DOI: 10.3390/diagnostics16213471 ISSN: 2075-4418

Germline AIP Testing in Pituitary Neuroendocrine Tumors: Diagnostic Yield, Variant Interpretation, and a Phenotype-Driven Clinical Approach—A Narrative Review

Cristiana Călin, Iulia Florentina Burcea, Ramona Dobre, Cătălina Poiană

Germline pathogenic variants in the aryl hydrocarbon receptor-interacting protein (AIP) gene are the most common identifiable genetic cause of familial isolated pituitary adenomas (FIPA) and are particularly associated with young-onset growth hormone-secreting pituitary neuroendocrine tumors (PitNETs). This review critically evaluates current evidence on patient selection for germline AIP testing, variant interpretation, and emerging multigene approaches. A narrative PubMed/MEDLINE search identified English-language studies published between January 2006 and April 2025, including original clinical studies, international cohorts, systematic reviews, consensus statements, and relevant guidelines. Evidence was synthesized with emphasis on diagnostic yield, genotype–phenotype correlations, clinical indications for testing, and contemporary American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant classification. The highest diagnostic yield is consistently observed in FIPA, pituitary gigantism, childhood- or young-onset growth hormone (GH)-secreting macroadenomas, and selected patients with aggressive or otherwise high-risk phenotypes. Contemporary ACMG/AMP classification has lowered and refined estimates of clinically actionable AIP variants compared with historical studies. Targeted AIP testing remains appropriate for characteristic phenotypes, whereas multigene panels may be useful when syndromic features, atypical presentations, or persistent suspicion of hereditary disease remain after negative AIP testing. Current evidence supports phenotype-driven rather than universal germline AIP testing in PitNETs. Integrating age at onset, family history, tumor subtype, tumor characteristics, and standardized variant interpretation may maximize diagnostic yield while limiting unnecessary testing.

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