ArticleVirchows Archiv : an international journal of pathology2021
An algorithmic approach utilizing CK7, TTF1, beta-catenin, CDX2, and SSTR2A can help differentiate between gastrointestinal and pulmonary neuroendocrine carcinomas.
Article in Virchows Archiv : an international journal of pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
- Machine Learning Uncovers Novel Predictors of Peptide Receptor Radionuclide Therapy Eligibility in Neuroendocrine Neoplasms.Cancers · 2025Article
- Patterns of Immunoreactivity with TTF-1 Antibodies 8G7G3/1 and SPT24 Suggest Distinct Immunoprofiles Between Most Pulmonary and Nonpulmonary Small Cell Carcinomas.International journal of surgical pathology · 2024Article
- Top 10 Histological Mimics of Neuroendocrine Carcinoma You Should Not Miss in the Head and Neck.Head and neck pathology · 2023Review
- An update on the development of concepts, diagnostic criteria, and challenging issues for neuroendocrine neoplasms across different digestive organs.Virchows Archiv : an international journal of pathology · 2022Review
- Review
- Neuroendocrine Differentiation in Conventional Colorectal Adenocarcinomas: Incidental Finding or Prognostic Biomarker?Cancers · 2021Article
- Second-Generation Neuroendocrine Immunohistochemical Markers: Reflections from Clinical Implementation.Biology · 2021Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Primary gastrointestinal neuroendocrine carcinoma (GI-NEC) cannot be distinguished morphologically from pulmonary neuroendocrine carcinoma (P-NEC). This can present a significant diagnostic challenge in cases where site of origin cannot be readily determined. To identify immunohistochemical (IHC) markers that can be used to reliably distinguish between GI-NECs and P-NECs, we constructed 3-mm tissue microarrays, one containing 13 GI-NECs and one containing 20 P-NECs. IHC was performed on both microarrays using 21 stains: AE1/AE3, CK7, CK20, synaptophysin, chromogranin, CD56, INSM1, SSTR2A, CDX2, SATB2, TTF1, Napsin A, PR, GATA3, PAX8, ISL1, beta-catenin, AFP, SMAD4, Rb, and p53. For GI-NEC, the most strongly expressed marker was synaptophysin (mean H-score 248), while AE1/AE3 was the most strongly expressed in P-NEC (mean H-score 230), which was stronger than in GI-NEC (p = 0.011). Other markers that were stronger overall in P-NEC than in GI-NEC included CK7 (p < 0.0001) and TTF1 (p < 0.0001). Markers that were stronger overall in GI-NEC than in P-NEC included SSTR2A (p = 0.0021), SATB2 (p = 0.018), CDX2 (p = 0.019), and beta-catenin (nuclear; p = 0.029). SMAD4, Rb, and p53 showed similar rates of abnormal protein expression. Based on these results, a stepwise algorithmic approach utilizing CK7, TTF1, beta-catenin, CDX2, and SSTR2A had a 91% overall accuracy in distinguishing these GI-NEC from P-NEC. This was tested on a second cohort of 10 metastatic GI-NEC and 10 metastatic P-NEC, with an accuracy in this cohort of 85% and an overall accuracy of 89% for the 53 cases tested. Our algorithm reasonably discriminates GI-NEC from P-NEC using currently available IHC stains.
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