Differential NEUROD1, ASCL1, and POU2F3 Expression Defines Molecular Subsets of Bladder Small Cell/Neuroendocrine Carcinoma With Prognostic Implications

IF 7.1 1区 医学 Q1 PATHOLOGY Modern Pathology Pub Date : 2024-07-02 DOI:10.1016/j.modpat.2024.100557
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Abstract

Small cell carcinomas (SMC) of the lung are now molecularly classified based on the expression of transcriptional regulators (NEUROD1, ASCL1, POU2F3, and YAP1) and DLL3, which has emerged as an investigational therapeutic target. PLCG2 has been shown to identify a distinct subpopulation of lung SMC with stem cell-like and prometastasis features and poor prognosis. We analyzed the expression of these novel neuroendocrine markers and their association with traditional neuroendocrine markers and patient outcomes in a cohort of bladder neuroendocrine carcinoma (NEC) consisting of 103 SMC and 19 large cell NEC (LCNEC) assembled in tissue microarrays. Coexpression patterns were assessed and integrated with detailed clinical annotation including overall (OS) and recurrence-free survival (RFS) and response to neoadjuvant/adjuvant chemotherapy. We identified 5 distinct molecular subtypes in bladder SMC based on the expression of ASCL1, NEUROD1, and POU2F3: ASCL1+/NEUROD1− (n = 33; 34%), ASCL1− /NEUROD1+ (n = 21; 21%), ASCL1+/NEUROD1+ (n = 17; 17%), POU2F3+ (n = 22, 22%), and ASCL1− /NEUROD1− /POU2F3− (n = 5, 5%). POU2F3+ tumors were mutually exclusive with those expressing ASCL1 and NEUROD1 and exhibited lower expression of traditional neuroendocrine markers. PLCG2 expression was noted in 33 tumors (32%) and was highly correlated with POU2F3 expression (P < .001). DLL3 expression was high in both SMC (n = 72, 82%) and LCNEC (n = 11, 85%). YAP1 expression was enriched in nonneuroendocrine components and negatively correlated with all neuroendocrine markers. In patients without metastatic disease who underwent radical cystectomy, PLCG2+ or POU2F3+ tumors had shorter RFS and OS (P < .05), but their expression was not associated with metastasis status or response to neoadjuvant/adjuvant chemotherapy. In conclusion, the NEC of the bladder can be divided into distinct molecular subtypes based on the expression of ASCL1, NEUROD1, and POU2F3. POU2F3-expressing tumors represent an ASCL1/NEUROD1-negative subset of bladder NEC characterized by lower expression of traditional neuroendocrine markers. Marker expression patterns were similar in SMC and LCNEC. Expression of PLCG2 and POU2F3 was associated with shorter RFS and OS. DLL3 was expressed at high levels in both SMC and LCNEC of the bladder, nominating it as a potential therapeutic target.

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NEUROD1、ASCL1 和 POU2F3 的差异表达定义了膀胱小细胞/神经内分泌癌的分子亚群,具有预后意义。
肺小细胞癌(SMC)目前根据转录调节因子(NEUROD1、ASCL1、POU2F3、YAP1)和DLL3的表达进行分子分类,DLL3已成为研究中的治疗靶点。PLCG2 已被证明能识别出肺部 SMC 中的一个独特亚群,该亚群具有干细胞样和促进转移的特征,预后较差。我们在一组膀胱神经内分泌癌(NEC)中分析了这些新型神经内分泌标记物的表达及其与传统神经内分泌标记物和患者预后的关联,这组NEC由103个SMC和19个大细胞神经内分泌癌(LCNEC)组成,并在组织芯片中进行了组装。我们评估了共表达模式,并将其与详细的临床注释相结合,包括总生存期(OS)、无复发生存期(RFS)和对新辅助/辅助化疗的反应。根据ASCL1、NEUROD1和POU2F3的表达,我们确定了膀胱SMC的五种不同分子亚型:ASCL1+/NEUROD1-(n=33;34%)、ASCL1-/NEUROD1+(n=21;21%)、ASCL1+/NEUROD1+(n=17;17%)、POU2F3+(n=22;22%)和ASCL1-/NEUROD1-/POU2F3-(n=5;5%)。POU2F3+肿瘤与表达ASCL1和NEUROD1的肿瘤相互排斥,传统神经内分泌标志物的表达较低。33个肿瘤(32%)中有PLCG2表达,与POU2F3表达高度相关(p < 0.001)。DLL3在SMC(72例,82%)和LCNEC(11例,85%)中均有高表达。YAP1 的表达富集于非神经内分泌成分,并与所有神经内分泌标志物呈负相关。在接受根治性膀胱切除术的无转移性疾病患者中,PLCG2+或POU2F3+肿瘤的RFS和OS较短(P<0.05)。
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来源期刊
Modern Pathology
Modern Pathology 医学-病理学
CiteScore
14.30
自引率
2.70%
发文量
174
审稿时长
18 days
期刊介绍: Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology. Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.
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