This SuperSeries is composed of the SubSeries listed below.
Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.
Specimen part
View SamplesThe reliability of differential expression analysis on FFPE expression profiles from Affymetrix arrays is questionable, due to the wide range of percent-present values reported in studies which profiled FFPE samples on Affymetrix arrays. Moreover the validity of externally defined gene-modules in FFPE microarray expression profiles is unknown. Using eight breast cancer tumors with available frozen and FFPE samples, five sample-matched data sets were generated from different combination of Affymetrix arrays, amplification-and-labeling kit and sample preservation method. The reliability of differential expression analysis was investigated by developing de novo ER/HER2 pathway gene-modules from matched data sets and validating it on external data set using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE-frozen expression profiles was used to measure reliability of externally defined gene-modules in FFPE expression profiles. Independent of array/amplification-kit/sample preservation method used, de novo ER/HER2 gene-modules derived from all matching data sets showed similar prediction performance during independent validation (AUC range; ER: 0.92-0.95, HER2: 0.88-0.91), except for de novo HER2 gene-module derived from FFPE data set with 3'IVT kit (AUC: 0.67-0.72). Further not all gene-module based biological signals present in frozen expression profiles can be recovered from matching FFPE microarray expression profiles using the currently available FFPE specific sample preparation kits. The gene-module based biological signal extracted from FFPE RNA, using microarrays, may not be as reliable as that from their frozen counterpart, if the sample preparation protocol used with FFPE RNA failed to recover relevant genes involved in the biological signal.
Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.
Specimen part
View SamplesThe reliability of differential expression analysis on FFPE expression profiles from Affymetrix arrays is questionable, due to the wide range of percent-present values reported in studies which profiled FFPE samples on Affymetrix arrays. Moreover the validity of externally defined gene-modules in FFPE microarray expression profiles is unknown. Using eight breast cancer tumors with available frozen and FFPE samples, five sample-matched data sets were generated from different combination of Affymetrix arrays, amplification-and-labeling kit and sample preservation method. The reliability of differential expression analysis was investigated by developing de novo ER/HER2 pathway gene-modules from matched data sets and validating it on external data set using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE-frozen expression profiles was used to measure reliability of externally defined gene-modules in FFPE expression profiles. Independent of array/amplification-kit/sample preservation method used, de novo ER/HER2 gene-modules derived from all matching data sets showed similar prediction performance during independent validation (AUC range; ER: 0.92-0.95, HER2: 0.88-0.91), except for de novo HER2 gene-module derived from FFPE data set with 3'IVT kit (AUC: 0.67-0.72). Further not all gene-module based biological signals present in frozen expression profiles can be recovered from matching FFPE microarray expression profiles using the currently available FFPE specific sample preparation kits. The gene-module based biological signal extracted from FFPE RNA, using microarrays, may not be as reliable as that from their frozen counterpart, if the sample preparation protocol used with FFPE RNA failed to recover relevant genes involved in the biological signal.
Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.
Specimen part
View SamplesThe reliability of differential expression analysis on FFPE expression profiles from Affymetrix arrays is questionable, due to the wide range of percent-present values reported in studies which profiled FFPE samples on Affymetrix arrays. Moreover the validity of externally defined gene-modules in FFPE microarray expression profiles is unknown. Using eight breast cancer tumors with available frozen and FFPE samples, five sample-matched data sets were generated from different combination of Affymetrix arrays, amplification-and-labeling kit and sample preservation method. The reliability of differential expression analysis was investigated by developing de novo ER/HER2 pathway gene-modules from matched data sets and validating it on external data set using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE-frozen expression profiles was used to measure reliability of externally defined gene-modules in FFPE expression profiles. Independent of array/amplification-kit/sample preservation method used, de novo ER/HER2 gene-modules derived from all matching data sets showed similar prediction performance during independent validation (AUC range; ER: 0.92-0.95, HER2: 0.88-0.91), except for de novo HER2 gene-module derived from FFPE data set with 3'IVT kit (AUC: 0.67-0.72). Further not all gene-module based biological signals present in frozen expression profiles can be recovered from matching FFPE microarray expression profiles using the currently available FFPE specific sample preparation kits. The gene-module based biological signal extracted from FFPE RNA, using microarrays, may not be as reliable as that from their frozen counterpart, if the sample preparation protocol used with FFPE RNA failed to recover relevant genes involved in the biological signal.
Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.
