title: Assessing the clinical utility of cancer genomic and proteomic data across tumor types.
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.
authors: 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].
authors: Yuan
authors: 0000000347067897
authors: Y
authors: Yuan
authors: 1] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [3].
authors: Eliezer M
authors: EM
authors: Van Allen
authors: 1] Sage Bionetworks, Seattle, Washington, USA. [2].
authors: Larsson
authors: L
authors: Omberg
authors: 1] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
authors: Nikhil
authors: N
authors: Wagle
authors: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
authors: Ali
authors: A
authors: Amin-Mansour
authors: Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
authors: Artem
authors: A
authors: Sokolov
authors: Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors: Lauren A
authors: LA
authors: Byers
authors: Division of Statistics and Scientific Computing, The University of Texas at Austin, Austin, Texas, USA.
authors: Yanxun
authors: Y
authors: Xu
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors: Kenneth R
authors: KR
authors: Hess
authors: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors: Lixia
authors: L
authors: Diao
authors: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors: Leng
authors: L
authors: Han
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors: Xuelin
authors: X
authors: Huang
authors: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
authors: Michael S
authors: MS
authors: Lawrence
authors: 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.
authors: John N
authors: JN
authors: Weinstein
authors: Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
authors: Josh M
authors: JM
authors: Stuart
authors: Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
authors: Gordon B
authors: GB
authors: Mills
authors: 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].
authors: Levi A
authors: LA
authors: Garraway
authors: 1] Sage Bionetworks, Seattle, Washington, USA. [2] [3].
authors: Adam A
authors: AA
authors: Margolin
authors: 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].
authors: Gad
authors: G
authors: Getz
authors: 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].
authors: Han
authors: 000000017633286X
authors: H
authors: Liang
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D004273:DNA, Neoplasm / Q000235:genetics*; D030541:Databases, Genetic; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics* / Q000401:mortality*; D015995:Prevalence; D020543:Proteome / Q000235:genetics*; D018570:Risk Assessment; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor; D004273:DNA, Neoplasm; D005819:Genetic Markers; D020543:Proteome