Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

In-depth Proteomics of Malignant Luminal Breast Cancer

ABSTRACT: In-depth clinical proteomic analysis of laser-microdissected HOT lesions of five luminal B breast cancer Ki-67 high subtype.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Toshihide Nishimura

PROVIDER: PXD021912 | JPOST Repository | Mon Oct 12 00:00:00 BST 2020

REPOSITORIES: jPOST

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	BT18-03_1-1_01.raw	Raw
	BT18-03_1-1_02.raw	Raw
	BT18-03_1-1_03.raw	Raw
	BT18-03_1-2_01.raw	Raw
	BT18-03_1-2_02.raw	Raw

Items per page:

1 - 5 of 20

Similar Datasets

Heterogeneity of Breast Cancer Vasculature

Project description:Tumor-associated breast vasculature was laser-cappture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-associated vasculature and identify gene expression signatures predictive of clinical outcome.

2010-09-19 | GSE15363 | GEO

Heterogeneity of Breast Cancer Vasculature

2010-09-19 | E-GEOD-15363 | biostudies-arrayexpress

Tumor-associated stroma derived from primary clinical breast cancer samples

Project description:Tumor-associated breast stroma was laser-capture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-assocaited stroma and identify gene expression signatures predictive of clinical outcome. Keywords: disease state analysis

2008-04-11 | GSE9014 | GEO

Gene expression profiling of LCM captured breast cancer cells

2014-09-16 | GSE54002 | GEO

Transcription profiling of human cohort of lymph node-negative breast cancer patients identifies a six-gene signature predicting breast cancer lung metastasis

Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Experiment Overall Design: We used microarrays to identify lung metastasis-related genes in a series of 23 patients with breast cancer metastases. No replicate, no reference sample.

2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress

Proteomic maps of breast cancer subtypes

Project description:Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.

2017-03-29 | MSV000080757 | MassIVE

Metabolic Microenvironments in Normal Breast and Breast Cancer (NMR analysis)

Project description:The purpose of this study was to understand the genomic and metabolomic changes in the breast tissue microenvironment at various stages of cancer development and progression (i.e. normal breast, DCIS, benign disease and invasive cancer). To understand the way in which metabolic microenvironments evolve with breast cancer, this study analyzed normal breast tissue adjacent to benign and malignant lesions at various stages of cancer development and studied samples adjacent to invasive cancers of distinct breast cancer subtypes.

| ST000053 | MetabolomicsWorkbench

Breast cancer development model

Project description:Breast cancer development model

2020-10-08 | 627 | FAIRDOMHub

Proteomic maps of breast cancer subtypes

2016-01-05 | PXD002619 | Pride

Gene expression profiling of LCM captured breast cancer cells

Project description:The purpose of this study is to obtain comprehensive gene expression profiles in breast cancer. Mammary gland cells were specifically isolated from 433 clinical tissue samples by laser capture microdissection (LCM). Total RNAs were extracted from LCM captured samples. We investigated gene expression profiles in 417 patients with breast cancer and 16 non-tumor tissues as a normal control using an Affymetrix GeneChip. Mammary gland cells were captured from clinical tissues of breast cancer patient by LCM. Gene expression profilings for 417 tumor and 16 non-tumor samples were acquired using GeneChip Human Genome U133 Plus 2.0 (Affymetrix, Santa Clara, CA). Microarray datasets were normalized and transformed to log2 values using robust multi-array average (RMA) method with R statistical software and BioConductor package.

2014-09-16 | E-GEOD-54002 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data