Specimen part
View SamplesThe reliability of differential expression analysis on FFPE expression profiles from Affymetrix arrays is questionable, due to the wide range of percent-present values reported in studies which profiled FFPE samples on Affymetrix arrays. Moreover the validity of externally defined gene-modules in FFPE microarray expression profiles is unknown. Using eight breast cancer tumors with available frozen and FFPE samples, five sample-matched data sets were generated from different combination of Affymetrix arrays, amplification-and-labeling kit and sample preservation method. The reliability of differential expression analysis was investigated by developing de novo ER/HER2 pathway gene-modules from matched data sets and validating it on external data set using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE-frozen expression profiles was used to measure reliability of externally defined gene-modules in FFPE expression profiles. Independent of array/amplification-kit/sample preservation method used, de novo ER/HER2 gene-modules derived from all matching data sets showed similar prediction performance during independent validation (AUC range; ER: 0.92-0.95, HER2: 0.88-0.91), except for de novo HER2 gene-module derived from FFPE data set with 3'IVT kit (AUC: 0.67-0.72). Further not all gene-module based biological signals present in frozen expression profiles can be recovered from matching FFPE microarray expression profiles using the currently available FFPE specific sample preparation kits. The gene-module based biological signal extracted from FFPE RNA, using microarrays, may not be as reliable as that from their frozen counterpart, if the sample preparation protocol used with FFPE RNA failed to recover relevant genes involved in the biological signal.
Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.
Specimen part
View SamplesThe reliability of differential expression analysis on FFPE expression profiles from Affymetrix arrays is questionable, due to the wide range of percent-present values reported in studies which profiled FFPE samples on Affymetrix arrays. Moreover the validity of externally defined gene-modules in FFPE microarray expression profiles is unknown. Using eight breast cancer tumors with available frozen and FFPE samples, five sample-matched data sets were generated from different combination of Affymetrix arrays, amplification-and-labeling kit and sample preservation method. The reliability of differential expression analysis was investigated by developing de novo ER/HER2 pathway gene-modules from matched data sets and validating it on external data set using ROC analysis. Spearman's rank correlation coefficient of module scores between matched FFPE-frozen expression profiles was used to measure reliability of externally defined gene-modules in FFPE expression profiles. Independent of array/amplification-kit/sample preservation method used, de novo ER/HER2 gene-modules derived from all matching data sets showed similar prediction performance during independent validation (AUC range; ER: 0.92-0.95, HER2: 0.88-0.91), except for de novo HER2 gene-module derived from FFPE data set with 3'IVT kit (AUC: 0.67-0.72). Further not all gene-module based biological signals present in frozen expression profiles can be recovered from matching FFPE microarray expression profiles using the currently available FFPE specific sample preparation kits. The gene-module based biological signal extracted from FFPE RNA, using microarrays, may not be as reliable as that from their frozen counterpart, if the sample preparation protocol used with FFPE RNA failed to recover relevant genes involved in the biological signal.
Feasibility of developing reliable gene expression modules from FFPE derived RNA profiled on Affymetrix arrays.
Specimen part
View SamplesUsing a dataset of 54 pregnant and 113 age/stage-matched non-pregnant breast cancer patients with complete clinical and survival data; we evaluated the pattern of hot spot somatic mutations and performed transcriptomic profiling using Sequenom and Affymetrix, respectively. Breast cancer molecular subtypes were defined using PAM50 and 3-Gene classifiers. We performed Gene set enrichment analysis (GSEA) to evaluate pathways associated with diagnosis during pregnancy. We investigated the differential expression of cancer-related genes and published gene sets according to pregnancy. We finally investigated genes associated with disease-free survival.
Biology of breast cancer during pregnancy using genomic profiling.
Age, Disease stage
View SamplesMicroarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated.
Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology.
Specimen part, Disease stage
View SamplesPurpose: There is growing evidence that interaction between stromal and tumor cells is pivotal in breast cancer progression and response to therapy. Since the pioneer work of Allinen et al. suggested that during breast cancer progression striking changes occur in CD10+ stromal cells, we aimed to better characterize this cell population and its clinical relevance.
Characterization and clinical evaluation of CD10+ stroma cells in the breast cancer microenvironment.
Specimen part, Disease stage
View SamplesThis SuperSeries is composed of the SubSeries listed below.
DNA methylation profiling reveals a predominant immune component in breast cancers.
Specimen part, Disease stage, Cell line, Treatment
View Samples