Our results, using a nude mouse model of experimental metastasis, demonstrate that EHop-016 significantly reduces mammary fat pad tumor growth and metastasis, as well as angiogenesis. The In Vitro assays with HUVEC cells, MDA-MB-435 cells, and PC3 cells further validate the use of EHop-016 to inhibit Rac, and thus, reduce cancer cell survival and proliferation, and inhibit metastatic cancer progression. Therefore, our data is significant for demonstrating the utility of developing chemical probes targeted at Rac, and the homolog Cdc42, as potential anti cancer therapeutics. We wish to acknowledge

Cristina Del Valle for excellent technical assistance. This study was supported by National Institute on Minority Ceritinib datasheet Health and Health Disparities of the National Institutes of Health (NIMHHD/NIH) U54MD008149, and Department of Defense/Breast Cancer Research Program (DoD/BCRP) W81XWH-07-1-0330 to SD; NIH/NIMHHD Research Centers in Minority Institutions (RCMI) 8G12MD007583, and Title V PPOHA P031S130068 from U.S.

Department of Education to UCC; UPR RCM NIH/NIMHHD grants 5U54CA096297 and R25GM061838 to THB; and 2012 American Association of Colleges of Pharmacy (AACP) New Investigator Award to EH. The authors have no conflicts of interest to declare. “
“To examine opportunities and challenges in the field of radiogenomics and the allied discipline

of computational bioinformatics, the NCI Cancer Imaging Lenvatinib cost Program (CIP) convened two related workshops on June 26 to 27, 2013, entitled “Correlating Imaging Phenotypes with Genomics Signatures Research” and “Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems.” The first workshop focused LY294002 on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) to enable cross-disciplinary research. A secondary goal of the workshops was to explore the importance of correlating in vivo imaging with digital pathology and the importance of including preclinical research.