Neuroendocrinology · 2026
Pathological Classification and Clinical Characteristics of Growth Hormone-Secreting PitNETs.
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Li Zhenwei, Wu Yinzi, Gao Yike, Feng Ming, Deng Kan, Xing Bing, Lian Wei, Yao Yong, Wang Renzhi, Sun Jian, Bao Xinjie
Аннотация
INTRODUCTION: Acromegaly/gigantism is a rare disease primarily caused by growth hormone (GH)-secreting pituitary neuroendocrine tumors (PitNETs). With the update in the WHO 5th edition classification of pituitary tumors, particularly the introduction of the novel PIT1/SF1 co-expressing tumor subtype, our understanding of these neoplasms has significantly advanced. However, systematic pathological classification and clinical characterization studies of GH-secreting PitNETs remain relatively scarce.
METHODS: A retrospective study was conducted on 143 patients with acromegaly/gigantism who underwent surgical treatment at Peking Union Medical College Hospital between June 2022 and December 2024. Tumor specimens were re-evaluated for pathological subtyping. Demographic data, radiological characteristics, hormone profiles, immunohistochemical findings, and clinical outcomes were collected and compared among subtypes.
RESULTS: Forty-five cases (32%) were classified as pure GH-secreting tumors, while the remainder showed co-expression of other hormones and transcription factors. Among PIT1-lineage tumors, sparsely granulated somatotroph tumors (SGSTs) demonstrated greater cavernous sinus invasion, larger maximal tumor diameter, lower GH secretion index, and immunostaining intensity, and reduced SSTR2 expression, suggesting a more aggressive biological behavior. Co-expression of prolactin increased the risk of hyperprolactinemia (OR = 2.843), though only 23.7% of mammosomatotroph tumors and 32.6% of mixed somatotroph-lactotroph tumors presented with hyperprolactinemia. Additionally, 19 PIT1/SF1 co-expressing tumors were identified, showing diverse hormonal profiles and significantly higher cavernous sinus invasion compared to PIT1-lineage tumors.
CONCLUSION: GH-secreting PitNETs exhibit marked heterogeneity in pathological subtypes. Current classification systems require clearer cutoff criteria to improve diagnostic consistency. SGSTs are associated with a more invasive phenotype, warranting close clinical monitoring and long-term follow-up. PIT1/SF1 co-expressing tumors represent a distinct and heterogeneous entity that should be considered in future classification frameworks.
METHODS: A retrospective study was conducted on 143 patients with acromegaly/gigantism who underwent surgical treatment at Peking Union Medical College Hospital between June 2022 and December 2024. Tumor specimens were re-evaluated for pathological subtyping. Demographic data, radiological characteristics, hormone profiles, immunohistochemical findings, and clinical outcomes were collected and compared among subtypes.
RESULTS: Forty-five cases (32%) were classified as pure GH-secreting tumors, while the remainder showed co-expression of other hormones and transcription factors. Among PIT1-lineage tumors, sparsely granulated somatotroph tumors (SGSTs) demonstrated greater cavernous sinus invasion, larger maximal tumor diameter, lower GH secretion index, and immunostaining intensity, and reduced SSTR2 expression, suggesting a more aggressive biological behavior. Co-expression of prolactin increased the risk of hyperprolactinemia (OR = 2.843), though only 23.7% of mammosomatotroph tumors and 32.6% of mixed somatotroph-lactotroph tumors presented with hyperprolactinemia. Additionally, 19 PIT1/SF1 co-expressing tumors were identified, showing diverse hormonal profiles and significantly higher cavernous sinus invasion compared to PIT1-lineage tumors.
CONCLUSION: GH-secreting PitNETs exhibit marked heterogeneity in pathological subtypes. Current classification systems require clearer cutoff criteria to improve diagnostic consistency. SGSTs are associated with a more invasive phenotype, warranting close clinical monitoring and long-term follow-up. PIT1/SF1 co-expressing tumors represent a distinct and heterogeneous entity that should be considered in future classification frameworks.