# Transcription Factor–Based Classification of Pituitary Neuroendocrine Tumors: Practical Immunohistochemical Algorithms, Molecular Correlates, and Diagnostic Challenges in the 5th WHO Era

## TL;DR

This paper explains how using specific transcription factors improves the classification of pituitary tumors, aiding diagnosis and treatment in the new WHO classification system.

## Contribution

A practical immunohistochemical algorithm integrating transcription factors and molecular markers for accurate PitNET classification.

## Key findings

- TF-based classification reduces the null cell category to about 1% of cases.
- High-risk histotypes like silent corticotroph tumors are better identified using TFs.
- Molecular correlates like GNAS and USP8 mutations guide personalized treatment strategies.

## Abstract

Pituitary neuroendocrine tumors (PitNETs) constitute a significant proportion of primary intracranial neoplasms and were historically differentiated based on clinical hormone excess syndromes and tinctorial properties. The 5th edition of the WHO classification introduces a paradigm shift towards the lineage-based taxonomy based on the cell-specific expression of transcription factors (TFs). This overview focuses on the biological justifications and diagnostic value of the core TFs of Pituitary-Specific Positive Transcription Factor 1 (PIT1), T-Box Pituitary Transcription Factor (TPIT), and Steroidogenic Factor 1 (SF1), which signify the somatotroph, lactotroph, thyrotroph, corticotroph, and gonadotroph lineages, respectively. By focusing on TF expressions instead of hormone immunoreactivity, pathologists can better subtype clinically non-functioning tumors, effectively relegating the previously overutilized null cell category to about 1% of cases. The TF-based classification is also essential in discriminating high-risk histotypes of silent corticotroph tumors, sparsely granulated somatotrophs, and immature PIT1-lineage PitNETs, which are linked to a higher invasiveness and recurrence. We suggest a practical, stepwise immunohistochemical diagnostic algorithm with the integration of ancillary markers (e.g., GATA3 and ERα) to refine lineage assignment. New molecular correlates such as GNAS and USP8 mutations also add to this framework and guide the use of individualized treatment involving somatostatin analogs or dopamine agonists. And lastly, we discuss the ongoing issues of diagnosis of triple-negative and multilineage tumors and the growing importance of DNA methylation profiling and artificial intelligence in standardized reporting and improving precision management.