Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics.
Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics.
复制标题
DOI：
10.1016/j.cels.2018.10.001
复制
发表时间：
2018-11-28
期刊：
影响因子：
9.3
通讯作者：
Bandyopadhyay S
中科院分区：
文献类型：
--
作者：
Webber JT;Kaushik S;Bandyopadhyay S
Leveraging insights from genomic studies of patient tumors is limited by the discordance between these tumors and the cell line models used for functional studies. We integrate -omics datasets using functional networks to identify gene modules reflecting variation between tumors and show that the structure of these modules can be evaluated in cell lines to discover clinically relevant biomarkers of therapeutic responses. Applied to breast cancer, we identify 219 gene modules that capture recurrent alterations, subtype patients and quantitate various cell types within the tumor microenvironment. Comparison of modules between tumors and cell lines reveals that many modules composed primarily of gene expression and methylation are poorly preserved. In contrast, preserved modules are highly predictive of drug responses in a manner that is robust and clinically relevant. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data. Webber et al. develop an approach to compress data from the multi-omic characterization of hundreds of cancers into a small set of gene modules using a modular analysis of gene networks in cancer, or MAGNETIC. Modules can be readily compared with cell lines revealing components of the tumor micro-environment and used to improve the development of biomarkers of therapeutic responses based on cell line collections.
登录
查看更多内容
影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
影响因子：
64.5
作者：
Basu A;Bodycombe NE;Cheah JH;Price EV;Liu K;Schaefer GI;Ebright RY;Stewart ML;Ito D;Wang S;Bracha AL;Liefeld T;Wawer M;Gilbert JC;Wilson AJ;Stransky N;Kryukov GV;Dancik V;Barretina J;Garraway LA;Hon CS;Munoz B;Bittker JA;Stockwell BR;Khabele D;Stern AM;Clemons PA;Shamji AF;Schreiber SL
通讯作者：
Schreiber SL
影响因子：
11.2
作者：
Hatzis C;Bedard PL;Birkbak NJ;Beck AH;Aerts HJ;Stem DF;Shi L;Clarke R;Quackenbush J;Haibe-Kains B
通讯作者：
Haibe-Kains B
影响因子：
6.6
作者：
Dorman, Stephanie N.;Baranoua, Katherina;Rogan, Peter K.
通讯作者：
Rogan, Peter K.
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel