小柯机器人

泛癌蛋白质基因组学将致癌驱动因素与功能状态联系起来，这一成果由美国圣路易斯华盛顿大学Li Ding、麻省理工学院和哈佛大学布罗德研究所Gad Getz和威尔康奈尔医学院Lewis C. Cantley团队。相关论文发表在2023年8月14日出版的《细胞》杂志上。

研究人员利用多组学泛癌症分析通过量化在RNA、蛋白质和磷酸化蛋白水平上显著的顺式效应和远端反式效应，揭示了对癌症驱动因素功能的见解。主要观察了包括点突变和拷贝数改变与蛋白质相互作用网络重新布线间的关联，值得注意的是，大多数癌症基因通过向基序序列的激酶活性谱相似的分子状态收敛。预测的新抗原负荷与测量的T细胞浸润之间的相关性表明免疫疗法具有潜在脆弱性。癌症标志的模式因多基因蛋白质丰度而异，从均匀到异质。

总体而言，该工作证明了综合蛋白质基因组学在理解致癌驱动因素功能状态及其与癌症进展的关联，打破了单个癌症类型研究的局限性。

据悉，癌症驱动事件是指诱导癌变的关键遗传畸变。然而，对其确切的分子机制仍然不够了解。

附：英文原文

Title: Pan-cancer proteogenomics connects oncogenic drivers to functional states

Author: Yize Li, Eduard Porta-Pardo, Collin Tokheim, Matthew H. Bailey, Tomer M. Yaron, Vasileios Stathias, Yifat Geffen, Kathleen J. Imbach, Song Cao, Shankara Anand, Yo Akiyama, Wenke Liu, Matthew A. Wyczalkowski, Yizhe Song, Erik P. Storrs, Michael C. Wendl, Wubing Zhang, Mustafa Sibai, Victoria Ruiz-Serra, Wen-Wei Liang, Nadezhda V. Terekhanova, Fernanda Martins Rodrigues, Karl R. Clauser, David I. Heiman, Qing Zhang, Francois Aguet, Anna P. Calinawan, Saravana M. Dhanasekaran, Chet Birger, Shankha Satpathy, Daniel Cui Zhou, Liang-Bo Wang, Jessika Baral, Jared L. Johnson, Emily M. Huntsman, Pietro Pugliese, Antonio Colaprico, Antonio Iavarone, Milan G. Chheda, Christopher J. Ricketts, David Feny, Samuel H. Payne, Henry Rodriguez, Ana I. Robles, Michael A. Gillette, Chandan Kumar-Sinha, Alexander J. Lazar, Lewis C. Cantley, Gad Getz, Li Ding, Eunkyung An, Meenakshi Anurag, Jasmin Bavarva, Michael J. Birrer, Anna Calinawan, Michele Ceccarelli, Daniel W. Chan, Arul M. Chinnaiyan, Hanbyul Cho, Shrabanti Chowdhury

Issue&Volume: 2023-08-14

Abstract: Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

DOI: 10.1016/j.cell.2023.07.014

Source: https://www.cell.com/cell/fulltext/S0092-8674(23)00780-8