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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: jhcheong@yuhs.ac or taehyun.hwang@utsouthwestern.edu or taehyun.cs@gmail.com, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Androgen receptor (AR) target genes direct development and survival of the prostate epithelial lineage, including prostate cancer (PCa). Thus, endocrine therapies that inhibit the AR ligand-binding domain (LBD) are effective in treating PCa. AR transcriptional reactivation is central to resistance, as evidenced by the efficacy of AR retargeting in castration-resistant PCa (CRPC) with next-generation endocrine therapies abiraterone and enzalutamide. However, resistance to abiraterone and enzalutamide limits this efficacy in most men, and PCa remains the second-leading cause of male cancer deaths. Here we show that AR gene rearrangements in CRPC tissues underlie a completely androgen-independent, yet AR-dependent, resistance mechanism. We discovered intragenic AR gene rearrangements in CRPC tissues, which we modeled using transcription activator-like effector nuclease (TALEN)-mediated genome engineering. This modeling revealed that these AR gene rearrangements blocked full-length AR synthesis, but promoted expression of truncated AR variant proteins lacking the AR ligand-binding domain. Furthermore, these AR variant proteins maintained the constitutive activity of the AR transcriptional program and a CRPC growth phenotype independent of full-length AR or androgens. These findings demonstrate that AR gene rearrangements are a unique resistance mechanism by which AR transcriptional activity can be uncoupled from endocrine regulation in CRPC.

Background: Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer., Results: We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis., Conclusions: In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/.