Abstract

   Clinically aggressive lactotroph pituitary neuroendocrine tumors
   (PitNET) are invasive tumors with an unusually rapid growth rate
   despite maximally tolerated doses of dopamine agonist (DA). We aimed to
   unravel the molecular heterogeneity of lactotroph PitNET and to
   identify biomarkers of aggressiveness and resistance to pharmacological
   treatment. A total of 13 patients harboring DA-resistant lactotroph
   PitNET were included in this study. Visium Spatial Transcriptomics
   (ST), whole transcriptome sequencing (WTS), and whole exome sequencing
   (WES) were performed in tumors from 4 of these patients; WTS and WES
   was carried out in 5; tumors from two patients underwent ST and WES and
   tumors from two other patients underwent only ST. Tumors were
   classified as null or partial responders according to their response to
   DA treatment. The eight PitNET analyzed by ST exhibited significant
   intratumoral heterogeneity, with clones showing alterations in PI3K/AKT
   and lipid metabolism pathways, particularly inositol phosphate,
   glycerophospholipid, and sphingolipid metabolism. The cell-cell
   communication analysis showed FGF-FGFR ligand receptor interaction
   whilst the transcription factors RXRA and CREM showed participation in
   both groups. A trajectory exploration was performed by including all
   PitNET together in a single analysis to determine whether there was a
   tendency or molecular pathway showing a differentiation pattern that
   would guide the transition from a partially responsive PitNET to a
   completely unresponsive one. We did not observe any such pattern. All
   of these findings were corroborated in the cohort of DA-resistant
   PitNETs in which only bulk WTS and WES were performed. The bulk WTS
   corroborated lipid metabolism and PI3K-AKT pathway alteration in
   PitNET, whereas the WES showed only SF3β1 and TP53 variants in one
   tumor each. Our work suggests that the PI3K/AKT pathway may constitute
   a molecular target at which to aim therapeutic strategies designed to
   treat aggressive and DA-resistant lactotroph PitNET.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s40478-025-02025-9.

   Keywords: Lactotroph PitNET, Cabergoline, Prolactinoma, Aggressive
   PitNET, Resistant, Spatial transcriptomic, Pharmacological resistant

Introduction

   Pituitary neuroendocrine tumors (PitNET) are epithelial neoplasms
   arising from adenohypophyseal cells and constitute the second most
   frequent intracranial tumor [[76]18, [77]19, [78]22]. Lactotroph PitNET
   (PRL-PitNET) are the most common type of functioning neoplasm of the
   pituitary gland and account for 50% of all pituitary tumors in both
   women and men [[79]24]. These highly heterogeneous lesions can behave
   invasively in 40–45% of cases, with up to 15% being clinically
   aggressive [[80]20]. Aggressive lactotroph PitNET are invasive tumors
   with an unusually rapid growth rate despite maximally tolerated doses
   of dopamine agonist (DA) and thus, frequently require adjuvant therapy,
   as they are at risk for persistence and recurrence [[81]21, [82]24].

   DA such as cabergoline suppress PRL production, reduce cell
   proliferation and constitute the pharmacological treatment of choice
   for lactotroph PitNET [[83]15]. In case of persistence or recurrence,
   the therapeutic alternatives include surgical resection, radiation
   therapy, and in very aggressive lesions or when metastases are present,
   chemotherapy with the alkylating agent temozolomide, with or without
   capecitabine [[84]24]. Predicting the response to any of these
   treatment modalities remains a challenge [[85]26]. In this context, the
   clinical, pathological, and molecular definitions of malignant and
   aggressive lactotroph PitNET remain to be clearly defined, as primary
   lactotroph carcinomas are similar to aggressive adenomas except for the
   presence of craniospinal or distant metastases [[86]21].

   To ascertain the molecular heterogeneity of lactotroph PitNET and to
   identify biomarkers of aggressiveness and resistance to pharmacological
   treatment, we carried out spatial transcriptomics as well as whole
   exome and whole transcriptome sequencing of tumoral tissues from
   patients with aggressive lactotroph PitNET resistant to DA who
   underwent transsphenoidal surgical resection.

Materials and methods

Patients

   A total of 13 patients harboring DA-resistant lactotroph PitNET were
   included in this study. Visium Spatial Transcriptomics (ST), whole
   transcriptome sequencing (WTS), and whole exome sequencing (WES) were
   performed in tumors from 4 of these patients; WTS and WES was carried
   out in 5; tumors from two patients underwent ST and WES and tumors from
   two other patients underwent only ST. One of the patients presented to
   the ear, nose and throat department with a mass in the sphenoid sinus,
   which was biopsied and turned out to be a PRL-secreting lesion
   documented by MRI, so she was treated with cabergoline. The remaining
   12 patients had been subjected to transsphenoidal surgery because of
   lack of response to DA treatment.

