Transcription profiling by array of 178 human breast cancer biopsy samples vs normal breast reference total RNA (Clontech) to identify exons associated with higher risk of relapse
Ontology highlight
ABSTRACT: We profiled breast cancer samples with exon arrays (244K Agilent Human exon) in order to identify exons associated with higher risk of relapse. This set contain 178 single dual color of samples (biopsies) versus normal breast reference (Normal breast Clontech) , 5 dye-swap (10 hybridizations) and 5 self self for controls.
Project description:transcription profiling of human head and neck squamous cell carcinoma (HNSCC) samples vs. normal tonsil samples using a two-color reference design experimental setting. Used to identify differentially expressed genes in tumor/normal samples, and compare the result to that of the same samples using the self-self hybridization experimental setting. Keywords: tumor/normal comparison
Project description:This series represents 180 lymph-node negative relapse free patients and 106 lymph-node negate patients that developed a distant metastasis. Please see attached patient clinical parameters sheet for more information (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?view=data&acc=GSE2034&id=40089&db=GeoDb_blob26). Keywords: other
Project description:The aim of this study was to compare the gene expression profile changes of DMBA-induced rat breast tumors from an initial stage to the moment of sacrifice. To this end, a cDNA microarray was performed (Affymetrix’s Rat Genome 230 2.0 array). This gene expression study was carried out on the umor biopsy samples and compared with matched tumor biopsy samples once the study ended (7 weeks after initial biopsy).
Project description:The aim of this study was to compare the gene expression profile changes of DMBA-induced rat breast tumors from an initial stage to the moment of sacrifice. To this end, a cDNA microarray was performed (Affymetrix’s Rat Genome 230 2.0 array). This gene expression study was carried out on the umor biopsy samples and compared with matched tumor biopsy samples once the study ended (7 weeks after initial biopsy). Breast tumors were induced with a single oral dosage of 7,12-dimethylbenz(alpha)anthracene (100 mg/kg body weight) in female Sprague-Dawley rats. Gene expression analysis was performed in paired samples as follows: DMBA final trucut tumor vs initial trucut tumor (DMBA final vs basal). For this assay, 5 samples were chosen according to histopathologic criteria (Bloom-Richardson grade II). Gene expression profiling was carried out using Affymetrix’s GeneChip technology, using the Rat Genome 230 2.0 array from this provider. All the protocols and apparatus were recommended by Affymetrix. Total RNA from frozen mammary tumors was extracted by RNeasy Mini kit and homogenized by QIAshredder columns according to manufacturer’s instructions. The quality and quantity of the obtained RNA was checked out through agarose electrophoresis and later spectrophotometry at 260/280 nm. Biotinylated cRNA was synthesized following the IVT labeling kit from Affymetrix and purified by the GeneChip Sample Cleanup Module from Affymetrix. The quality and quantity of the obtained cRNA was again checked out through agarose electrophoresis and posterior spectrophotometry at 260/280 nm. After hybridization, slides were washed and scanned following the manufacturer’s standard protocol. Intensity values were normalized by Robust Multichip Average method and subsequently these were filtered to remove the control sequences and those with a hybridization signal close to background. The spike controls were: BioB, BioC, BioD and Cre; because BioB was the least abundant in the samples, it was used to estimate the sensitivity of the experiment. The housekeeping control was GAPDH. After non-supervised clustering using Pearson correlation coefficient, statistical significance of gene expression was estimated by Student’s T test for paired samples, using GeneSpring GX 7.3 software (Agilent).
Project description:Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.
Project description:The purpose of this study was to define reference intervals for total thyroxine (tT4) in dried blood samples (DBSs) obtained for newborn screening. The aim of our study was to assess the possible benefit of measuring tT4 concentrations directly in DBSs obtained for newborn screening in premature and term-born infants. In order to have a sufficient number of samples for the extremely premature infants (<30 weeks), we set up a retrospective study, measuring the concentrations in DBSs collected over the previous 21 weeks. This time frame was a result of the included miniature study of tT4 stability in DBSs. We found that tT4 strongly correlated with gestational age (GA) in premature infants, highlighting the need for age-specific reference ranges. For term-born infants, the tT4 ranges did not vary significantly among different gestational ages, allowing for the use of one single reference range.
Project description:Selection of lung cancer patients for therapy with tyrosine kinase inhibitors directed at EGFR requires the identification of specific EGFR mutations. In most patients with advanced, inoperable lung carcinoma limited tumor samples often represent the only material available for both histologic typing and molecular analysis. We defined a next generation sequencing protocol targeted to EGFR exons 18-21 suitable for the routine diagnosis of such clinical samples. The protocol was validated in an unselected series of 80 small biopsies (n=14) and cytology (n=66) specimens representative of the material ordinarily submitted for diagnostic evaluation to three referral medical centers in Italy. Specimens were systematically evaluated for tumor cell number and proportion relative to non-neoplastic cells. They were analyzed in batches of 100-150 amplicons per run, reaching an analytical sensitivity of 1% and obtaining an adequate number of reads, to cover all exons on all samples analyzed. Next generation sequencing was compared with Sanger sequencing. The latter identified 15 EGFR mutations in 14/80 cases (17.5%) but did not detected mutations when the proportion of neoplastic cells was below 40%. Next generation sequencing identified 31 EGFR mutations in 24/80 cases (30.0%). Mutations were detected with a proportion of neoplastic cells as low as 5%. All mutations identified by the Sanger method were confirmed. In 6 cases next generation sequencing identified exon 19 deletions or the L858R mutation not seen after Sanger sequencing, allowing the patient to be treated with tyrosine kinase inhibitors. In one additional case the R831H mutation associated with treatment resistance was identified in an EGFR wild type tumor after Sanger sequencing. Next generation sequencing is robust, cost-effective and greatly improves the detection of EGFR mutations. Its use should be promoted for the clinical diagnosis of mutations in specimens with unfavorable tumor cell content.
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.