Over the past four years, a remarkable sequencing technology explosion has occurred, opening many new avenues of biological experimentation and discovery. We describe a platform for large- scale sequencing and analysis that extends the incredible potential of next-generation sequencing to transform biomedical research and significantly impact medical practice. Our eight specific aims are intertwined across six major research areas that overall address the NHGRI mission and provide an interdisciplinary approach that is already producing medically relevant results in the areas of cancer genomics, heritable disease, and microbial/metagenomics. Our platform offers a consistently cost-effective approach to sequencing-related research projects and we have established a scalable incoming sample pipeline that can intake and track >40,000 samples per year into a flexible and innovative sequencing pipeline. Equally scalable are our LIMS and analysis pipeline capabilities, having established systems that processed several hundred cancer cases through whole genome sequencing and analysis in the last year alone, along with multiple other project types. We describe a research plan that will further our explorations of human health and disease, in a more comprehensive manner than ever before, and will investigate third-generation sequencing technologies, incorporating their unique attributes and integrating them to our production repertoire. One important aspect of our proposed work includes efforts to begin translating our discoveries and procedures into the clinical setting, effectively setting the stage for genomic diagnosis and personalized medicine. This important transition will be required to bring DNA sequencing to clinical medicine, and our innovative combination of sequencing technology, data analysis, and outstanding clinical collaborators increase the potential for success. Overall, we are enthusiastic about the future of genome sequencing at high scale, and we combine years of success in DNA sequencing and analysis with the necessary collaboration expertise, infrastructure support, and shared vision with NHGRI to make significant progress on these aims in the next four years.
The potential for DNA sequencing to transform medical practice and change our understanding of disease has never been higher. Our group combines years of experience in DNA sequencing with key clinical researchers to address important questions in patient care and outcome. These early efforts will translate to a revolution in medicine, centered around DNA-based diagnostics including whole genome sequencing.
| 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 |
| 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 |
| Magrini, Vincent; Gao, Xin; Rosa, Bruce A et al. (2018) Improving eukaryotic genome annotation using single molecule mRNA sequencing. BMC Genomics 19:172 |
| Blue, Elizabeth E; Bis, Joshua C; Dorschner, Michael O et al. (2018) Genetic Variation in Genes Underlying Diverse Dementias May Explain a Small Proportion of Cases in the Alzheimer's Disease Sequencing Project. Dement Geriatr Cogn Disord 45:1-17 |
| 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 |
| Ricketts, Christopher J; De Cubas, Aguirre A; Fan, Huihui et al. (2018) The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma. Cell Rep 23:313-326.e5 |
Showing the most recent 10 out of 234 publications
