Assessing the clinical utility of cancer genomic and proteomic data across tumor types

被引:212
作者
Yuan, Yuan [1 ,2 ]
Van Allen, Eliezer M. [3 ,4 ]
Omberg, Larsson [5 ]
Wagle, Nikhil [3 ,4 ]
Amin-Mansour, Ali [4 ]
Sokolov, Artem [6 ]
Byers, Lauren A. [7 ]
Xu, Yanxun [8 ]
Hess, Kenneth R. [9 ]
Diao, Lixia [2 ]
Han, Leng [2 ]
Huang, Xuelin [9 ]
Lawrence, Michael S. [4 ]
Weinstein, John N. [2 ,10 ]
Stuart, Josh M. [6 ]
Mills, Gordon B. [10 ]
Garraway, Levi A. [3 ,4 ,11 ]
Margolin, Adam A. [5 ]
Getz, Gad [4 ,11 ,12 ,13 ]
Liang, Han [1 ,2 ]
机构
[1] Baylor Coll Med, Grad Program Struct & Computat Biol & Mol Biophys, Houston, TX 77030 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77030 USA
[3] Dana Farber Canc Inst, Dept Med Oncol, Boston, MA 02115 USA
[4] Broad Inst MIT & Harvard, Cambridge, MA USA
[5] Sage Bionetworks, Seattle, WA USA
[6] Univ Calif Santa Cruz, Dept Biomol Engn, Santa Cruz, CA 95064 USA
[7] Univ Texas MD Anderson Canc Ctr, Dept Thorac Head & Neck Med Oncol, Houston, TX 77030 USA
[8] Univ Texas Austin, Div Stat & Sci Comp, Austin, TX 78712 USA
[9] Univ Texas MD Anderson Canc Ctr, Dept Biostat, Houston, TX 77030 USA
[10] Univ Texas MD Anderson Canc Ctr, Dept Syst Biol, Houston, TX 77030 USA
[11] Harvard Univ, Sch Med, Boston, MA USA
[12] Massachusetts Gen Hosp, Ctr Canc, Boston, MA 02114 USA
[13] Massachusetts Gen Hosp, Dept Pathol, Boston, MA 02114 USA
基金
美国国家卫生研究院;
关键词
CELL LUNG-CANCER; SURVIVAL; EVEROLIMUS; BREAST; VALIDATION; RESISTANCE; SUPPRESSOR; EXPRESSION; BIOMARKERS; MUTATIONS;
D O I
10.1038/nbt.2940
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.
引用
收藏
页码:644 / +
页数:11
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