Liquid Biopsy Analysis Unit, Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain.
Department of Medical Oncology, Complexo Hospitalario Universitario de Santiago de Compostela (SERGAS), Santiago de Compostela, Spain; Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; CIBERONC, Centro de Investigación Biomédica en Red Cáncer, Madrid, Spain.
Mol Cell Proteomics. 2024 Oct;23(10):100834. doi: 10.1016/j.mcpro.2024.100834. Epub 2024 Aug 29.
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.
免疫疗法已经提高了癌症患者的生存率,但确定哪些患者对治疗有反应仍然是一个挑战。蛋白质组学技术的进步使得在单个实验中鉴定和定量几乎所有表达的蛋白质成为可能。将质谱与高通量技术相结合,促进了癌症血浆蛋白质组的全面分析,有助于早期诊断和个性化治疗。在这种情况下,我们的研究旨在使用 SWATH-MS(序贯窗口采集所有理论质谱)策略研究新诊断的接受派姆单抗治疗的非小细胞肺癌(NSCLC)患者的血浆蛋白质组分析的预测和预后价值。我们招募了 64 名新诊断的接受派姆单抗治疗的晚期 NSCLC 患者。所有患者在治疗前和治疗期间采集血样。使用 SWATH-MS 策略分析了总共 171 个血样。分析了接受派姆单抗治疗前转移性 NSCLC 患者的血浆蛋白质表达。第一队列(发现队列)用于鉴定预测免疫治疗反应的蛋白质组学特征。因此,在应答者和无应答者患者之间鉴定出 324 个差异表达蛋白。此外,我们开发了一个预测模型,发现包括 ATG9A、DCDC2、HPS5、FIL1L、LZTL1、PGTA 和 SPTN2 在内的七种蛋白质的组合具有比 PD-L1 表达更强的预测价值。此外,生存分析表明,ATG9A、DCDC2、SPTN2 和 HPS5 的水平与无进展生存期(PFS)和/或总生存期(OS)之间存在关联。我们的研究结果强调了蛋白质组学技术在检测 NSCLC 患者血液样本中的预测生物标志物方面的潜力,强调了免疫治疗反应与鉴定的蛋白质组之间的相关性。