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研究人员绘制出肺腺癌蛋白质基因组特征图谱
作者：小柯机器人 发布时间：2020/7/12 23:48:43

美国哈佛大学Steven A. Carr等研究人员合作绘制出肺腺癌蛋白质基因组特征图谱，从而揭示了肿瘤的治疗弱点。相关论文于2020年7月9日发表在《细胞》杂志上。

为了探索肺腺癌（LUAD）的生物学特性并发现新的疗法，研究人员对110个肿瘤和101个匹配的正常邻近组织（NAT）进行了全面的蛋白质组学表征，其中包括基因组学、表观基因组学、深层蛋白质组学、磷酸化蛋白质组学和乙酰蛋白质组学。多组学聚类分析显示了由关键驱动突变、国家和性别定义的四个亚群。
 
蛋白质组学和磷酸化蛋白质组学数据阐明了拷贝数畸变、体细胞突变和融合的下游生物学，并确定了与涉及KRAS、EGFR和ALK的驱动基因事件相关的治疗弱点。免疫亚型揭示了复杂的情况，加强了STK11与免疫冷行为的联系，并强调了嗜中性粒细胞脱粒的潜在免疫抑制作用。吸烟相关的LUAD与其他环境暴露特征和NAT的场效应相关。匹配的NAT可以鉴定具有潜在诊断和治疗用途的差异表达蛋白。
 
这个蛋白质组学数据集为更好地了解和治疗肺腺癌提供了独特的公共资源。
 
附：英文原文
 
Title: Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Author: Michael A. Gillette, Shankha Satpathy, Song Cao, Saravana M. Dhanasekaran, Suhas V. Vasaikar, Karsten Krug, Francesca Petralia, Yize Li, Wen-Wei Liang, Boris Reva, Azra Krek, Jiayi Ji, Xiaoyu Song, Wenke Liu, Runyu Hong, Lijun Yao, Lili Blumenberg, Sara R. Savage, Michael C. Wendl, Bo Wen, Kai Li, Lauren C. Tang, Melanie A. MacMullan, Shayan C. Avanessian, M. Harry Kane, Chelsea J. Newton, MacIntosh Cornwell, Ramani B. Kothadia, Weiping Ma, Seungyeul Yoo, Rahul Mannan, Pankaj Vats, Chandan Kumar-Sinha, Emily A. Kawaler, Tatiana Omelchenko, Antonio Colaprico, Yifat Geffen, Yosef E. Maruvka, Felipe da Veiga Leprevost, Maciej Wiznerowicz, Zeynep H. Gümü, Rajwanth R. Veluswamy, Galen Hostetter, David I. Heiman, Matthew A. Wyczalkowski, Tara Hiltke, Mehdi Mesri, Christopher R. Kinsinger, Emily S. Boja, Gilbert S. Omenn, Arul M. Chinnaiyan, Henry Rodriguez, Qing Kay Li, Scott D. Jewell, Mathangi Thiagarajan, Gad Getz, Bing Zhang, David Feny, Kelly V. Ruggles, Marcin P. Cieslik, Ana I. Robles, Karl R. Clauser, Ramaswamy Govindan, Pei Wang, Alexey I. Nesvizhskii, Li Ding, D.R. Mani, Steven A. Carr, Alex Webster, Alicia Francis, Alyssa Charamut, Amanda G. Paulovich, Amy M. Perou, Andrew K. Godwin

Issue&Volume: 2020/07/09

Abstract: To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

DOI: 10.1016/j.cell.2020.06.013

Source: https://www.cell.com/cell/fulltext/S0092-8674(20)30744-3

期刊信息
Cell：《细胞》，创刊于1974年。隶属于细胞出版社，最新IF：36.216
官方网址：https://www.cell.com/