A notion of graph likelihood and an infinite monkey theorem

We play with a graph-theoretic analogue of the folklore infinite monkey theorem. We define a notion of graph likelihood as the probability that a given graph is constructed by a monkey in a number of time steps equal to the number of vertices. We present an algorithm to compute this graph invariant and closed formulas for some infinite classes. We have to leave the computational complexity of the likelihood as an open problem.Comment: 6 pages, 1 EPS figur

Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology.Comment: 20 pages, 5 figures. In Press in Sci Rep 201

Antitumor activity in RAS-driven tumors by blocking AKT and MEK.

PURPOSE: KRAS is the most commonly mutated oncogene in human tumors. KRAS-mutant cells may exhibit resistance to the allosteric MEK1/2 inhibitor selumetinib (AZD6244; ARRY-142886) and allosteric AKT inhibitors (such as MK-2206), the combination of which may overcome resistance to both monotherapies. EXPERIMENTAL DESIGN: We conducted a dose/schedule-finding study evaluating MK-2206 and selumetinib in patients with advanced treatment-refractory solid tumors. Recommended dosing schedules were defined as MK-2206 at 135 mg weekly and selumetinib at 100 mg once daily. RESULTS: Grade 3 rash was the most common dose-limiting toxicity (DLT); other DLTs included grade 4 lipase increase, grade 3 stomatitis, diarrhea, and fatigue, and grade 3 and grade 2 retinal pigment epithelium detachment. There were no meaningful pharmacokinetic drug-drug interactions. Clinical antitumor activity included RECIST 1.0-confirmed partial responses in non-small cell lung cancer and low-grade ovarian carcinoma. CONCLUSION: Responses in KRAS-mutant cancers were generally durable. Clinical cotargeting of MEK and AKT signaling may be an important therapeutic strategy in KRAS-driven human malignancies (Trial NCT number NCT01021748).The Drug Development Unit of the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research is supported in part by a programme grant from Cancer Research UK (grant number: C347/A18077). Support was also provided by an Experimental Cancer Medicine Centre grant (no grant number) and the National Institute for Health Research Biomedical Research Centre (jointly to the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research) (grant numbers: A46/CCR - CCR4057 & CCR4058).This is the accepted manuscript of a paper published in Clinical Cancer Research, February 15, 2015 21; 739, doi: 10.1158/1078-0432.CCR-14-190

17-allyamino-17-demethoxygeldanamycin treatment results in a magnetic resonance spectroscopy-detectable elevation in choline-containing metabolites associated with increased expression of choline transporter SLC44A1 and phospholipase A2

Abstract Introduction 17-allyamino-17-demethoxygeldanamycin (17-AAG), a small molecule inhibitor of Hsp90, is currently in clinical trials in breast cancer. However, 17-AAG treatment often results in inhibition of tumor growth rather than shrinkage, making detection of response a challenge. Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) are noninvasive imaging methods than can be used to monitor metabolic biomarkers of drug-target modulation. This study set out to examine the MRS-detectable metabolic consequences of Hsp90 inhibition in a breast cancer model. Methods MCF-7 breast cancer cells were investigated, and MRS studies were performed both on live cells and on cell extracts. 31P and 1H MRS were used to determine total cellular metabolite concentrations and 13C MRS was used to probe the metabolism of [1,2-13C]-choline. To explain the MRS metabolic findings, microarray and RT-PCR were used to analyze gene expression, and in vitro activity assays were performed to determine changes in enzymatic activity following 17-AAG treatment. Results Treatment of MCF-7 cells with 17-AAG for 48 hours caused a significant increase in intracellular levels of choline (to 266 ± 18% of control, P = 0.05) and phosphocholine (PC; to 181 ± 10% of control, P = 0.001) associated with an increase in expression of choline transporter SLC44A1 and an elevation in the de novo synthesis of PC. We also detected an increase in intracellular levels of glycerophosphocholine (GPC; to 176 ± 38% of control, P = 0.03) associated with an increase in PLA2 expression and activity. Conclusions This study determined that in the MCF-7 breast cancer model inhibition of Hsp90 by 17-AAG results in a significant MRS-detectable increase in choline, PC and GPC, which is likely due to an increase in choline transport into the cell and phospholipase activation. 1H MRSI can be used in the clinical setting to detect levels of total choline-containing metabolite (t-Cho, composed of intracellular choline, PC and GPC). As Hsp90 inhibitors enter routine clinical use, t-Cho could thus provide an easily detectable, noninvasive metabolic biomarker of Hsp90 inhibition in breast cancer patients

The creatine kinase pathway is a metabolic vulnerability in EVI1-positive acute myeloid leukemia

