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鉴定内源性逆转录病毒特征以预测晚期透明细胞肾细胞癌的抗PD1反应:三项临床试验的综合分析

Identification of an endogenous retroviral signature to predict anti-PD1 response in advanced clear cell renal cell carcinoma: an integrated analysis of three clinical trials.

作者信息

Zhou Jian-Guo, Zeng Yu, Wang Haitao, Jin Su-Han, Wang Yun-Jia, He Sisi, Frey Benjamin, Fietkau Rainer, Hecht Markus, Ma Hu, Zhang Wenchuan, Gaipl Udo S

机构信息

Department of Oncology, The second affiliated Hospital of Zunyi Medical University, Zunyi, China Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Erlangen, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Erlangen, Germany Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.

Department of Neurosurgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Ther Adv Med Oncol. 2022 Sep 24;14:17588359221126154. doi: 10.1177/17588359221126154. eCollection 2022.

DOI:10.1177/17588359221126154
PMID:37614979
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10442641/
Abstract

BACKGROUND

Endogenous retrovirus (ERV) elements are genomic footprints of ancestral retroviral infections within the human genome. Previous studies have demonstrated that dysregulated ERV transcription level is associated with immune cell infiltration in cancers, but the association between ERV expression and programmed cell death protein 1 (PD-1) blockade response is currently unraveled for solid cancers, such as advanced clear cell renal cell carcinoma (ccRCC).

METHODS

ERV mRNA profiles were obtained from three clinical trials of ccRCC where the patients were treated with anti-PD-1 (CM-009, CM-010, CM-025, and TCGA-KIRC data). Patients treated with nivolumab were divided into training and test cohort, while the TCGA-KIRC cohort was used as an external validation. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression were used to establish the signature. Immune cell infiltration analysis and gene set enrichment analysis were performed to explore potential biological mechanisms.

RESULTS

An ERV signature was established based on nine ERV expression patterns. In the training cohort, the median overall survival in the low- and high-risk group was 45.2 and 19.6 months [hazard ratio (HR) = 0.49, 0.32-0.75,  < 0.001], respectively. The results were confirmed in the test (HR = 0.41, 0.20-0.83,  = 0.013), and in the TCGA-KIRC cohort (HR = 0.55, 0.34-0.90,  = 0.017). Moreover, in the CM-025 cohort, the low-risk group that received nivolumab had a more favorable survival compared with those that received the mTOR inhibitor everolimus, while no significant differences were observed in the high-risk group. CD8+ T cells were enriched in the low-risk group, while immune suppressive pathways were suppressed.

CONCLUSION

The newly identified ERV signature is not only a prognostic, but also a predictive biomarker for advanced ccRCC patients who received anti-PD-1 therapy, which can guide personalized treatment in cancer patients in the future.

摘要

背景

内源性逆转录病毒(ERV)元件是人类基因组中祖先逆转录病毒感染的基因组印记。先前的研究表明,ERV转录水平失调与癌症中的免疫细胞浸润有关,但目前对于实体癌,如晚期透明细胞肾细胞癌(ccRCC),ERV表达与程序性细胞死亡蛋白1(PD-1)阻断反应之间的关联尚不清楚。

方法

从三项ccRCC临床试验中获取ERV mRNA谱,这些试验中的患者接受了抗PD-1治疗(CM-009、CM-010、CM-025以及TCGA-KIRC数据)。接受纳武单抗治疗的患者被分为训练组和测试组,而TCGA-KIRC队列用作外部验证。采用单变量Cox回归分析和最小绝对收缩和选择算子回归来建立特征。进行免疫细胞浸润分析和基因集富集分析以探索潜在的生物学机制。

结果

基于九种ERV表达模式建立了一个ERV特征。在训练组中,低风险组和高风险组的中位总生存期分别为45.2个月和19.6个月[风险比(HR)=0.49,0.32 - 0.75,<0.001]。在测试组(HR = 0.41,0.20 - 0.83,= 0.013)和TCGA-KIRC队列(HR = 0.55,0.34 - 0.90,= 0.017)中证实了该结果。此外,在CM-025队列中,接受纳武单抗的低风险组的生存期比接受mTOR抑制剂依维莫司的患者更有利,而在高风险组中未观察到显著差异。CD8 + T细胞在低风险组中富集,而免疫抑制途径受到抑制。

结论

新鉴定的ERV特征不仅是晚期ccRCC患者接受抗PD-1治疗的预后生物标志物,也是预测生物标志物,可为未来癌症患者的个性化治疗提供指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/06d0e7bf9707/10.1177_17588359221126154-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/1879a6c22b76/10.1177_17588359221126154-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/492b9324b768/10.1177_17588359221126154-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/a73653755955/10.1177_17588359221126154-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/e7db6319fbe7/10.1177_17588359221126154-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/6747e2403f1e/10.1177_17588359221126154-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/385f51f8e386/10.1177_17588359221126154-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/06d0e7bf9707/10.1177_17588359221126154-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/1879a6c22b76/10.1177_17588359221126154-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/492b9324b768/10.1177_17588359221126154-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/a73653755955/10.1177_17588359221126154-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/e7db6319fbe7/10.1177_17588359221126154-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/6747e2403f1e/10.1177_17588359221126154-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/385f51f8e386/10.1177_17588359221126154-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd57/10442641/06d0e7bf9707/10.1177_17588359221126154-fig7.jpg

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