 years), and the sole mast cell found (n=1) was detected in a 54 years-old peritumoral sample (Suppl. Fig.2). Histological immunostaining of tryptase further confirmed that the percentage of tryptase-positive mast cells was significantly higher in FFPE tissue-sections from the tumors we sequenced than in normal pituitary tissues from young or old control cases (Fig.2C, Suppl Table1c). Finally, we confirmed these observations in the pituitary of aged mice that had developed spontaneous tumors as shown in Fig.2D, where Tpsab1-positive mast cells were only present in pituitaries harboring spontaneous tumors and not in aged-matched or younger control animals (Fig.2D). Taken together, these results support that mast cells are specifically recruited in both mouse and human pituitary tumor tissues.

275

## Intratumoral mast cells are present in the vicinity of endothelial cells and blood vessels

We next used spatial transcriptomics to map the location of mast cells in gonadotroph tumors and address their intratumoral interactions and functional roles. Cryosections from 2 gonadotroph tumors
279 (Suppl Table1d) were sequenced using the Visium technology. The expression of TPASB1, TPSB2 and 280 CPA3 was mapped (Fig.3A) and these genes were used to predict the Visium spots containing mast 281 cells. Through this approach we identified 144 (4.71%) and 107 (3.21%) spots that were suspected to 282 contain mast cells (Fig.3B, Suppl.Fig.3A). We next performed a clustering and a differential expression 283 analysis following an integration of both Visium datasets (Fig.3C). Gene ontology analysis based on the 284 most significantly expressed genes in spots containing mast cells (Suppl Table3) highlighted an 285 enrichment in gene signatures and gene markers associated with endothelial cells and pericytes, which 286 supports the presence of mast cells as well as blood vessels in the vicinity of mast cells (Fig.3D,E and 287 Suppl.Fig.3B). Using Spata2 36, we confirmed the existence of an overlapping spatial enrichment of 288 biological processes associated with the gene expression signatures of endothelial- and mast-cell 289 activation (Fig.3E). Using Cell2location 43, we further deconvoluted the spatial distribution of the cells 290 annotated in the GoPITs previously sequenced, to map the spatial distribution of mast, endothelial and 291 tumor cells (Fig.3F, Suppl.Fig.4). Immunofluorescence staining performed on adjacent sections to the 292 one used for the Visium sequencing confirmed that the spatial distribution of CD34- and TPSAB1- 293 immune reactive cells matched our predictive deconvolution (Fig.3F, Suppl.Fig.5). These analyses 294 revealed the existence of intratumoral regions where endothelial cells colocalize with mast cells in 295 both Visium sequenced samples (Fig3.F, Suppl.Fig.5). 296

297 Mast cells are associated with intratumoral microvessel density and tumor recurrence

298 Given that mast cells have so far not been reported in human gonadotroph tumors, we performed 299 tryptase immunostaining on a cohort of 40 patients with gonadotroph tumors, among which 9 had 300 recurring tumors (Suppl Table4). Quantification of tryptase immune reactive cells performed on whole 301 tissue sections revealed that mast cells were present in all analyzed tumors and that their proportion 302 was comparable to that of our single-cell transcriptomics analysis (median of the 49 tumors 0.18%, 303 range 0.01-1.96%, Fig.4A). The percentage of mast cells was significantly higher in recurring tumors 304 compared to their initial counterpart (p = 0.007, Fig.4B), suggesting that a remodeling of the TME
implicating mast cells may take place with tumor progression and/or following a first surgical resection.
In addition, mast cells appeared to be associated with a worse prognosis, as more mast cells were also
found in tumors progressing/relapsing more rapidly after surgery (p = 0.002, Fig.4C), suggesting a pro-
tumorigenic role of mast cells in gonadotroph tumors. Aside from these observations, the percentage
of mast cells was not statistically associated with other clinicopathological characteristics (data not
shown). Given the association between mast cells and endothelial cells observed through our spatial
transcriptomic analysis (Fig.3E,F), we next evaluated the association between mast cells and
microvessel density (MVD) in human gonadotroph tumors. We performed CD34 immunostaining on
tissue sections from our cohort of gonadotroph tumors and determined the overall MVD on whole
tissue sections. Interestingly, the percentage of mast cells was positively correlated with the MVD
(Spearman's rho = 0.39, Fig.4D), and though not statistically significant, the correlation was weaker
when analyzing only the initial 40 tumors and stronger when analyzing only the nine recurring tumors
(Spearman's rho = 0.29 and 0.52, respectively, Suppl.Fig.6). Next, we focused on the heterogeneity of
the intratumoral spatial colocalization of mast cells compared to endothelial cells. To address this, we
performed overlay analysis of CD34 and tryptase staining on serial tumor sections (Fig.4E), and defined
intratumoral areas containing high (dense) or low (sparse) numbers of tryptase immune reactive mast
cells. Mast dense areas contained a significantly higher number of blood vessels and an increased MVD
(Fig.4F,G). Moreover, the vessels found in mast dense areas were also morphologically different with
a higher number of solid blood vessels showing thicker walls (Fig.4H). Taken together, these findings
suggest a possible functional role of mast cells on the intratumoral vasculature.

