Human CORO1A blocking peptide

BackgroundAbundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy.MethodsUsing four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification.ResultsWe identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype.ConclusionsThe identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

The European InnoMed-PredTox project was a collaborative effort between 15 pharmaceutical companies, 2 small and mid-sized enterprises, and 3 universities with the goal of delivering deeper insights into the molecular mechanisms of kidney and liver toxicity and to identify mechanism-linked diagnostic or prognostic safety biomarker candidates by combining conventional toxicological parameters with "omics" data. Mechanistic toxicity studies with 16 different compounds, 2 dose levels, and 3 time points were performed in male Crl: WI(Han) rats. Three of the 16 investigated compounds, BI-3 (FP007SE), Gentamicin (FP009SF), and IMM125 (FP013NO), induced kidney proximal tubule damage (PTD). In addition to histopathology and clinical chemistry, transcriptomics microarray and proteomics 2D-DIGE analysis were performed. Data from the three FTD studies were combined for a cross-study and cross-omics meta-analysis of the target organ. The mechanistic interpretation of kidney FTD-associated deregulated transcripts revealed, in addition to previously described kidney damage transcript biomarkers such as KIM-1, CLU and TIMP-1, a number of additional deregulated pathways congruent with histopathology observations on a single animal basis, including a specific effect on the complement system. The identification of new, more specific biomarker candidates for PTD was most successful when transcriptomics data were used. Combining transcriptomics data with proteomics data added extra value. (C) 2011 Elsevier Inc. All rights reserved.