title: Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
abstract: BACKGROUND
  Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures.
  
  
  RESULTS
  An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer.
  
  
  METHODS
  The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/.
  
  
  BACKGROUND
  yudi.pawitan@ki.se
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: School of Life Sciences, Peking University, Beijing, China, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors: Chen
authors: C
authors: Suo
authors: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors: Olga
authors: O
authors: Hrydziuszko
authors: Department of Statistics, Ewha Womans University, Seoul, South Korea.
authors: Donghwan
authors: D
authors: Lee
authors: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Computational Statistics, Institute of Statistics, Jakarta, Indonesia and.
authors: Setia
authors: S
authors: Pramana
authors: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors: Dhany
authors: D
authors: Saputra
authors: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors: Himanshu
authors: H
authors: Joshi
authors: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy.
authors: Stefano
authors: S
authors: Calza
authors: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors: Yudi
authors: Y
authors: Pawitan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000235:genetics*; D001940:Breast / Q000378:metabolism; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality* / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000235:genetics / Q000401:mortality / Q000473:pathology; D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods; D059472:Exome / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008875:Middle Aged; D009154:Mutation / Q000235:genetics*; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011379:Prognosis; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor