Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform.
Eschrich S, Zhang H, Zhao H, Boulware D, Lee JH, Bloom G, Torres-Roca JF
Int J Radiat Oncol Biol Phys. 2009 Oct 1;75(2):497-505
Abstract
PURPOSE: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. METHODS AND MATERIALS: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). RESULTS: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. CONCLUSION: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.
Pubmed ID:19735874
A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation.
Eschrich SA, Pramana J, Zhang H, Zhao H, Boulware D, Lee JH, Bloom G, Rocha-Lima C, Kelley S, Calvin DP, Yeatman TJ, Begg AC, Torres-Roca JF
Int J Radiat Oncol Biol Phys. 2009 Oct 1;75(2):489-96
Abstract
PURPOSE: Development of a radiosensitivity predictive assay is a central goal of radiation oncology. We reasoned a gene expression model could be developed to predict intrinsic radiosensitivity and treatment response in patients. METHODS AND MATERIALS: Radiosensitivity (determined by survival fraction at 2 Gy) was modeled as a function of gene expression, tissue of origin, ras status (mut/wt), and p53 status (mut/wt) in 48 human cancer cell lines. Ten genes were identified and used to build a rank-based linear regression algorithm to predict an intrinsic radiosensitivity index (RSI, high index = radioresistance). This model was applied to three independent cohorts treated with concurrent chemoradiation: head-and-neck cancer (HNC, n = 92); rectal cancer (n = 14); and esophageal cancer (n = 12). RESULTS: Predicted RSI was significantly different in responders (R) vs. nonresponders (NR) in the rectal (RSI R vs. NR 0.32 vs. 0.46, p = 0.03), esophageal (RSI R vs. NR 0.37 vs. 0.50, p = 0.05) and combined rectal/esophageal (RSI R vs. NR 0.34 vs. 0.48, p = 0.001511) cohorts. Using a threshold RSI of 0.46, the model has a sensitivity of 80%, specificity of 82%, and positive predictive value of 86%. Finally, we evaluated the model as a prognostic marker in HNC. There was an improved 2-year locoregional control (LRC) in the predicted radiosensitive group (2-year LRC 86% vs. 61%, p = 0.05). CONCLUSIONS: We validate a robust multigene expression model of intrinsic tumor radiosensitivity in three independent cohorts totaling 118 patients. To our knowledge, this is the first time that a systems biology-based radiosensitivity model is validated in multiple independent clinical datasets.
Pubmed ID:19735873
Predicting response to clinical radiotherapy: past, present, and future directions.
Torres-Roca JF, Stevens CW.
Cancer Control. 2008 Apr;15(2):151-6. Review.
Abstract
BACKGROUND: Personalized radiation therapy holds the promise that the diagnosis, prevention, and treatment of cancer will be based on individual assessment of risk. Although advances in personalized radiation therapy have been achieved, the biological parameters that define individual radiosensitivity remain unclear. METHODS: This review focuses on discussing the field of radiosensitivity predictive assays, a technology central to the concept of personalized medicine in radiation oncology. Two novel approaches, DNA end-binding complexes and gene expression classifiers, show promise in solving some of the logistic problems associated with previous assays. RESULTS: Current data suggest that predicting clinical response to radiotherapy is possible. The delivery of this promise depends on the ability to define the variables that define response to clinical radiotherapy. A successful predictive assay is key to the development of personalized treatment strategies in radiation oncology. CONCLUSIONS: Novel technologies need to be developed that will improve our understanding of the biological variables that define clinical tumor response and will lead to the development of a clinically useful assay.
PMID:18376382
Prediction of radiation sensitivity using a gene expression classifier.
Torres-Roca JF, Eschrich S, Zhao H, Bloom G, Sung J, McCarthy S, Cantor AB, Scuto A, Li C, Zhang S, Jove R, Yeatman T
Cancer Res. 2005 Aug 15;65(16):7169-76
Abstract
The development of a successful radiation sensitivity predictive assay has been a major goal of radiation biology for several decades. We have developed a radiation classifier that predicts the inherent radiosensitivity of tumor cell lines as measured by survival fraction at 2 Gy (SF2), based on gene expression profiles obtained from the literature. Our classifier correctly predicts the SF2 value in 22 of 35 cell lines from the National Cancer Institute panel of 60, a result significantly different from chance (P = 0.0002). In our approach, we treat radiation sensitivity as a continuous variable, significance analysis of microarrays is used for gene selection, and a multivariate linear regression model is used for radiosensitivity prediction. The gene selection step identified three novel genes (RbAp48, RGS19, and R5PIA) of which expression values are correlated with radiation sensitivity. Gene expression was confirmed by quantitative real-time PCR. To biologically validate our classifier, we transfected RbAp48 into three cancer cell lines (HS-578T, MALME-3M, and MDA-MB-231). RbAp48 overexpression induced radiosensitization (1.5- to 2-fold) when compared with mock-transfected cell lines. Furthermore, we show that HS-578T-RbAp48 overexpressors have a higher proportion of cells in G2-M (27% versus 5%), the radiosensitive phase of the cell cycle. Finally, RbAp48 overexpression is correlated with dephosphorylation of Akt, suggesting that RbAp48 may be exerting its effect by antagonizing the Ras pathway. The implications of our findings are significant. We establish that radiation sensitivity can be predicted based on gene expression profiles and we introduce a genomic approach to the identification of novel molecular markers of radiation sensitivity.
Pubmed ID:16103067
