SUMMARY The target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6 – collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic, and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/Cyclin A/E – implicated in resistance to CDK4/6 inhibition – and CDK1/Cyclin B. The multi-faceted experimental and computational approaches described here therefore uncover under-appreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.

Current benchmark reports of classification algorithms generally concern common classifiers and their variants but do not include many algorithms that have been introduced in recent years. Moreover, important properties such as the dependency on number of classes and features and CPU running time are typically not examined. In this paper, we carry out a comparative empirical study on both established classifiers and more recently proposed ones on 71 data sets originating from different domains, publicly available at UCI and KEEL repositories. The list of 11 algorithms studied includes Extreme Learning Machine (ELM), Sparse Representation based Classification (SRC), and Deep Learning (DL), which have not been thoroughly investigated in existing comparative studies. It is found that Stochastic Gradient Boosting Trees (GBDT) matches or exceeds the prediction performance of Support Vector Machines (SVM) and Random Forests (RF), while being the fastest algorithm in terms of prediction efficiency. ELM also yields good accuracy results, ranking in the top-5 , alongside GBDT, RF, SVM, and C4.5 but this performance varies widely across all data sets. Unsurprisingly, top accuracy performers have average or slow training time efficiency. DL is the worst performer in terms of accuracy but second fastest in prediction efficiency. SRC shows good accuracy performance but it is the slowest classifier in both training and testing.

Differential privacy (DP) is a widely accepted mathematical framework for protecting data privacy. Simply stated, it guarantees that the distribution of query results changes only slightly due to the modification of any one tuple in the database. This allows protection, even against powerful adversaries, who know the entire database except one tuple. For providing this guarantee, differential privacy mechanisms assume independence of tuples in the database-a vulnerable assumption that can lead to degradation in expected privacy levels especially when applied to real-world datasets that manifest natural dependence owing to various social, behavioral, and genetic relationships between users. In this paper, we make several contributions that not only demonstrate the feasibility of exploiting the above vulnerability but also provide steps towards mitigating it. First, we present an inference attack, using real datasets, where an adversary leverages the probabilistic dependence between tuples to extract users' sensitive information from differentially private query results (violating the DP guarantees). Second, we introduce the notion of dependent differential privacy (DDP) that accounts for the dependence that exists between tuples and propose a dependent perturbation mechanism (DPM) to achieve the privacy guarantees in DDP. Finally, using a combination of theoretical analysis and extensive experiments involving different classes of queries (e.g., machine learning queries, graph queries) issued over multiple large-scale real-world datasets, we show that our DPM consistently outperforms state-of-the-art approaches in managing the privacy-utility tradeoffs for dependent data. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH’s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or ‘dark’ kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

Background: Hepatocellular carcinoma (HCC) is a prominent cancer type, with long non-coding RNAs (lncRNAs) being known to be relevant to its progression. We therefore investigated how a particular lncRNA known as the metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) was associated with HCC.Methods: Quantitative reverse transcriptase PCR (qPCR) was used to assess expression of MALAT1, Forkhead Box M1 (FOXM1) and miR-125a-3p in HCC tissue samples. How MALAT1 regulates HCC proliferation and metastasis was assessed through appropriate assays. FOXM1 was identified as a miR-125a-3p target using luciferase assays, and how MALAT1/miR-125a-3p alter FOXM1 expression was explored via qPCR and Western blotting.Results: HCC tissues exhibited MALAT1 upregulation. miR-125a-3p targeted FOXM1 and could be regulated by MALAT1. MALAT1 knockdown disrupted proliferation and invasion, whereas miR-125a-3p knockdown partially reversed this phenotype.Conclusions: These results indicate that MALAT1 modulates FOXM1 expression via being a miR-125a-3p sponge, thus promoting HCC progression.

