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Assessment of an RNA interference screen-derived mitotic and ceramide pathway metagene as a predictor of response to neoadjuvant paclitaxel for primary triple-negative breast cancer: a retrospective analysis of five clinical trials

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LANCET ONCOLOGY 2010年第4期11卷 358-365页

作者： Juul, Nicolai Szallasi, Zoltan Eklund, Aron C. Li, Qiyuan Burrell, Rebecca A. Gerlinger, Marco Valero, Vicente Andreopoulou, Eleni Esteva, Francisco J. Symmans, W. Fraser Desmedt, Christine Haibe-Kains, Benjamin Sotiriou, Christos Pusztai, Lajos Swanton, Charles Canc Res UK London Res Inst Translat Canc Therapeut Lab London WC2A 3PX England Tech Univ Denmark Ctr Biol Sequence Anal DK-2800 Lyngby Denmark Harvard Univ Sch Med Harvard Mit Div Hlth Sci & Technol Childrens Hosp Informat Program Boston MA USA Queen Mary Univ London Baits & London Sch Med & Dent Inst Canc London England Univ Texas MD Anderson Canc Ctr Dept Breast Med Oncol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Pathol Houston TX 77030 USA Inst Jules Bordet Dept Med Oncol B-1000 Brussels Belgium Univ Libre Bruxelles Machine Learning Grp Brussels Belgium Royal Marsden Hosp Dept Med Breast Unit Sutton Surrey England

Background Addition of taxanes to preoperative chemotherapy in breast cancer increases the proportion of patients who have a pathological complete response (pCR). However, a substantial proportion of patients do not respond, and the prognosis is particularly poor for patients with oestrogen-receptor (ER)/progesterone-receptor (PR)/human epidermal growth factor receptor 2 (HER2;ERBB2)-negative (triple-negative) disease who do not achieve a pCR. Reliable identification of such patients is the first step in determining who might benefit from alternative treatment regimens in clinical trials. We previously identified genes involved in mitosis or ceramide metabolism that influenced sensitivity to paclitaxel, with an RNA interference (RNAi) screen in three cancer cell lines, including a triple-negative breast-cancer cell line. Here, we assess these genes as a predictor of pCR to paclitaxel combination chemotherapy in triple-negative breast cancer. Methods We derived a paclitaxel response metagene based on mitotic and ceramide genes identified by functional genomics studies. We used area under the curve (AUC) analysis and multivariate logistic regression to retrospectively assess the metagene in six cohorts of patients with triple-negative breast cancer treated with neoadjuvant chemotherapy;two cohorts treated with paclitaxel (n=27, 30) and four treated without paclitaxel (n=88 ,28, 48, 39). Findings The metagene was associated with pCR in paclitaxel-treated cohorts (AUC 0.79 [95% CI 0.53-0.93], 0.72 [0.48-0.90]) but not in non-paclitaxel treated cohorts (0.53 [0.31-0.77], 0.59 [0.22-0.82], 0.53 [0.36-0.71], 0.64 [0.43-0.81]). In multivariate logistic regression, the metagene was associated with pCR (OR 19.92, 2.62-151.57;p=0.0039) with paclitaxel-containing chemotherapy. Interpretation The paclitaxel response metagene shows promise as a paclitaxel-specific predictor of pCR in patients with triple-negative breast cancer. The metagene is suitable for development into a reve

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Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing

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BIOINFORMATICS 2010年第12期26卷 i7-i12页

作者： Coelho, Luis Pedro Peng, Tao Murphy, Robert F. Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Bioimage informat Pittsburgh PA 15213 USA Carnegie Mellon Univ Joint Carnegie Mellon Univ Univ Pittsburgh Ph D Program Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA Univ Freiburg Freiburg Inst Adv Studies D-79104 Freiburg Germany

Motivation: Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them. Methods: We developed two approaches to the problem, both based on identifying types of objects present in images and representing patterns by frequencies of those object types. One is a basis pursuit method (which is based on a linear mixture model), and the other is based on latent Dirichlet allocation (LDA). For testing both approaches, we used images previously acquired for testing supervised unmixing methods. These images were of cells labeled with various combinations of two organelle-specific probes that had the same fluorescent properties to simulate mixed patterns of subcellular location. Results: We achieved 0.80 and 0.91 correlation between estimated and underlying fractions of the two probes (fundamental patterns) with basis pursuit and LDA approaches, respectively, indicating that our methods can unmix the complex subcellular distribution with reasonably high accuracy.

