Identification of novel targets for breast cancer by exploring gene switches on a genome scale

<p>Abstract</p> <p>Background</p> <p>An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.</p> <p>Results</p> <p>We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.</p> <p>Conclusions</p> <p>Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.</p

Comprehensive characterization of the Published Kinase Inhibitor Set

Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein-coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome

Observations of Lyα\alpha Emitters at High Redshift

In this series of lectures, I review our observational understanding of high-$z$ Ly$\alpha$ emitters (LAEs) and relevant scientific topics. Since the discovery of LAEs in the late 1990s, more than ten (one) thousand(s) of LAEs have been identified photometrically (spectroscopically) at $z\sim 0$ to $z\sim 10$. These large samples of LAEs are useful to address two major astrophysical issues, galaxy formation and cosmic reionization. Statistical studies have revealed the general picture of LAEs' physical properties: young stellar populations, remarkable luminosity function evolutions, compact morphologies, highly ionized inter-stellar media (ISM) with low metal/dust contents, low masses of dark-matter halos. Typical LAEs represent low-mass high-$z$ galaxies, high-$z$ analogs of dwarf galaxies, some of which are thought to be candidates of population III galaxies. These observational studies have also pinpointed rare bright Ly$\alpha$ sources extended over $\sim 10-100$ kpc, dubbed Ly$\alpha$ blobs, whose physical origins are under debate. LAEs are used as probes of cosmic reionization history through the Ly$\alpha$ damping wing absorption given by the neutral hydrogen of the inter-galactic medium (IGM), which complement the cosmic microwave background radiation and 21cm observations. The low-mass and highly-ionized population of LAEs can be major sources of cosmic reionization. The budget of ionizing photons for cosmic reionization has been constrained, although there remain large observational uncertainties in the parameters. Beyond galaxy formation and cosmic reionization, several new usages of LAEs for science frontiers have been suggested such as the distribution of {\sc Hi} gas in the circum-galactic medium and filaments of large-scale structures. On-going programs and future telescope projects, such as JWST, ELTs, and SKA, will push the horizons of the science frontiers.Comment: Lecture notes for `Lyman-alpha as an Astrophysical and Cosmological Tool', Saas-Fee Advanced Course 46. Verhamme, A., North, P., Cantalupo, S., & Atek, H. (eds.) --- 147 pages, 103 figures. Abstract abridged. Link to the lecture program including the video recording and ppt files : https://obswww.unige.ch/Courses/saas-fee-2016/program.cg

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Progress towards a public chemogenomic set for protein kinases and a call for contributions

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases