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Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that define stromal components of breast cancer.

ABSTRACT: Multiple studies have shown that the tumor microenvironment (TME) of carcinomas can play an important role in the initiation, progression, and metastasis of cancer. Here we test the hypothesis that specific benign fibrous soft tissue tumor gene expression profiles may represent distinct stromal fibroblastic reaction types that occur in different breast cancers. The discovered stromal profiles could classify breast cancer based on the type of stromal reaction patterns in the TME.Next generation sequencing-based gene expression profiling (3SEQ) was performed on formalin fixed, paraffin embedded (FFPE) samples of 10 types of fibrous soft tissue tumors. We determined the extent to which these signatures could identify distinct subsets of breast cancers in four publicly available breast cancer datasets.A total of 53 fibrous tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the gene signatures derived from elastofibroma (EF) and fibroma of tendon sheath (FOTS) demonstrated robust outcome results for survival in the four breast cancer datasets. The breast cancers positive for the EF signature (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature-positive breast cancers (11-35% of the cohort) had a worse outcome.We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Our group has previously identified novel cancer stromal gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of three soft tissue tumors, desmoid-type fibromatosis (DTF), solitary fibrous tumor (SFT), and tenosynovial giant cell tumor (TGCT/CSF1), as surrogates for stromal expression patterns. By combining the stromal signatures of EF and FOTS, with our previously identified DTF and TGCT/CSF1 signatures we can now characterize clinically relevant stromal expression profiles in the TME for between 74% to 90% of all breast cancers.

SUBMITTER: Guo X

PROVIDER: S-EPMC3978842 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that define stromal components of breast cancer.

Guo Xiangqian X Zhu Shirley X SX Brunner Alayne L AL van de Rijn Matt M West Robert B RB

Breast cancer research : BCR 20131217 6

<h4>Introduction</h4>Multiple studies have shown that the tumor microenvironment (TME) of carcinomas can play an important role in the initiation, progression, and metastasis of cancer. Here we test the hypothesis that specific benign fibrous soft tissue tumor gene expression profiles may represent distinct stromal fibroblastic reaction types that occur in different breast cancers. The discovered stromal profiles could classify breast cancer based on the type of stromal reaction patterns in the ...[more]

PMID: 24342436

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Project description:BackgroundObesity is a metabolic disease caused by environmental and genetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to investigate the role of skeletal muscle DNA methylation in combination with transcriptomic changes in obesity.ResultsMuscle biopsies were obtained basally from lean (n = 12; BMI = 23.4 ± 0.7 kg/m(2)) and obese (n = 10; BMI = 32.9 ± 0.7 kg/m(2)) participants in combination with euglycemic-hyperinsulinemic clamps to assess insulin sensitivity. We performed reduced representation bisulfite sequencing (RRBS) next-generation methylation and microarray analyses on DNA and RNA isolated from vastus lateralis muscle biopsies. There were 13,130 differentially methylated cytosines (DMC; uncorrected P < 0.05) that were altered in the promoter and untranslated (5' and 3'UTR) regions in the obese versus lean analysis. Microarray analysis revealed 99 probes that were significantly (corrected P < 0.05) altered. Of these, 12 genes (encompassing 22 methylation sites) demonstrated a negative relationship between gene expression and DNA methylation. Specifically, sorbin and SH3 domain containing 3 (SORBS3) which codes for the adapter protein vinexin was significantly decreased in gene expression (fold change -1.9) and had nine DMCs that were significantly increased in methylation in obesity (methylation differences ranged from 5.0 to 24.4 %). Moreover, differentially methylated region (DMR) analysis identified a region in the 5'UTR (Chr.8:22,423,530-22,423,569) of SORBS3 that was increased in methylation by 11.2 % in the obese group. The negative relationship observed between DNA methylation and gene expression for SORBS3 was validated by a site-specific sequencing approach, pyrosequencing, and qRT-PCR. Additionally, we performed transcription factor binding analysis and identified a number of transcription factors whose binding to the differentially methylated sites or region may contribute to obesity.ConclusionsThese results demonstrate that obesity alters the epigenome through DNA methylation and highlights novel transcriptomic changes in SORBS3 in skeletal muscle.

Project description:The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.

Project description:The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.

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Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that define stromal components of breast cancer.

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Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that define stromal components of breast cancer.

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