A mammalian functional-genetic approach to characterizing cancer therapeutics

Supplementary information is available online at http://www.nature.com/naturechemicalbiology/. Reprints and permissions information is available online at http://npg.nature.com/reprintsandpermissions/.Identifying mechanisms of drug action remains a fundamental impediment to the development and effective use of chemotherapeutics. Here we describe an RNA interference (RNAi)–based strategy to characterize small-molecule function in mammalian cells. By examining the response of cells expressing short hairpin RNAs (shRNAs) to a diverse selection of chemotherapeutics, we could generate a functional shRNA signature that was able to accurately group drugs into established biochemical modes of action. This, in turn, provided a diversely sampled reference set for high-resolution prediction of mechanisms of action for poorly characterized small molecules. We could further reduce the predictive shRNA target set to as few as eight genes and, by using a newly derived probability-based nearest-neighbors approach, could extend the predictive power of this shRNA set to characterize additional drug categories. Thus, a focused shRNA phenotypic signature can provide a highly sensitive and tractable approach for characterizing new anticancer drugs.National Institute of Mental Health (U.S.) (grant RO1 CA128803-03)American Association for Cancer ResearchMassachusetts Institute of Technology. Dept. of BiologyNational Cancer Institute (U.S.). Integrative Cancer Biology Program (grant 1-U54-CA112967

Software module clustering: An in-depth literature analysis

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field

Improved K-means clustering algorithms : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, New Zealand

K-means clustering algorithm is designed to divide the samples into subsets with the goal that maximizes the intra-subset similarity and inter-subset dissimilarity where the similarity measures the relationship between two samples. As an unsupervised learning technique, K-means clustering algorithm is considered one of the most used clustering algorithms and has been applied in a variety of areas such as artificial intelligence, data mining, biology, psychology, marketing, medicine, etc. K-means clustering algorithm is not robust and its clustering result depends on the initialization, the similarity measure, and the predefined cluster number. Previous research focused on solving a part of these issues but has not focused on solving them in a unified framework. However, fixing one of these issues does not guarantee the best performance. To improve K-means clustering algorithm, one of the most famous and widely used clustering algorithms, by solving its issues simultaneously is challenging and significant. This thesis conducts an extensive research on K-means clustering algorithm aiming to improve it. First, we propose the Initialization-Similarity (IS) clustering algorithm to solve the issues of the initialization and the similarity measure of K-means clustering algorithm in a unified way. Specifically, we propose to fix the initialization of the clustering by using sum-of-norms (SON) which outputs the new representation of the original samples and to learn the similarity matrix based on the data distribution. Furthermore, the derived new representation is used to conduct K-means clustering. Second, we propose a Joint Feature Selection with Dynamic Spectral (FSDS) clustering algorithm to solve the issues of the cluster number determination, the similarity measure, and the robustness of the clustering by selecting effective features and reducing the influence of outliers simultaneously. Specifically, we propose to learn the similarity matrix based on the data distribution as well as adding the ranked constraint on the Laplacian matrix of the learned similarity matrix to automatically output the cluster number. Furthermore, the proposed algorithm employs the L2,1-norm as the sparse constraints on the regularization term and the loss function to remove the redundant features and reduce the influence of outliers respectively. Third, we propose a Joint Robust Multi-view (JRM) spectral clustering algorithm that conducts clustering for multi-view data while solving the initialization issue, the cluster number determination, the similarity measure learning, the removal of the redundant features, and the reduction of outlier influence in a unified way. Finally, the proposed algorithms outperformed the state-of-the-art clustering algorithms on real data sets. Moreover, we theoretically prove the convergences of the proposed optimization methods for the proposed objective functions

Systems level characterizations of single and combination drug mechanisms of action in vitro and in vivo

