The Cell Analysis Core is designed to provide support to the clinical and basic projects by centralizing common procedures. These include sample acquisition, characterization, and distribution and banking; cell sorting; analytical flow cytometry; and histology and immunohistochemistry. By centralizing sample acquisition and storage we will be able to track samples and maintain records of expected data for each sample used, thus increasing the efficiency of data collection. A priority list for sample distribution will be established by the Core Oversight Committee and updated periodically to accommodate requirements of the different projects in terms of cell numbers, sample type, patient characteristics, and other ielevant criteria. This mechanism will greatly enhance the efficiency of sample utilization by the different projects. A centralized histology and immunohistochemistry service will avoid the need to establish the procedures in each investigator's laboratory, providing for uniformity of procedures and efficient use of materials. The flow cytometry and cell sorting service offered will provide state-of-the-art flow cytometry and cell sorting support to the projects in this program application. Able individuals experienced in the various procedures will supervise the different functions of the core.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA055819-14
Application #
7650106
Study Section
Subcommittee G - Education (NCI)
Project Start
Project End
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
14
Fiscal Year
2008
Total Cost
$631,443
Indirect Cost
Name
University of Arkansas for Medical Sciences
Department
Type
DUNS #
122452563
City
Little Rock
State
AR
Country
United States
Zip Code
72205
Rasche, L; Alapat, D; Kumar, M et al. (2018) Combination of flow cytometry and functional imaging for monitoring of residual disease in myeloma. Leukemia :
Went, Molly; Sud, Amit; Försti, Asta et al. (2018) Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma. Nat Commun 9:3707
Mehdi, Syed J; Johnson, Sarah K; Epstein, Joshua et al. (2018) Mesenchymal stem cells gene signature in high-risk myeloma bone marrow linked to suppression of distinct IGFBP2-expressing small adipocytes. Br J Haematol :
Rasche, Leo; Angtuaco, Edgardo J; Alpe, Terri L et al. (2018) The presence of large focal lesions is a strong independent prognostic factor in multiple myeloma. Blood 132:59-66
Stein, Caleb K; Pawlyn, Charlotte; Chavan, Shweta et al. (2017) The varied distribution and impact of RAS codon and other key DNA alterations across the translocation cyclin D subgroups in multiple myeloma. Oncotarget 8:27854-27867
Chavan, S S; He, J; Tytarenko, R et al. (2017) Bi-allelic inactivation is more prevalent at relapse in multiple myeloma, identifying RB1 as an independent prognostic marker. Blood Cancer J 7:e535
Went, M; Sud, A; Law, P J et al. (2017) Assessing the effect of obesity-related traits on multiple myeloma using a Mendelian randomisation approach. Blood Cancer J 7:e573
McDonald, James E; Kessler, Marcus M; Gardner, Michael W et al. (2017) Assessment of Total Lesion Glycolysis by 18F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma. Clin Cancer Res 23:1981-1987
Rasche, Leo; Weinhold, Niels; Morgan, Gareth J et al. (2017) Immunologic approaches for the treatment of multiple myeloma. Cancer Treat Rev 55:190-199
Rasche, L; Chavan, S S; Stephens, O W et al. (2017) Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 8:268

Showing the most recent 10 out of 290 publications