Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

Abstract

The transcriptome of breast cancers have been extensively screened with microarrays and large sets of genes associated with clinical features have been established. The aim of this study was to validate original gene sets on a large cohort of raw breast cancer microarray data with known clinical follow-up. We recovered 20 publications and matched them to Affymetrix HGU133A annotations. Raw Affymetrix HGU133A microarray data were extracted from GEO and MAS5 normalized. For classifying patients using the selected gene sets, we applied prediction analysis of microarrays and constructed KaplanMeier plots. A new classification including all patients was generated using supervised principal components analysis. Seven studies including 1,470 patients were downloaded from GEO. Notably, we uncovered 641 microarrays representing 251 individual tumor specimens among them, which were repeatedly described under independent GEO identifiers. We excluded all redundant data and used the remaining 1,079 samples. Eight of the 20 gene sets were able to predict response at a significance of P \ 0.05. The discrimination of good and poor prognosis groups exclusively relying on gene expression data resulted in high significance (P = 1.8E-12). A model including genes fitted by both gene expression and clinical covariates (lymph node status and grade) contains 44 genes and can predict response at P = 9.5E-7. The outcome provides a ranking of the gene lists regarding applicability on an independent dataset. We established a consensus predictor combining the available clinical and gene expression data. The database comprising expression profiles of 1,079 breast cancers can be used to classify individual patients.

Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

“…Furthermore, no signature produced convincing HRs when applied to the Blaveri data set, except the Blaveri_surv_MI signature, which was derived from the same data set. These findings are in contrast to what has been observed for breast cancer for which survival gene signatures have been validated and significant HR have been obtained in external data sets (47)(48)(49)(50)(51)(52).…”

Acid ceramidase 1 expression correlates with a better prognosis in ER-positive breast cancer

“…Still, these methods can have severe disadvantages caused by the inhomogeneity of the data, patient groups and treatment modalities. Since microarray datasets generally contain only tens or hundreds of patients ASAH1, acid ceramidase 1; LASS4, ceramide synthase 4; LASS6, ceramide synthase 6; HR, hazard ratio; 95% CI, 95% confidence interval; ER, estrogen receptor; LNN, lymph node-negative; pos., positive; neg., negative because of the expenditure and complexity of this method, data-pooling has been increasingly applied 17,18,[49][50][51][52] . Moreover, recent studies 53,54 suggest that the pooling microarray datasets generates more accurate results and advocate the analysis of new data within the context of a compendium, rather than analysis in isolation.…”

An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients

“…After an initial quality control, redundant samples (n=384) were excluded [12]. The raw .CEL files were MAS5 normalized in the R statistical environment (www.r-project.org) using the affy Bioconductor library [13].…”