Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors.
Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors.
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DOI：
10.1038/s42003-022-04075-4
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发表时间：
2022-12-13
影响因子：
5.9
通讯作者：
Vaske, Olena M.
中科院分区：
文献类型：
--
作者：
Sanders, Lauren M.;Chandra, Rahul;Zebarjadi, Navid;Beale, Holly C.;Lyle, A. Geoffrey;Rodriguez, Analiz;Kephart, Ellen Towle;Pfeil, Jacob;Cheney, Allison;Learned, Katrina;Currie, Rob;Gitlin, Leonid;Vengerov, David;Haussler, David;Salama, Sofie R.;Vaske, Olena M.
几十年来，癌细胞系已被广泛用于研究驱动癌症发展的生物过程，并识别对治疗药物反应的生物标志物。基因组测序的进步使得对癌细胞系和原发性肿瘤集合的大规模基因组表征成为可能，例如癌细胞系百科全书（CCLE）和癌症基因组图谱（TCGA）。这些研究首次对癌细胞系和原发性肿瘤在基因组和蛋白质组水平上的可比性进行了全面评估。在这里，我们利用来自 CCLE 和 TCGA 中数千个样本的大量 mRNA 和 micro-RNA 测序数据，以及来自 MD 安德森细胞系项目 (MCLP) 和癌症蛋白质组图谱 (TCPA) 合作伙伴研究的蛋白质组数据，来表征癌细胞系再现肿瘤的程度。我们发现癌细胞系中长非编码 RNA 和 microRNA 调控网络的失调，与细胞系和原发性肿瘤之间四个关键癌症驱动途径中的差异表达相关：KRAS 信号传导、NFKB 信号传导、IL2/STAT5 信号传导和 TP53 信号传导。我们的结果强调了仔细解释癌细胞系实验的必要性，特别是针对这些重要癌症途径的治疗方法。使用支持向量机学习方法和多组学数据，与原发性肿瘤相比，癌细胞系中关键癌症驱动途径的失调被揭示。
Cancer cell lines have been widely used for decades to study biological processes driving cancer development, and to identify biomarkers of response to therapeutic agents. Advances in genomic sequencing have made possible large-scale genomic characterizations of collections of cancer cell lines and primary tumors, such as the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). These studies allow for the first time a comprehensive evaluation of the comparability of cancer cell lines and primary tumors on the genomic and proteomic level. Here we employ bulk mRNA and micro-RNA sequencing data from thousands of samples in CCLE and TCGA, and proteomic data from partner studies in the MD Anderson Cell Line Project (MCLP) and The Cancer Proteome Atlas (TCPA), to characterize the extent to which cancer cell lines recapitulate tumors. We identify dysregulation of a long non-coding RNA and microRNA regulatory network in cancer cell lines, associated with differential expression between cell lines and primary tumors in four key cancer driver pathways: KRAS signaling, NFKB signaling, IL2/STAT5 signaling and TP53 signaling. Our results emphasize the necessity for careful interpretation of cancer cell line experiments, particularly with respect to therapeutic treatments targeting these important cancer pathways. Using a support vector machine learning approach and multi-omics data, dysregulation of key cancer driver pathways is revealed in cancer cell lines compared to primary tumors.
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影响因子：
8.8
作者：
Eser, S.;Schnieke, A.;Schneider, G.;Saur, D.
通讯作者：
Saur, D.
影响因子：
16.6
作者：
Domcke, Silvia;Sinha, Rileen;Levine, Douglas A.;Sander, Chris;Schultz, Nikolaus
通讯作者：
Schultz, Nikolaus
影响因子：
8.8
作者：
Kodack DP;Farago AF;Dastur A;Held MA;Dardaei L;Friboulet L;von Flotow F;Damon LJ;Lee D;Parks M;Dicecca R;Greenberg M;Kattermann KE;Riley AK;Fintelmann FJ;Rizzo C;Piotrowska Z;Shaw AT;Gainor JF;Sequist LV;Niederst MJ;Engelman JA;Benes CH
通讯作者：
Benes CH
影响因子：
11.2
作者：
Hoshida Y;Nijman SM;Kobayashi M;Chan JA;Brunet JP;Chiang DY;Villanueva A;Newell P;Ikeda K;Hashimoto M;Watanabe G;Gabriel S;Friedman SL;Kumada H;Llovet JM;Golub TR
通讯作者：
Golub TR
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.