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2012 Summer Cancer Research Fellowships/Internships
PROJECT DESCRIPTIONS
Broad Institute/Dana-Farber Cancer Institute
Boston, MA
Website: http://www.broadinstitute.org/science/programs/cancer/icbp/broad-institute-icbp
Principal Investigators: Todd R. Golub, M.D.
William Hahn, Ph.D.
Mentor: Jesse Boehm, Ph.D.
Duration of Program: June 4 through August 3, 2012
Project description:
The overarching goal of the Broad Institute Center for Cancer Systems Biology is to predict the vulnerabilities of tumors based upon their genomic features. To accomplish this goal we are systematically cataloging cancer dependencies utilizing genome-wide RNA interference screens on hundreds of cancer cell lines that have been undergone comprehensive genomic characterization. Despite this exciting experimental progress, major challenges in computational modeling (e.g., can we develop statistical and analytical methods to link tumor features with dependencies?) as well as experimental confirmation (e.g., are experiments done in cell lines predictive of the vulnerabilities of established tumors in vivo?) must be overcome if we are to understand how to use genomic biomarkers to predict how to effectively kill real tumors in humans.
We are looking for a highly motivated, dedicated and talented individual for the 2012 Summer Internship Program to help further this work on either computational or experimental fronts (or both), depending on the interests of the applicant. The project may include work in the cancer laboratory performing over-expression and RNAi experiments in cancer cell lines as well as confirmatory studies in mouse models, and/or developing computational methods on these genome-scale tumor vulnerability data sets.
Requirements: The ideal applicant will have a strong passion and drive to learn new concepts and work with others. Prior experience in a molecular biology laboratory and having basic programming skills are ideal but not a requirement.
Project type: In their application, the student should state which type of project (computational or experimental) they are interested in and describe their rationale.
Columbia University
New York, NY
Websites: http://magnet.c2b2.columbia.edu/
Principal Investigator: Andrea Califano, Ph.D.
Mentor: Itsik Pe’er, Ph.D.
Duration of Program: June 4 through August 3, 2012
Title: Allele specific chromatin states in prostate tumors
Background: CHromatin Immuno-Precipitation SEQuencing (ChIP-Seq) data is a genome wide, rich source of information regarding transcriptional activation states of all genes. SNPs within enhancer or promoter states that are often associated with transcription level of the relevant genes.
Hypothesis: At heterozygous sites within enhancer or promoter regions, ChIP-Seq signal for such SNPs will often be allele-specific.
Study design: The project involves analysis of ChIP-Seq data from multiple prostate tumor samples. Heterozygous sites will be identified directly from the sequence data. At each such site, ChIP-Seq marks will be determined on an allele-specific manner. Allele-specificity of marks will be compared across sites that are known to be associated with local gene expression, or not. Analysis will take into account the often aberrant copy-number status of each locus along the tumor genome. The role of the summer student involves application and improvement of statistical tests to large datasets, interpreting and controlling the quality of the results. Such automated analysis inevitably involves writing significant amount of computer code, from short, helper scripts to longer programs.
Primary field of study: genetics/epigenetics
Requirements: (1) ability to independently program, (2) familiarity with the Unix environment, (3) command of basic probability and statistics, and (4) preferred: knowledge of a scripting language (Perl/Python/Ruby/Matlab/R).
Project type: Computational
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Georgetown University
Washington, D.C. (Georgetown area)
Website: http://lombardi.georgetown.edu/breastcancer/ccsb/
Principal Investigator: Robert Clarke, Ph.D.
Mentor: Robert Clarke, Ph.D.
Duration of Program: June 4 through August 3, 2012
Title: The unfolded protein response in anti-estrogen resistance in breast cancer
Project description: About 70% of human breast cancers are initially dependent upon estrogen-related signals for proliferation and survival, and are thus sensitive to a wide range of hormonal antagonists or anti-estrogens. However, many such tumors develop resistance to these agents, and ultimately develop resistance to common chemotherapy agents as well. We have shown that anti-estrogen resistant breast cancer cells can utilize singling involved in the unfolded protein response (UPR) to prolong survival upon treatment with anti-estrogens. We now plan to extend these studies by determining the influences of genes related to apoptosis and autophagy, alone and in combination with genes related to the estrogen signaling network, on resistance to hormonal antagonists and chemotherapy agents. Students will learn to conduct concurrent cell viability and protein measurement assays under different drug treatment conditions to determine the regulation of essential signaling molecules in development of anti-estrogen resistance.
Primary field of study: Molecular and cellular biology
Requirements: Some familiarity with cell culture and a basic understanding of biochemistry and/or molecular biology
Key words: Breast cancer, anti-estrogens and molecular biology
Project type: Experimental biology
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Massachusetts Institute of Technology (MIT)
Cambridge, MA
Website: http://web.mit.edu/icbp/
Principal Investigator: Douglas Lauffenburger, Ph.D.
Mentor: Douglas Lauffenburger, Ph.D.
Duration of Program: June 4 through August 3, 2012
Title: Building large scale shRNA libraries
Project description: RNA interference (RNAi) is a sequence-specific gene silencing process used to investigate gene function in human diseases. Short hairpin RNAs (shRNAs) are one type of RNAi effectors that result in stable gene knockdown and low nonspecific interferon responses in virally infected cells. Large shRNA libraries are constructed to systematically suppress gene function and rapidly identify modulators of a specific phenotype. The first and arguably most critical step in library construction is the selection of genes that should be targeted based on their association with a particular disease. An effective target list can be intelligently expanded or contracted using pathway-specific data from external databases. Algorithms have been developed to predict effective shRNA sequences but further database searches are often required to identify genes that may be targeted inadvertently. The goal of this project is to build a practical computational framework for designing targeted shRNA libraries. In its first use, the program will be developed to identify genes that are important for the intravasation of breast cancer cells through the endothelial cell layer lining blood vessels.
Requirements: Due to the relatively short duration of this project, familiarity with a scripting language such as, but not limited to, Python is a requirement.
Project type: Computational
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Memorial Sloan-Kettering Cancer Center
New York City, NY
Website: http://www.mskcc.org/mskcc/html/11655.cfm
Principal Investigator: Chris Sander, Ph.D.
Mentor: Grégoire Altan-Bonnet, Ph.D.
Duration of Program: June 4 through August 3, 2012
Title: Modeling signaling cross-talk in tumor/stromal cell interactions
Project Description: We aim at dissecting the signaling cross-talks between receptor-tyrosine kinase signals and cytokine signals in tumor cells and stromal cells. Quantitative modeling of this cross-talk (of critical functional relevance in new targeted drug therapies) will be carried out. The goal is to quantitatively optimize the timing and dosing of drug perturbation to maximize cytotoxicity. Modeling predictions will be validated with state-of-the-art cytometric phospho-profiling with single-cell resolution.
Requirements: Strong quantitative skills (computer modeling, statistical and analytical skills learned from physics, engineering or applied math) are required. Experimental experience in cell signaling and/or biophysics is a plus. Students interested in the field of Systems Biology through Cancer Biology, Immunology, Biophysics, or Chemical Engineering, are welcome to apply.
Key Words: Biophysical modeling, differential equations, single-cell phospho-profiling, signal transduction
Project Type: The project is a combination of computational and biological aspects with a computational emphasis.
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Methodist Hospital Research Institute
Houston, TX
Website: http://www.methodisthealth.com/tmhri.cfm?id=39163
Principal Investigator: Stephen Wong, Ph.D.
Co-Principal Investigator: Ming Zhan, Ph.D.
Mentors: Jaykrishna Singh, Ph.D.
Fuhai Li, Ph.D.
Duration of Program: June 4 through August 3, 2012
Title: Computational modeling of cell-cell communication in breast cancer stem cells
Project description: The Center for Modeling Cancer Development, headquartered at the Methodist Hospital Research Institute, focuses on the analysis of cancer as a complex biological system through integrating experimental biology with mathematical modeling. Cancer stem cells have been implicated in tumor growth and considered as a major cause of resistance to chemotherapy. The microenvironment or niche of cancer stem cells plays a critical role in determining the fate of the cells. We are interested in exploring the microenvironment of cancer stem cells through computational modeling. The project for the student will be part of this effort. Specifically, the student will a) construct the cell-cell communication network of breast cancer stem cells and their interacting cells, b) integrate the inter-cellular network with intra-cellular network, c) develop mathematical models of the integrated network, and d) conduct computational simulation based on the models to understand the dynamic behavior, particularly the response to drug perturbation. The study will contribute to the final goals of our research, that is, new insights into cancer biology and new approaches to the management of cancer. The tasks of the study will be exclusively computational, and will also include data collection and curation from the literature.
Primary field of study: Computational biology, bioinformatics, cancer biology.
Requirements: Background in mathematics or statistics, computer programming, or bioinformatics; basic knowledge in molecular biology or cancer biology.
Key words: Computational modeling, breast cancer, cell-cell communication, microenvironment, niche.
Project type: Computational
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Oregon Health & Science University/UC Berkeley
Fellowship site: Berkeley, CA
Websites: http://www.ohsu.edu/xd/
Principal Investigator: Joe Gray, Ph.D.
Mentor: Soulaiman Itani, Ph.D.
Duration of Program: June 4 through August 3, 2012
Project Description: In this project, the student's main responsibility is to add new functionalities to systems biology visualization tool. A piece of software is being developed at the lab to simplify the visualization and analysis of dynamic models of biological systems. Even though this project is computational, a student interested in wet lab work can do some experiments. The systems studied involve the interactions of proteins in cancer cells. Since these interactions are basically chemical reactions, they can be modeled with differential equations that arise based on the laws of chemistry. Working on this project would expose the student to molecular biology concepts, entities and experiments, signaling pathways in cancer, and the modeling, analysis, and simulation of some chemical and biological processes. The student would work on adding some new processing and visualization functionalities, for example, finding invariant parameter spaces and plotting them on demand.
