Cancer Genome Consortium Data Research Articles

A cancer cell-line titration series for evaluating somatic classification.

BackgroundAccurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies.ResultsCell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease.ConclusionsOur cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium’s Data Access Compliance Office (ICGC DACO).Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-1803-7) contains supplementary material, which is available to authorized users.

Abstract LB-308: A novel data safe haven approach to bring analyses to the International Cancer Genome Consortium data

Abstract To target and personalize cancer therapies to the genomic aberrations present in a particular patient's tumor, researchers need to identify the genes that drive the progression of malignant tumors. This requires analysis of somatic mutations from large samples of patients to identify driver mutations up to the “tail end” of the frequency distribution. Community genomics data sets from the TCGA and ICGC projects represent a valuable resource to which researchers can add their own data to gain statistical power in their analyzes. The current issue to this methodology is the highly fragmented storage of public and private data and the inefficient access to public data. Researchers spend weeks to months downloading hundreds of terabytes of data from central repositories before computations can begin. What is needed is a data “safe haven” where researchers can bring compute to the reference data without the need to incur in bulky data transfers or duplicative storage costs, in an environment that protects the privacy of the patients’ data. In collaboration with the International Cancer genome Consortium, we developed ShareSeq, a genomic data safe haven platform that provides an informatics solution for storing, handling and analyzing protected identifiable genomic data. This resource leverages Annai-GNOS, the technology which we developed to create and manage the CGHub TCGA repository together with UCSC, and that is being used in the ICGC Pan Cancer Analysis of Whole Genomes project, and combines it with a high-performance compute environment and an array of tools to process and analyze genomic data. Built using a walled garden approach, where the data is stored, processed and managed within the security of the system, ShareSeq avoids the complexity of assured end point encryption. GeneTorrent, our fast and secure file transfer mechanism, enables researchers’ private information to be transferred into the walled garden simply and securely to combine it with the public datasets. ShareSeq differs dramatically from the traditional cloud in two features: (i) formal mechanisms and a service level agreement to store protected identifiable genomic data securely and safely, built into the system from the ground up; (ii) the system is specifically designed for genomic computing over large shared data sets supporting common bioinformatics workflow tools; (iii) Fast download and access to raw genomic information and its metadata; and (iv) access controls leveraging federated authentication systems that Data Access Committees utilize to authorize access to the restricted data. ShareSeq is initially hosting raw, normalized, and processed data from the ICGC, but we envision that over time it will host an increasing number of high value reference genomic public datasets and add standards-based interfaces promoted by the Global Alliance of Genomes and Health to allow broader data discovery and sharing. Citation Format: Francisco M. De La Vega, Ying Wu, Tal Shmaya, Thomas Schlumpberger, James Wiley, Akshay Patel, Raja Hayek. A novel data safe haven approach to bring analyses to the International Cancer Genome Consortium data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-308. doi:10.1158/1538-7445.AM2015-LB-308