Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene expression analysis of a series of HER2+ of breast tumors

ABSTRACT: Abstract Motivation Breast cancer is a heterogeneous disease with distinct subtypes. Even within these subtypes differences at the molecular level are present which are reflected in variable responses to chemotherapy. We set out to identify genes associated with chemotherapy resistance by analyzing a set of HER2-positive breast cancers. Methods We collected, gene expression profiled and analyzed 60 HER2-positive breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy. In addition to conventional supervised approaches for the detection of reporters of resistance, we report on a novel approach specifically tailored to the detection of small groups of resistant samples that show aberrant gene expression patterns. Results We propose a novel analytical approach that takes heterogeneity in response into account. We show that this approach is more powerful than classical approaches for detecting small subgroups of samples showing aberrant expression in a controlled setting. We applied this approach to our 60 breast cancer samples prior to neoadjuvant chemotherapy, and generated candidate response reporter lists for each subtype. Discussion Using a novel analytical approach we report on the mRNA gene expression analysis of a cohort of breast cancers prior to neoadjuvant chemotherapy. An important characteristic of this approach is that it takes heterogeneity in neoadjuvant treatment response into account. Such approaches are needed to identify biomarkers for predicting treatment response. We collected, gene expression profiled and analyzed 60 breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.

ORGANISM(S): Homo sapiens

SUBMITTER: Jelle ten Hoeve

PROVIDER: E-GEOD-41656 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Identifying subgroup markers in heterogeneous populations.

de Ronde Jorma J JJ Rigaill Guillem G Rottenberg Sven S Rodenhuis Sjoerd S Wessels Lodewyk F A LF

Nucleic acids research 20130922 21

Traditional methods that aim to identify biomarkers that distinguish between two groups, like Significance Analysis of Microarrays or the t-test, perform optimally when such biomarkers show homogeneous behavior within each group and differential behavior between the groups. However, in many applications, this is not the case. Instead, a subgroup of samples in one group shows differential behavior with respect to all other samples. To successfully detect markers showing such imbalanced patterns o ...[more]

PMID: 24062158

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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

Gene expression analysis of a series of HER2+ of breast tumors

Publications

Identifying subgroup markers in heterogeneous populations.

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