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references.Small molecule characterization is a critical limiting step in cancer drug development. At the present time, high throughput screens of natural products and combinatorial synthesis libraries generate more pharmaceutical leads than can be characterized in detail. Lead optimization further generates many derivatives of these cytotoxic hits in an attempt to generate optimized compounds with better physical or chemical properties. This leaves many promising agents stranded in drug development and poorly characterized. In addition, most small molecules interact biochemically with a diverse set of proteins. While characterizing the diversity of biochemical interactions that can occur is important to understanding function, only a subset are likely to be necessary or sufficient for therapeutic efficacy. In light of this diversity, the functional characterization of the mechanisms of cell death by cytotoxic agents should improve drug discovery by allowing for the early prioritization of cytotoxic leads, derivatized compounds, and targeted inhibitors on the basis of the mechanisms by which they cause death in intact cells. Using RNAi mediated suppression of key mediators of apoptosis; we found that we could predict the functional mechanisms of drug action in lymphoma cells across many categories of cytotoxic therapeutics with as few as 8 shRNAs. Beyond single drug mechanisms, most drugs used in cancer are used as drug combinations. These combinations were largely formulated on two principles: compounds must have a unique mechanism of action so that more cumulative drug can be dosed with non-overlapping toxicity, and they must have statistically independent mechanisms of drug resistance. However, beyond clinical efficacy, the basic mechanisms of combination therapy have never been examined. Thus, in light of the central role of apoptosis in guiding mammalian cell death to cancer therapy, we sought to examine the functional signatures of cell death in the face of combination therapy. Surprisingly we find that RNAi mediated suppression of cell death mediators in response to common cytotoxic regimens, averages both sensitivity and resistance to therapy and neutralizes the effects of genetic variation. This suggests that common cytotoxic regimens are intrinsically depersonalized and difficult to genetically stratify.by Justin Pritchard.Ph.D

Automatic discovery of drug mode of action and drug repositioning from gene expression data

2009 - 2010The identification of the molecular pathway that is targeted by a compound, combined with the dissection of the following reactions in the cellular environment, i.e. the drug mode of action, is a key challenge in biomedicine. Elucidation of drug mode of action has been attempted, in the past, with different approaches. Methods based only on transcriptional responses are those requiring the least amount of information and can be quickly applied to new compounds. On the other hand, they have met with limited success and, at the present, a general, robust and efficient gene-expression based method to study drugs in mammalian systems is still missing. We developed an efficient analysis framework to investigate the mode of action of drugs by using gene expression data only. Particularly, by using a large compendium of gene expression profiles following treatments with more than 1,000 compounds on different human cell lines, we were able to extract a synthetic consensual transcriptional response for each of the tested compounds. This was obtained by developing an original rank merging procedure. Then, we designed a novel similarity measure among the transcriptional responses to each drug, endingending up with a “drug similarity network”, where each drug is a node and edges represent significant similarities between drugs. By means of a novel hierarchical clustering algorithm, we then provided this network with a modular topology, contanining groups of highly interconnected nodes (i.e. network communities) whose exemplars form secondlevel modules (i.e. network rich-clubs), and so on. We showed that these topological modules are enriched for a given mode of action and that the hierarchy of the resulting final network reflects the different levels of similarities among the composing compound mode of actions. Most importantly, by integrating a novel drug X into this network (which can be done very quickly) the unknown mode of action can be inferred by studying the topology of the subnetwork surrounding X. Moreover, novel potential therapeutic applications can be assigned to safe and approved drugs, that are already present in the network, by studying their neighborhood (i.e. drug repositioning), hence in a very cheap, easy and fast way, without the need of additional experiments. By using this approach, we were able to correctly classify novel anti-cancer compounds; to predict and experimentally validate an unexpected similarity in the mode of action of CDK2 inhibitors and TopoIsomerase inhibitors and to predict that Fasudil, a known and FDA-approved cardiotonic agent, could be repositioned as novel enhancer of cellular autophagy. Due to the extremely safe profile of this drug and its potential ability to traverse the blood-brain barrier, this could have strong implications in the treatment of several human neurodegenerative disorders, such as Huntington and Parkinson diseases. [edited by author]IX n.s

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Metropolitan Research

Metropolitan research requires multidisciplinary perspectives in order to do justice to the complexities of metropolitan regions. This volume provides a scholarly and accessible overview of key methods and approaches in metropolitan research from a uniquely broad range of disciplines including architectural history, art history, heritage conservation, literary and cultural studies, spatial planning and planning theory, geoinformatics, urban sociology, economic geography, operations research, technology studies, transport planning, aquatic ecosystems research and urban epidemiology. It is this scope of disciplinary - and increasingly also interdisciplinary - approaches that allows metropolitan research to address recent societal challenges of urban life, such as mobility, health, diversity or sustainability