Prediction of Radiation Sensitivity Using a Gene Expression Classifier

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 G 2 -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. (Cancer Res 2005; 65(16): 7169-76)

Precision medicine in early breast cancer—can this apply to radiotherapy?

“…This is contrary to present knowledge from other groups who have done similar work in developing radiation-specific assays, the most prominent being the radiosensitivity index (RSI) (14). The RSI is a validated ten-gene expression signature which has been shown to be predictive of distant metastasis-free survival in breast cancer patients treated with radiotherapy (15).…”

Development and Validation of a Gene Profile Predicting Benefit of Postmastectomy Radiotherapy in Patients with High-Risk Breast Cancer: A Study of Gene Expression in the DBCG82bc Cohort

“…There is no overlap between the 34 genes and the genes identified in this study. Neither was there any overlap with the genes in a 10-gene signature predictive of cellular radiosensitivity that has been developed from microarray analysis on 35 cell lines (38), and subsequently clinically validated, primarily in terms of risk of DM, in two breast cancer data set (39). Nuyten and colleagues (33) and Kreike and colleagues (40) did not identify a specific geneset predicting risk of recurrence after BCT, though a gene profile based on the wound response signature was described as being of independent prognostic value.…”

Biomarkers in Lung Cancer: Integration with Radiogenomics Data

“…The authors establish that radiation sensitivity can be predicted based on gene expression profiles and they introduce a genomic approach to the identification of novel molecular markers of radiation sensitivity. Despite of results in different tumor cell lines, this work included only four NSCLC cell lines and they were able to predict correct SF2 values for only two of them [74]. So, the study should be performed on a broader panel of NSCLC cell lines.…”

“…This may be quite reasonable since the mechanisms of radioresistance are a complex multigene interaction. In this sense, Torres-Roca et al [74] in 2005, hypothesized that a radiation sensitivity classifier or predictor could be developed based on gene expression profiles derived from DNA microarrays. This hypothesis was based in the fact of three main biological mechanisms partially correlated with clinical failure to radiotherapy, which are: hypoxia, intrinsic radiosensitivity and proliferation.…”