Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

Trey Ideker is Chief of Genetics at the UCSD School of Medicine. He also serves as Professor of Bioengineering, Adjunct Professor of Computer Science and Member of the Moores UCSD Cancer Center. Ideker received Bachelor’s and Master’s degrees from MIT in Electrical Engineering and Computer Science and his Ph.D. from the University of Washington in Molecular Biology under the supervision of Dr. Leroy Hood. He is a pioneer in using genome-scale measurements to construct network models of cellular processes and disease. His recent research activities include assembly of networks governing the response to DNA damage, development of software for protein network cross-species comparisons, and network-based diagnosis of disease. Ideker serves on the Editorial Boards for Bioinformatics and PLoS Computational Biology, Board of Directors for US-HUPO and the Cytoscape Consortium, and is a regular consultant for companies such as Monsanto, Genstruct, and Mendel Biotechnology. He was named one of the Top 10 Innovators of 2006 by Technology Review magazine and the 2009 Overton Prize recipient from the International Society for Computational Biology. His work has been featured in news outlets such as The Scientist, the San Diego Union Tribune, and Forbes magazine.

The establishment of a highly accurate human reference genome sequence in 2004 ushered in the "genomics era" and promised a genetic basis for disease diagnosis, prognosis and treatment tailored specifically to each patient. Cancer, often described as "a disease of the chromosomes", is under intense scrutiny of next generation sequencing technologies. Remarkable successes have emerged from recent large-scale studies, but significant challenges remain. This talk will review recent progress, and outline the challenges toward application of the evolving sequencing technologies to personalized treatment.

David A. Wheeler received his B.S. degrees in Biochemistry and Xoology from the University of Maryland, an M.S. in Biochemistry and Ph.D. in Molecular Genetics from the George Washington University. Dr. Wheeler did postdoctoral research in behavioral genetics at Brandeis University with Dr. Jeffrey Hall, where he participated in the cloning of the D. melanogaster period locus, the first gene with an established role in regulating behavior (circadian rhyhthms) in any organism. Through this work in the late 1980s, Dr. Wheeler became interested in the new area of computational biology. He joined the faculty at Baylor College of Medicine in 1991 to develop computational tools for moloecular biology. He was Director the Molecular Biology Computation Resource at Baylor College of Medicine for 10 years and in 2001 joined the Human Genome Sequencing Center at BCM where he guided the finishing of the D. melanogaster chromosome 3 and X genome sequence followed by the human genome sequence, chromosomes 3, and 12. Currently Dr. Wheeler is Director of Cancer Genomics in the Human Genome Sequencing Center, where he develops methods for discovery of genome variation in human and animal populations using DNA sequencing technologies with the goal of relating polymorphism and mutation to cancer.

The dynamic usage of mRNA 3' untranslated region (3'UTR) resulting from alternative polyadenylation (APA) is emerging as a pervasive mechanism to regulate approximately 70% of human genes. The importance of APA in human diseases such as cancer is only beginning to be appreciated. Current APA profiling protocols use the partitioning and fragmentation of mRNA to enrich for polyA sites followed by high throughput sequencing (polyA-seq). These polyA-seq protocols, although powerful, have not been widely adopted. Therefore, global studies of APA in cancer are very limited. In contrast, whole transcriptome RNA-seq has been broadly employed in almost every large-scale genomics project, including The Cancer Genome Atlas (TCGA). We therefore developed a novel bioinformatics algorithm, termed Dynamic analysis of Alternative PolyAdenylation from RNA-Seq (DaPars), to directly infer dynamic APA events through standard RNA-seq. DaPars used a linear regression model to identify the exact location of the de novo APA site, and quantify the lengthening or shortening of 3'UTRs between different conditions.

When applied to 291 TCGA clinical samples across 6 tumor types, we discovered 416 genes with highly recurrent tumor-specific dynamic APAs. Most of these genes (94%) have shorter 3'UTRs and accordingly have higher expression in tumors than in matched normal tissues, likely through loss of microRNA-mediated repression. Interestingly, we found a novel link between APA regulation and cancer metabolism, and strong evidence that a critical component of the 3'-end processing machinery is a master activator of proximal APA usage in tumorigenesis. Together, through the reanalysis of TCGA RNA-seq data using DaPars, our work is the first to demonstrate the feasibility of APA analysis through standard RNA-seq, and expands our knowledge of the mechanisms and consequences of APA regulation during tumorigenesis.

Wei Li is an Associate Professor in the Dan L. Duncan Cancer Center and Department of Molecular and Cellular Biology at Baylor College of Medicine. He received his PhD in Bioinformatics from the Chinese Academy of Sciences and postdoctoral training in the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute and Harvard School of Public Health. Dr. Li’s research is focused on the design and application of statistical and computational algorithms to elucidate epigenetic mechanisms in various disease models such as cancer.