Novel biomarkers derived from the Maintenance of Wakefulness Test as predictors of sleepiness and response to treatment.

The Maintenance of Wakefulness Test (MWT) is a widely accepted objective test used to evaluate daytime somnolence and is commonly used in clinical studies evaluating novel therapeutics for excessive daytime sleepiness. In the latter, sleep onset latency (SOL) is typically the sole MWT endpoint. Here, we explored microsleeps, sleep probability measures derived from automated sleep scoring, and quantitative electroencephalography (qEEG) features as additional MWT biomarkers of daytime sleepiness, using data from a phase 1B trial of the selective orexin receptor 2 agonist danavorexton (TAK-925) in people with narcolepsy type 1 (NT1) or type 2 (NT2). Danavorexton treatment reduced the rate and duration of microsleeps during the MWT in NT1 (days 1 and 7; p ≤ 0.005) and microsleep rate in NT2 (days 1 and 7; p < 0.0001). Use of an EEG-sleep-staging-derived measure to determine the probability of wakefulness for each minute revealed a novel metric to track changes in daytime sleepiness, which were consistent with the θ/α ratio, a known biomarker of drowsiness. The slopes of line-fits to both the log-transformed sleepiness score or log-transformed θ/α ratio correlated well to (inverse) MWT SOL for NT1 (R = 0.93 and R = 0.83, respectively) and NT2 (R = 0.97 and R = 0.84, respectively), suggesting that individuals with narcolepsy have increased sleepiness immediately after lights-off. These analyses demonstrate that novel EEG-based biomarkers can augment SOL as predictors of sleepiness and its response to treatment and provide a novel framework for the analysis of wake EEG in hypersomnia disorders.

Patient understanding and impression of hematology/oncology fellows.

BACKGROUND: Hematologists/Oncologists spend years of training in a fellowship program. At academic centers, patients receiving treatment are often seen by fellows. It has not been established what patients understand about fellowship training, therefore the purpose of this study was to explore their understanding and whether they are content with fellows taking part in their care. METHODS: At West Virginia University/Mary Babb Randolph Cancer Center, the authors drafted a survey. This anonymous and voluntary survey abstracted basic patient demographic data and experience being cared for by fellows and basic knowledge of a Hematology/Oncology fellowship. Multiple-choice questions were drafted with 4 to 6 answer choices with no option for unknown. Surveys were collected over a 3-week period in July 2012. Patients were surveyed at outpatient appointments, infusion center visits, and laboratory draws. RESULTS: Two hundred twenty-six surveys were collected. Statistical analysis was performed and a binomial regression was fit to the data. There is evidence that higher levels of education are more likely to give correct answers (P = 0.035). Patients who stated that they had not seen a fellow or were unsure whether they had seen a fellow were more likely to select incorrect answers (P = 0.001). There is no statistical significance differentiating between cancer types in likelihood of getting answers correct. Of those surveyed, 1.77% felt that they completely understand the role of a fellow in their care, whereas 80.45% desired further information about fellows. Only 2.2% disliked having a fellow involved in their care. CONCLUSIONS: Patients at academic centers being seen by Hematology/Oncology fellows appear to have a lack of knowledge of a fellow's role and background but have a desire to be educated. Educational initiatives can be introduced to teaching institutions to help patients better understand the role of a fellow.

NEDD9 depletion leads to MMP14 inactivation by TIMP2 and prevents invasion and metastasis.

UNLABELLED: The scaffolding protein NEDD9 is an established prometastatic marker in several cancers. Nevertheless, the molecular mechanisms of NEDD9-driven metastasis in cancers remain ill-defined. Here, using a comprehensive breast cancer tissue microarray, it was shown that increased levels of NEDD9 protein significantly correlated with the transition from carcinoma in situ to invasive carcinoma. Similarly, it was shown that NEDD9 overexpression is a hallmark of highly invasive breast cancer cells. Moreover, NEDD9 expression is crucial for the protease-dependent mesenchymal invasion of cancer cells at the primary site but not at the metastatic site. Depletion of NEDD9 is sufficient to suppress invasion of tumor cells in vitro and in vivo, leading to decreased circulating tumor cells and lung metastases in xenograft models. Mechanistically, NEDD9 localized to invasive pseudopods and was required for local matrix degradation. Depletion of NEDD9 impaired invasion of cancer cells through inactivation of membrane-bound matrix metalloproteinase MMP14 by excess TIMP2 on the cell surface. Inactivation of MMP14 is accompanied by reduced collagenolytic activity of soluble metalloproteinases MMP2 and MMP9. Reexpression of NEDD9 is sufficient to restore the activity of MMP14 and the invasive properties of breast cancer cells in vitro and in vivo. Collectively, these findings uncover critical steps in NEDD9-dependent invasion of breast cancer cells. IMPLICATIONS: This study provides a mechanistic basis for potential therapeutic interventions to prevent metastasis.

Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS.

BACKGROUND: The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms. RESULTS: In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants. CONCLUSION: We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.