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A Generative Model of Microtubule Distributions, and Indirect Estimation of its Parameters from Fluorescence Microscopy Images

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CYTOMETRY PART A 2010年第5期77A卷 457-466页

作者： Shariff, Aabid Murphy, Robert F. Rohde, Gustavo K. Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Bioimage Informat Pittsburgh PA 15213 USA Univ Pittsburgh Joint Carnegie Mellon Univ PhD Program Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA Univ Freiburg Freiburg Inst Adv Studies D-79104 Freiburg Germany Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

The microtubule network plays critical roles in many cellular processes, and quantitative models of how its organization varies across cell types and conditions are required for understanding those roles and as input to cell simulations. High-throughput image acquisition technologies are potentially valuable for this purpose, but do not provide sufficient resolution for current analysis methods that rely on tracing of individual microtubules. We describe a parametric conditional model of microtubule distribution that can generate a microtubule network in intact cells using a persistent random walk approach. The model parameters are physically meaningful as they directly describe the spatial distribution of microtubules and include the number of microtubules as well as the mean of the length distribution. We also present an indirect method for estimating the parameters of the model from three-dimensional fluorescence microscope images of cells that relies on comparing acquired images with simulated images generated from the model. Our results show that our method can reasonably recover parameters for a given query image, and we present the distributions of parameters estimated by our method for a collection of HeLa cell images. (C) 2010 International Society for Advancement of Cytometry

关键词： microtubule networks parameter estimation fluorescence microscopy inverse modeling cell simulation

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Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2010年第7期107卷 2944-2949页

作者： Peng, Tao Bonamy, Ghislain M. C. Glory-Afshar, Estelle Rines, Daniel R. Chanda, Sumit K. Murphy, Robert F. Carnegie Mellon Univ Ctr Bioimage Informat Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Genom Inst Novartis Res Fdn San Diego CA 92121 USA Hudson Alpha Inst Biotechnol Huntsville AL 35806 USA Burnham Inst Med Res Program Inflammatory Dis Res San Diego CA 92037 USA Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA Univ Freiburg Freiburg Inst Adv Studies D-79104 Freiburg Germany

Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in conjunction with the macromolecule of interest. Alternatively, tailored algorithms allow finding particular regions in images and quantifying the amount of fluorescence they contain. Unfortunately, this approach requires extensive hand-tuning of algorithms and is often cell type-dependent. Here we describe a machine-learning approach for estimating the amount of fluorescent signal in different subcellular compartments without hand tuning, requiring only the acquisition of separate training images of markers for each compartment. In testing on images of cells stained with mixtures of probes for different organelles, we achieved a 93% correlation between estimated and expected amounts of probes in each compartment. We also demonstrated that the method can be used to quantify drug-dependent protein translocations. The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts.

关键词： automated microscopy fluorescence microscopy location proteomics pattern recognition high content analysis

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Faster graphical models for point-pattern matching

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SPATIAL VISION 2009年第5期22卷 443-453页

作者： Caetano, Tiberio S. McAuley, Julian J. NICTA Stat Machine Learning Program Canberra ACT Australia Australian Natl Univ Res Sch Informat Sci & Engn Canberra ACT Australia

It has been shown that isometric matching problems call be solved exactly in polynomial time. by means of a Junction Tree with Small maximal clique size. Recently, in iterative algorithm was presented which converges to the same solution in order of magnitude faster. Here, we build oil both of these ideas to produce an algorithm with the Same asymptotic running time as the iterative solution, but which requires only a single Iteration of belief propagation. Thus our algorithm IS much faster in practice, while maintaining Similar error rates.

关键词： Point-pattern matching, graphical models Isometric matching

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Amplification of LAPTM4B and YWHAZ contributes to chemotherapy resistance and recurrence of breast cancer

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NATURE MEDICINE 2010年第2期16卷 214-U121页