Intratumoral mast cells drive a pro-angiogenic and pro-tumorigenic signal through the production
of VEGFA

We next investigated the functional connection between mast and endothelial cells in GoPITs by
exploring their intercellular communications. We performed a ligand-receptor network (LRN) analysis
using CellPhoneDB and CellChat 31,44. We identified 3,029 (CellPhoneDB) and 977 (CellChat)
interactions within the sequenced GoPITs (Fig.5A, Suppl.Fig.7A-C, Suppl.Table 5 and Suppl.Table6).

While mast cells showed functional connections with all cell populations, their interactions with MoMacs and with endothelial cells were the most frequent (Fig.5B), with the highest number of interactions being the ones implicating mast cells and endothelial cells (Suppl.Fig.7D,E). The most expressed secreted ligand-receptor pair identified by CellPhoneDB was VEGFA – VEGF receptor 1 (FLT1) (Fig.5B, Suppl.Fig.8A, Suppl Table5), which was also the second most probable interacting pair inferred by CellChat (Suppl Table6). Interestingly, mast-derived VEGFA is associated with tumor angiogenesis ¹⁶,⁴⁵, supporting a pro-angiogenic role for mast cells in gonadotroph tumors. Although VEGFA was not expressed exclusively by mast cells, VEGFA was more frequently expressed by mast cells (27%) compared to all the other cell types, p < 0.0001 (Fig.5C, Suppl Fig.8B). From the list of 16 unique ligands expressed by mast cells and inferred by CellPhoneDB to act on endothelial cells, histamine (HDC) that was specific to mast cells, and CD44 that had the highest expression in mast cells (Fig.5C, Suppl.Fig.8A), have also been reported to play pro-angiogenic roles ⁴⁶,⁴⁷. In addition, our data corroborate previous findings on the potent pro-angiogenic role of tryptase (Fig.5C) ⁴⁸. Interestingly, although not a pro-angiogenic factor per se, LIF, which was identified as a signaling factor between mast and endothelial cells (Fig.5B,C), was previously shown to upregulate genes encoding molecules implicated in angiogenesis (i.e., pro-angiogenic) ⁴⁹. An autocrine pro-angiogenic role for LIF in mast cells from gonadotroph tumors may therefore be plausible, since a much higher percentage of LIF+ mast cells expressed VEGFA (52.4%) compared to LIF- mast cells that expressed VEGFA in only 15.4% of cases (p < 0.0001) (Fig.5D). In addition, LIF was more frequently expressed in mast cells (32%), compared to all other cell types (p < 0.0001) (Suppl.Fig8C). Interestingly, in mast cells, the ratio between VEGFA and the tumor necrosis factor (TNF) has been associated with their anti-/pro-tumorigenic roles in cancers and with a better/worse clinical outcome ¹⁶. Here, by comparing the number of VEGFA- and TNF-expressing mast cells from our single-cell data we found that mast cells were more frequently associated with a VEGFA than TNF expression in all patients analyzed (Suppl.Fig.8D), suggesting a pro-tumorigenic role of mast cells in gonadotroph tumors. We thus
357 decided to focus on the VEGFA signaling as this LRN was the highest-ranked candidate derived from
358 both CellPhoneDB and CellChat. Analysis of the distribution and the composition of the VEGF signaling
359 network among tumor and TME cell populations confirmed that the VEGF-driven mast cell signaling
360 towards endothelial cells was mediated through its cognate receptors VEGFR1 (FLT1) and VEGFR2
361 (KDR) (Fig.5E). This result was subsequently confirmed through the analysis of the expression pattern
362 of these genes (Fig.5F) and further validated by the identification of intratumoral mast cells capable of
363 producing VEGFA, as demonstrated by the identification of TPSAB1/VEGFA double immunofluorescent
364 cells in gonadotroph tumors (Fig.5G). Finally, we further confirmed the spatial overlap between the
365 localization of mast cells and endothelial cells by mapping the GO signature of VEGF Activated Receptor
366 Activity, as well as the spatial expression of VEGFA, KDR and FLT1, among Visium-sequenced GoPITs
367 (Fig.5H, Suppl.Fig.9). Taken together, these results support the existence of a functional and spatial
368 connection, as well as a plausible pro-angiogenic action of mast cells on endothelial cells in
369 gonadotroph pituitary tumors.
370

