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Nat Biotechnol. 2014 Jul;32(7):644-52. doi: 10.1038/nbt.2940. Epub 2014 Jun 22.

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

Author information

1
1] Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA. [2] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [3].
2
1] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3].
3
1] Sage Bionetworks, Seattle, Washington, USA. [2].
4
1] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
5
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
6
Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
7
Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
8
Division of Statistics and Scientific Computing, The University of Texas at Austin, Austin, Texas, USA.
9
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
10
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
11
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
12
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
13
1] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3] Harvard Medical School, Boston, Massachusetts, USA. [4].
14
1] Sage Bionetworks, Seattle, Washington, USA. [2] [3].
15
1] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [2] Harvard Medical School, Boston, Massachusetts, USA. [3] Massachusetts General Hospital, Cancer Center and Department of Pathology, Boston, Massachusetts, USA. [4].

Abstract

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.

PMID:
24952901
PMCID:
PMC4102885
DOI:
10.1038/nbt.2940
[Indexed for MEDLINE]
Free PMC Article

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