A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status.

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient. Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small.

An inhibitor of interaction between the transcription factor NRF2 and the E3 ubiquitin ligase adapter ß-TrCP delivers anti-inflammatory responses in mouse liver.

It is widely accepted that activating the transcription factor NRF2 will blast the physiological anti-inflammatory mechanisms, which will help combat pathologic inflammation. Much effort is being put in inhibiting the main NRF2 repressor, KEAP1, with either electrophilic small molecules or disrupters of the KEAP1/NRF2 interaction. However, targeting ß-TrCP, the non-canonical repressor of NRF2, has not been considered yet. After in silico screening of ∼1 million compounds, we now describe a novel small molecule, PHAR, that selectively inhibits the interaction between ß-TrCP and the phosphodegron in transcription factor NRF2. PHAR upregulates NRF2-target genes such as Hmox1, Nqo1, Gclc, Gclm and Aox1, in a KEAP1-independent, but ß-TrCP dependent manner, breaks the ß-TrCP/NRF2 interaction in the cell nucleus, and inhibits the ß-TrCP-mediated in vitro ubiquitination of NRF2. PHAR attenuates hydrogen peroxide induced oxidative stress and, in lipopolysaccharide-treated macrophages, it downregulates the expression of inflammatory genes Il1b, Il6, Cox2, Nos2. In mice, PHAR selectively targets the liver and greatly attenuates LPS-induced liver inflammation as indicated by a reduction in the gene expression of the inflammatory cytokines Il1b, TNf, and Il6, and in F4/80-stained liver resident macrophages. Thus, PHAR offers a still unexplored alternative to current NRF2 activators by acting as a ß-TrCP/NRF2 interaction inhibitor that may have a therapeutic value against undesirable inflammation.

A Collection of Designed Peptides to Target SARS-CoV-2 Spike RBD-ACE2 Interaction.

The angiotensin-converting enzyme 2 (ACE2) is the receptor used by SARS-CoV and SARS-CoV-2 coronaviruses to attach to cells via the receptor-binding domain (RBD) of their viral spike protein. Since the start of the COVID-19 pandemic, several structures of protein complexes involving ACE2 and RBD as well as monoclonal antibodies and nanobodies have become available. We have leveraged the structural data to design peptides to target the interaction between the RBD of SARS-CoV-2 and ACE2 and SARS-CoV and ACE2, as contrasting exemplar, as well as the dimerization surface of ACE2 monomers. The peptides were modelled using our original method: PiPreD that uses native elements of the interaction between the targeted protein and cognate partner(s) that are subsequently included in the designed peptides. These peptides recapitulate stretches of residues present in the native interface plus novel and highly diverse conformations surrogating key interactions at the interface. To facilitate the access to this information we have created a freely available and dedicated web-based repository, PepI-Covid19 database, providing convenient access to this wealth of information to the scientific community with the view of maximizing its potential impact in the development of novel therapeutic and diagnostic agents.

Evaluation of Computationally Designed Peptides against TWEAK, a Cytokine of the Tumour Necrosis Factor Ligand Family.

The tumour necrosis factor-like weak inducer of apoptosis (TWEAK) is a member of the tumour necrosis factor ligand family and has been shown to be overexpressed in tumoral cells together with the fibroblast growth factor-inducible 14 (Fn14) receptor. TWEAK-Fn14 interaction triggers a set of intracellular pathways responsible for tumour cell invasion and migration, as well as proliferation and angiogenesis. Hence, modulation of the TWEAK-Fn14 interaction is an important therapeutic goal. The targeting of protein-protein interactions by external agents, e.g., drugs, remains a substantial challenge. Given their intrinsic features, as well as recent advances that improve their pharmacological profiles, peptides have arisen as promising agents in this regard. Here, we report, by in silico structural design validated by cell-based and in vitro assays, the discovery of four peptides able to target TWEAK. Our results show that, when added to TWEAK-dependent cellular cultures, peptides cause a down-regulation of genes that are part of TWEAK-Fn14 signalling pathway. The direct, physical interaction between the peptides and TWEAK was further elucidated in an in vitro assay which confirmed that the bioactivity shown in cell-based assays was due to the targeting of TWEAK. The results presented here are framed within early pre-clinical drug development and therefore these peptide hits represent a starting point for the development of novel therapeutic agents. Our approach exemplifies the powerful combination of in silico and experimental efforts to quickly identify peptides with desirable traits.

In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan.

Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/.

Proximal Pathway Enrichment Analysis for Targeting Comorbid Diseases via Network Endopharmacology.

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.

Transcription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach.

Systems medicine has a mechanism-based rather than a symptom- or organ-based approach to disease and identifies therapeutic targets in a nonhypothesis-driven manner. In this work, we apply this to transcription factor nuclear factor (erythroid-derived 2)-like 2 (NRF2) by cross-validating its position in a protein-protein interaction network (the NRF2 interactome) functionally linked to cytoprotection in low-grade stress, chronic inflammation, metabolic alterations, and reactive oxygen species formation. Multiscale network analysis of these molecular profiles suggests alterations of NRF2 expression and activity as a common mechanism in a subnetwork of diseases (the NRF2 diseasome). This network joins apparently heterogeneous phenotypes such as autoimmune, respiratory, digestive, cardiovascular, metabolic, and neurodegenerative diseases, along with cancer. Importantly, this approach matches and confirms in silico several applications for NRF2-modulating drugs validated in vivo at different phases of clinical development. Pharmacologically, their profile is as diverse as electrophilic dimethyl fumarate, synthetic triterpenoids like bardoxolone methyl and sulforaphane, protein-protein or DNA-protein interaction inhibitors, and even registered drugs such as metformin and statins, which activate NRF2 and may be repurposed for indications within the NRF2 cluster of disease phenotypes. Thus, NRF2 represents one of the first targets fully embraced by classic and systems medicine approaches to facilitate both drug development and drug repurposing by focusing on a set of disease phenotypes that appear to be mechanistically linked. The resulting NRF2 drugome may therefore rapidly advance several surprising clinical options for this subset of chronic diseases.

Genetic and functional characterization of disease associations explains comorbidity.

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn's disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer's disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations.

The N-Terminal Phosphorylation of RB by p38 Bypasses Its Inactivation by CDKs and Prevents Proliferation in Cancer Cells.

Control of the G1/S phase transition by the Retinoblastoma (RB) tumor suppressor is critical for the proliferation of normal cells in tissues, and its inactivation is one of the most fundamental events leading to cancer. Cyclin-dependent kinase (CDK) phosphorylation inactivates RB to promote cell cycle-regulated gene expression. Here we show that, upon stress, the p38 stress-activated protein kinase (SAPK) maximizes cell survival by downregulating E2F gene expression through the targeting of RB. RB undergoes selective phosphorylation by p38 in its N terminus; these phosphorylations render RB insensitive to the inactivation by CDKs. p38 phosphorylation of RB increases its affinity toward the E2F transcription factor, represses gene expression, and delays cell-cycle progression. Remarkably, introduction of a RB phosphomimetic mutant in cancer cells reduces colony formation and decreases their proliferative and tumorigenic potential in mice.

ADP-ribose-derived nuclear ATP synthesis by NUDIX5 is required for chromatin remodeling.

Key nuclear processes in eukaryotes, including DNA replication, repair, and gene regulation, require extensive chromatin remodeling catalyzed by energy-consuming enzymes. It remains unclear how the ATP demands of such processes are met in response to rapid stimuli. We analyzed this question in the context of the massive gene regulation changes induced by progestins in breast cancer cells and found that ATP is generated in the cell nucleus via the hydrolysis of poly(ADP-ribose) to ADP-ribose. In the presence of pyrophosphate, ADP-ribose is used by the pyrophosphatase NUDIX5 to generate nuclear ATP. The nuclear source of ATP is essential for hormone-induced chromatin remodeling, transcriptional regulation, and cell proliferation.

Intramolecular trimerization, a novel strategy for making multispecific antibodies with controlled orientation of the antigen binding domains.

Here, we describe a new strategy that allows the rapid and efficient engineering of mono and multispecific trivalent antibodies. By fusing single-domain antibodies from camelid heavy-chain-only immunoglobulins (VHHs) to the N-terminus of a human collagen XVIII trimerization domain (TIE(XVIII)) we produced monospecific trimerbodies that were efficiently secreted as soluble functional proteins by mammalian cells. The purified VHH-TIE(XVIII) trimerbodies were trimeric in solution and exhibited excellent antigen binding capacity. Furthermore, by connecting with two additional glycine-serine-based linkers three VHH-TIE(XVIII) modules on a single polypeptide chain, we present an approach for the rational design of multispecific tandem trimerbodies with defined stoichiometry and controlled orientation. Using this technology we report here the construction and characterization of a tandem VHH-based trimerbody capable of simultaneously binding to three different antigens: carcinoembryonic antigen (CEA), epidermal growth factor receptor (EGFR) and green fluorescence protein (GFP). Multispecific tandem VHH-based trimerbodies were well expressed in mammalian cells, had good biophysical properties and were capable of simultaneously binding their targeted antigens. Importantly, these antibodies were very effective in inhibiting the proliferation of human epidermoid carcinoma A431 cells. Multispecific VHH-based trimerbodies are therefore ideal candidates for future applications in various therapeutic areas.

FN14 and GRP94 expression are prognostic/predictive biomarkers of brain metastasis outcome that open up new therapeutic strategies.