371 **Mast cell depletion impacts gonadotroph tumor progression**

372 Given the importance of VEGFA expression in mast cells in gonadotroph tumors, we next wondered
373 what factors drove this expression. Bioinformatics analysis using NicheNet highlighted interleukin 18
374 (IL18, acting via IL18R1) and the KIT ligand (KITLG, acting via KIT) to be the most probable drivers
375 of VEGFA expression in mast cells from gonadotroph tumors (Suppl.Fig.10A). While the “IL18-IL18
376 receptor complex” interaction was not inferred by CellPhoneDB, likely because mast cells do not
377 express the co-receptor IL-18 receptor beta (IL18RAP) required for IL18 signal transduction 50, the
378 KITLG-KIT was the top ranking ligand-receptor pair between both endothelial and mast cells, and
379 stromal and mast cells (Suppl.Fig.10B, Suppl.Table5). KITLG-KIT was also the second ranking
380 interaction between proliferating tumor cells and mast cells (Suppl.Table5), suggesting that multiple
381 cell populations are capable of recruiting and activating mast cells within the gonadotroph TME via the
382 KIT ligand. Interestingly, significantly more proliferating tumor cells expressed KITLG compared to non-
proliferating tumor cells (12.7% versus 3.7%, p=0.004) (Suppl.Fig.11), suggesting that the former may be more potent at recruiting and activating mast cells in gonadotroph tumors. The availability of a c-Kit blocking antibody, used to deplete mast cells in mice ⁵¹, prompted us to investigate the consequences of such a depletion on the growth of gonadotroph tumors. We performed subcutaneous injections of the well establish LβT2 gonadotroph tumor cell line in Rag2KO mice, having previously confirmed that this approach results in the formation of gonadotroph tumors showing vascularization and macrophage infiltration ⁵². LβT2 tumors collected following the treatment of engrafted mice with an anti-c-KIT-antibody or an isotype control, revealed no significant difference in their volumes or their weights (Suppl.Fig.10C). Immunohistological examination confirmed the presence of mast cells in tumors developed within control mice and their depletion in anti-c-Kit treated animals (Suppl.Fig.10D). Interestingly, control LβT2 tumors had a percentage of mast cells (median of 0.12%) comparable to that found in human GoPITs (median of 0.18%) (Fig.4A). Immunohistological analysis of tumor cells, assessed through their expression of the LargeT antigen, highlighted a significantly reduced density of tumor cells in anti-c-KIT-antibody-treated mice compared to control tumors (Suppl.Fig.10E), and an increase in apoptosis as evidenced by the cleavage of caspase3 seen in LβT2 tumor cells (Suppl.Fig.10F, Suppl.Fig.12).

