Comparative Oncogenomic Analysis of Copy Number Alterations in Human and Zebrafish Tumors Enables Cancer Driver Discovery

The identification of cancer drivers is a major goal of current cancer research. Finding driver genes within large chromosomal events is especially challenging because such alterations encompass many genes. Previously, we demonstrated that zebrafish malignant peripheral nerve sheath tumors (MPNSTs) are highly aneuploid, much like human tumors. In this study, we examined 147 zebrafish MPNSTs by massively parallel sequencing and identified both large and focal copy number alterations (CNAs). Given the low degree of conserved synteny between fish and mammals, we reasoned that comparative analyses of CNAs from fish versus human MPNSTs would enable elimination of a large proportion of passenger mutations, especially on large CNAs. We established a list of orthologous genes between human and zebrafish, which includes approximately two-thirds of human protein-coding genes. For the subset of these genes found in human MPNST CNAs, only one quarter of their orthologues were co-gained or co-lost in zebrafish, dramatically narrowing the list of candidate cancer drivers for both focal and large CNAs. We conclude that zebrafish-human comparative analysis represents a powerful, and broadly applicable, tool to enrich for evolutionarily conserved cancer drivers.Kathy and Curt Marble Cancer Research FundArthur C. MerrillNational Institutes of Health (U.S.) (Grant CA106416)National Institutes of Health (U.S.) (Grant ROI RR020833)National Institutes of Health (U.S.) (Grant 1F32GM095213-01

Accuracy Improvement in Rapid Prototyping Machine (FDM-1650)

Over the past few years, improvements in equipment, materials, and processes have enabled significant improvements in the accuracy of Fused Deposition Modeling (FDM) technology. This project will investigate the present in-plane accuracy of a particular FDM machine using the benchmark “User Part” developed by the North American StereoLithography User Group (NASUG) and show the effect of optimal Shrinkage Compensation Factors (SCF) on the accuracy of the prototyped parts. The benchmark parts were built on the FDM-1650 prototyping machine and a total of 46 measurements were taken in the X and Y planes using a Brown & Sharpe Coordinate Measuring Machine (CMM). The data was then analyzed for accuracy using standard formulas and statistics, such as mean error, standard deviation, residual error, rms error, etc. The optimal SCF for the FDM-1650 machine was found to be 1.007 or 0.7%.This work was funded by a National Science Foundation (NSF) grant to Loyola Marymount University for their Research Experience for Undergraduates program.Mechanical Engineerin