Brain metastasis is a devastating problem in patients with breast, lung and melanoma tumors. GRP94 and FN14 are predictive biomarkers over-expressed in primary breast carcinomas that metastasized in brain. To further validate these brain metastasis biomarkers, we performed a multicenter study including 318 patients with breast carcinomas. Among these patients, there were 138 patients with metastasis, of whom 84 had brain metastasis. The likelihood of developing brain metastasis increased by 5.24-fold (95%CI 2.83-9.71) and 2.55- (95%CI 1.52-4.3) in the presence of FN14 and GRP94, respectively. Moreover, FN14 was more sensitive than ErbB2 (38.27 vs. 24.68) with similar specificity (89.43 vs. 89.55) to predict brain metastasis and had identical prognostic value than triple negative patients (p < 0.0001). Furthermore, we used GRP94 and FN14 pathways and GUILD, a network-based disease-gene prioritization program, to pinpoint the genes likely to be therapeutic targets, which resulted in FN14 as the main modulator and thalidomide as the best scored drug. The treatment of mice with brain metastasis improves survival decreasing reactive astrocytes and angiogenesis, and down-regulate FN14 and its ligand TWEAK. In conclusion our results indicate that FN14 and GRP94 are prediction/prognosis markers which open up new possibilities for preventing/treating brain metastasis.

Knowledge-based modeling of peptides at protein interfaces: PiPreD.

MOTIVATION: Protein-protein interactions (PPIs) underpin virtually all cellular processes both in health and disease. Modulating the interaction between proteins by means of small (chemical) agents is therefore a promising route for future novel therapeutic interventions. In this context, peptides are gaining momentum as emerging agents for the modulation of PPIs. RESULTS: We reported a novel computational, structure and knowledge-based approach to model orthosteric peptides to target PPIs: PiPreD. PiPreD relies on a precompiled and bespoken library of structural motifs, iMotifs, extracted from protein complexes and a fast structural modeling algorithm driven by the location of native chemical groups on the interface of the protein target named anchor residues. PiPreD comprehensive and systematically samples the entire interface deriving peptide conformations best suited for the given region on the protein interface. PiPreD complements the existing technologies and provides new solutions for the disruption of selected interactions. AVAILABILITY AND IMPLEMENTATION: Database and accessory scripts and programs are available upon request to the authors or at http://www.bioinsilico.org/PIPRED. CONTACT: narcis.fernandez@gmail.com.

Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer.

Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the "guilt-by-association" principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the 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.

CDK2-dependent activation of PARP-1 is required for hormonal gene regulation in breast cancer cells.

Eukaryotic gene regulation implies that transcription factors gain access to genomic information via poorly understood processes involving activation and targeting of kinases, histone-modifying enzymes, and chromatin remodelers to chromatin. Here we report that progestin gene regulation in breast cancer cells requires a rapid and transient increase in poly-(ADP)-ribose (PAR), accompanied by a dramatic decrease of cellular NAD that could have broad implications in cell physiology. This rapid increase in nuclear PARylation is mediated by activation of PAR polymerase PARP-1 as a result of phosphorylation by cyclin-dependent kinase CDK2. Hormone-dependent phosphorylation of PARP-1 by CDK2, within the catalytic domain, enhances its enzymatic capabilities. Activated PARP-1 contributes to the displacement of histone H1 and is essential for regulation of the majority of hormone-responsive genes and for the effect of progestins on cell cycle progression. Both global chromatin immunoprecipitation (ChIP) coupled with deep sequencing (ChIP-seq) and gene expression analysis show a strong overlap between PARP-1 and CDK2. Thus, progestin gene regulation involves a novel signaling pathway that connects CDK2-dependent activation of PARP-1 with histone H1 displacement. Given the multiplicity of PARP targets, this new pathway could be used for the pharmacological management of breast cancer.

A taxonomy of organ-specific breast cancer metastases based on a protein-protein interaction network.

We carried out a systems-level study of the mechanisms underlying organ-specific metastases of breast cancer. We followed a network-based approach using microarray expression data from human breast cancer metastases to select organ-specific proteins that exert a range of functions allowing cell survival and growth in the microenvironment of distant organs. MinerProt, a home-made software application, was used to group organ-specific signatures of brain (1191 genes), bone (1623 genes), liver (977 genes) and lung (254 genes) metastases by function and select the most differentially expressed gene in each function. As a result, we obtained 19 functional representative proteins in brain, 23 in bone, 15 in liver and 9 in lung, with which we constructed four organ-specific protein-protein interaction networks. The network taxonomy included seven proteins that interacted in brain metastasis, which were mainly associated with signal transduction. Proteins related to immune response functions were bone specific, while those involved in proteolysis, signal transduction and hepatic glucose metabolism were found in liver metastasis. No experimental protein-protein interaction was found in lung metastasis; thus, computationally determined interactions were included in this network. Moreover, three of these selected genes (CXCL12, DSC2 and TFDP2) were associated with progression to specific organs when tested in an independent dataset. In conclusion, we present a network-based approach to filter information by selecting key protein functions as metastatic markers or therapeutic targets.

Extending signaling pathways with protein-interaction networks. Application to apoptosis.

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.

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.