399

400 **Depletion of mast cells is associated with a defective tumor vascularization and an increased apoplexy**

401 Based on their macroscopic examination, LβT2 tumors treated with the anti-c-Kit blocking antibody displayed a darker red color, consistent with the presence of hemorrhagic tissues (Fig.6A, **Suppl.Fig.10C**). We confirmed the presence of a significant number of apoplectic regions in treated tumors by histological examination and quantification of hemorrhagic areas (Fig.6B). As seen in human GoPITs, double immunofluorescent staining demonstrated the presence of mast cells in the vicinity of endothelial cells (Suppl.Fig.13A), with a subset able to produce Vegfa (Suppl.Fig.13B). Based on this latter observation, we analyzed the vasculature of treated and control tumors, by quantifying the MVD

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based on CD34 immunostaining. Through these approaches, we found that the vascular network was
significantly reduced in mast cell-depleted tumors (Fig.6C). As for human tumors, we also quantified
the number of blood vessels, the MVD and their morphologies in mast cell dense and sparse areas, as
well as in mast-depleted animals. While the number of blood vessels and the MVD were lower (albeit
not significantly) in mast cell sparse regions compared to dense regions in control animals, they were
significantly higher in mast dense areas compared to treated animals (Fig.6D-F). Similar observations
were made regarding the morphological analysis of solid vessels with thicker walls in those
corresponding regions (Fig.6G,H). Collectively, these observations suggest that the reduced tumor
growth seen in treated animals may be a direct consequence of a reduced tumor vascularization, and
hence, support that mast cell targeting may be of therapeutic interest.

419

## Discussion

Here, we report the identification of mast cells within the TME of pituitary gonadotroph tumors. Our work further provides evidence on their pro-tumorigenic association in tumor relapse and their pro-angiogenic action in gonadotroph tumors through a series of approaches combining single-cell and spatial omics and their functional depletion in tumor-engrafted mice. While the characterization of the immune TME of pituitary tumors has recently attracted interest ¹⁰, the existence of mast cells in these tumors has never been proven nor quantified. It is however worth mentioning that their presence was previously suggested by bioinformatics deconvolution of bulk transcriptomes and even proposed that they may represent the third most abundant immune population when addressed in all pituitary tumor subtypes ¹⁷. Aside from the identification of mast cells within gonadotroph pituitary tumors, our work also supports that these cells are recruited by tumors rather than being a resident cell population or a population increasing with age. Our work is thus consistent with the rare studies that have explored the presence of mast cells in the normal human anterior pituitary gland. While the presence of mast cells as a rare population "limited to 1 or 2 cells" was first described in 1935 in the normal human anterior pituitary ⁵³, mast cells were also later reported as a tryptase-positive cell type occasionally
found in the normal anterior pituitary and significantly increased in chronic lymphocytic hypophysitis
condition <sup>54</sup>. This latter point combined with our observations suggest that pathological conditions
such as hypophysitis or tumorigenesis are associated with an enrichment in mast cells within pituitary
tissues, further supporting an active role of those cells in such pathologies.

Interestingly, all of the tumors we analyzed contained mast cells. These cells represented less than 1%
of the tumor composition and some variability was found between patients. While it is now well-
accepted that rare cells can play important roles in tumorigenesis-related processes <sup>55</sup>, the low and
variable number of mast cells in gonadotroph tumors raises questions regarding their functional
impact in these tumors. Mast cells are considered to play both pro- and anti-tumorigenic roles in
tumors. Mast cells induce angiogenesis, promote the remodeling of the extracellular matrix, modulate
the anti-tumor immune response, and may also exert direct effects on tumor cells <sup>16,56</sup>. Among these
multiple explanations, the pro-angiogenic role of mast cells appears to prevail in tumorigenic processes
<sup>16</sup>. The pro-angiogenic role of mast cells in gonadotroph tumors is supported by several of our findings,
including the tight spatial and functional connection we demonstrated between mast and endothelial
cells/blood vessels in both human and mouse gonadotroph tumors. Moreover, our results reveal that
the spatial distribution of mast cells within tumor tissues is heterogenous and may have a direct impact
on the heterogeneity and the structure of the intratumoral vasculature in certain tumor areas. Indeed,
we found that intratumoral regions that contain more mast cells present an increased number of blood
vessels, an increased MVD but also morphological changes as supported by the presence of more solid
blood vessels with thicker walls. These findings are consistent with numerous *in vitro* and *in vivo* results
supporting the pro-angiogenic role of mast cells in various contexts <sup>45,47,57,58</sup>. In addition, and similar to
our observations, mast cells were shown to be positively correlated with the MVD in several human
cancers, including pancreatic <sup>59</sup>, colorectal <sup>60</sup>, and breast cancer <sup>61</sup>, as well as in chronic lymphocytic
hypophysitis <sup>54</sup>.

