MSACL 2023 Abstract

Mass Spectrometry for Classification of Pituitary Neuroendocrine Tumors

William Phipps, William Catungal, Kelly Smith, Luis Gonzalez-Cuyar, Niklas Krumm, and Andrew Hoofnagle
University of Washington, Seattle, WA

William Phipps, MD (Presenter) University of Washington >> POSTER (PDF)

Presenter Bio: Bill Phipps is an Assistant Professor at the University of Washington in the Department of Laboratory Medicine and Pathology, where he serves as Assistant Director of Clinical Chemistry and is Clinical Pathology Residency Program director. He completed his residency training at UT Southwestern Medical Center in Dallas, TX.

Relevant Financial Disclosures (within past 24 months, reported on Apr 21, 2026) Grant/Research Support UNIVERSITY OF WASHINGTON Conflict mitigation NOT REQUIRED.

INTRODUCTION
Immunohistochemistry (IHC) is the primary tool used clinically for measuring proteins in histologic tissue samples. This technique is subject to innumerable problems with respect to standardization and performance. Mass spectrometry (MS) offers an alternative with the potential to overcome these challenges. A representative application for which MS may be better suited is the classification of pituitary neuroendocrine tumors (PitNETs) based on protein hormone and transcription factor expression.

OBJECTIVES
The primary objective of this study was to develop a liquid chromatograph-tandem mass spectrometry (LC-MS/MS) assay to classify pituitary neuroendocrine tumors (PitNETs) using formalin-fixed paraffin-embedded (FFPE) tissue.

METHODS
Tumor regions from archived FFPE PitNET tissue specimens (prolactin, ACTH and growth hormone producing tumors) were microdissected and processed using heat-denaturation and overnight trypsin digestion. Mass spectrometry analyses were performed on the tryptic peptides using data-dependent acquisition (DDA) and parallel reaction monitoring (PRM) on a hybrid orbitrap mass spectrometer coupled to a nanoflow chromatograph. Untargeted DDA results were utilized to inform development of a targeted PRM assay.

RESULTS
Untargeted LC-MS/MS analyses correctly differentiated tumor hormone expression in all cases (n = 6), based on spectral counting for the key protein hormone markers and exactly matched the historical typing for these specimens using anti-pituitary hormone IHC (follicle-stimulating hormone, luteinizing hormone, prolactin, proopiomelanocortin, somatotropin, and thyrotropin). For all 3 PitNET subtypes evaluated (somatroph, lactotroph, and corticotroph tumors), at least two novel and high-quality surrogate peptides were identified for their respective hormone to enable complementary analyses by PRM.

CONCLUSION
The application of LC-MS/MS to solid tissues represents an alternative pathway to IHC that allows for more objective and highly parallel quantication protein expression in tissues to improve diagnosis. Classification of PitNET tumors by LC-MS/MS was robust and successful in cases with limited tissue. The novel surrogate peptides identified can be used to develop similar testing using other MS instrumentation, such as triple quadrupole mass spectrometers. This study provides a model for the development of more sophisticated and quantitative analyses of protein expression in a broader set of pituitary tumors and additional tumor types.