Krauze Andra Valentina, Sierk Michael, Nguyen Trinh, Chen Qingrong, Yan Chunhua, Hu Ying, Jiang William, Tasci Erdal, Zgela Theresa Cooley, Sproull Mary, Mackey Megan, Shankavaram Uma, Meerzaman Daoud, Camphausen Kevin
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, United States.
Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, United States.
Front Oncol. 2023 Aug 10;13:1127645. doi: 10.3389/fonc.2023.1127645. eCollection 2023.
Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response.
We 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.
82 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.
3 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 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.
Distinct 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.
胶质母细胞瘤（GBM）是进展迅速、几乎无一例外会致命的脑肿瘤。蛋白质组学分析为GBM的无创分类以及对治疗反应的生物学理解提供了契机。
我们分析了患者血清样本在同步放化疗（CRT）完成前后的蛋白质组差异表达，以探索蛋白质组学改变，并通过整合临床和蛋白质组学参数对GBM进行分类。
对82例GBM患者进行临床注释，并在CRT前后采集血清样本。然后使用基于适配体的SOMAScan®蛋白质组学检测方法对血清样本进行筛查。将单变量和多变量Cox模型中与总生存期（OS）相关的显著特征指定为独立预后因素，并进行主成分分析（PCA）。使用配对t检验计算蛋白质信号的差异表达，使用KOBAS识别相关的KEGG通路。对差异表达蛋白（DEP）的总体列表进行GSEA预排名分析，对照MSigDB标志性通路、基因本体生物学过程和Reactome数据库，同时使用加权基因共表达网络分析（WGCNA）和Enrichr在内部验证通路命中情况。
确定了3个具有不同生存期的临床聚类。确定了389个治疗前后显著差异表达的蛋白，包括284个上调和105个下调的蛋白，代表了几个与癌症代谢和进展相关的通路。生存期最短的组（中位OS为13.2个月）与增殖通路相关的DEP有关，并且与表现较好的组（中等生存期组，中位OS为22.4个月；生存期最长组，中位OS为28.7个月）相比，在包括放疗相关通路等方面表现出明显相反的反应。在几个通路（特别是脂肪酸代谢、NOTCH、TNFα - NF-κB、Myc靶标V1信号传导、紫外线反应、未折叠蛋白反应、过氧化物酶体和干扰素反应）的多项分析中，不同临床生存期组之间存在相反的信号模式，并且得到了WGCNA的支持。23种蛋白对OS具有统计学意义，其中5种（NETO2、CST7、SEMA6D、CBLN4、NPS）得到了KM的支持。
在GBM患者CRT前后采集的血清样本中，发现了具有癌症特征（包括进展、耐药、干性和侵袭）的明显蛋白质组学改变，并且与临床生存期相对应。蛋白质组有可能用于胶质瘤分类和癌症通路的生物学研究。