A pan-cancer single-cell transcriptional atlas across 15 human cancer types recently linked the
prevailing pro-angiogenic role of mast cells to their pro-tumorigenic effect <sup>16</sup>. In that study, cancers in
461 which an abundance of mast cells expressing VEGFA rather than TNF, were associated with a worse
462 outcome, suggesting a pro-tumorigenic role when their pro-angiogenic role prevailed ¹⁶. Here, our
463 single-cell analysis reveals that gonadotroph tumors contain mast cells that also express more VEGFA
464 than TNF, suggesting their pro-tumorigenic properties in gonadotroph tumors. This potential pro-
465 tumorigenic role was consistent with the fact that gonadotroph tumors progressing/relapsing more
466 rapidly after surgery had more mast cells, further confirming that tumors containing VEGFA-expressing
467 mast cells are usually associated with a worse prognosis ¹⁶.

468 Human and animal studies having used mast cell-depleting agents in the cancer context have reported
469 controversial results. Various agents have been used, including non-specific multi-tyrosine kinase
470 inhibitors, such as imatinib, sunitinib, and masitinib ⁶¹,⁶². Based on our LRN analysis and the
471 identification of a VEGFA-VEGFR communication axis between mast and endothelial cells, we chose to
472 target mast cell function with a c-Kit blocking antibody as Kit signaling was shown to regulate VEFGA
473 in mast cells. Depleting mast cells through this strategy resulted in a reduced tumor growth associated
474 with an increased apoptosis and a massive apoplexy likely due to the reduced number of blood vessels
475 and reduced MVD in treated animals. These observations highlight the potential benefit of targeting
476 mast cells in gonadotroph tumors, although the exact mechanism implicated needs to be further
477 determined.

478 Aside from the identification of the VEGFA-VEGFR communication axis, it is important to note that our
479 LRN analysis also revealed a series of mast cell-produced molecules with recognized pro-angiogenic
480 roles, including *HDC* and *CD44*, in addition to tryptase which is also a potent pro-angiogenic factor ⁴⁶-
481 ⁴⁸, suggesting mast cells could promote angiogenesis via several different mechanisms and pathways
482 in these tumors. The exact contributions of those candidates to potential therapeutic strategies remain
483 to be fully investigated.

484 Future studies exploring the effect of mast cell targeting in gonadotroph tumors, including the best
485 way to target them (i.e., using mast cell depletion, mast cell stabilizers, modulating the effect of mast
486 cell molecules) are therefore required. Identifying the best way to target them is particularly important
given that the vast majority of gonadotroph tumors that would benefit from medical treatment in the post-surgical setting are benign, and that even the most aggressive gonadotroph tumors have low malignant potential.

In conclusion, our study identified mast cells as new players in human gonadotroph tumors with pro-angiogenic and pro-tumorigenic roles, and proposes mast cell targeting as a potential avenue for the treatment of gonadotroph tumors. At the same time, our study generated single-cell and spatial transcriptomics data on human gonadotroph tumors that will benefit the scientific community.

**Funding:** Fonds de dotation pour la Recherche et l'Innovation en Endocrinologie et Maladies Métaboliques/Société Française d'Endocrinologie (Prix FRIEMM); la Ligue contre le Cancer (Comité départemental de la Drôme CD26, Comité départemental du Rhône CD69); Agence Nationale de la Recherche (SpaceTranscriptomiX ANR-23-CE45-0017, PlasCan SpaceNet ANR-17-CONV-0002, SingleStatOmics ANR-18-CE45-0023, AI4scMed ANR-22-PESN-0002); Fondation ARC pour la recherche sur le cancer (ARCMD-DOC12020020001361, ARCMD-DOC2021120004293).

