Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

Abstract

Background: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy.

Results: Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway.

Conclusions: Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.

Figure 1

Study scheme of analysis of data from four microarray experiments.

Figure 2

Identification of 31 radiosensitivity signature genes in NCI-60 cell lines. A. Venn diagram showing selection of 31 common radiosensitivity signature genes and 179 genes that were selected in more than three platforms from four microarray experiments. B. Principal component analysis with gene expression profile using 31 radiosensitivity signature genes. Each cell line is represented as a radiosensitive group (SF2 <0.2; black), an intermediate group (SF2 between 0.2 and 0.8; red), and a radioresistant group (SF2 >0.8; blue).

Figure 3

Integrin signaling pathway and its interaction as a radiosensitive target. A. Statistical ranking of pathways with the commonly selected 179 genes using SAM analysis. The x-axis displays the -log of the p-value calculated by Fisher's exact test, right-tailed. B. Gene plot showing the influence of individual genes of the integrin signaling pathway produced by a global test. The influence on the y-axis is represented as the p-value, the extent of correlation between SF2 (radiosensitivity) and gene expression in a gene set. A lower p-value means that the gene is well correlated between SF2 and the gene expression value. C. Integrin signaling pathway interaction with identified adhesion molecules from the 31 radiosensitivity signature. (References from Ingenuity knowledge base, Additional file 6).