Expression of the MECOM (also known as EVI1) proto-oncogene is deregulated by chromosomal translocations in some cases of acute myeloid leukemia (AML) and is associated with poor clinical outcome. Here, through transcriptomic and metabolomic profiling of hematopoietic cells, we reveal that EVI1 overexpression alters cellular metabolism. A screen using pooled short hairpin RNAs (shRNAs) identified the ATP-buffering, mitochondrial creatine kinase CKMT1 as necessary for survival of EVI1-expressing cells in subjects with EVI1-positive AML. EVI1 promotes CKMT1 expression by repressing the myeloid differentiation regulator RUNX1. Suppression of arginine-creatine metabolism by CKMT1-directed shRNAs or by the small molecule cyclocreatine selectively decreased the viability, promoted the cell cycle arrest and apoptosis of human EVI1-positive cell lines, and prolonged survival in both orthotopic xenograft models and mouse models of primary AML. CKMT1 inhibition altered mitochondrial respiration and ATP production, an effect that was abrogated by phosphocreatine-mediated reactivation of the arginine-creatine pathway. Targeting CKMT1 is thus a promising therapeutic strategy for this EVI1-driven AML subtype that is highly resistant to current treatment regimens. Keywords: AML; RUNX1; CKMT1; cyclocreatine; arginine metabolismNational Cancer Institute (U.S.) (NIH 1R35 CA210030-01)Stand Up To CancerBridge ProjectNational Cancer Institute (U.S.) (David H. Koch Institute for Integrative Cancer Research at MIT. Grant P30-CA14051

Genome-Wide Linkage Scan to Identify Loci Associated with Type 2 Diabetes and Blood Lipid Phenotypes in the Sikh Diabetes Study

In this investigation, we have carried out an autosomal genome-wide linkage analysis to map genes associated with type 2 diabetes (T2D) and five quantitative traits of blood lipids including total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, very low-density lipoprotein (VLDL) cholesterol, and triglycerides in a unique family-based cohort from the Sikh Diabetes Study (SDS). A total of 870 individuals (526 male/344 female) from 321 families were successfully genotyped using 398 polymorphic microsatellite markers with an average spacing of 9.26 cM on the autosomes. Results of non-parametric multipoint linkage analysis using Sall statistics (implemented in Merlin) did not reveal any chromosomal region to be significantly associated with T2D in this Sikh cohort. However, linkage analysis for lipid traits using QTL-ALL analysis revealed promising linkage signals with p≤0.005 for total cholesterol, LDL cholesterol, and HDL cholesterol at chromosomes 5p15, 9q21, 10p11, 10q21, and 22q13. The most significant signal (p = 0.0011) occurred at 10q21.2 for HDL cholesterol. We also observed linkage signals for total cholesterol at 22q13.32 (p = 0.0016) and 5p15.33 (p = 0.0031) and for LDL cholesterol at 10p11.23 (p = 0.0045). Interestingly, some of linkage regions identified in this Sikh population coincide with plausible candidate genes reported in recent genome-wide association and meta-analysis studies for lipid traits. Our study provides the first evidence of linkage for loci associated with quantitative lipid traits at four chromosomal regions in this Asian Indian population from Punjab. More detailed examination of these regions with more informative genotyping, sequencing, and functional studies should lead to rapid detection of novel targets of therapeutic importance

A catalogue of structural and morphological measurements for DES Y1

We present a structural and morphological catalogue for 45 million objects selected from the first year data of the Dark Energy Survey (DES). Single Sersic fits and non-parametric ´ measurements are produced for g, r, and i filters. The parameters from the best-fitting Sersic ´ model (total magnitude, half-light radius, Sersic index, axis ratio, and position angle) are mea- ´ sured with GALFIT; the non-parametric coefficients (concentration, asymmetry, clumpiness, Gini, M20) are provided using the Zurich Estimator of Structural Types (ZEST+). To study the statistical uncertainties, we consider a sample of state-of-the-art image simulations with a realistic distribution in the input parameter space and then process and analyse them as we do with real data: this enables us to quantify the observational biases due to PSF blurring and magnitude effects and correct the measurements as a function of magnitude, galaxy size, Sersic ´ index (concentration for the analysis of the non-parametric measurements) and ellipticity. We present the largest structural catalogue to date: we find that accurate and complete measurements for all the structural parameters are typically obtained for galaxies with SEXTRACTOR MAG AUTO I ≤ 21. Indeed, the parameters in the filters i and r can be overall well recovered up to MAG AUTO ≤ 21.5, corresponding to a fitting completeness of ∼90 per cent below this threshold, for a total of 25 million galaxies. The combination of parametric and non-parametric structural measurements makes this catalogue an important instrument to explore and understand how galaxies form and evolve. The catalogue described in this paper will be publicly released alongside the DES collaboration Y1 cosmology data products at the following URL: https://des.ncsa.illinois.edu/releases