Translational Engineering in Cancer The Translational Engineering in Cancer (TEC) Program brings together 25 faculty, representing 12 departments at Dartmouth?s Geisel School of Medicine (Geisel), its Thayer School of Engineering and its Guarini School of Graduate and Advanced Studies; and the Dartmouth-Hitchcock Medical Center (DH), with research interests relevant to three central TEC Program themes: 1) Surgical image-guidance, 2) Radiation monitoring and guidance, and 3) Cellular/molecular detection and contrast. The shared goals of the TEC Program are to: 1) Utilize biophysical and bioengineering approaches to develop and translate new imaging, engineering and treatment technologies that improve the care of cancer patients; 2) Impact cancer detection, diagnosis, and treatment ? nationally, regionally, and within the NCCC catchment area especially for malignancies of the brain/CNS, breast, skin, head & neck, pancreas and prostate ? by providing access to novel imaging modalities, interventional protocols enhanced by imaging and image-guidance, and dosimetry technology for assessing radiation treatments; and 3) Translate laboratory results into clinical studies via development of novel technologies that fuel academic-industrial partnerships, as well as start-up ventures and other industry relationships, to drive Dartmouth-generated technology towards clinical dissemination with the goal of changing and improving practice. Peer-reviewed cancer-related research direct cost support currently totals $6.4M, with NCI funding representing 53% ($3.4M) and total direct costs summing to $6.5M. Thirteen (13) TEC Program Members currently have a total of 17 CCSG-defined R01-equivalent awards. Using the same definition of cancer-related direct costs in 2014 (i.e., excluding all indirects as well as training and administrative direct costs), peer-reviewed cancer-related research direct costs ($6.4M) are 44% less compared to 2014 ($11.3M), but 60% of the 2014 total ($7.0M) was attributable to one large BARDA award. Since 2015, the Program has 306 cancer-related publications, 51% (157) intra-programmatic, 25% (76) inter- programmatic, 51% (156) with investigators from other institutions, and 11% (28) in high impact journals (i.e., impact factor >8), which are uncommon in engineering publications. Compared to 2014, intra-programmatic remains high (51% and 60%), and inter-programmatic has increased, to 25% from 20%. During the proposed funding period, TEC will leverage new Center for Imaging Medicine facilities in the Williamson building and the significant expansion of Dartmouth?s Thayer School of Engineering Faculty and co-location with Computer Science, as part of a major capital campaign recently announced to grow significantly its imaging and image- guidance research base in surgical oncology.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Center Core Grants (P30)
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Subcommittee I - Transistion to Independence (NCI)
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Dartmouth College
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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 :
Trentham-Dietz, Amy; Ergun, Mehmet Ali; Alagoz, Oguzhan et al. (2018) Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening. Breast Cancer Res Treat 168:229-239
Moulton, Haley; Tosteson, Tor D; Zhao, Wenyan et al. (2018) Considering Spine Surgery: A Web-Based Calculator for Communicating Estimates of Personalized Treatment Outcomes. Spine (Phila Pa 1976) 43:1731-1738
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
Ferreiro-Iglesias, Aida; Lesseur, Corina; McKay, James et al. (2018) Fine mapping of MHC region in lung cancer highlights independent susceptibility loci by ethnicity. Nat Commun 9:3927
Bronson, Mackenzie R; Kapadia, Nirav S; Austin, Andrea M et al. (2018) Leveraging Linkage of Cohort Studies With Administrative Claims Data to Identify Individuals With Cancer. Med Care 56:e83-e89
Ji, Xuemei; Bossé, Yohan; Landi, Maria Teresa et al. (2018) Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 9:3221
Hancock, D B; Guo, Y; Reginsson, G W et al. (2018) Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence. Mol Psychiatry 23:1-9

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