Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

IGR_BREX_JF

ABSTRACT: The tissue stroma plays a major role in tumors natural history. Important programs for tumor progression such as epithelial mesenchymal transition are not activated as cell-autonomous processes but under the conditions of cross-talk between tumor and stroma. Adipose tissue is a major component of the stroma and, because of their extended half-life, adipocytes are witnesses of tissue exposures. This study compares breast fat in women with breast tumors to those in healthy women to try to define a signature that could be translated into biomarkers for cancer and / or cancer risk. In the tumor-bearing breasts, we sampled adipose tissues adjacent to or distant from the tumor. Parameters studied include: adipocytes size and density; immune cells infiltration, vascularization, functional excretion and gene expression. Adipose tissues from tumor-bearing breasts whether adjacent to or distant from the tumor, did not differ from each other by any of these parameters. By contrast, when compared to adipose tissues from tumor-free breasts, tissues from tumor-bearing breasts displayed functional characteristics pointing to some sort of \\"activation\\". They have the capacity to excrete twice as much of interleukin 8 (p=0.001) and they differentially express a set of around 200 genes, at a threshold of p-value 0.001, of which many belong to inflammation mediated by chemokine and cytokine signaling and integrin signaling pathways. The presence of mast cells appears as a strong predictor of IL-8 secretion capacity (Log2 scale p = 0.002). Therefore, breast fat tissues from breast cancer patients have distinct functional and expression profiles. Although a dialogue effect with the tumor cannot be ruled out, it is also possible that this status is individually defined thus determining the constitutive permissiveness of the tissue for the development of cancer. The activation of pathways that was observed could potentially be translated into biomarkers or therapeutic targets. This transcriptomic analysis has been performed on two types of samples at short and long distances from the tumour in comparison with samples from normal tissues on Illumina beadarrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Dessen Philippe

PROVIDER: E-MTAB-8638 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Adipose Tissue Properties in Tumor-Bearing Breasts.

Miran Isabelle I Scherer Dominique D Ostyn Pauline P Mazouni Chafika C Drusch Françoise F Bernard Marine M Louvet Emilie E Adam Julien J Mathieu Marie-Christine MC Haffa Mariam M Antignac Jean-Philippe JP Le Bizec Bruno B Vielh Philippe P Dessen Philippe P Perdry Hervé H Delaloge Suzette S Feunteun Jean J

Frontiers in oncology 20200821

The tissue stroma plays a major role in tumors' natural history. Most programs for tumor progression are not activated as cell-autonomous processes but under the conditions of cross-talks between tumor and stroma. Adipose tissue is a major component of breast stroma. This study compares adipose tissues in tumor-bearing breasts to those in tumor-free breasts with the intention of defining a signature that could translate into markers of cancer risk. In tumor-bearing breasts, we sampled adipose ti ...[more]

PMID: 32974182

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Project description:Breast cancers display a high degree of diversity between and within tumors due to variations in both tumor cells and non-malignant cells of the tumor microenvironment (TME). While most studies on the TME have focused on stroma located within the tumor mass, few studies have examined the peri-tumoral microenvironment, which extends beyond the tumor margins and includes the surrounding, benign-appearing tissues. Here, we examined the heterogeneity in tumors and peri-tumoral stroma from 10 ER+PR+HER2- invasive breast carcinomas, through multi-region transcriptomic profiling. Tumor samples were obtained from the center, superior, inferior, medial, and lateral (C, S, Inf, M, L) regions of each tumor. Peritumoral samples were obtained from four radial directions corresponding to the regions the tumor samples were collected from (S, Inf, M, L) and from three zones of increasing distances from the tumor margins (Zone 1: 1-3 cm, Zone 2: 3-5 cm, Zone 3: 5-7 cm). To represent the normal breast transcriptomic state, 5 non-directional samples were each obtained from 3 non-tumor bearing breasts (NTB) - 1 patient had undergone cosmetic reduction mammoplasty and 2 patients were germline BRCA2-mutation carriers who had undergone risk-reducing mammoplasties. Total RNA was isolated and microarray was performed using the human Clariom™ S Assay (ThermoFisher Scientific). In total, 144 transcriptomic profiles (47 Tumor, 82 peri-tumoral stroma, 15 NTB) were analyzed. Heterogeneity was observed both within and between the tumor samples, highlighting that unlike the histological classification, an individual tumor is often comprised of several molecular sub-types. Similarly, the peri-tumoral stroma was also heterogeneous, albeit independent of the tumor heterogeneity. Four distinct stromal clusters were noted, which differed in their molecular pathways and cell type composition. Among these, the adipose-enriched stroma that had inflammatory cancer-associated fibroblasts and innate immune cells was significantly associated with poorer overall survival, in contrast to the myofibroblast-enriched stroma. These data together suggest that the peri-tumoral heterogeneity may be an important determinant of the evolution and treatment of breast cancers.

Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate â¤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics. Affymetrix GeneChip Mouse Genome 430 2.0 microarrays were used to profile whole tissues from 5 different tissue types of 25 tumor-bearing and 25 control mice of the Her2/Neu breast cancer mouse model. The 5 tissues tested were from breast, liver, spleen, blood cell, and thymus. The tumor-bearing mice were bitransgenic for MMTV-rtTA/TetO-NeuNT, and the control mice were transgenic for TetO-NeuNT only. The control mice were age- and cage-matched to the tumor-bearing mice. All samples were lysed and total RNA isolated and amplified prior to hybridization.

Project description:Background: We hypothesize that important genomic differences between breast cancer subtypes occur early in carcinogenesis. Therefore, gene expression might distinguish histologically normal breast epithelium (NlEpi) from breasts containing estrogen receptor positive (ER+) compared with estrogen receptor negative (ER-) cancers. Methods: We examined gene expression in 46 cases of microdissected NlEpi from previously untreated women undergoing breast cancer surgery. From 30 age-matched cases (15 ER+, 15 ER-) we used Affymetryix U133A arrays. From 16 independent cases (9ER+, 7 ER-), we validated seven selected genes using qPCR. We then compared gene expression between NlEpi and invasive breast cancer using 4 publicly available datasets. Results: 216 probes (corresponding to 198 unique genes) distinguished the NlEpi from breasts with ER+ (NlEpiER+) compared to ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers themselves, (e.g., ESR1, GATA3, and CX3CL1, FABP7, respectively). QPCR validated the microarray results in both a sampling of the 30 original cases (84%) and all of the 16 independent cases (77%). Gene expression in NlEpiER+ and NlEPIERNlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 36%-53% of the genes or probes examined in each the 4 external datasets overlapped between NlEpi and the corresponding cancer subtype. Conclusions: Gene expression differs in NlEpi of breasts containing ER+ compared to ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. Thus, breast cancer subtypes may be detectable before histologic abnormalities. NlEpi gene expression may help define subtype-specific risk signatures, identify initial subtype specific genomic differences, and suggest new targets for subtype-specific prevention and therapy. We determined that 216 probesets significantly differed between histologically normal epithelium from ER+ breast cancer patients and from ER- breast cancer patients, and that gene expression in each type of histologically normal epithelium resembles expression of the corresponding subtype of invasive breast cancer (i.e., ER+ or ER-). These findings suggest that characteristic features of breast cancer subtypes are detectable prior to any histologic abnormality. This suggestion has implications for understanding breast cancer biology and devising new tools for assessing breast cancer risk. 30 total laser capture microdissected histologically normal breast tissue samples were analyzed with Affymetrix HU133A microarrays. All samples were age-matched between histologically normal epithelial samples from ER+ breast cancer patients (n=15) and histologically normal epithelial samples from ER- breast cancer patients (n=15). Sample numbers correspond to individual patient samples. Of the 4 publicly available datasets mentioned above, the only dataset with a GEO number was GSE3494, corresponding to the Miller dataset. The supplementary file below lists the 251 Samples used from the GSE3494 study. We did not reanalyze the data - there was no change made to the Miller dataset; we only used these data for confirmation of our own dataset.

Dataset Information

IGR_BREX_JF

Publications

Adipose Tissue Properties in Tumor-Bearing Breasts.

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