Proceedings of the 49th Annual ASTRO Meeting S595

FANCD2 in the regulation of pro-survival pathways that respond to reactive oxygen species. In part supported by Susan G. Komenfor the Cure (to LAK and HW) and NIH P01 CA095227 (to KDH).

Author Disclosure: V.M. Kuhnert, None; L.A. Kachnic, None; L. Li, None; M. Purschke, None; R. Lee, None; J. Dunst, None;K.D. Held, None; H. Willers, None.

2713 Towards Personalized Radiation Therapy: Translation of a Mathematical Model of the Radiosensitivity

Network to the Prediction of Clinical Radiation Response

S. Eschrich, H. Zhang, H. Zhao, J. Lee, G. Bloom, D. Boulware, C. Rocha-Lima, D. P. Calvin, T. J. Yeatman, J. F. Torres-Roca

H. Lee Moffitt Cancer Center, Tampa, FL

Purpose/Objective(s): Understanding the biological variables that influence the inherent radiosensitivity of tumors is central to thedevelopment of personalized medicine in radiation oncology. We hypothesized that a systems biology view of the radiation sen-sitivity network would impact our ability to define these variables.

Materials/Methods: We applied a mathematical model to the analysis of gene expression (GE) profiles (Affymetrix HU-6800)and radioresponse (SF2) in a 48 cell line database. We created the model by using linear regression. ANOVA is used to determinesignificant interactions.

Results: Previously, we developed a linear regression model of GE/SF2 in 35 cell lines showing that cellular radiosensitivity waspredictable from gene expression profiles. To clinically translate this model, we sought to understand the intricacies of its operation.First, we reasoned that a predictive model should identify genes that are involved in regulating radioresponse. We tested this andbiologically validated 3/4 genes selected. Next, we expanded the GE/SF2 database to include 48 cell lines and incorporated net-work dynamics into the model, a key step in generating a systems view. We defined dynamics by variables that have been reportedto perturb the radioresponse network: tissue of origin (TO), ras (mut/wt), p53 (mut/wt) and GE. This showed that some factors aremore influential in gene selection. For example, TO and ras along with GE but not p53 are key variables in defining the 3 observedsignificant dynamic states. Additionally, the 3 states were biologically distinct, implying that the key pathways/genes that regulateradioresponse vary depending on the biological context of GE, which must be accounted for by a successful model. We tested thisby evaluating the model-proposed hypothesis that c-jun knockout affects radiophenotype in a tissue-dependent manner. Predictionswere experimentally validated with 2/3 confirmed correct: Lung cancer (radioresistance, p = 0.005) and colon cancer lines (nochange). Next, we generated the network topology from the top 500 genes and their primary interconnections, revealing 8 centraland 11 minor hubs. Remarkably, each central hub has been reported to be involved in radiation signaling and 6/8 are current targetsfor radiosensitizer development. To explore the clinical relevance of the topology, we queried the network with 10 radiosensitiza-tion targets, all of which linked by primary interconnection to at least one central hub. Finally using the 8 central hubs we appliedthe cell line model to 14 patients with locally-advanced rectal cancer treated with preoperative concurrent radiochemotherapy. The8 gene model significantly separated pathological non-responders (NR, n = 6) from responders (R, n = 8) (mean predicted SF2 NRvs. R, 0.45 vs. 0.31, p = 0.03).

Conclusions: We developed a model with biological and clinical implications. First, it identifies network components and accu-rately captures biological context into predictions, a novel capability. The clinical relevance is demonstrated by the network linkageof known radiosensitization targets and by the generation of a tissue-independent radiosensitivity signature which correlated withclinical response in rectal cancer. We think this represents a major advance towards the goal of personalized RT.

Author Disclosure: S. Eschrich, None; H. Zhang, None; H. Zhao, None; J. Lee, None; G. Bloom, None; D. Boulware, None;C. Rocha-Lima, None; D.P. Calvin, None; T.J. Yeatman, None; J.F. Torres-Roca, None.

2714 Optimization of siRNA-Transgene Expression in Adenoviral Vector Systems for Radiation Sensitizing

Strategies

T. L. DeWeese1, S. J. Collis1, S. E. Lupold2, W. Chowdury2, T. Kudrolli2, M. Swartz1, R. Rodriguez2

1Department of Radiation Oncology and Molecular Radiation Sciences, 2Department of Urology, The Johns HopkinsUniversity School of Med., Baltimore, MD

Purpose/Objective(s): We recently developed U6 promoter-based siRNA vectors targeting mammalian DNA repair proteins asa molecular radiation sensitizing strategy. Although these siRNA are effective in vitro following exogenous delivery of plasmidDNA, such a plasmid-based system is not ideal for clinical translation because of low transfection efficiency. Moreover, tissuespecificity is critical to any such approach to avoid unwanted radiation sensitization of non-target tissues. Viral-based systemsare ideal to meet these endpoints. Lentiviral and adeno-associated virus systems are not ideal for clinical translation given the risksassociated with genomic integration. An adenoviral system is a much safer alternative but to date, no system has been shown todeliver high levels of functional siRNA at a clinically usable multiplicity of infection (MOI). In order to understand the necessaryelements for construction of an adenoviral-siRNA vector capable of target protein reductions at a low and clinically relevant MOI,we undertook a step wise analysis to determine the reason for low siRNA transgene expression in the context of two different com-monly used adenoviral promoters.

Materials/Methods: ATM, DNA-PK and control U6 promoter-siRNA and VA1 promoter-siRNA sequences were sub-clonedfrom pSHAG-1 into pShuttle. 293, DU145, LNCaP and T-24 human embryonic kidney, prostate and bladder tumor cell lineswere transfected and transfection efficiencies were assessed by quantifying Renilla firefly expression in cells following co-trans-fection with Firefly luciferase-expressing and siRNA-containing pShuttle plasmids. SDS-PAGE/immunoblotting was used to de-termine target protein knockdown.

Results: Expression of U6-siRNA transgenes was severely compromised when placed in the commonly used E1A region withinthe adenoviral genome. This reduced expression was not due to proximity of the E1A region to the siRNA site and was not sec-ondary to a trans-acting factor produced during viral infection. However, promiscuous pol II-based transcription from within theU6 sequence and from downstream viral open reading frames did result in reduced expression of downstream siRNA transgenes. Asynthetic promoter based on the native adenoviral VA1 promoter, which yields effective siRNAs in plasmid constructs, also failed