一种用于克服非小细胞肺癌治疗中抗原异质性的多靶向 CAR-T 细胞平台
A Multi-Targeting Chimeric Antigen Receptor-T Cell Platform to Overcome Antigen Heterogeneity in the Treatment of Non-Small Cell Lung Cancer.
这些发现支持采用多靶点CAR-T 策略来应对NSCLC及可能其他实体瘤中的抗原异质性。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
小细胞肺癌的新型免疫组化特征分析：肿瘤亚型与免疫微环境之间的相关性
英文原题：Novel Immunohistochemical Profiling of Small-Cell Lung Cancer: Correlations Between Tumor Subtypes and Immune Microenvironment.
查看英文原题
Novel Immunohistochemical Profiling of Small-Cell Lung Cancer: Correlations Between Tumor Subtypes and Immune Microenvironment.
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研究概要
本研究强调，有必要将新型 SCLC 分类与 TIME 相关数据整合，以更好地指导患者预后和治疗。
中文摘要
背景与目的
小细胞肺癌（SCLC）是一种侵袭性极强的恶性肿瘤，正依据转录因子ASCL1、NEUROD1和POU2F3表达进行新型分子分型。本研究旨在探索这些新亚型与肿瘤免疫微环境（TIME）的关系，特别关注CD8+和CD4+TIL（肿瘤浸润淋巴细胞）。
方法
研究对51例SCLC患者样本进行ASCL1、NEUROD1、POU2F3、CD56、Ki67、CD8和CD4免疫组化染色。计算新型转录因子的H评分，以确定肿瘤亚型。对CD8+和CD4+ TIL计数取10个高倍视野的平均值。采用Kruskal-Wallis检验及后续Dunn事后检验，比较各亚型间转录因子表达及TIL差异。
结果
本队列中，SCLC-A占68.62%，SCLC-N占9.80%，SCLC-P占7.84%，SCLC-I占13.72%。不同亚型间ASCL1、NEUROD1和POU2F3表达存在显著差异。SCLC-P和SCLC-I中的CD8+ TIL更丰富。CD8+ TIL与ASCL1表达呈负相关（p<0.05），与POU2F3表达呈正相关（p<0.005）。
结论
本研究突显将SCLC新型分子分型与TIME数据结合的重要性，有助于更准确地指导患者预后判断和治疗。
展开英文摘要原文
METHODS
In 51 cases of patients with SCLC, immunohistochemical (IHC) stains for ASCL1, NEUROD1, POU2F3, CD56, Ki67, CD8, and CD4 were performed. H-scores for the novel transcription factors were calculated to determine tumor subtype. CD8+ and CD4+ TIL counts were averaged across 10 high-power fields. The Kruskal-Wallis test and subsequent post hoc Dunn tests were used to determine the differences in transcription factor expression and TILs across subtypes.
RESULTS
In our cohort, 68.62% of our cases were SCLC-A, 9.80% were SCLC-N, 7.84% were SCLC-P, and 13.72% were SCLC-I. Significant differences were observed in the expression of ASCL1, NEUROD1, and POU2F3 across subtypes. CD8+ TILs were more abundant in SCLC-P and SCLC-I. CD8+ TILs were negatively correlated with ASCL1 expression ( p < 0.05) and positively correlated with POU2F3 expression ( p < 0.005).
CONCLUSIONS
This study highlights the need to integrate the novel SCLC classification with data regarding the TIME to better inform patient prognosis and treatment.
论文信息
- 作者
- Vigdorovits A、Olteanu GE、Pascalau AV、Pirlog R、Berindan-Neagoe I、Pop OL
- 单位
- Department of Morphological Disciplines, University of Oradea, 410087 Oradea, Romania.Italy
- 期刊
- Diagnostics (Basel, Switzerland)2024 Nov 26
- 原文标识
- PubMed 39682568 · DOI 10.3390/diagnostics14232660
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