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.
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.
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.
Project description:Breast cancer can classify molecular subtype, luminal A, B, and HER2-positive and triple-negative breast cancer. Especially TNBC, there is no therapeutic target compared to other molecular subtypes. To investigate dysregulated miRNAs in TNBC, we performed miRNA microarray using breast cancer tissue with matched normal tissue in each subtype.