PSMD2过表达作为免疫检查点和酪氨酸激酶抑制剂治疗肾细胞癌耐药性和预后的生物标记物。

IF 4.9 2区 医学 Q2 CELL BIOLOGY Cellular Oncology Pub Date : 2024-10-01 Epub Date: 2024-09-02 DOI:10.1007/s13402-024-00977-z
Xianglai Xu, Jiahao Wang, Ying Wang, Yanjun Zhu, Jiajun Wang, Jianming Guo
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引用次数: 0

摘要

背景:综合免疫检查点抑制剂(ICIs)加酪氨酸激酶抑制剂(TKIs)是目前治疗肾细胞癌(mRCC)的推荐一线疗法。肿瘤中蛋白酶体26S亚基非ATP酶2(PSMD2)的过表达与肿瘤的进展相关。目前,mRCC缺乏一种已确定的生物标志物,可用于ICI+TKI的联合治疗:本研究对两组接受 ICI+TKI 治疗的 RCC 患者(ZS-MRCC 和 JAVELIN-Renal-101)进行了 RNA 测序。我们在使用流式细胞术的同时还使用了免疫组化技术,旨在评估高风险局部RCC样本中的免疫细胞浸润和功能。根据 RECIST 标准评估了反应和无进展生存期(PFS):结果:PSMD2在晚期RCC和对ICI+TKI疗法无应答者中明显过表达。在ZS-MRCC和JAVELIN-101队列中,PSMD2的过表达与不良的PFS相关。多变量Cox分析验证了PSMD2是一个独立的PFS预测因子。PSMD2的过表达与CD8+ T细胞,尤其是GZMB+ CD8+ T细胞的减少有关,此外还与PD1+ CD4+ T细胞的增加有关。此外,PSMD2水平高的肿瘤显示出更高的T细胞衰竭水平和更高的调节性T细胞存在。基于PSMD2表达和其他筛选因素的机器学习(ML)模型随后被开发出来,用于预测ICI+TKI的有效性:结论:PSMD2表达升高与接受ICI+TKI治疗的mRCC患者的耐药性和PFS下降有关。高 PSMD2 水平还与肿瘤浸润淋巴细胞功能受损和耗竭增加有关。包含 PSMD2 表达的 ML 模型有可能识别出更有可能从 ICI+TKI 治疗中获益的患者。
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PSMD2 overexpression as a biomarker for resistance and prognosis in renal cell carcinoma treated with immune checkpoint and tyrosine kinase inhibitors.

Background: Integrated immune checkpoint inhibitors (ICIs) plus tyrosine kinase inhibitors (TKIs) are now the recommended first-line therapy to manage renal cell carcinoma (mRCC). Proteasome 26S subunit non-ATPase 2 (PSMD2) overexpression in tumors has been correlated with tumor progression. Currently, mRCC lacks an established biomarker for the combination of ICI+TKI.

Methods: This study involved RNA sequencing of RCC patients from two cohorts treated with ICI+TKI (ZS-MRCC and JAVELIN-Renal-101). We utilized immunohistochemistry alongside flow cytometry, aiming at assessing immune cell infiltration and functionality in high-risk localized RCC samples. Response and progression-free survival (PFS) were evaluated relying upon RECIST criteria.

Results: PSMD2 was significantly overexpressed in advanced RCC and among non-responders to ICI+TKI therapy. Overexpressed PSMD2 was correlated with poor PFS in the ZS-MRCC and JAVELIN-101 cohorts. Multivariate Cox analysis validated PSMD2 as an independent PFS predictor. PSMD2 overexpression was related to a reduction in CD8+ T cells, especially GZMB+ CD8+ T cells, besides an increase in PD1+ CD4+ T cells. Additionally, tumors with high PSMD2 levels showed enhanced T cell exhaustion levels and a higher regulatory T cell presence. A Machine Learning (ML) model based on PSMD2 expression and other screened factors was subsequently developed to predict the effectiveness of ICI+TKI.

Conclusions: Elevated PSMD2 expression is linked to resistance and decreased PFS in mRCC patients undergoing ICI+TKI therapy. High PSMD2 levels are also associated with impaired function and increased exhaustion of tumor-infiltrating lymphocytes. An ML model incorporating PSMD2 expression could potentially identify patients who may have a higher likelihood of benefiting from ICI+TKI.

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来源期刊
Cellular Oncology
Cellular Oncology ONCOLOGY-CELL BIOLOGY
CiteScore
10.30
自引率
1.50%
发文量
86
审稿时长
12 months
期刊介绍: The Official Journal of the International Society for Cellular Oncology Focuses on translational research Addresses the conversion of cell biology to clinical applications Cellular Oncology publishes scientific contributions from various biomedical and clinical disciplines involved in basic and translational cancer research on the cell and tissue level, technical and bioinformatics developments in this area, and clinical applications. This includes a variety of fields like genome technology, micro-arrays and other high-throughput techniques, genomic instability, SNP, DNA methylation, signaling pathways, DNA organization, (sub)microscopic imaging, proteomics, bioinformatics, functional effects of genomics, drug design and development, molecular diagnostics and targeted cancer therapies, genotype-phenotype interactions. A major goal is to translate the latest developments in these fields from the research laboratory into routine patient management. To this end Cellular Oncology forms a platform of scientific information exchange between molecular biologists and geneticists, technical developers, pathologists, (medical) oncologists and other clinicians involved in the management of cancer patients. In vitro studies are preferentially supported by validations in tumor tissue with clinicopathological associations.
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