背景:免疫治疗提高了癌症患者的生存率,但是确定谁会对治疗有反应仍然是一个挑战。蛋白质组学技术的最新进展使得能够在单个实验中鉴定和定量几乎所有表达的蛋白质。质谱与其他高通量技术的整合为全面和系统的分析癌症血浆蛋白质组铺平了道路。促进早期诊断和个性化治疗。在这种情况下,我们的研究目的是研究使用SWATH-MS(所有理论质谱的序贯窗口采集)策略对接受派姆单抗治疗的新诊断NSCLC患者进行血浆蛋白质组分析的预测和预后价值.
方法:为此,纳入64例用派姆单抗治疗的新诊断的晚期NSCLC患者,并在治疗前和治疗期间从所有患者收集血液样品。总共收集了171份血液样本,和血浆样本采用SWATH-MS策略进行分析。接下来,我们比较了接受pembrolizumab治疗前转移性NSCLC患者的血浆蛋白表达,并将队列分为两组,以鉴定蛋白质组学特征,使我们能够预测免疫治疗反应.
结果:通过SWATH-MS策略进行的蛋白质组学分析使我们能够在应答者和非应答者患者之间鉴定出324种差异表达的蛋白质。此外,我们开发了一个预测模型,发现了七种蛋白质的组合,包括ATG9A,DCDC2,HPS5,FIL1L,LZTL1,PGTA,和SPTN2,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OS较差相关。
结论:我们的工作强调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志物的潜在价值。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。
BACKGROUND: Immunotherapy has improved survival rates in cancer patients, but identifying those who will respond to treatment remains a challenge. Recent advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integration of mass spectrometry with other high-throughput technologies has paved the way for comprehensive and systematic analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, the objective of our study was 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 NSCLC patients who received pembrolizumab therapy.
METHODS: For this purpose, 64 newly diagnosed advanced NSCLC patients treated with pembrolizumab therapy were enrolled and blood samples were collected from all patients before and during therapy. In total 171 blood samples were collected, and plasma samples were analysed employing SWATH-MS strategy. Next, we compared the plasma protein expression of metastatic NSCLC patients prior to receiving pembrolizumab treatment and divided the cohort into two groups in order to identify a proteomic signature that allow us to predict immunotherapy response.
RESULTS: Proteomic analyses by SWATH-MS strategy allow us to identified 324 differentially expressed proteins between responder and non-responder patients. 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 that low levels of ATG9A, DCDC2, and HPS5 were associated with longer progression-free survival (PFS) and overall survival (OS), while low levels of SPTN2 were associated with worse OS.
CONCLUSIONS: Our work highlights the potential value of proteomic technologies to detect predictive biomarkers in blood samples of NSCLC patients. These analyses shed light on the correlation between the response to immunotherapy in patients with NSCLC and the set of 7 proteins.