**Conflict of interest:** The authors declare no competing interests.

**Authorship:** Study conception and design: MDI, GR and PB. Bioinformatic analyses: MDI, BS, ML, OG, FP, PB. Statistical analyses, collection of clinical data: MDI. Pathological diagnosis, annotation and providing of human pituitary tissues and sections: AV, EJ and LC. Performing and analysis of the *in vivo* experiment: MC, AFM, AL. Scientific discussion and editing: MDI, AFM, BS, MC, OG, FP, DB, GR, PB.

**Data availability:** Single-cell sequenced raw data have been deposited in the NCBI's Gene Expression Omnibus database under the accession number: GSE239803. Public scRNAseq data were collected from the National Genomic Data Centre (NGDC accession #HRA003483 27 and #HRA003483 28).
513 **Acknowledgments:** The authors thank Hector HERNANDEZ-VARGAS, Cyril DEGLETAGNE, Laurie
514 TONON and the CRCL Cancer Genomics Platform (Lyon, France) for their help to perform single-cell
515 and spatial sequencing. We thank Brigitte MANSHIP for English proofreading. We also thank the CRCL
516 platforms (P-PAC animal facility, Research Pathology and Cell Imaging) as well as the CIQLE (Lyon,
517 France) and Bruno CHAPUIS for their assistance. We gratefully acknowledge the support of the CRB
518 HCL—Tissue Tumorothèque Est for their biological samples and the PSMN (Pôle Scientifique de
519 Modélisation Numérique) of the ENS de Lyon for providing computing resources. Fig.2A was created,
520 in part, with BioRender.com.
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655
656 **Figure Legends**

657 **Figure.1: Identification of mast cells in the TME of gonadotroph tumors through single cell sequencing. (A)** Uniform Manifold Approximation and Projection (UMAP) representation of cell clusters in the tumor and tumor microenvironment (TME) through the integration of 24,471 cells coming from six human gonadotroph tumors (3 male and 3 female patients). (B) Stacked violin plots showing the mRNA expression levels of the main markers used to annotate tumor and TME cell clusters. (C) Distribution of the cell type clusters in the six integrated gonadotroph tumors and in individual patients. (D) Number of identified mast cells and their distribution between sequenced tumors (n = number). (E) Violin plots showing the mRNA expression levels of the top 10 differentially expressed genes in mast cells. (F) UMAP representation of mast cells from the integration of the six gonadotroph tumors after their sub-clustering. Markers of the 2 forced mast cell sub-clusters are shown on the violin plots.

668

669 **Figure.2: Analysis of mast cells in gonadotroph tumors and aging normal pituitary glands. (A)** Schematic view of the integration of 7 normal pituitary single-cell datasets with our GoPITs sequenced data. Used datasets that were obtained from peritumoral tissues (n=4, study PRJCA009690) or post-mortem pituitaries (n=3, PRJCA012771). UMAP of the integrated data are shown. Graph created using BioRender. (B) Split UMAP and analysis of the annotated clusters between gonadotroph tumor and normal pituitary sequenced tissues. Number (n) and distribution of each annotated cell type are shown in the bar plot. (C) Immunohistological (IHC) identification of tryptase immune reactive mast cells in normal pituitaries and in the sequenced gonadotroph tumors. Graph shows whole-slide quantification and median of tryptase-positive cells from 7 normal samples and 5 of the sequenced tumor samples. The reference and age of the patients are indicated. Statistical test: Mann-Whitney test, *: p<0.05. (D) Hematoxylin & eosin-stained sections from mouse pituitaries collected in young (5 months-old) and old (22 months-old) animals. Dashed lines indicate tumors (Tu.). Lower panels show representative images of Tpsab1 immunofluorescent staining counterstained with DAPI. The inset shows an enlarged
682 view of the dashed area highlighting the presence of Tpsab1-immune reactive mast cells in the aged
683 sample with tumors.

