Angels Sierra

A Six-Gene Signature Predicting Breast Cancer Lung Metastasis

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.

The lipid phenotype of breast cancer cells characterized by Raman microspectroscopy: towards a stratification of malignancy.

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.

Peroxiredoxin 2 specifically regulates the oxidative and metabolic stress response of human metastatic breast cancer cells in lungs

Little is known about metastatic pathways that are specific to the lung rather than other organs. We previously showed that antioxidant proteins such as peroxiredoxins were specifically upregulated in lung metastatic breast cancer cells. We hypothesize that cancer cells that live under aerobic conditions, as might be the case in lungs, protect themselves against the damage caused by reactive oxygen species (ROS). To examine this hypothesis, we studied the role of peroxiredoxin-2 (PRDX2) in lung vs bone metastasis formation. A metastatic variant of MDA-MB-435 breast cancer cells that specifically metastasize to lungs (435-L3) was transduced with short hairpin RNAs to specifically silence PRDX2. Conversely, a bone metastatic variant of MDA-MB-231 cells (BO2) was stably transfected to overexpress PRDX2. The 435-L3 cells silenced for PRDX2 were significantly more sensitive to H2O2-induced oxidative stress than the parental and scrambled transfected cells. BO2/PRDX2 cells produced less ROS than BO2/green fluorescent protein control cells under oxidative stress. Moreover, PRDX2 knockdown inhibited the growth of 435-L3 cells in the lungs, whereas lymph node metastasis remained unaffected. In contrast, PRDX2 overexpression in bone metastatic BO2 breast cancer cells led to drastic inhibition of the skeletal tumor burden and reduction of bone destruction. Furthermore, PRDX2 expression in breast cancer cells was associated with a glucose-dependent phenotype, different from bone metastatic cells. Overall, our results strongly suggest that PRDX2 is a targetable ‘metabolic adaptor’ driver protein implicated in the selective growth of metastatic cells in the lungs by protecting them against oxidative stress.

Expression of Endoplasmic Reticulum Stress Proteins Is a Candidate Marker of Brain Metastasis in both ErbB-2+ and ErbB-2− Primary Breast Tumors

The increasing incidence of breast cancer brain metastasis in patients with otherwise well-controlled systemic cancer is a key challenge in cancer research. It is necessary to understand the properties of brain-tropic tumor cells to identify patients at risk for brain metastasis. Here we attempt to identify functional phenotypes that might enhance brain metastasis. To obtain an accurate classification of brain metastasis proteins, we mapped organ-specific brain metastasis gene expression signatures onto an experimental protein-protein interaction network based on brain metastatic cells. Thirty-seven proteins were differentially expressed between brain metastases and non-brain metastases. Analysis of metastatic tissues, the use of bioinformatic approaches, and the characterization of protein expression in tumors with or without metastasis identified candidate markers. A multivariate analysis based on stepwise logistic regression revealed GRP94, FN14, and inhibin as the best combination to discriminate between brain and non-brain metastases (ROC AUC = 0.85, 95% CI = 0.73 to 0.96 for the combination of the three proteins). These markers substantially improve the discrimination of brain metastasis compared with ErbB-2 alone (AUC = 0.76, 95% CI = 0.60 to 0.93). Furthermore, GRP94 was a better negative marker (LR = 0.16) than ErbB-2 (LR = 0.42). We conclude that, in breast carcinomas, certain proteins associated with the endoplasmic reticulum stress phenotype are candidate markers of brain metastasis.

Bcl-xL-Mediated Changes in Metabolic Pathways of Breast Cancer Cells : From Survival in the Blood Stream to Organ-Specific Metastasis

Bcl-x(L) protein plays a role in breast cancer dormancy, promoting survival of cells in metastatic foci by counteracting the proapoptotic signals in the microenvironment. The aim of this study was to identify phenotypes mediated by Bcl-x(L) in breast cancer cells that enhance in vivo survival of clinical metastases. 435/Bcl-x(L) or 435/Neo human breast cancer cells were injected into the inguinal mammary gland of nude mice, and tumors, metastases in lymph node, lung, and bone, and bloodstream surviving cells were examined. Proteomic analysis identified 17 proteins that were overexpressed (more than twofold) or underexpressed (less than twofold) in metastases. A protein interaction program allowed us to functionally associate peroxiredoxin 3, peroxiredoxin 2, carbonyl reductase 3, and enolase 1, suggesting a role for cellular responses to oxidative stress in metastasis organ selection. The prediction included proteins involved in redox systems, kinase pathways, and the ATP synthase complex. Furthermore, the interaction of redox proteins with enolase 1 suggests a connection between glycolysis and antioxidant pathways, enabling achievement of a high metastatic activity. In conclusion, Bcl-x(L) mediates a phenotype in which redox pathways and glycolysis are coupled to protect breast cancer metastatic cells during transit from the primary tumor to the metastatic state.

Biological pathways contributing to organ-specific phenotype of brain metastatic cells.

