Table_2_Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.xlsx

BackgroundGlioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response.
PurposeWe analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters.
Materials and methods82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally.
Results3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM.
ConclusionDistinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.

背景 胶质母细胞瘤（Glioblastomas, GBM）是一类进展迅猛、几乎均为致死性的颅内肿瘤。蛋白质组学分析为胶质母细胞瘤的无创分型以及解析治疗应答的生物学机制提供了全新的研究契机。
目的 本研究旨在分析同步放化疗（concurrent chemoirradiation, CRT）完成前后患者血清样本的差异蛋白质组表达谱，以探索蛋白质组学改变，并整合临床与蛋白质组学参数对胶质母细胞瘤进行分型。
材料与方法 本研究纳入82例完成临床信息标注的胶质母细胞瘤患者，收集其同步放化疗前后的血清样本。随后采用基于适配体的SOMAScan®蛋白质组检测技术对血清样本进行筛选。通过单变量与多变量Cox比例风险模型筛选出与总生存期（overall survival, OS）相关的显著特征，将其定为独立预后因素，并开展主成分分析（principal component analysis, PCA）。采用配对t检验计算蛋白质信号的差异表达情况，借助KOBAS数据库识别相关的KEGG通路。针对差异表达蛋白质（differentially expressed proteins, DEPs）的整体列表，采用基因集富集分析（Gene Set Enrichment Analysis, GSEA）预排序分析，以MSigDB特征基因集、基因本体（Gene Ontology, GO）生物过程及Reactome数据库作为参考，并通过加权基因共表达网络分析（weighted gene correlation network analysis, WGCNA）与Enrichr工具对内通路命中结果进行内部验证。
结果 本研究鉴定出3个具有显著生存差异的患者临床聚类亚组。共鉴定出389个治疗前后存在显著差异表达的蛋白质，其中284个表达上调、105个表达下调，这些蛋白质涉及多条与肿瘤代谢及进展相关的通路。相较于预后更佳的两个亚组（中位总生存期分别为22.4个月的中间亚组、28.7个月的最优亚组），生存预后最差的亚组（中位总生存期13.2个月）其差异表达蛋白质富集于增殖相关通路，且针对放疗相关通路等呈现出截然不同的对立应答模式。多条通路在多组分析中呈现出相反的信号调控模式，尤其涵盖脂肪酸代谢、NOTCH通路、经NF-κB激活的TNFα信号、Myc靶标V1信号、紫外线应答、未折叠蛋白应答、过氧化物酶体以及干扰素应答等，这些模式在不同临床生存亚组间差异显著，并得到了WGCNA分析的验证支持。另有23个蛋白质与总生存期具有统计学相关性，其中5个蛋白质（NETO2、CST7、SEMA6D、CBLN4、NPS）经Kaplan-Meier（KM）生存分析验证有效。
结论 本研究在胶质母细胞瘤患者同步放化疗前后的血清样本中，鉴定出带有肿瘤标志性特征的特异性蛋白质组学改变，涵盖肿瘤进展、治疗抵抗、干细胞特性及侵袭等特征，且这些改变与患者的临床生存结局密切相关。蛋白质组学特征有望用于胶质瘤的无创分型以及肿瘤通路的生物学解析。