684

685 **Figure.3: Identification of the Mast/Endothelial cell colocalization through spatial transcriptomic analysis. (A)** Visium analysis of TPSAB1, TPSB2 and CPA3 spatial gene expression in two independent gonadotroph tumors. (B) Mapping of the Visium spots containing mast cells based on TPSAB1, TPSB2 and CPA3 spatial gene expression. (C) Integrated clustering of Visium#1 and Visium#2 sequenced spots. The lower panel shows the UMAP localization of mast-containing spots from merged datasets. (D) Results of the GeneOntology analysis performed with genes differentially expressed in spots with mast cells vs other spots. The p-values are shown in the graph of ranked HDsignDB human 2021 and Tabula Sapiens references. On the right panel, violin plots show the expression of mast, endothelial and pericyte gene markers in spots containing mast cells vs other spots. (E) Spatial distribution of Mast_cell_activation and Endothelial_cell_activation signature on Visium sequenced tumors, graphs were generated through the Spata2 analysis pipeline. (F) Left panel shows a Semla-based deconvolution mapping the location of TumorCells, EndothelialCells and MastCells identified in our single-cell RNA sequencing integrated references on Visium#1. a and b insets are magnified views of the dashed areas. The right panels show CD34/TPSAB1 double immunofluorescence staining performed on an adjacent serial section used for the sequencing of the Visium#1. a and b rows correspond to the enlarged areas.

701

702 **Figure.4: Quantification, clinical associations of mast cells in a cohort of gonadotroph tumors, and their impact on the tumor vasculature. (A)** Representative picture of tryptase-immunostained mast cells in 8 independent gonadotroph tumors with the highest (top row) and the lowest (bottom row) percentage of mast cells. The graph on the right summarizes the percentage of mast cells quantified on whole-scans of tryptase IHCs from 49 gonadotroph tumors. Median with interquartile range is shown on the graph. Each point represents a tumor. Scale bar = 100 µm. (B) Percentage of mast cells
708 in the initial versus the recurrent tumor sample for the nine patients. Graph: each point represents a tumor; the lines connect the initial tumor sample to the recurrent tumor sample for the same patient.
709
710 Statistical test: Wilcoxon matched paired test, ** p = 0.007. (C) Percentage of mast cells in tumors with early (<3 years) versus late (≥3 years) progression/recurrence after surgery. Graph: median with range;
711
712 each point represents a tumor. Statistical test: Mann-Whitney test, ** p = 0.002. (D) Correlation analysis between the percentage of mast cells and the microvessel density in the 49 tumors. Graph: scatter plot; each point represents a tumor. Statistical test: Spearman's correlation. (E) Representative whole slide density map analysis of CD34+ and tryptase+ cells in a gonadotroph tumor. The merged density map highlights non-overlapping (CD34+ only; blue) and overlapping (CD34+/tryptase+, white) areas. The right panels show a split-magnified view of the indicated regions in the density map (dashed boxes), illustrating TPSAB1 and CD34 immunostaining in both dense and sparse zones. (F-H)
713
714
715
716
717 Quantification of the number of blood vessels (F), the microvessel density (MVD) (G), the number of solid vessels and (H) the thickness of vessel walls. Quantification was performed on 12 independent mast cell-dense or -sparse regions from 6 gonadotroph tumors. Graph: median with range; each point represents a tumor. Statistical test: Mann-Whitney test, * p < 0.05, ** p < 0.01. Right panel in (H) shows representative CD34 IHC of blood vessel morphologies found in mast cell-dense or -sparse regions.
718
719
720
721
722
723
724

725 **Figure.5: Analysis of signaling ligand-receptor networks in gonadotroph tumors. (A) Interactions**
726 inferred by CellPhoneDB between mast cells and the other cell populations following the integration
727 of six gonadotroph tumors. Each line represents an interaction. (B) Enriched interactions between
728 mast cells, endothelial cells and MoMacs. (C) Violin plots showing gene expression of the most
729 representative mast cell-produced pro-angiogenic molecules. (D) Association analysis between LIF and
730 VEGFA expression in mast cells. Statistical test: Fisher's exact test, **** = p < 0.0001. (E) CellChat
731 analysis of VEGF signaling network. On the left, the VEGF signaling network in gonadotroph tumors,
732 and on the right, the cell-type contribution and cell type of each ligand-receptor (L-R) pair to the VEGF
733 signaling network in gonadotroph tumors. (F) Violin plots showing the expression of genes contributing
734 to the VEGF signaling network in each cluster. (G) Representative immunofluorescence staining 735 colocalizing VEGFA expression in TPSAB1 expressing mast cells. Staining was performed on a human 736 gonadotroph tumor. a and b panels are enlarged views of the indicated dashed areas. (H) Spatial 737 mapping of GO_VEGF_ACTIVATED RECEPTOR_ACTIVITY expression score on Visium#1 and Visium#2 738 samples. Spatial localization of mast and endothelial cells obtained by deconvolution are shown as 739 references. 740

