Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets

Mechanotransduction by bone cells in vitro: mechanobiology of bone tissue

Mechanical force plays an important role in the regulation of bone remodelling in intact bone and bone repair. In vitro, bone cells demonstrate a high responsiveness to mechanical stimuli. Much debate exists regarding the critical components in the load profile and whether different components, such as fluid shear, tension or compression, can influence cells in differing ways. During dynamic loading of intact bone, fluid is pressed through the osteocyte canaliculi, and it has been demonstrated that fluid shear stress stimulates osteocytes to produce signalling molecules. It is less clear how mechanical loads act on mature osteoblasts present on the surface of cancellous or trabecular bone. Although tissue strain and fluid shear stress both cause cell deformation, these stimuli could excite different signalling pathways. This is confirmed by our experimental findings, in human bone cells, that strain applied through the substrate and fluid flow stimulate the release of signalling molecules to varying extents. Nitric oxide and prostaglandin E2 values increased by between two- and nine-fold after treatment with pulsating fluid flow (0.6±0.3 Pa). Cyclic strain (1000 μstrain) stimulated the release of nitric oxide two-fold, but had no effect on prostaglandin E2. Furthermore, substrate strains enhanced the bone matrix protein collagen I two-fold, whereas fluid shear caused a 50% reduction in collagen I. The relevance of these variations is discussed in relation to bone growth and remodelling. In applications such as tissue engineering, both stimuli offer possibilities for enhancing bone cell growth in vitro