Secondary to the increased survival following chemotherapy, brain metastases have recently become a significant clinical problem for breast cancer patients. The aim of this study was to characterize those functional phenotypes that might enhance brain metastasis in breast cancer cells. We first analyzed by two-dimensional electrophoresis (2DE-DIGE) differences in protein expression between parental MDA-MB 435 cells and the brain metastatic variant 435-Br1, obtaining 19 identified proteins by peptide mass fingerprinting, 11 under-expressed (<2-fold) and 8 overexpressed (>2-fold) in 435-Br1. We created and analyzed protein interaction networks with a bioinformatic program (PIANA) from protein data, and it allowed us to associate 34/67-laminin receptor functionally with HSP 27, through a chaperone glucose-regulated protein GRP 94. Moreover, HSP 27 had the largest amount of direct and indirect protein interactions, forming a cluster of chaperones and cochaperones, associated through kinases to a set of intermediated filament proteins. In addition, functional groups of proteins identified were peptidase, DNA binding transcription factors, ATP synthase complex, anion transporters, and carbohydrate metabolism. Further functional analyses in cells, expression analyses in experimental tissues, and in human brain metastasis were addressed to validate the biological pathways contributing to organ-specific phenotype of brain metastasis.

A Transcriptome-proteome Integrated Network Identifies Endoplasmic Reticulum thiol oxidoreductase (ERp57) as a Hub that Mediates Bone Metastasis

Bone metastasis is the most common distant relapse in breast cancer. The identification of key proteins involved in the osteotropic phenotype would represent a major step toward the development of new prognostic markers and therapeutic improvements. The aim of this study was to characterize functional phenotypes that favor bone metastasis in human breast cancer. We used the human breast cancer cell line MDA-MB-231 and its osteotropic BO2 subclone to identify crucial proteins in bone metastatic growth. We identified 31 proteins, 15 underexpressed and 16 overexpressed, in BO2 cells compared with parental cells. We employed a network-modeling approach in which these 31 candidate proteins were prioritized with respect to their potential in metastasis formation, based on the topology of the protein-protein interaction network and differential expression. The protein-protein interaction network provided a framework to study the functional relationships between biological molecules by attributing functions to genes whose functions had not been characterized. The combination of expression profiles and protein interactions revealed an endoplasmic reticulum-thiol oxidoreductase, ERp57, functioning as a hub that retained four down-regulated nodes involved in antigen presentation associated with the human major histocompatibility complex class I molecules, including HLA-A, HLA-B, HLA-E, and HLA-F. Further analysis of the interaction network revealed an inverse correlation between ERp57 and vimentin, which influences cytoskeleton reorganization. Moreover, knockdown of ERp57 in BO2 cells confirmed its bone organ-specific prometastatic role. Altogether, ERp57 appears as a multifunctional chaperone that can regulate diverse biological processes to maintain the homeostasis of breast cancer cells and promote the development of bone metastasis.

Functional Clustering of Metastasis Proteins Describes Plastic Adaptation Resources of Breast-Cancer Cells to New Microenvironments†

To examine the molecular mechanisms underlying breast cancer metastasis in liver and search for potential markers of metastatic progression in soft-tissue, we analyzed metastatic variants developed from the highly metastatic MDA-MB 435 cell line through in vivo stepwise selection in the athymic mice. Comparative proteomic analysis using two-dimensional electrophoresis (2DE-DIGE) revealed that 74 protein spots were reproducibly more than doubled in liver metastatic cells compared to parental counterpart. From 22 proteins identified by MALDI-TOF, belonging to intermediate filaments, intracellular transport and ATP synthesis, we generated a protein-protein interaction network containing 496 nodes, 12 of which interacted. GRP 75 was connected with four other proteins: prohibitin, HSP 27, elongin B and macropain delta chain. After functional classification, we found that pathways including hepatocyte growth factor receptor (p = 0.014), platelet-derived growth factor (p = 0.018), vascular endothelial growth factor (p = 0.021) and epidermal growth factor (p = 0.050) were predominant in liver metastatic cells, but not in lung metastatic cells. In conclusion, we suggest that GRP 75 is involved in cell proliferation, tumorigenesis and stress response in metastatic cells by recruiting signals in which the transmembrane receptor protein tyrosine kinase signaling pathway (p-value FDR = 1.71 x 10(-2)) and protein amino acid phosphorylation (p-value FDR = 3.28 x 10(-2)) might be the most significant biological process differentially increased in liver metastasis.

Functional pathways shared by liver and lung metastases: a mitochondrial chaperone machine is up-regulated in soft-tissue breast cancer metastasis

Genes that mediate breast cancer metastasis to lung are different from those which mediate bone metastasis. However, which markers accounts for the diversity of breast cancer metastasis remains unknown. The aim of this study was identify proteins associated with the soft-tissue metastatic ability of breast cancer tumors in metastases, coupling microarray data from clinical metastases and immunohistochemistry, for further screening for early detection at the first diagnosis in patients. We use a bioinformatic program to create and analyze protein interaction networks from protein experimental data, and to translate RNA expression analysis of breast cancer human metastases to protein, in a search for the phenotype associated with soft-tissue metastases. The pre-validated proteins constituted the protein signature for each metastasis: 37 (8.9%) from liver, 92 (8.5%) from lung and 167 (13%) from bone. Pleiotrophin, BAG 2, HSP 60 and vinculin were pre-validated in liver and lung metastases performing the soft-tissue phenotype. After IHC validation, we conclude that HSP 60, one of the best-known mitochondrial chaperone machines, is a key protein in soft-tissue metastases phenotype interacting with BAG 2, which competes for binding to GRP 75, the other mitochondrial chaperone. The relationship between HSP 60/GRP 75 and BAG 2 might result in the activation of several transcription pathways, different in liver from in lung metastases, as a nodal point coupling positive and negative actuators in the multiple survival-signal pathways and so achieving metastatic growth.

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy.