肺癌
药品
医学
蛋白质组
仿形(计算机编程)
肿瘤科
计算生物学
癌症研究
生物信息学
药理学
生物
计算机科学
操作系统
作者
Cui Liu,Ruiqi Huang,Xiaopeng Ma,Wenting Du,Cunjing Yu,Xikun Wu,Yongjia Tong,XU Lan-jun,Si Mou
出处
期刊:Cancer Research
[American Association for Cancer Research]
日期:2025-04-21
卷期号:85 (8_Supplement_1): 1873-1873
标识
DOI:10.1158/1538-7445.am2025-1873
摘要
Abstract Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of various cancers. A better understanding of ICI induced dynamics and response / resistance mechanisms may contribute to the optimization of treatment strategies. In early-stage non-small cell lung cancer (NSCLC) cohort with neoadjuvant chemoimmunotherapy, we utilized the plasma proteomics platform to deepen the understanding of disease biology and explore potential response and resistance biomarkers. Methods: Plasma samples collected at various time points during neoadjuvant chemoimmunotherapy were analyzed using an unbiased mass spectrometry-based plasma proteomics platform. Taking advantage of the protein corona formed on the surface of nanoparticles, low-abundant proteins related to immunomodulation and signal transduction were enriched and identified. A comprehensive statistical analysis framework was adapted to reveal mechanism and explore predictive and prognostic biomarkers. Immunotherapy-induced changes of blood proteins were estimated by linear mixed model. Molecular insights were studied through mediation analysis. In the association analysis with clinical outcomes, classic logistic regression was applied for binary clinical outcomes, while cox proportional hazard model for survival outcomes. Results: 180 plasma samples from 92 individuals were profiled, resulting in the quantification of 2303 proteins in peripheral blood. The consistency between quantitative proteomics and established techniques were demonstrated by known biomarker CA125, CRP and gender related proteins. A total of 541 upregulated proteins and 504 downregulated proteins after neoadjuvant chemoimmunotherapy were identified. Pathway enrichment analysis revealed that neoadjuvant chemoimmunotherapy promoted immune activation. Conclusion: Deep plasma proteome profiling enables a deeper understanding of the dynamics and mechanisms induced by ICI treatment. Citation Format: Cui Liu, Ruiqi Huang, Xiaopeng Ma, Wenting Du, Cunjing Yu, Xikun Wu, Yongchun Tong, Lanjun Xu, Si Mou. Deep plasma proteome profiling to discover drug treatment related novel biomarkers in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1873.
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