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.
Project description:Background:Despite the important role of radiotherapy in cancer treatment, a subset of patients responds poorly to treatment majorly due to radioresistance. Particularly the role of radiotherapy has not been established in gastric cancer (GC). Herein, we aimed to identify a radiosensitivity gene signature and to discover relevant targets to enhance radiosensitivity in GC cells. Methods:An oligonucleotide microarray (containing 22,740 probes) was performed in 12 GC cell lines prior to radiation. A clonogenic assay was performed to evaluate the survival fraction at 2 Gy (SF2) as a surrogate marker for radiosensitivity. Genes differentially expressed (fold change > 6, q-value < 0.025) were identified between radiosensitive and radioresistant cell lines, and quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was performed for validation. Gene set and pathway analyses were performed using Ingenuity Pathway Analysis (IPA). Results:Radiosensitive (SF2 < 0.4) and radioresistant cell lines (SF2 ? 0.6) exhibited a marked difference in gene expression. We identified 68 genes that are differentially expressed between radiosensitive and radioresistant cell lines. The identified genes showed interactions via AKT, HIF1A, TGFB1, and TP53, and their functions were associated with the genetic networks associated with cellular growth and proliferation, cellular movement, and cell cycle. The Akt signaling pathway exhibited the highest association with radiosensitivity. Combinatorial treatment with MK-2206, an allosteric Akt inhibitor, and radiotherapy significantly increased cell death compared with radiotherapy alone in two radioresistant cell lines (YCC-2 and YCC-16). Conclusion:We identified a GC-specific radiosensitivity gene signature and suggest that the Akt signaling pathway could serve as a therapeutic target for GC radiosensitization.
Project description:Intrinsic radiosensitivity is an important factor underlying radiotherapy response, but there is no method for its routine assessment in human tumours. Gene signatures are currently being derived and some were previously generated by expression profiling the NCI-60 cell line panel. It was hypothesised that focusing on more homogeneous tumour types would be a better approach. Two cell line cohorts were used derived from cervix [n = 16] and head and neck [n = 11] cancers. Radiosensitivity was measured as surviving fraction following irradiation with 2 Gy (SF2) by clonogenic assay. Differential gene expression between radiosensitive and radioresistant cell lines (SF2</> median) was investigated using Affymetrix GeneChip Exon 1.0ST (cervix) or U133A Plus2 (head and neck) arrays. There were differences within cell line cohorts relating to tissue of origin reflected by expression of the stratified epithelial marker p63. Of 138 genes identified as being associated with SF2, only 2 (1.4%) were congruent between the cervix and head and neck carcinoma cell lines (MGST1 and TFPI), and these did not partition the published NCI-60 cell lines based on SF2. There was variable success in applying three published radiosensitivity signatures to our cohorts. One gene signature, originally trained on the NCI-60 cell lines, did partially separate sensitive and resistant cell lines in all three cell line datasets. The findings do not confirm our hypothesis but suggest that a common transcriptional signature can reflect the radiosensitivity of tumours of heterogeneous origins.
Project description:Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53.We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.
Project description:Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.
Project description:The focus of analyzing data from microarray experiments has shifted from the identification of associated individual genes to that of associated biological pathways or gene sets. In bioinformatics, a feature selection algorithm is usually used to cope with the high dimensionality of microarray data. In addition to those algorithms that use the biological information contained within a gene set as a priori to facilitate the process of feature selection, various gene set analysis methods can be applied directly or modified readily for the purpose of feature selection. Significance analysis of microarray to gene-set reduction analysis (SAM-GSR) algorithm, a novel direction of gene set analysis, is one of such methods. Here, we explore the feature selection property of SAM-GSR and provide a modification to better achieve the goal of feature selection. In a multiple sclerosis (MS) microarray data application, both SAM-GSR and our modification of SAM-GSR perform well. Our results show that SAM-GSR can carry out feature selection indeed, and modified SAM-GSR outperforms SAM-GSR. Given pathway information is far from completeness, a statistical method capable of constructing biologically meaningful gene networks is of interest. Consequently, both SAM-GSR algorithms will be continuously revaluated in our future work, and thus better characterized.
Project description:BACKGROUND: Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA. METHODS AND FINDINGS: Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78+/-6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan real-time PCR assays. Classification based on the TaqMan data replicated the microarray results and achieved 80% classification accuracy on the testing set. CONCLUSIONS: This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan real-time PCR, define a set of promising potential diagnostic markers, setting the stage for a blood-based gene expression test to facilitate early detection of TAA.
Project description:We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2?Gy ?-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
Project description:A 31-gene signature derived by integrating four different microarray experiments, has been found to have a potential for predicting radiosensitivity of cancer cells, but it was seldom validated in clinical cancer samples. We proposed that the gene signature may serve as a predictive biomarker for estimating the overall survival of radiation-treated patients. The significance of gene signature was tested in two previously published datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Altas (TCGA), respectively. In GEO data set, patients predicted to be radiosensitive(RS) had an improved overall survival when compared with radioresistant(RR) patients in either radiotherapy(RT)-treated or non radiotherapy(RT)-treated subgroups(p<0.0001 in the RT-treated group). Multivariate Cox regression analysis showed that the gene signature is the strongest predictor(p=0.0093) in the RT-treated subgroup of patients. However, it does not remain significant (p=0.7668) in non radiotherapy-treated group when adjusting for age and Karnofsky performance score (KPS) as covariates. Similarly, in the TCGA data set, radiotherapy-treated glioblastoma multiforme(GBM) patients assigned to RS group had an improved overall survival compared with RR group(p<0.0001). Geneset enrichment analysis(GSEA) analysis revealed that enrichment of epithelial mesenchymal transition(EMT) pathway was observed with radioresistant phenotype. These results suggest that the signature is a predictive biomarker for radiation-treated glioma patients' prognostic.
Project description:Objective: Glioma is the most common and fatal primary brain tumor that has a high risk of recurrence in adults. Identification of predictive biomarkers is necessary to optimize therapeutic strategies. This study investigated the predictive efficacy of a previously identified radiosensitivity signature as well as Exportin 1 (XPO1) expression levels. Methods: A total of 1,552 patients diagnosed with glioma were analyzed using the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. The radiosensitive and radioresistant groups were identified based on a radiosensitivity signature. Patients were also stratified into XPO1-high and XPO1-low groups based on XPO1 mRNA expression levels. Overall survival rates were compared across patient groups. Differential gene expression was detected and analyzed through pathway enrichment and Gene Set Enrichment Analysis (GSEA). To predict 1-, 3-, and 5-years survival rates for glioma patients, a nomogram was established combining the radiosensitivity gene signature, XPO1 status, and clinical characteristics. An artificial intelligence clustering system and a survival prediction system of glioma were developed to predict individual risk. Results: This proposed classification based on a radiosensitivity gene signature and XPO1 expression levels provides an independent prognostic factor for glioma. The RR-XPO1-high group shows a poor prognosis and may benefit most from radiotherapy-combined anti-XPO1 treatment. The nomogram based on the radiosensitivity gene signature, XPO1 expression, and clinical characteristics performs more optimally compared to the WHO classification and IDH status in predicting survival rates for glioma patients. The online clustering and prediction systems make it accessible to predict risk and optimize treatment for a special patient. The cell cycle, p53, and focal adhesion pathways are associated with more invasive glioma cases. Conclusion: Combining the radiosensitivity signature and XPO1 expression is a favorable approach to predict outcomes as well as determine optimal therapeutic strategies for glioma patients.
Project description:Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between 'self-contained' versus 'competitive' methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of 'core members' that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.