High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer.

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Molecular & Cellular Proteomics Pub Date : 2024-05-01 Epub Date: 2024-03-20 DOI:10.1016/j.mcpro.2024.100749
Liyuan Dai, Qiaoyun Tan, Lin Li, Ning Lou, Cuiling Zheng, Jianliang Yang, Liling Huang, Shasha Wang, Rongrong Luo, Guangyu Fan, Tongji Xie, Jiarui Yao, Zhishang Zhang, Le Tang, Yuankai Shi, Xiaohong Han
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Abstract

Chemoimmunotherapy has evolved as a standard treatment for advanced non-small cell lung cancer (aNSCLC). However, inevitable drug resistance has limited its efficacy, highlighting the urgent need for biomarkers of chemoimmunotherapy. A three-phase strategy to discover, verify, and validate longitudinal predictive autoantibodies (AAbs) for aNSCLC before and after chemoimmunotherapy was employed. A total of 528 plasma samples from 267 aNSCLC patients before and after anti-PD1 immunotherapy were collected, plus 30 independent formalin-fixed paraffin-embedded samples. Candidate AAbs were firstly selected using a HuProt high-density microarray containing 21,000 proteins in the discovery phase, followed by validation using an aNSCLC-focused microarray. Longitudinal predictive AAbs were chosen for ELISA based on responders versus non-responders comparison and progression-free survival (PFS) survival analysis. Prognostic markers were also validated using immunohistochemistry and publicly available immunotherapy datasets. We identified and validated a panel of two AAbs (MAX and DHX29) as pre-treatment biomarkers and another panel of two AAbs (MAX and TAPBP) as on-treatment predictive markers in aNSCLC patients undergoing chemoimmunotherapy. All three AAbs exhibited a positive correlation with early responses and PFS (p < 0.05). The kinetics of MAX AAb showed an increasing trend in responders (p < 0.05) and a tendency to initially increase and then decrease in non-responders (p < 0.05). Importantly, MAX protein and mRNA levels effectively discriminated PFS (p < 0.05) in aNSCLC patients treated with immunotherapy. Our results present a longitudinal analysis of changes in prognostic AAbs in aNSCLC patients undergoing chemoimmunotherapy.

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高通量抗原芯片确定晚期非小细胞肺癌化疗免疫疗法的纵向预后自身抗体。
化学免疫疗法已发展成为晚期非小细胞肺癌(aNSCLC)的标准疗法。然而,不可避免的耐药性限制了它的疗效,这凸显了对化学免疫疗法生物标志物的迫切需求。我们采用了一种三阶段策略,在化疗免疫疗法前后发现、验证和确认aNSCLC的纵向预测性自身抗体(AAbs)。在抗PD1免疫疗法前后,共收集了267名aNSCLC患者的528份血浆样本,以及30份独立的福尔马林固定石蜡包埋样本。在发现阶段,首先使用包含 21,000 个蛋白质的 HuProtTM 高密度芯片筛选出候选 AAbs,然后使用以 aNSCLC 为重点的芯片进行验证。根据应答者与非应答者的比较和无进展生存期(PFS)生存分析,选择了纵向预测性 AAbs 进行酶联免疫吸附试验(ELISA)。我们还利用免疫组化技术和公开的免疫疗法数据集对预后标志物进行了验证。在接受化疗免疫疗法的非小细胞肺癌患者中,我们发现并验证了由两种 AAbs(MAX 和 DHX29)组成的治疗前生物标记物,以及由两种 AAbs(MAX 和 TAPBP)组成的治疗中预测标记物。所有三种 AAb 都与早期反应和 PFS 呈正相关(p < 0.05)。MAX AAb的动力学在应答者中呈上升趋势(p < 0.05),而在非应答者中呈先升后降的趋势(p < 0.05)。重要的是,MAX蛋白和mRNA水平能有效区分接受免疫疗法治疗的非小细胞肺癌患者的PFS(p < 0.05)。我们的研究结果对接受化疗免疫治疗的 aNSCLC 患者预后 AAbs 的变化进行了纵向分析。
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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
期刊最新文献
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