The Genomics Shared Resource has operated since 2002 as a core facility. The GSR was developed within the NCCC as a resource to meet the research needs of the NCCC but has expanded its reach to provide services to the entire Dartmouth research community. The major objectives of the GSR are to: (1) implement state-of-the-art high-throughput technologies for the global analysis of gene regulation and expression;(2) develop a competitive low-cost structure for microarray services that would promote use by NCCC investigators;and (3) develop new technologies to exploit additional avenues in genomics. Services currently offered by the GSR include (1) analysis of RNA samples to determine quality, amount, and purity;(2) experimental design consultations in conjunction with the Biostatistics and Bioinformatics Snared Resources; (3) Affymetrix gene expression profiling;(4) Agilent gene expression profiling;(5) Agilent miRNA arrays;(6) Agilent CGH arrays from frozen and FFPE samples;and (7) Agilent CpG island arrays. Services that are to be offered in the next year include: ChlP-on-chip arrays, Agilent genome tiling arrays, and SNP analysis. Information and ordering by the investigator is available electronically via the interactive website , which was established in 2006. Some of the new technologies being developed at the GSR include the use of microarrays to determine global transcription rates, hnRNA processing, cytoplasmic turnover rates, polysome entry rates, and differential nuclear vs. cytoplasmic steady-state RNA levels. During the fiscal year ending in 2007,14 NCCC members and their laboratories (of a total of 24 laboratories at Dartmouth) used the GSR with services valued at $238,000 (-65% of $366,410 total revenues). The overall goal of the GSR is to provide NCCC researchers with state-of-the-art technical support and services for their cancer research at as low a cost as possible so that they can successfully achieve their research objectives.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Center Core Grants (P30)
Project #
Application #
Study Section
Subcommittee G - Education (NCI)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Dartmouth College
United States
Zip Code
Shee, Kevin; Jiang, Amanda; Varn, Frederick S et al. (2018) Cytokine sensitivity screening highlights BMP4 pathway signaling as a therapeutic opportunity in ER+ breast cancer. FASEB J :fj201801241R
Rodriguez-Garcia, Marta; Fortier, Jared M; Barr, Fiona D et al. (2018) Aging impacts CD103+ CD8+ T cell presence and induction by dendritic cells in the genital tract. Aging Cell 17:e12733
Shajani-Yi, Zahra; de Abreu, Francine B; Peterson, Jason D et al. (2018) Frequency of Somatic TP53 Mutations in Combination with Known Pathogenic Mutations in Colon Adenocarcinoma, Non-Small Cell Lung Carcinoma, and Gliomas as Identified by Next-Generation Sequencing. Neoplasia 20:256-262
Szczepiorkowski, Zbigniew M; Burnett, Christine A; Dumont, Larry J et al. (2018) Apheresis buffy coat collection without photoactivation has no effect on apoptosis, cell proliferation, and total viability of mononuclear cells collected using photopheresis systems. Transfusion 58:943-950
Bossé, Yohan; Amos, Christopher I (2018) A Decade of GWAS Results in Lung Cancer. Cancer Epidemiol Biomarkers Prev 27:363-379
Pande, Mala; Joon, Aron; Brewster, Abenaa M et al. (2018) Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies. PLoS One 13:e0196245
Smith, T Jarrod; Sondermann, Holger; O'Toole, George A (2018) Co-opting the Lap System of Pseudomonas fluorescens To Reversibly Customize Bacterial Cell Surfaces. ACS Synth Biol 7:2612-2617
Gorlova, Olga Y; Li, Yafang; Gorlov, Ivan et al. (2018) Gene-level association analysis of systemic sclerosis: A comparison of African-Americans and White populations. PLoS One 13:e0189498
Schmit, Stephanie L; Edlund, Christopher K; Schumacher, Fredrick R et al. (2018) Novel Common Genetic Susceptibility Loci for Colorectal Cancer. J Natl Cancer Inst :
Cai, Yunliang; Wu, Shaoju; Zhao, Wei et al. (2018) Concussion classification via deep learning using whole-brain white matter fiber strains. PLoS One 13:e0197992

Showing the most recent 10 out of 1911 publications