Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Molecular & Cellular Proteomics Pub Date : 2024-10-01 Epub Date: 2024-08-29 DOI:10.1016/j.mcpro.2024.100834
Patricia Mondelo-Macía, Jorge García-González, Luis León-Mateos, Alicia Abalo, Susana Bravo, María Del Pilar Chantada Vazquez, Laura Muinelo-Romay, Rafael López-López, Roberto Díaz-Peña, Ana B Dávila-Ibáñez
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引用次数: 0

Abstract

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

确定预测转移性非小细胞肺癌患者免疫疗法反应的蛋白质组特征。
背景:免疫疗法提高了癌症患者的存活率,但识别对治疗有反应的患者仍是一项挑战。蛋白质组学技术的最新进展使我们能够在一次实验中鉴定和量化几乎所有表达的蛋白质。质谱技术与其他高通量技术的整合为全面系统地分析癌症患者的血浆蛋白质组铺平了道路,有助于早期诊断和个性化治疗。在此背景下,我们的研究旨在探讨使用 SWATH-MS(所有理论质谱的顺序窗口获取)策略对接受 pembrolizumab 治疗的新诊断 NSCLC 患者进行血浆蛋白质组分析的预测和预后价值:为此,研究人员招募了64名接受pembrolizumab治疗的新确诊晚期NSCLC患者,并在治疗前和治疗期间采集了所有患者的血液样本。共采集了 171 份血液样本,并采用 SWATH-MS 策略对血浆样本进行了分析。接下来,我们比较了转移性NSCLC患者在接受pembrolizumab治疗前的血浆蛋白表达情况,并将患者分为两组,以确定能预测免疫治疗反应的蛋白质组特征:通过SWATH-MS策略进行的蛋白质组学分析,我们发现了324种在应答患者和非应答患者之间表达不同的蛋白质。此外,我们还建立了一个预测模型,发现ATG9A、DCDC2、HPS5、FIL1L、LZTL1、PGTA和SPTN2等7种蛋白质的组合比单独的PD-L1表达具有更强的预测价值。此外,生存分析表明,低水平的ATG9A、DCDC2和HPS5与较长的无进展生存期(PFS)和总生存期(OS)相关,而低水平的SPTN2与较差的OS相关:我们的工作凸显了蛋白质组学技术在NSCLC患者血液样本中检测预测性生物标记物的潜在价值。这些分析揭示了NSCLC患者对免疫疗法的反应与7种蛋白质之间的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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