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.

Project description:In this study 3 pooling experiments were performed. In each of the 3 cohorts, a 'case' and a 'control' blood pool was compared - the goal being to identify single nucleotide polymorphisms with significantly different estimated pooling allele frequencies between cases and controls. For cohort 1, 100 individuals with blue eye color were placed in one pool (the 'control' pool) and 100 individuals with brown eye color were placed in another pool ( the 'case' pool). In cohort 2, 131 individuals with age-related macular degeneration were placed in one pool, with 216 control individuals in another pool. In cohort 3, 100 individuals with pseudoexfoliation syndrome were placed in a case pool - in this case the cohort 2 control sample was used as 'controls'. The blue/brown pools were hybridized to Illumina HumanHap550 arrays. The cohort 2 and 3 pools were hybridized to Illumina 1M arrays. After processing, the raw data are summarized to give pooling allele frequency estimates for each pool. The abstract from the paper describing these data is as follows: Genome-wide association studies (GWAS) have now successfully identified important genetic variants associated with many human traits and diseases. The high cost of genotyping arrays in large datasets remains the major barrier to wider utilization of GWAS. We have developed a novel method in which whole blood from cases and controls respectively is pooled prior to DNA extraction for genotyping. We demonstrate proof of principle by clearly identifying the associated variants for eye color, age-related macular degeneration and pseudoexfoliation syndrome in cohorts not previously studied. Blood pooling has the potential to reduce GWAS cost by several orders of magnitude and dramatically shorten gene discovery time. This method has profound implications for translation of modern genetic approaches to a multitude of diseases and traits yet to be analysed by GWAS, and will enable developing nations to participate in GWAS.

Project description:In this study 3 pooling experiments were performed. In each of the 3 cohorts, a 'case' and a 'control' blood pool was compared - the goal being to identify single nucleotide polymorphisms with significantly different estimated pooling allele frequencies between cases and controls. For cohort 1, 100 individuals with blue eye color were placed in one pool (the 'control' pool) and 100 individuals with brown eye color were placed in another pool ( the 'case' pool). In cohort 2, 131 individuals with age-related macular degeneration were placed in one pool, with 216 control individuals in another pool. In cohort 3, 100 individuals with pseudoexfoliation syndrome were placed in a case pool - in this case the cohort 2 control sample was used as 'controls'. The blue/brown pools were hybridized to Illumina HumanHap550 arrays. The cohort 2 and 3 pools were hybridized to Illumina 1M arrays. After processing, the raw data are summarized to give pooling allele frequency estimates for each pool. The abstract from the paper describing these data is as follows: Genome-wide association studies (GWAS) have now successfully identified important genetic variants associated with many human traits and diseases. The high cost of genotyping arrays in large datasets remains the major barrier to wider utilization of GWAS. We have developed a novel method in which whole blood from cases and controls respectively is pooled prior to DNA extraction for genotyping. We demonstrate proof of principle by clearly identifying the associated variants for eye color, age-related macular degeneration and pseudoexfoliation syndrome in cohorts not previously studied. Blood pooling has the potential to reduce GWAS cost by several orders of magnitude and dramatically shorten gene discovery time. This method has profound implications for translation of modern genetic approaches to a multitude of diseases and traits yet to be analysed by GWAS, and will enable developing nations to participate in GWAS. Pools were typed with Illumina arrays. Estimates of pooling allele frequency were obtained.

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Breast Cancer Gene Expression Data from Frankfurt Series

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