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Proc Natl Acad Sci U S A. 2015 Aug 11;112(32):9978-83. doi: 10.1073/pnas.1423101112. Epub 2015 Jul 27.

MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation.

Author information

1
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115;
2
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115;
3
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115;
4
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 nathalie_agar@dfci.harvard.edu.

Abstract

We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making.

KEYWORDS:

in-source decay; intrasurgical diagnosis; mass spectrometry imaging; molecular pathology; pituitary

PMID:
26216958
PMCID:
PMC4538649
DOI:
10.1073/pnas.1423101112
[Indexed for MEDLINE]
Free PMC Article

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