Our objective is to establish and operate a high throughput Genome Characterization Centre (GCC) that specializes in sequencing libraries, prepared from mRNAs and microRNAs purified from cancer cells and tissues. To achieve our objective, we will build upon existing """"""""next generation"""""""" sequencing and analysis strengths at the BC Genome Sciences Centre. We have accumulated world-leading experience in the operation of lllumina sequencers as a consequence of becoming one of the four Early Access Partners originally engaged by Solexa (now lllumina) in November of 2006. Since then, we have led in the development and implementation of protocols for preparing and sequencing numerous library types on the lllumina GA platform, including both microRNA and mRNA libraries. To build sequencing libraries, we have established an lllumina library construction core that has constructed 1,075 libraries, from which we have generated more than 934 billion base-pairs of high quality sequence information. These data have so far provided the basis for 19 publications describing results obtained from sequencing genomes and transcriptomes, and describing the creation of new software tools and algorithms to manage and analyze the wealth of data such instruments produce. To develop and critically appraise instrument upgrades, new instruments and new protocols, we have established a next generation technology development core. Technologies developed and appraised by this group are approved for use by our lllumina production sequencing core. Under-pinning this enterprise is a formal system for quality assurance and quality control that encompasses laboratory and bioinformatics activities, and a laboratory information management system that regulates and monitors laboratory activity, inventory, and features real time error avoidance. Our experience in assembling these teams and this infrastructure will serve as a basis for scaling up our library production capacity to meet our objectives, which are to sequence 4,400 transcriptome libraries in the first year of our operation. Using bioinformatics tools already in production, we will analyze these sequences to profile the expression of genes, individual exons and microRNAs, and analyze the sequences to discover mutations and RNA editing events in expressed sequences. These analyses will provide an unprecedented view of transcription in cancer cells;identify promising new mutated and expressed therapeutic targets, and identify genes and pathways that are drivers of oncogenesis.
| Radovich, Milan; Pickering, Curtis R; Felau, Ina et al. (2018) The Integrated Genomic Landscape of Thymic Epithelial Tumors. Cancer Cell 33:244-258.e10 |
| Shen, Hui; Shih, Juliann; Hollern, Daniel P et al. (2018) Integrated Molecular Characterization of Testicular Germ Cell Tumors. Cell Rep 23:3392-3406 |
| Berger, Ashton C; Korkut, Anil; Kanchi, Rupa S et al. (2018) A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers. Cancer Cell 33:690-705.e9 |
| Hoadley, Katherine A; Yau, Christina; Hinoue, Toshinori et al. (2018) Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell 173:291-304.e6 |
| Schaub, Franz X; Dhankani, Varsha; Berger, Ashton C et al. (2018) Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas. Cell Syst 6:282-300.e2 |
| Liu, Jianfang; Lichtenberg, Tara; Hoadley, Katherine A et al. (2018) An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173:400-416.e11 |
| Bailey, Matthew H; Tokheim, Collin; Porta-Pardo, Eduard et al. (2018) Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 173:371-385.e18 |
| Hmeljak, Julija; Sanchez-Vega, Francisco; Hoadley, Katherine A et al. (2018) Integrative Molecular Characterization of Malignant Pleural Mesothelioma. Cancer Discov 8:1548-1565 |
| Sanchez-Vega, Francisco; Mina, Marco; Armenia, Joshua et al. (2018) Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 173:321-337.e10 |
| Way, Gregory P; Sanchez-Vega, Francisco; La, Konnor et al. (2018) Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep 23:172-180.e3 |
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