PO.CL01.02 · 临床研究
CXCR4/LITAF作为伴肝转移胰腺癌尼妥珠单抗联合AG方案疗效的预测性生物标志物:整合单细胞与空间分析
CXCR4/LITAF as predictive biomarkers for nimotuzumab plus AG therapy in pancreatic cancer with liver metastasis: Integrative single-cell and spatial analysis
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:伴肝转移的胰腺癌(PCLM)占大多数晚期胰腺癌病例,预后不良。虽然吉西他滨联合白蛋白结合型紫杉醇(AG)仍是NCCN推荐用于身体状况良好患者的方案,但疗效仍不理想。既往研究提示加用尼妥珠单抗可能改善转化切除率,但疗效的分子决定因素尚不明确(2025 AACR# CT167,NCT06405685)。本研究整合临床与相关性分析,以识别尼妥珠单抗+AG疗法的预测性生物标志物。
方法:初治PCLM患者接受尼妥珠单抗(400 mg 静脉注射,每周一次)联合AG(吉西他滨1000 mg/m²和白蛋白结合型紫杉醇125 mg/m²,第1天、第8天,每3周一次)治疗。主要终点为客观缓解率(ORR)和疾病控制率(DCR);次要终点包括转化率和R0切除率。同时,对配对的原发灶和转移灶的单细胞RNA测序数据(GEO)进行分析,通过hdWGCNA和机器学习算法(SVM、LASSO、随机森林)识别PCLM特异性亚群。为进行临床验证,对PCLM肿瘤组织进行了CXCR4和LITAF的免疫荧光染色。使用自动化的CK-pan引导上皮分割进行空间定量。强度经过标准化处理,通过热点加权聚合为每位患者生成CXCR4/LITAF联合评分,反映共激活情况。
结果:在23例可评估的PCLM患者中,ORR为17.4%,DCR为78.3%,43.5%实现了转化手术,与既往发现一致。单细胞分析识别出22个细胞簇,包括一个在肝转移灶中富集的独特PCLM特异性亚群。CXCR4和LITAF成为核心枢纽基因,与这些转移特异性细胞的丰度呈正相关,且与较差的临床结局相关。基于成像的空间定量显示,CXCR4/LITAF联合评分较高的患者治疗反应显著较差(疾病进展,PD)。从上皮分割(CK-pan引导)到联合分子评分的自动化流程,在各样本间实现了稳健的可重复性,并准确区分了缓解者与非缓解者。初步分析提示KRAS突变型肿瘤可能表现出更强的CXCR4/LITAF共激活,从而导致治疗异质性。
结论:尼妥珠单抗联合AG在PCLM中显示出良好的疾病控制和转化潜力。整合单细胞和空间分析识别出CXCR4和LITAF作为与治疗反应相关的候选生物标志物。CXCR4/LITAF联合空间评分为预测转移性胰腺癌对尼妥珠单抗+AG的反应提供了一种定量成像工具。
查看英文原文 English abstract
Background: Pancreatic cancer with liver metastasis (PCLM) accounts for most advanced pancreatic cancer cases and carries poor prognosis. While Gemcitabine plus Nab-paclitaxel (AG) remains the NCCN-recommended regimen for fit patients, outcomes remain suboptimal. Previous studies suggested that adding Nimotuzumab may improve conversion to resection, but the molecular determinants of response are unclear (2025 AACR# CT167, NCT06405685) . This study integrates clinical and correlative analyses to identify predictive biomarkers for Nimotuzumab + AG therapy.
Methods: Treatment-naïve PCLM patients received Nimotuzumab (400 mg iv, qw) plus AG (Gemcitabine 1000 mg/m² and Nab-paclitaxel 125 mg/m², d1, d8, q3w). Primary endpoints were objective response rate (ORR) and disease control rate (DCR); secondary endpoints included conversion and R0 resection rates. In parallel, single-cell RNA-seq data from paired primary and metastatic lesions (GEO) were analyzed to identify PCLM-specific subpopulations via hdWGCNA and machine-learning algorithms (SVM, LASSO, random forest). For clinical validation, immunofluorescence staining of CXCR4 and LITAF was performed on PCLM tumor tissues. Spatial quantification was performed using an automated CK-pan-guided epithelial segmentation. Intensities were normalized and a hotspot-weighted aggregation produced a combined CXCR4/LITAF score per patient, reflecting co-activation.
Results: Among 23 evaluable PCLM patients, the ORR was 17.4%, DCR 78.3%, and 43.5% achieved conversion surgery, consistent with previous findings. Single-cell analysis identified 22 cell clusters, including a distinct PCLM-specific subpopulation enriched
in hepatic metastases. CXCR4 and LITAF emerged as central hub genes positively correlated with the abundance of these metastatic-specific cells and with poorer clinical outcomes. Imaging-based spatial quantification showed that patients with higher CXCR4/LITAF combined scores exhibited significantly worse treatment responses (progressive disease, PD). The automated pipeline-from epithelial segmentation (CK-pan-guided) to combined molecular scoring-achieved robust reproducibility across samples and accurately stratified responders versus non-responders. Preliminary analyses suggest that KRAS-mutant tumors may exhibit enhanced CXCR4/LITAF co-activation, contributing to therapeutic heterogeneity.
Conclusions: Nimotuzumab plus AG demonstrates favorable disease control and conversion potential in PCLM. Integrative single-cell and spatial analyses identify CXCR4 and LITAF as candidate biomarkers linked to treatment response. The CXCR4/LITAF combined spatial score offers a quantitative imaging tool for predicting response to Nimotuzumab + AG in metastatic pancreatic cancer.
利益披露 Disclosure
L. Xu, None..
Y. Liu, None..
L. Fu, None..
D. Wu, None..
J. Zhang, None..
H. Wang, None..
H. Li, None..
J. Hao, None.