We discuss the synthesis of a composite of ZrO 2 nanoparticles supported on multiwalled carbon nanotubes (MWCNT). Using X-ray diffraction and high-resolution transmission electron microscopy (HR-TEM), the ZrO 2 were found to be 2−3 nm tetragonal crystalline nanoparticles. Strong interfacial interaction between the ZrO 2 nanoparticles and the MWCNT surface was observed by near-edge X-ray absorption fine structure spectroscopy (NEXAFS) at the carbon K-edge and the oxygen K-edge, and this strong metal oxide/support interaction leads to small ZrO 2 particle size and thermal stability. The ZrO 2 /MWCNT was converted into a solid acid catalyst by sulfation, and the properties of S-ZrO 2 / MWCNT were studied. The nature of the acid sites was probed by S K-edge and Zr L-edges (L 3 , L 2 , L 1 ) XANES as well as catalytic probe reaction of cyclohexane dehydrogenation/cracking. Such composites would be good candidates for potential catalysis applications in fuel cell electrodes and biomass processing.

Social relationships present a critical foundation for many real-world applications. However, both users and online social network (OSN) providers are hesitant to share social relationships with untrusted external applications due to privacy concerns. In this work, we design LinkMirage, a system that mediates privacy-preserving access to social relationships. LinkMirage takes users' social relationship graph as an input, obfuscates the social graph topology, and provides untrusted external applications with an obfuscated view of the social relationship graph while preserving graph utility.Our key contributions are (1) a novel algorithm for obfuscating social relationship graph while preserving graph utility, (2) theoretical and experimental analysis of privacy and utility using real-world social network topologies, including a large-scale Google+ dataset with 940 million links. Our experimental results demonstrate that LinkMirage provides up to 10x improvement in privacy guarantees compared to the state-of-the-art approaches. Overall, LinkMirage enables the design of real-world applications such as recommendation systems, graph analytics, anonymous communications, and Sybil defenses while protecting the privacy of social relationships.

BackgroundAutophagy is a major cellular process by which cytoplasmic components such as damaged organelles and misfolded proteins are recycled. Although low levels of autophagy occur in cells under basal conditions, certain cellular stresses including nutrient depletion, DNA damage, and oxidative stress are known to robustly induce autophagy. Krüppel-like factor 4 (KLF4) is a zinc-finger transcription factor activated during oxidative stress to maintain genomic stability. Both autophagy and KLF4 play important roles in response to stress and function in tumor suppression.MethodsTo explore the role of KLF4 on autophagy in mouse embryonic fibroblasts (MEFs), we compared wild-type with Klf4 deficient cells. To determine the levels of autophagy, we starved MEFs for different times with Earle’s balanced salts solution (EBSS). Rapamycin was used to manipulate mTOR activity and autophagy. The percentage of cells with γ-H2AX foci, a marker for DNA damage, and punctate pattern of GFP-LC3 were counted by confocal microscopy. The effects of the drug treatments, Klf4 overexpression, or Klf4 transient silencing on autophagy were analyzed using Western blot. Trypan Blue assay and flow cytometry were used to study cell viability and apoptosis, respectively. qPCR was also used to assay basal and the effects of Klf4 overexpression on Atg7 expression levels.ResultsHere our data suggested that Klf4−/− MEFs exhibited impaired autophagy, which sensitized them to cell death under nutrient deprivation. Secondly, DNA damage in Klf4-null MEFs increased after treatment with EBSS and was correlated with increased apoptosis. Thirdly, we found that Klf4−/− MEFs showed hyperactive mTOR activity. Furthermore, we demonstrated that rapamycin reduced the increased level of mTOR in Klf4−/− MEFs, but did not restore the level of autophagy. Finally, re-expression of Klf4 in Klf4 deficient MEFs resulted in increased levels of LC3II, a marker for autophagy, and Atg7 expression level when compared to GFP-control transfected Klf4−/− MEFs.ConclusionTaken together, our results strongly suggest that KLF4 plays a critical role in the regulation of autophagy and suppression of mTOR activity. In addition, we showed that rapamycin decreased the level of mTOR in Klf4−/− MEFs, but did not restore autophagy. This suggests that KLF4 regulates autophagy through both mTOR-dependent and independent mechanisms. Furthermore, for the first time, our findings provide novel insights into the mechanism by which KLF4 perhaps prevents DNA damage and apoptosis through activation of autophagy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-015-0373-6) contains supplementary material, which is available to authorized users.