Integrated Genomic and Clinicopathologic Approach Distinguishes Pancreatic Grade 3 Neuroendocrine Tumor From Neuroendocrine Carcinoma and Identifies a Subset With Molecular Overlap.

molecular profile neuroendocrine carcinoma neuroendocrine tumor next generation sequencing pancreas

Journal

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
ISSN: 1530-0285
Titre abrégé: Mod Pathol
Pays: United States
ID NLM: 8806605

Informations de publication

Date de publication:
03 2023
Historique:
received: 18 04 2022
revised: 02 08 2022
accepted: 06 10 2022
pubmed: 15 2 2023
medline: 22 3 2023
entrez: 14 2 2023
Statut: ppublish

Résumé

Distinguishing grade 3 pancreatic neuroendocrine tumor (G3 PanNET) from neuroendocrine carcinoma (PanNEC) is a known diagnostic challenge, and accurate classification is critical because clinical behavior and therapies differ. Although current recommendations suggest that immunohistochemistry for p53, Rb, ATRX, and DAXX can distinguish most cases, some cases remain difficult to classify using this approach. In this study, we reviewed 47 high-grade neoplasms originally diagnosed as pancreatic neuroendocrine neoplasms. In addition to the currently recommended stains, we performed capture-based sequencing of approximately 500 cancer genes and immunohistochemistry for p16 and trypsin or chymotrypsin. Using an integrated molecular and clinicopathologic approach, 42 (89%) of 47 cases had a clear final diagnosis of either G3 PanNET (n = 17), PanNEC (n = 17), or mixed acinar-NEC (n = 8). The 17 G3 PanNETs demonstrated frequent alterations in MEN1 (71%), DAXX (47%), ATRX (24%), TSC2 (35%), SETD2 (42%), and CDKN2A (41%). Contrary to prior reports, TP53 alterations were also common in G3 PanNETs (35%) but were always mutually exclusive with CDKN2A alterations in this group. The 17 PanNECs demonstrated frequent alterations in TP53 (88%), cell cycle genes RB1 (47%), CCNE1/CCND1 (12%), CDKN2A (29%), and in KRAS (53%) and SMAD4 (41%); TP53 was coaltered with a cell cycle gene in 76% of PanNECs. Diffuse strong p16 staining was observed in 69% of PanNECs in contrast to 0% of G3 PanNETs. The 8 acinar-NECs had recurrent alterations in ATM (25%), APC (25%), and STK11 (25%). Five cases remained difficult to classify, 3 of which exhibited overlapping molecular features with alterations in MEN1 with or without ATRX, and RB1 with or without TP53, making it unclear whether to classify as PanNET or PanNEC. Our data demonstrate that molecular profiling and immunohistochemistry for p16 greatly improve the diagnostic accuracy of high-grade pancreatic neuroendocrine neoplasms and identify a subset of rare cases with overlapping features of both PanNET and PanNEC.

Identifiants

pubmed: 36788102
pii: S0893-3952(22)05461-8
doi: 10.1016/j.modpat.2022.100065
pii:
doi:

Types de publication

Review Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

100065

Informations de copyright

Published by Elsevier Inc.

Auteurs

Sarah E Umetsu (SE)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Sanjay Kakar (S)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Olca Basturk (O)

Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York.

Grace E Kim (GE)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Deyali Chatterjee (D)

Department of Pathology, MD Anderson Cancer Center, Houston, Texas.

Kwun Wah Wen (KW)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Gillian Hale (G)

Department of Pathology, University of Utah, Salt Lake City, Utah.

Nafis Shafizadeh (N)

Department of Pathology, Southern California Permanente Medical Group, Woodland Hills Medical Center, Los Angeles, California.

Soo-Jin Cho (SJ)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Julia Whitman (J)

Department of Pathology, Washington University School of Medicine, St. Louis, Missouri.

Ryan M Gill (RM)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Kirk D Jones (KD)

Department of Pathology, University of California, San Francisco, San Francisco, California.

Pooja Navale (P)

Department of Pathology, Washington University School of Medicine, St. Louis, Missouri.

Emily Bergsland (E)

Department of Medicine, University of California, San Francisco, San Francisco, California.

David Klimstra (D)

Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York; Paige.AI, New York, New York.

Nancy M Joseph (NM)

Department of Pathology, University of California, San Francisco, San Francisco, California. Electronic address: Nancy.Joseph@ucsf.edu.

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Classifications MeSH