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 of using tissue surgically resected from lung metastatic lesions and comparing their gene expression profiles with those from nonpulmonary sites, all coming from breast cancer patients. We show 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 6-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the 6-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we show that the signature improves risk stratification independently of known standard clinical variables and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. [Cancer Res 2008;68(15):6092–9]

Wistar rats, eight days old, were subjected to permanent bilateral forebrain ischemia, followed by hypoxia for 15 minutes. A cerebral infarct, mainly involving the cerebral neocortex, hippocampus, amygdala, striatum and subcortical white matter was produced. Neurons and glia showing punctate chromatin condensation and karyorrhectic cells were observed 12 hours after hypoxia‐ischemia. Their number increased during the first two days and recruitment of cells with degenerating nuclei occurred until day five. In situ labeling of nuclear DNA fragmentation stained many normal‐appearing nuclei, as well as punctate chromatin condensations and nuclear fragments in karyorrhectic cells. Delayed neuronal death in the CA1 area of the hippocampus was observed after 20 minutes of transient forebrain ischemia in the adult gerbil. In situ labeling of nuclear DNA fragmentation demonstrated stained punctate chromatin condensation in a few degenerating cells at 48 hours post‐ischemia. Substantial labeling of CA1 neurons occurred in the fourth day.Agarose gel electrophoresis of extracted brain DNA from ischemic infant rats and adult gerbils showed a ladder‐type pattern which is typical of nuclear DNA fragmentation into oligonucleosomal fragments (internucleosomal cleavage). These findings suggest that endonuclease(s) activation may play a role in cell death induced by different forms of hypoxia‐ischemia.

Although molecular classification brings interesting insights into breast cancer taxonomy, its implementation in daily clinical care is questionable because of its expense and the information supplied in a single sample allocation is not sufficiently reliable. New approaches, based on a panel of small molecules derived from the global or targeted analysis of metabolic profiles of cells, have found a correlation between activation of de novo lipogenesis and poorer prognosis and shorter disease-free survival for many tumors. We hypothesized that the lipid content of breast cancer cells might be a useful indirect measure of a variety of functions coupled to breast cancer progression. Raman microspectroscopy was used to characterize metabolism of breast cancer cells with different degrees of malignancy. Raman spectra from MDA-MB-435, MDA-MB-468, MDA-MB-231, SKBR3, MCF7 and MCF10A cells were acquired with an InVia Raman microscope (Renishaw) with a backscattered configuration. We used Principal Component Analysis and Partial Least Squares Discriminant Analyses to assess the different profiling of the lipid composition of breast cancer cells. Characteristic bands related to lipid content were found at 3014, 2935, 2890 and 2845 cm⁻¹, and related to lipid and protein content at 2940 cm⁻¹. A classificatory model was generated which segregated metastatic cells and non-metastatic cells without basal-like phenotype with a sensitivity of 90% and a specificity of 82.1%. Moreover, expression of SREBP-1c and ABCA1 genes validated the assignation of the lipid phenotype of breast cancer cells. Indeed, changes in fatty acid unsaturation were related with the epithelial-to-mesenchymal transition phenotype. Raman microspectroscopy is a promising technique for characterizing and classifying the malignant phenotype of breast cancer cells on the basis of their lipid profiling. The algorithm for the discrimination of metastatic ability is a first step towards stratifying breast cancer cells using this rapid and reagent-free tool.