F. Ruf Zamojski*a (Dr), D. Marrerob (Dr), K. Taniguchib (Dr), Z. Zhangc (Dr), V. Yiannid (Dr), GR. Smitha (Dr), AB. Rubensteina (Dr), CM. Abascal Sherwell S├ínchezd (Dr), N. Mendeleva (Mrs), MAS. Ampera (Mrs), H. Pincasa (Dr), M. Zamojskia (Dr), VD. Naira (Dr), E. Zaslavskya (Dr), O. Trojanskayae (Prof), C. Andoniadoud (Prof), M. Mercadob (Prof), SC. Sealfona (Prof)

a Icahn School of Medicine at Mount Sinai, New York, Ny, UNITED STATES ; b Instituto Mexicano del Seguro Social, Mexico, MEXICO ; c Princeton University, Princeton, Nj, UNITED STATES ; d King College London, London, UNITED KINGDOM ; e Princeton University and Flatiron Institute, Simons Foundation, Princeton, Nj And New York, Ny, UNITED STATES

* frederique.ruf-zamojski@mssm.edu

Introduction

Patients with pituitary neuroendocrine tumors (PitNETs)/pituitary adenomas have increased co-morbidities and mortality compared to the general population. Standard therapies consist of surgery, medical therapy, and radiotherapy. The present WHO grading guidelines include tumor type, size, invasion, and proliferation rates (1). However, current invasion and proliferation markers are insufficient for adenoma subtype classification, prognosis, and patient management. Although bulk genomic studies have led the way to improved classification and targeted treatments, these methods fail to capture cell subpopulations both within tumors and across patients, and address potential tumor heterogeneity within a given subtype. We recently characterized normal human post-mortem pituitaries at single nucleus resolution (2). Here we extend this work to adenomas.

Materials and Methods

We analyzed eight clinically-characterized adenomas, including four non-functioning gonadotroph, two corticotroph, and two somatotroph adenomas, using single nucleus multi-omics approaches encompassing same-cell RNA-seq and ATAC-seq. We identified cell types in each sample based on the combined integration of gene expression and chromatin accessibility using well-established clustering analysis software (e.g. Seurat). Unlike normal pituitaries, some of these datasets demonstrated high levels of artifactual ambient RNA for the highly expressed hormones, requiring additional filtering.

Results

Gene expression profiles of hormone-secreting tumor cells were distinct from healthy donor hormone-secreting cell types. Tumor samples were very heterogeneous, with clusters varying from a mixed composition of distinct cell types, to closely related groups of cells clustering together. Analysis of coordinated genes identified latent variables separating different adenoma types, and non-invasive vs. invasive non-functional gonadotroph adenomas. Our data reveal a high degree of inter-donor variability, as well as intra-adenoma heterogeneity even within the same tumor type at both the RNA and chromatin accessibility levels.

Discussion

Our data analysis brings new insights into the molecular pathogenesis of PitNETs and represents a first step towards sub-classifying pituitary adenomas and identifying tumor type-specific markers. Given the cellular heterogeneity observed in tumors, we anticipate that single-cell analysis of the epigenetic landscape will advance tumor classification, prognosis, and elucidation of the mechanisms underlying tumorigenesis.

References:

(1) Trouillas et al, Cancers, 12(2), 2020.

(2) Zhang et al, Cell Reports, 38(10), 2022.

The author has declared no conflict of interest.