   Response to cabergoline treatment was defined as follows: complete
   response, normal PRL levels and > 50% reduction of tumor mass; partial
   or stable response, > 50% reduction in PRL levels and any reduction in
   tumor mass; and null response, less than 50% reduction in PRL levels
   and no tumor mass reduction. All tumors included in the study were
   sporadic and were collected from patients diagnosed, treated and
   followed at the Endocrinology Service and the Neurosurgical department
   of Hospital de Especialidades, Centro Médico Nacional Siglo XXI of the
   Instituto Mexicano del Seguro Social. All participating patients were
   recruited with signed informed consent and ethical approval from the
   Comisión Nacional de Ética e Investigación Científica of the Instituto
   Mexicano del Seguro Social in accordance with the Helsinki declaration.

Immunophenotyping of PitNET: immunohistochemistry (IHC) for hormones and
transcription factors (TF)

   Immunohistochemistry was performed as previously described [[87]35].
   Briefly, paraffin-embedded, formalin-fixed tissue blocks were obtained
   and 3 μm sections were stained with hematoxylin-eosin and reviewed by a
   neuropathologist. Tumors were represented with a 2-fold redundancy.
   Sections were cut and placed onto coated slides. Immunostaining was
   performed by means of the HiDef detection HRP polymer system (Cell
   Marque, CA, USA), using specific antibodies against each pituitary
   hormone (TSH, GH, PRL, FSH, LH and ACTH) and the lineage specific
   transcription factors (TF) TBX19 (T-PIT), POU1F1 (PIT-1) and NR5A1
   (SF1), as previously described. Interpretation of IHC for pituitary
   hormones and TF was carried out by two independent observers.

Visium Spatial transcriptomics

   Pathologist reviewed the hematoxylin and eosin-stained (H&E) slides and
   selected representative areas from each tumor. Visium Spatial Gene
   Expression for FFPE (10X Genomics) instructions were followed as
   provided by manufacturers. The FFPE tissue blocks were cut 5 μm-thick
   as recommended. Slices were placed onto Visium Spatial Gene Expression
   Slide, and the tissue was deparaffinized at 60ºC for 2 h and xylene
   baths followed by decreasing concentrations of ethanol and finally
   molecular grade water. Once the tissue was deparaffinized and
   re-hydrated it was stained with H&E. The tissue images were digitalized
   with the Aperio CS2 by Leica. Decrosslinking was performed with 0.1 N
   HCl as recommended. Libraries were constructed with the Visium Spatial
   Gene Expression Reagent Kit as follows, FFPE probes were hybridized
   overnight and subsequently were ligated, the RNA was digested, and the
   probes released for extension and eluted for amplification and index
   ligation and finally library cleanup. The libraries were pair-end
   sequenced in a NextSeq2000.

Visium bioinformatic analysis

   The bioinformatic analysis was conducted using Ubuntu 22.04.5 LTS,
   based on Linux. Statistical analyses and figure constructions were
   carried out using R version 4.4.1, unless otherwise specified as in the
   following sections.

   Data preprocessing was performed using Space Ranger 2.1.1 with GRCh38
   as the reference genome. For further analysis, the Seurat package was
   used, whereby data was normalized with the “SCTransform” function using
   default parameters on the “Spatial” assay, followed by dimensionality
   reduction and clustering. Variable features were calculated using the
   “FindAllMarkers” function and “FindSpatiallyVariableFeatures” for
   spatial variogram variable features.

   Cell-cell communication analysis was conducted using CellChat v. 2.1.2
   with the full CellChatDB.human database. Communication
   probability/strength was calculated using the “computeCommunProb”
   function with default parameters, overexpressed genes and filtering of
   overexpressed interactions, and network centrality was also calculated
   by means of “netAnalysis_computeCentrality.”

   Enrichment analysis was performed using the “clusterProfiler” package,
   v. 4.12.6. The “enrichKEGG” function was used for enrichment with
   default parameters, and results from “FindAllMarkers” were filtered by
   absolute avg_log2FC > 0.5 and an adjusted p value of < 0.01. Term
   similarity and figures were generated using the same package. Figures
   over tissue images were constructed by adding an assay to individual
   Seurat objects.

   The “monocle3” package, v. 1.3.7, was used for trajectory analysis on
   the aggregate, utilizing its functions “as.cell_data_set,”
   “cluster_cells,” “learn_graph,” and “plot_cells,” as default
   parameters.

   Gene regulatory network analysis using “Scenic” required transforming
   the Seurat object to loom using the “build_loom” function from
   “SCopeLoomR” v. 0.13.0. Loom files were then processed with the “grn,”
   “ctx,” and “aucell” functions from “pyscenic” v. 0.12.1, using the
   following references: