CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：KPNA2 expression as a biomarker for immunosuppressive microenvironment predicting response to TKI and immunotherapy in metastatic renal cell carcinoma.
KPNA2 expression as a biomarker for immunosuppressive microenvironment predicting response to TKI and immunotherapy in metastatic renal cell carcinoma.
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KPNA2 表达介导免疫抑制和对 TKI + IO 治疗的耐药性。这些发现将 KPNA2 定位为晚期 RCC 中个体化治疗选择的有前景的生物标志物，特别是用于优化 TKI + IO 与替代方案的选择。
免疫治疗（IO）联合酪氨酸激酶抑制剂（TKI）现已成为晚期肾细胞癌（RCC）的一线治疗，但可靠的预测性生物标志物仍然难以确定。近期证据表明，核转运调节因子核转运蛋白α2亚基（KPNA2）在肿瘤发生和治疗耐药中发挥关键作用。
分析了两个队列：转移性RCC患者的机构队列（ZS-MRCC）和III期JAVELIN Renal 101试验队列。RNA测序定量了所有样本中KPNA2的表达。使用流式细胞术和多重免疫组化（IHC）评估了免疫浸润和T细胞功能。
KPNA2低表达患者在两个队列中均表现出更优的客观缓解率（55 % vs. 20 %）和延长的无进展生存期（PFS）。缓解者显示KPNA2表达显著降低（P < 0.05）。尽管高KPNA2肿瘤通过IHC（P < 0.05）和流式细胞术（P < 0.05）显示TIL（肿瘤浸润淋巴细胞）（TILs）增加，但CD8 + T细胞表现出功能受损，颗粒酶B（GZMB）表达减少。KPNA2升高与调节性T细胞（Tregs）呈正相关，与M1样巨噬细胞呈负相关。一个纳入KPNA2和T细胞耗竭标志物的机器学习模型生成了一个预测评分。
Immunotherapy (IO) combined with tyrosine kinase inhibitors (TKI) are now first-line therapy for advanced renal cell carcinoma (RCC), though reliable predictive biomarkers remain elusive. Recent evidence demonstrates that karyopherin α2 subunit (KPNA2), a nuclear transport regulator, plays key roles in tumorigenesis and therapy resistance.
Two cohorts were analyzed: an institutional cohort of metastatic RCC patients (ZS-MRCC) and the phase III JAVELIN Renal 101 trial cohort. RNA sequencing quantified KPNA2 expression in all samples. Immune infiltration and T-cell functionality were evaluated using flow cytometry and multiplex immunohistochemistry (IHC).
Patients with low KPNA2 expression demonstrated superior objective response rates (55 % vs. 20 %) and prolonged progression-free survival (PFS) in both cohorts. Responders showed significantly decreased KPNA2 expression (P < 0.05). Although, high-KPNA2 tumors exhibited increased tumor-infiltrating lymphocytes (TILs) by IHC (P < 0.05) and flow cytometry (P < 0.05), CD8 + T cells displayed functional impairment with reduced granzyme B (GZMB) expression. Elevated KPNA2 correlated positively with regulatory T cells (Tregs) and negatively with M1-like macrophages. A machine learning model incorporating KPNA2 and T-cell exhaustion markers generated a predictive score.
KPNA2 expression mediates immunosuppression and resistance to TKI + IO therapy. These findings position KPNA2 as a promising biomarker for personalizing treatment selection in advanced RCC, particularly for optimizing TKI + IO versus alternative regimens.
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