Unknown,Transcriptomics,Genomics,Proteomics

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Multi-platform Characterization of Stage I Lung Adenocarcinoma (Genome-wide DNA Methylation)

ABSTRACT: Genome wide DNA methylation profiling of Stage I Lung Adenocarcinoma and non-tumor adjacent tissues. The Illumina Infinium 27k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 27,000 CpGs. Samples included tumor and adjacent non-tumor tissues excised from a cohort of 35 patients with Stage I Lung Adenocarcinoma. Candidate prognostic biomarkers were validated by pyrosequencing in independent cohorts. Total genomic DNA was bisulfite converted and hybridized to Infinium Human Methylation27Beadchips. Fluorescent signals were collated via Bead Studio software and converted into β-values. Illumina Bead Studio software output data were normalized using SSN (simple scaling normalization), to correct for dye bias and transformed into M-values (M = log2 intensity of methylated probe/ intensity of unmethylated probe), using the R package lumi.

ORGANISM(S): Homo sapiens

SUBMITTER: Ana Robles

PROVIDER: E-GEOD-63384 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Abstract Introduction Response rates to chemotherapy remain highly variable in breast cancer patients. We set out to identify genes associated with chemotherapy resistance. We analyzed what is currently the largest single-institute set of gene expression profiles derived from breast cancers prior to a single neoadjuvant chemotherapy regimen (dose-dense doxorubicine and cyclophophamide). Methods We collected, gene expression-profiled and analyzed 178 HER2-negative breast tumor biopsies (M-bM-^@M-^XNKI datasetM-bM-^@M-^Y). We employed a recently developed approach for detecting imbalanced differential signal (DIDS) in order to identify markers of resistance to treatment. In contrast to traditional methods, DIDS is able to identify markers that show aberrant expression in only a small subgroup of the non-responder samples. Results We found a number of markers of resistance to anthracycline-based chemotherapy. We validated our findings by the analysis of three external datasets, which contained 456 HER2-negative samples in total. Since these external sets included patients who received differing treatment regimens we could only validate markers of general chemotherapy resistance. There was a highly significant overlap in the markers identified in the NKI dataset and the other three datasets. Five resistance markers, SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B, were identified in three of the four datasets (p-value overlap <1e-6). These five genes identified resistant tumors that could not have been identified by merely taking ER-status or proliferation into account. Conclusion The identification of these genes might lead to a better understanding of the mechanisms involved in (clinically) observed chemotherapy resistance and could possibly assist in the recognition of breast cancers in which chemotherapy does not contribute to response or survival. We collected, gene expression profiled and analyzed 178 HER2-negative breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.

Dataset Information

Multi-platform Characterization of Stage I Lung Adenocarcinoma (Genome-wide DNA Methylation)

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