NEDD9 depletion destabilizes Aurora A kinase and heightens the efficacy of Aurora A inhibitors: implications for treatment of metastatic solid tumors.

Aurora A kinase (AURKA) is overexpressed in 96% of human cancers and is considered an independent marker of poor prognosis. While the majority of tumors have elevated levels of AURKA protein, few have AURKA gene amplification, implying that posttranscriptional mechanisms regulating AURKA protein levels are significant. Here, we show that NEDD9, a known activator of AURKA, is directly involved in AURKA stability. Analysis of a comprehensive breast cancer tissue microarray revealed a tight correlation between the expression of both proteins, significantly corresponding with increased prognostic value. A decrease in AURKA, concomitant with increased ubiquitination and proteasome-dependent degradation, occurs due to depletion or knockout of NEDD9. Reexpression of wild-type NEDD9 was sufficient to rescue the observed phenomenon. Binding of NEDD9 to AURKA is critical for AURKA stabilization, as mutation of S296E was sufficient to disrupt binding and led to reduced AURKA protein levels. NEDD9 confers AURKA stability by limiting the binding of the cdh1-substrate recognition subunit of APC/C ubiquitin ligase to AURKA. Depletion of NEDD9 in tumor cells increases sensitivity to AURKA inhibitors. Combination therapy with NEDD9 short hairpin RNAs and AURKA inhibitors impairs tumor growth and distant metastasis in mice harboring xenografts of breast tumors. Collectively, our findings provide rationale for the use of AURKA inhibitors in treatment of metastatic tumors and predict the sensitivity of the patients to AURKA inhibitors based on NEDD9 expression.

Src binds cortactin through an SH2 domain cystine-mediated linkage.

Tyrosine-kinase-based signal transduction mediated by modular protein domains is critical for cellular function. The Src homology (SH)2 domain is an important conductor of intracellular signaling that binds to phosphorylated tyrosines on acceptor proteins, producing molecular complexes responsible for signal relay. Cortactin is a cytoskeletal protein and tyrosine kinase substrate that regulates actin-based motility through interactions with SH2-domain-containing proteins. The Src kinase SH2 domain mediates cortactin binding and tyrosine phosphorylation, but how Src interacts with cortactin is unknown. Here we demonstrate that Src binds cortactin through cystine bonding between Src C185 in the SH2 domain within the phosphotyrosine binding pocket and cortactin C112/246 in the cortactin repeats domain, independent of tyrosine phosphorylation. Interaction studies show that the presence of reducing agents ablates Src-cortactin binding, eliminates cortactin phosphorylation by Src, and prevents Src SH2 domain binding to cortactin. Tandem MS/MS sequencing demonstrates cystine bond formation between Src C185 and cortactin C112/246. Mutational studies indicate that an intact cystine binding interface is required for Src-mediated cortactin phosphorylation, cell migration, and pre-invadopodia formation. Our results identify a novel phosphotyrosine-independent binding mode between the Src SH2 domain and cortactin. Besides Src, one quarter of all SH2 domains contain cysteines at or near the analogous Src C185 position. This provides a potential alternative mechanism to tyrosine phosphorylation for cysteine-containing SH2 domains to bind cognate ligands that may be widespread in propagating signals regulating diverse cellular functions.

The ensemble bridge algorithm: a new modeling tool for drug discovery problems.

Ensemble algorithms have been historically categorized into two separate paradigms, boosting and random forests, which differ significantly in the way each ensemble is constructed. Boosting algorithms represent one extreme, where an iterative greedy optimization strategy, weak learners (e.g., small classification trees), and stage weights are employed to target difficult-to-classify regions in the training space. On the other extreme, random forests rely on randomly selected features and complex learners (learners that exhibit low bias, e.g., large regression trees) to classify well over the entire training data. Because the approach is not targeting the next learner for inclusion, it tends to provide a natural robustness to noisy labels. In this work, we introduce the ensemble bridge algorithm, which is capable of transitioning between boosting and random forests using a regularization parameter nu in [0,1]. Because the ensemble bridge algorithm is a compromise between the greedy nature of boosting and the randomness present in random forests, it yields robust performance in the presence of a noisy response and superior performance in the presence of a clean response. Often, drug discovery data (e.g., computational chemistry data) have varying levels of noise. Hence, this method enables a practitioner to employ a single method to evaluate ensemble performance. The method's robustness is verified across a variety of data sets where the algorithm repeatedly yields better performance than either boosting or random forests alone. Finally, we provide diagnostic tools for the new algorithm, including a measure of variable importance and an observational clustering tool.

Graph-based semisupervised learning.

Graph-based learning provides a useful approach for modeling data in classification problems. In this modeling scenario, the relationship between labeled and unlabeled data impacts the construction and performance of classifiers, and therefore a semi-supervised learning framework is adopted. We propose a graph classifier based on kernel smoothing. A regularization framework is also introduced, and it is shown that the proposed classifier optimizes certain loss functions. Its performance is assessed on several synthetic and real benchmark data sets with good results, especially in settings where only a small fraction of the data are labeled.