Combination immunohistochemistry for CK5/6, p63, GATA6, and HNF4a predicts clinical outcome in treatment-naïve pancreatic ductal adenocarcinoma

Author:

Shibayama Takahiro,Hayashi Akimasa,Toki Masao,Kitahama Keiichiro,Ho Yu-Jui,Kato Kenichiro,Yamada Takahiro,Kawamoto Sho,Kambayashi Komei,Ochiai Kazushige,Gondo Koichi,Okano Naohiro,Melchor Jerry P.,Iacobuzio-Donahue Christine A.,Sakamoto Yoshihiro,Hisamatsu Tadakazu,Shibahara Junji

Abstract

AbstractAlthough sequence-based studies show that basal-like features lead to worse prognosis and chemotherapy-resistance compared to the classical subtype in advanced pancreatic ductal adenocarcinoma (PDAC), a surrogate biomarker distinguishing between these subtypes in routine diagnostic practice remains to be identified. We aimed to evaluate the utility of immunohistochemistry (IHC) expression subtypes generated by unsupervised hierarchical clustering based on staining scores of four markers (CK5/6, p63, GATA6, HNF4a) applied to endoscopic ultrasound-guided fine needle aspiration biopsy (EUS-FNAB) materials. EUS-FNAB materials taken from 190 treatment-naïve advanced PDAC patients were analyzed, and three IHC patterns were established (Classical, Transitional, and Basal-like pattern). Basal-like pattern (high co-expression of CK5/6 and p63 with low expression of GATA6 and HNF4a) was significantly associated with squamous differentiation histology (p < 0.001) and demonstrated the worst overall survival among our cohort (p = 0.004). IHC expression subtype (Transitional, Basal vs Classical) was an independent poor prognosticator in multivariate analysis [HR 1.58 (95% CI 1.01–2.38), p = 0.047]. Furthermore, CK5/6 expression was an independent poor prognostic factor in histological glandular type PDAC [HR 2.82 (95% CI 1.31–6.08), p = 0.008]. Our results suggest that IHC expression patterns successfully predict molecular features indicative of the Basal-like subgroup in advanced PDAC. These results provide the basis for appropriate stratification for therapeutic selection and prognostic estimation of advanced PDAC in a simplified manner.

Funder

Grant-in-Aid for Scientific Research

Mochida Memorial Foundation for Medical and Pharmaceutical Research

Publisher

Springer Science and Business Media LLC

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