The Reactivity of the Cellular Transcriptome to Xenobiotic Compound Perturbation


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The Reactivity of the Cellular Transcriptome to Xenobiotic Compound Perturbation

Monday, October 24, 2011

The New York Academy of Sciences

Presented By


The steady state level of transcripts in a given cellular system is a function of multiple external input variables. Among these input signals are small molecules that potentially can affect the intracellular homeostasis. Cells are equipped with a series of reagents (e.g. cytochromes) that allow them to maintain cellular metabolites levels within their respective range. In the events when the perturbation is such that the cells are not readily able to cope with it, it must react by amending its metabolic networks. To do so requires regulating the amount of enzymes and regulatory agents. De novo transcription of components of the cellular system occurs. The measurement and analyses of the transcriptome of cellular systems’ submitted to compounds’ treatments provide the means to characterize the systemic biological effect of candidate therapies.


Next Generation Connectivity Map
Arvind Subramanian, Broad Institute of MIT and Harvard University

Dose-Dependent Transcriptome Response to Compound Perturbation: a New Paradigm to Characterize Drug Activity
Rui-Ru Ji, Bristol-Myers Squibb

Chemical Genomics in Cancer: Rebalancing the Unbalanced Equation Inherent in the Programming of the Disease
Duane C. Hassane, Weill Cornell Medical College

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Andrea Califano, PhD

Columbia University

Dr. Califano's doctoral thesis in physics, at the University of Florence, was on the behavior of high-dimensional dynamical systems. From 1986 to 1990, as a Research Staff Member in the Exploratory Computer Vision Group at the IBM TJ Watson Research Center he worked on several algorithms for machine learning, more specifically for the interpretation of 2D and 3D visual scenes. In 1990 Dr. Califano started his activities in Computational Biology and, in 1997, became the program director of the IBM Computational Biology Center, a worldwide organization active in several research areas related to bioinformatics, chemoinformatics, complex biological system modeling/simulation, microarray analysis, protein structure prediction, and molecular-dynamics. In 2000 he co-founded First Genetic Trust, Inc. to actively pursue translational genomics research and infrastructure related activities in the context of large-scale patient studies with a genetic components. Finally, in 2003, he joined Columbia University and is currently Professor of Systems Biology at Columbia University, Director of the Columbia Initiative in Systems Biology, Director of the JP Sulzberger Columbia Genome Center, and Associate Director for Bioinformatics of the Herbert Irving Comprehensive Cancer Center. Dr. Califano serves on numerous scientific advisory boards, including the Board of Scientific Advisors of the National Cancer Institute.

Dr. Califano’s interests reside in dissecting and interrogating the cell-context specific gene regulatory networks that determine the cell pathophysiological behavior, using a combination of in silico reverse engineering methods and high-throughput experimental assays. His lab has pioneered a variety of algorithms for the dissection of transcriptional, post-transcriptional, and post-translational regulatory interactions in mammalian cells and for their interrogation to identify master regulators of aberrant transformation and physiological differentiation/maturation events. His lab has also pioneered regulatory network based methods to dissect the mechanisms of action of drugs and to identify genetic alterations that contribute to the aberrant activity of master regulators. In collaboration with a number of colleagues within the Columbia scientific community, the Califano lab has been the first to publish fully context-specific molecular interaction networks (referred to as Interactomes) for normal and tumor-related human cells including neoplastic malignancies of lymphoma and glioma subtypes. Ongoing projects in the lab aim to define the regulatory networks for various neoplasmic states of the breast, ovary, prostate, germ cell, colon, and lung, for the study of pluripotency and lineage differentiation in stem cells, and for the elucidation of mechanisms associated with the onset and progression of neurodegenerative diseases.

Manuel Duval, PhD

Network Therapeutics Inc.

Drug Discovery and Development is clearly one of the most rewarding human experiences. This is a collective endeavor aimed at providing healing and life saving solutions. How many human lives have been rescued thanks to antibiotics? How many premature deaths have been prevented thanks to beta-blockers and angiotensin-converting enzyme inhibitors for the treatment of heart and kidney failure? How many individuals had the opportunity to seize the joy of a meaning full existence thanks to serotonin reuptake inhibitor? How many of us won’t need to go through debilitating cardio-vascular surgeries thanks to statin? This aforementioned list is just a small excerpt of the medical breakthroughs achieved by the aggregate contributions of talents in wide areas of science and project management deployed in multicultural environments. I, Manuel Duval, am a French American Life Scientist, trained both in France (PhD in Biochemistry in 1996 at the University Joseph Fourier Grenoble, Fr) and in the US (Post-Doc at Texas A&M with computer science training), and with professional experiences in both continents (Rhône-Poulenc and Pfizer). Although I like to acknowledge the incredible benefit for the public health of the aforementioned achievements of medicinal chemistry, I also am frustrated by the lack of new treatments needed for addressing yet unmet critical medical needs. I have spent the last ten years as a computational biologist practitioner in an outstanding centenarian Drug R&D organization headquartered in the great metropolis of New York City, and funded by German entrepreneur chemists, Charles Erhardt and Charles Pfizer. As another European scientist immigrant to New England, I am following the footsteps of my glorious predecessors and co-founded a Drug Discovery 2.0 organization called Network Therapeutics. The scientific and entrepreneurial heritage of the medicinal chemists’ pioneers deserves great respect. On the other hand, the advent of a new age of Drug Discovery is about to start: it will rely on the leadership of quantitative biologists and on the deployment of lean and decentralized organizations. In that context, the New York Academy of Science is instrumental in fostering scientific communication and progress for the great advantage of professional and amateur scientists around the world.

Aris Economides, PhD

Regeneron Pharmaceuticals

Dr Aris N. Economides joined Regeneron Pharmaceuticals Inc in 1992 and he currently holds the position of Sr. Director, leading two groups: Genome Engineering Technologies, and Skeletal Diseases TFA. Dr. Economides is a co-inventor of the Cytokine Trap technology that led to the development of the IL-1 trap, a currently approved biologic drug (ARCALYST™). He is also a co-inventor of the VelociGene® technology, that has led to the development of VelocImmune®, a method for the generation of all-human antibodies in mice. More recently, he has been spearheading the development of new methods for the generation of transgenic mice using BAC as transgene vectors, and has also pioneered a new method for generating conditional alleles as well as a new method for the generation of multifunctional alleles

Gustavo Stolovitzky, PhD

IBM Research

Gustavo Stolovitzky received his M.Sc. in Physics, from the University of Buenos Aires (1987) and his Ph.D. in Mechanical Engineering from Yale University (1994) receiving the Henry Prentiss Becton Prize, for Excellence in Engineering and Applied Sciences. In 1998 he joined the IBM Computational Biology center at IBM Research.

His most recent interests are in the field of high-throughput biological-data analysis, reverse engineering biological circuits, the mathematical modeling of biological processes and new generation technologies for DNA sequencing.

Gustavo leads the DREAM project on assessment of systems biology models, has co-authored more than 90 scientific publications, 15 patents and edited 2 books. His work has been highlighted in The New York Times, The Economist, Technology Review and Scientific American (where his DNA transistor project was chosen as one of the 10 world changing ideas of 2010) among other media. Gustavo is a Fellow of the NY Academy of Sciences, a Fellow of the American Physical Society, a fellow of the American Association for the Advancement of Science, an adjunct Associate Professor at Columbia University and the Manager of the IBM Functional Genomics and Systems Biology Group.

Jennifer Henry, PhD

The New York Academy of Sciences


Duane C. Hassane, PhD

Weill Cornell Medical College

Duane Hassane is an Assistant Professor of Pathology and Laboratory Medicine at the Institute for Computational Biomedicine of Weill Cornell Medical College in New York City. Dr Hassane received his BS in 1995 at the University of Rochester. In 2002, he received his PhD in Microbiology, Immunology, and Molecular Genetics at the University of Kentucky, subsequently returning to the University of Rochester where he trained as postdoctoral fellow in the laboratory of Dr Craig Jordan, focusing on leukemia stem cell targeting. Currently, at Weill Cornell Medical College, Dr Hassane’s laboratory focuses on the use of chemical genomic strategies to accelerate the translation of pre-clinical discoveries to clinic through development of novel treatment modalities and therapeutic combinations.

Rui-Ru Ji, PhD

Bristol-Myers Squibb

Rui-Ru Ji graduated from the University of Science and Technology of China with a BS in Biology. After college Rui-Ru came to the United States and obtained a PhD in Molecular Biology and a MS in Computer Science from the Purdue University. Rui-Ru joined Celera Genomics in 2000 and was part of the team decoding the human and mouse genomes. In 2002 Rui-Ru moved to New Jersey and has since been working in the pharmaceutical industry. Rui-Ru first worked at Purdue Pharma LP and led their bioinformatics effort to support target identification and validation. In 2005 Rui-Ru joined Bristol-Myers Squibb and has been working on various Oncology and Immunology discovery and development programs.

