The cancer genome atlas pan-cancer analysis project

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile

Validating the concept of mutational signatures with isogenic cell models.

The diversity of somatic mutations in human cancers can be decomposed into individual mutational signatures, patterns of mutagenesis that arise because of DNA damage and DNA repair processes that have occurred in cells as they evolved towards malignancy. Correlations between mutational signatures and environmental exposures, enzymatic activities and genetic defects have been described, but human cancers are not ideal experimental systems-the exposures to different mutational processes in a patient's lifetime are uncontrolled and any relationships observed can only be described as an association. Here, we demonstrate the proof-of-principle that it is possible to recreate cancer mutational signatures in vitro using CRISPR-Cas9-based gene-editing experiments in an isogenic human-cell system. We provide experimental and algorithmic methods to discover mutational signatures generated under highly experimentally-controlled conditions. Our in vitro findings strikingly recapitulate in vivo observations of cancer data, fundamentally validating the concept of (particularly) endogenously-arising mutational signatures

PIK3CA dependence and sensitivity to therapeutic targeting in urothelial carcinoma

Background Many urothelial carcinomas (UC) contain activating PIK3CA mutations. In telomerase-immortalized normal urothelial cells (TERT-NHUC), ectopic expression of mutant PIK3CA induces PI3K pathway activation, cell proliferation and cell migration. However, it is not clear whether advanced UC tumors are PIK3CA-dependent and whether PI3K pathway inhibition is a good therapeutic option in such cases. Methods We used retrovirus-mediated delivery of shRNA to knock down mutant PIK3CA in UC cell lines and assessed effects on pathway activation, cell proliferation, migration and tumorigenicity. The effect of the class I PI3K inhibitor GDC-0941 was assessed in a panel of UC cell lines with a range of known molecular alterations in the PI3K pathway. Results Specific knockdown of PIK3CA inhibited proliferation, migration, anchorage-independent growth and in vivo tumor growth of cells with PIK3CA mutations. Sensitivity to GDC-0941 was dependent on hotspot PIK3CA mutation status. Cells with rare PIK3CA mutations and co-occurring TSC1 or PTEN mutations were less sensitive. Furthermore, downstream PI3K pathway alterations in TSC1 or PTEN or co-occurring AKT1 and RAS gene mutations were associated with GDC-0941 resistance. Conclusions Mutant PIK3CA is a potent oncogenic driver in many UC cell lines and may represent a valuable therapeutic target in advanced bladder cancer

Algorithms for effective querying of compound graph-based pathway databases

<p>Abstract</p> <p>Background</p> <p>Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.</p> <p>Results</p> <p>Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool P<smcaps>ATIKA</smcaps><it>web </it>(Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.</p> <p>Conclusion</p> <p>The P<smcaps>ATIKA</smcaps> Project Web site is <url>http://www.patika.org</url>. P<smcaps>ATIKA</smcaps><it>web </it>version 2.1 is available at <url>http://web.patika.org</url>.</p