MOTIVATION: Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers. RESULTS: We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4790 cancer patients with 19 different types of tumors. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and targets of currently available drugs. To investigate the clinical significance of these altered pathways, we performed consensus clustering for patient stratification using member genes in the altered pathways coupled with gene expression datasets from 4870 patients from TCGA, and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal. AVAILABILITY AND IMPLEMENTATION: The code is available at: http://www.taehyunlab.org CONTACT: [email protected] or [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Advances in technology have made different microarray platforms available. Among the many, Illumina BeadArrays are relatively new and have captured significant market share. With BeadArray technology, high data quality is generated from low sample input at reduced cost. However, the analysis methods for Illumina BeadArrays are far behind those for Affymetrix oligonucleotide arrays, and so need to be improved. RESULTS: In this article, we consider the problem of background correction for BeadArray data. One distinct feature of BeadArrays is that for each array, the noise is controlled by over 1000 bead types conjugated with non-specific oligonucleotide sequences. We extend the robust multi-array analysis (RMA) background correction model to incorporate the information from negative control beads, and consider three commonly used approaches for parameter estimation, namely, non-parametric, maximum likelihood estimation (MLE) and Bayesian estimation. The proposed approaches, as well as the existing background correction methods, are compared through simulation studies and a data example. We find that the maximum likelihood and Bayes methods seem to be the most promising. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.