Arvind Subramanian, PhD

Broad Institute of MIT and Harvard University

Arvind Subramanian is a Research Scientist in the Cancer Program at the Broad Institute of MIT and Harvard. He leads a team of molecular biologists, computational biologists and software engineers whose focus is on developing new technologies and algorithms for large-scale mRNA profiling and analysis. As a graduate student in the Whitehead Institute Center for Genome Research, Arvind helped develop Gene Set Enrichment Analysis (GSEA), a widely cited knowledge-based algorithm for the interpretation of high-dimensionality genomic datasets. In collaboration with colleagues in the RNAi platform at the Broad Institute, he developed computational methods to analyze genome-scale pooled shRNA screens for the identification of essential genes in cancer cells (RIGER). Arvind is currently collaborating with members of the Todd Golub laboratory to implement a high-throughput, medium-density, low-cost gene expression-profiling platform. The current focus of the group is to use this technology to massively scale-up the Connectivity Map database to include over 1M perturbational profiles.


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Next Generation Connectivity Map
Aravind Subramanian, PhD, Broad Institute of MIT and Harvard University

Genome-wide expression arrays have proven to be useful tools in the definition and characterization of biological states. However, studies using commercial microarray solutions are frequently restricted to small sample sizes and narrow choices of experimental conditions due to the high cost of the arrays. We have developed a new approach to expression profiling based on a reduced representation of the human transcriptome. Namely, we have identified 1,000 transcripts from which the remainder of the transcriptome can be computationally inferred. We have implemented a platform to cost-effectively measure these 1,000 'Landmark' transcripts using a combination of ligation-mediated amplification with an optically addressed microsphere and flow cytometric detection system. With a low-cost, high-throughput expression-profiling method in hand, it is now possible to contemplate the generation of a dataset unprecedented in scope and scale. I will present our work on the 'Connectivity Map' that aims to provide a comprehensive functional characterization of the genome by capturing the consequences of small-molecule and genetic perturbations at library scale, and associating these disparate perturbagens with each other and external phenotypes to discover functional connections between drugs, genes and diseases.

Dose-Dependent Transcriptome Response to Compound Perturbation: a New Paradigm to Characterize Drug Activity
Rui-Ru Ji, PhD, Bristol-Myers Squibb

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling. Single-dose experiments will co-mingle effects that can occur with different potency (e.g., effects on the known target versus effects on additional undesired targets), and have little comparability to the dose-response bioassays, which are now used throughout the drug discovery processes. One reason for the disparity between experimental approaches is that existing analytical methods for dose-response bioassays can't cope with the dimensionality of microarray data. Conversely, existing methods for microarray data analysis can identify patterns, but provide no quantitative dose-response information. To overcome these problems, we developed novel algorithms and visualization methods that allow anyone to apply transcriptional profiling as a conventional dose-response assay. The approach greatly enriches the information that can be obtained from standard transcriptional profiling technology, yet is economical (typically 12 arrays/compound). With this new analytical framework, it is now possible to identify distinct transcriptional responses at distinct regions of the dose range, to link these impacts to biological pathways, and to make realistic connections to drug targets and to other bioassays.

Chemical Genomics in Cancer: Rebalancing the Unbalanced Equation Inherent in the Programming of the Disease
Duane C. Hassane, PhD, Weill Cornell Medical College

Cancer cells reside in state of imbalance, marked by unchecked regulation of numerous cellular processes resulting in their abnormal persistence and expansion. Adding to this complexity, some cancers such as acute myeloid leukemia (AML) consist of different cell types within a single patient, including leukemic stem cells. In analogy to normal stem cells that regenerate normal tissue, leukemic stem cells regenerate leukemia and resist chemotherapy. Thus, new drugs are needed to eliminate these cells and maintain durable remissions in patients. We have identified pre-clinical drugs that are promising. Massive screening for drugs that target leukemia stem cells is difficult since these cells are a rare component of the bulk tumor. Overall, the process of taking discoveries from bench to bedside process is slow. Therefore, to improve the speed of clinical translation, we have focused efforts on gaining an understanding of the molecular genetic programming inherent to leukemia and its response to therapeutic challenge. Our work in developing new therapeutic approaches and rationally combining compounds by searching for drugs that normalize aberrant cancer programs based on transcriptomic information will be discussed.

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