作者： Li, Yang Zou, Lihua Li, Qiyuan Haibe-Kains, Benjamin Tian, Ruiyang Li, Yan Desmedt, Christine Sotiriou, Christos Szallasi, Zoltan Iglehart, J. Dirk Richardson, Andrea L. Wang, Zhigang Charles Harvard Univ Sch Med Dana Farber Canc Inst Dept Canc Biol Boston MA 02115 USA Harvard Univ Sch Med Brigham & Womens Hosp Dept Surg Boston MA 02115 USA Harvard Univ Sch Publ Hlth Dept Biostat Boston MA 02115 USA Tech Univ Denmark Ctr Biol Sequence Anal DK-2800 Lyngby Denmark Inst Jules Bordet Dept Med Oncol B-1000 Brussels Belgium Univ Libre Bruxelles Machine Learning Grp Brussels Belgium Harvard Univ Sch Med Harvard Mit Div Hlth Sci & Technol Childrens Hosp Informat Program Boston MA USA Harvard Univ Brigham & Womens Hosp Sch Med Dept Pathol Boston MA 02115 USA

Adjuvant chemotherapy for breast cancer after surgery has effectively lowered metastatic recurrence rates(1). However, a considerable proportion of women suffer recurrent cancer at distant metastatic sites despite adjuvant treatment. Identification of the genes crucial for tumor response to specific chemotherapy drugs is a challenge but is necessary to improve outcomes(2). By using integrated genomics, we identified a small number of overexpressed and amplified genes from chromosome 8q22 that were associated with early disease recurrence despite anthracycline-based adjuvant chemotherapy. We confirmed the association in an analysis of multiple independent cohorts. SiRNA-mediated knockdown of either of two of these genes, the antiapoptotic gene YWHAZ and a lysosomal gene LAPTM4B, sensitized tumor cells to anthracyclines, and overexpression of either of the genes induced anthracycline resistance. Overexpression of LAPTM4B resulted in sequestration of the anthracycline doxorubicin, delaying its appearance in the nucleus. Overexpression of these two genes was associated with poor tumor response to anthracycline treatment in a neoadjuvant chemotherapy trial in women with primary breast cancer. Our results suggest that 8q22 amplification and overexpression of LAPTM4B and YWHAZ contribute to de novo chemoresistance to anthracyclines and are permissive for metastatic recurrence. Overexpression of these two genes may predict anthracycline resistance and influence selection of chemotherapy.

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Performance evaluation of the NVIDIA GeForce 8800 GTX GPU for machine learning

Performance evaluation of the NVIDIA GeForce 8800 GTX GPU fo...

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8th International Conference on Computational Science

作者： El Zein, Ahmed McCreath, Eric Rendell, Alistair Smola, Alex Australian Natl Univ Dept Comp Sci Canberra ACT Australia NICTA Stat Machine Learning Program Canberra Australia

ISBN: (纸本)9783540693833

NVIDIA have released a new platform (CUDA) for general purpose computing on their graphical processing units (GPU). This paper evaluates use of this platform for statistical machine learning applications. The transfer rates to and from the CPU are measured, as is the performance of matrix vector operations on the CPU. An implementation of a sparse matrix vector product on the CPU is outlined and evaluated. Performance comparisons are made with the host processor.

关键词： Graphics processing unit

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Kernel methods in machine learning

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ANNALS OF STATISTICS 2008年第3期36卷 1171-1220页

作者： Hofmann, Thomas Schoelkopf, Bernhard Smola, Alexander J. Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Max Planck Inst Biol Cybernet Tubingen Germany Natl ICT Australia Stat Machine Learning Program Canberra ACT Australia

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.

关键词： machine learning reproducing kernels support vector machines graphical models

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Discrete-time expectation maximization algorithms for Markov-modulated Poisson processes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2008年第1期53卷 247-256页

作者： Elliott, Robert J. Malcolm, W. R. Univ Calgary Haskayne Sch Business Calgary AB T2N 1N4 Canada NICTA Stat Machine Learning Program Canberra ACT 2600 Australia Australian Natl Univ Inst Math Sci Canberra ACT 0200 Australia

In this paper, we consider parameter estimation Markov-modulated Poisson processes via robust filtering and smoothing techniques. Using the expectation maximization algorithm framework, our filters and smoothers can be applied to estimate the parameters of our model in either an online configuration or an offline configuration. Further, our estimator dynamics do not involve stochastic integrals and our new formulas, in terms of time integrals, are easily discretized, and are written in numerically stable forms in W. P. Malcolm, R. J. Elliott, and J. van der Hoek, "On the numerical stability of time-discretized state estimation via clark transformations," presented at the IEEE Conf. Decision Control, Mauii, HI, Dec. 2003.

关键词： change of measure counting processes expectation maximization (EM) algorithm martingales

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender Systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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