741 **Figure.6: Mast cell depletion impacts the tumor vasculature of mice engrafted with LβT2 tumors. (A)**
742 Macroscopic view of LβT2 tumors in control and treated mice. Pictures show tumors 21 days after LβT2 743 cell injection at the subcutaneous implantation site. (B) Whole-scan images of Hematoxylin & Eosin 744 (H&E)-stained tumor sections collected from control and treated animals. Left panels are a magnified 745 view of the dashed areas highlighted in whole-section images. Quantification of hemorrhagic areas is 746 shown on the graph for all treated and control tumors. Data are shown as median with range, statistical 747 test: Mann-Whitney test, * p < 0.05. (C) Representative images of Cd34-immunostained (IHC) tumor 748 serial sections adjacent to the H&E stained-images. Enlarged views of the Cd34 (IHC) are shown in the 749 middle panel. a, b, c and d, indicate the different regions highlighted by dashed lines in tumors 750 collected from control (a, b) and treated (c, d) animals. Data are shown as median, statistical test: 751 Mann-Whitney test, * p < 0.05. (D) Representative of Tpsab1 and Cd34 immunostained serial tumor 752 sections collected from control and treated animals. In control animals, Cd34-immune reactive cells 753 are shown in regions containing (Dense) or lacking (Sparse) mast cells (arrows), respectively. (E-H) 754 Quantification of the number of blood vessels (E), MVD (F), number of solid vessels (G), and wall 755 thickness of vessels (H) performed on mast cell-dense or -sparse regions from 6 tumors collected from 756 control mice and 6 tumors collected from treated mice. Data are presented as median, statistical test: 757 Mann-Whitney test, *: p<0.05, **: p<0.01.
<caption>Figure 1</caption>

Fig.1 file et al.

A

<figure><img src="image_2.png" /></figure>

C

<figure><img src="image_3.png" /></figure>

D

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /></figure>

E

<figure><img src="image_6.png" /></figure>

F

<figure><img src="image_7.png" /></figure>
<caption>Figure 2</caption>

<figure><img src="image_8.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_11.png" /></figure>
<caption>Fig. 3 file et al.</caption>

A

<figure><img src="image_12.png" /></figure>

B

<figure><img src="image_13.png" /></figure>

C

<figure><img src="image_14.png" /></figure>

D

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

E

<figure><img src="image_17.png" /></figure>

F

<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /><figcaption>Figure 4</figcaption></figure>
<caption>Figure 5</caption>

Fig. 5 file et al.

<figure><img src="image_20.png" /></figure>
<figure><img src="image_21.png" /><figcaption>Treated</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>Control</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>Hemorrhagic area (%)</figcaption></figure>

<figure><img src="image_24.png" /></figure>

Cd34

<figure><img src="image_25.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_28.png" /></figure>
Single Cell & Spatial Sequencing

Pituitary
tumors

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

Mast cells

<figure><img src="image_31.png" /></figure>

TME

Cartography

<figure><img src="image_32.png" /></figure>

Mast cells
Endothelial cells

Functional interactions

Endothelial cell

<figure><img src="image_33.png" /></figure>

Mast cell

KITLG

<figure><img src="image_34.png" /></figure>

KIT

<figure><img src="image_35.png" /></figure>

Mast cell

VEGF-A

<figure><img src="image_36.png" /></figure>

Endothelial cell

Mast cells → pro-tumorigenic & pro-angiogenic

Patient cohort

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

Relapse

Mouse grafted tumor cells

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

Mast cell
depletion

<figure><img src="image_41.png" /></figure>

Micro Vascular Density

Apoplexy/Hemorrhages