Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

ACTuDB, a new database for the integrated analysis of array-CGH and clinical data for tumors.

In recent years, an increasing number of projects have investigated tumor genome structure, using microarray-based techniques like array comparative genomic hybridization (array-CGH) or single nucleotide polymorphism (SNP) arrays. The forthcoming studies have to integrate these former results and compare their findings to the existing sets of copy number data for validation. These sets also form the basis from which many comparative retrospective analyses can be carried out. Nevertheless, exploitation of this mass of data relies on a homogeneous preparation of copy number data, which will make it possible to compare them together, and their integration into a unified bioinformatics environment with ad hoc analysis tools and interfaces. To our knowledge, no such data integration has been proposed yet. Therefore the biologists and clinicians involved in cancer research urgently need such an integrative tool, which motivated us to undertake the construction of a database for array-CGH and other DNA copy number data for tumors (ACTuDB). When available, the associated clinical, transcriptome and loss of heterozygosity data were also integrated into ACTuDB. ACTuDB contains currently about 1500 genomic profiles for tumors and cell lines for the bladder, brain, breast, colon, liver, lymphoma, neuroblastoma, mouth and pancreas, together with data for replication timing experiments. The CGH array data were processed, using ad hoc algorithms (probe mapping, breakpoint detection, gain or loss status assignment and visualization) developed at Institut Curie. The database is available from http://bioinfo.curie.fr/actudb/ and can be browsed with a user-friendly interface. This database will be a useful resource for the genomic profiling of tumors, a field of highly active research. We invite research groups involved in tumor genome profiling to submit their data to ACTuDB.

Challenges in the stratification of breast tumors for tailored therapies.

Studying the molecular stratification of breast carcinoma is a real challenge considering the extreme heterogeneity of these tumors. Many patients are now treated following recommendation established at several NIH and St Gallen consensus conferences. However a significant fraction of these breast cancer patients do not need adjuvant chemotherapies while other patients receive inefficacious therapies. High density gene expression arrays have been designed to attempt to establish expression profiles that could be used as prognostic indicators or as predictive markers for response to treatment. This review is intended to discuss the potential value of these new indicators, but also the current weaknesses of these new genomic and bioinformatic approaches. The combined analysis of transcriptomic and genomic alteration data from relatively large numbers of well annotated tumor specimens may offer an opportunity to overcome the current difficulties in validating recently published non overlapping gene lists as prognostic or therapeutic indicators. There is also hope for identifying and deciphering signal transduction pathways driving tumor progression with newly developed algorithms and semi quantitative parameters obtained in simplified in vitro or in vivo models for specific transduction pathways.

Computation of recurrent minimal genomic alterations from array-CGH data.

MOTIVATION: The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH data has been studied extensively, but subsequent steps in the analysis require flexible, efficient algorithms, particularly if the number of available profiles exceeds a few tens or the number of array probes exceeds a few thousands. RESULTS: We propose two algorithms for computing minimal and minimal constrained regions of gain and loss from discretized CGH profiles. The second of these algorithms can handle additional constraints describing relevant regions of copy number change. We have validated these algorithms on two public array-CGH datasets. AVAILABILITY: From the authors, upon request. CONTACT: celine@lri.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.