Requirements: Although the primary field of the student's study is not restricted, the student must have some working knowledge of calculus, differential equations, and programming. The software is implemented as MATLAB code, and so previous knowledge of MATLAB is a plus.
Project type: Computational (with an optional experimental biology component)
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St. Elizabeth’s Medical Center/Tufts University
Boston, MA
Website: http://www.cancer-systems-biology.org
Principal Investigator: Lynn Hlatky, Ph.D.
Mentors: Tyson McDonald, Ph.D.
Edward Rietman, Ph.D.
Duration of Program: June 4 through August 3, 2012
Title: Characterizing the regenerative potential of irradiated brain tumor cell populations
Project description: Glioblastomas are the most common and aggressive type of brain tumor, with a median survival of about14 months due to tumor recurrence post-treatment. Essentially all glioblastomas are treated with radiation therapy. This project investigates how radiation exposure changes the overall character of the glioblastoma cell population, thereby altering its tumor growth potential. In particular, we investigate both how the molecular fingerprint of the cancer cell is altered and how the “cancer stem cell” compartment of the cell population is modulated by irradiation. Of great interest to both basic and translational cancer research is idea that cancer ‘stem’ cells (those cancer cells capable of initiating tumor growth or post-therapy regrowth) are a particularly treatment-resistant, subpopulation of cancer cells that drive tumor recurrence.
Glioblastoma cell populations will be irradiated, in vitro, with high, clinically relevant doses. The subpopulations of cells surviving the irradiation will be molecularly profiled and their regenerative potential assessed. Through this investigation, the student, working with the mentoring team, will gain exposure to a panel of multiscale approaches to analyze cancer cells: including molecular characterization using Illumina gene array platforms for global gene expression, RT PCR, western blotting, immunohistochemical analysis, in vitro cell kinetic studies, and in vivo tumor growth studies. In turn, the student will participate in quantitative analysis of this data, including pathway analysis of the array data, in conjunction with the overall tumor modeling efforts ongoing in the lab. To this end, the student will learn to use software for building protein-protein interaction networks as a function of time and dose, and will explore the use of novel presentations of Gene Ontology annotation associated with these time/dosage data sets. Thus, in addition to exposure to wet-lab studies, these investigations provide an opportunity for the student to participate in computational modeling and bioinformatic analysis, while investigating the important issue of brain tumor recurrence from a population dynamic perspective.
Keywords: Glioblastoma, cancer stem cells, radiation, gene arrays, cell culture, in vitro culture, in vivo tumor models, bioinformatics, data mining, quantitative analysis and protein-protein networks.
Requirements: General experience in a biological laboratory setting and computer expertise desirable.
Project type: Combination of biological and computational
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Stanford University School of Medicine
Palo Alto, CA
Website: http://ccsb.stanford.edu
Principal Investigator: Sylvia Plevritis, Ph.D.
Mentor: Sylvia Plevritis, Ph.D.
Duration of Program: approximately June 17 through August 17, 2012
Project description: The aim of Stanford CCSB is to understand the role of differentiation in cancer progression. Our primary focus is on hematological malignancies including follicular lymphoma (FL), acute lymphoblastic leukemia (ALL), and acute myeloid leukemia (AML). In AML, it is well established that there is a hierarchy of cells in the tumor, with leukemic stem cells at the apex, which give rise to more differentiated cell types. This is analogous to differentiation of normal stem cells. There are a large number of high-throughput datasets, such as gene expression microarray experiments, that can be mined for insights into how normal differentiation processes are impaired in cancer, or how processes that maintain “stem cell like behavior” might be hijacked and employed by cancer cells. Analyzing these data requires applying computational methods such as algorithms for reconstructing molecular regulatory networks.
Our summer project involves applying computational tools to large datasets to gain insights into de-regulation of differentiation programs in cancer. For example, in AML the presence of large populations of leukemic stem cells is associated with much worse prognosis for patients, and frequent resistance to treatment. However, the underlying cellular processes driving this are unknown. Less is known about these aspects in ALL and FL. We have methods for inferring underlying patterns of gene expression that might reflect progression, and for analyzing these in the context of gene regulation. This project will apply them to data in AML, ALL, and FL.
Requirements: Applicants for this project should have interests in applying computational methods to large scale data mining of cancer datasets. Experience with a high-level programming language such as R, Matlab, or Perl is required – some experience with Unix systems would be advantageous. Knowledge of basic statistical methods will be useful. Although experience with data such as gene expression is not essential, an interest in learning about them and what they can tell us about cancer systems biology is.
Project type: Computational
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University of Texas Health Science Center at San Antonio
San Antonio, TX
Website: http://www.uthscsa.edu
Principal Investigator: Tim Huang, Ph.D.
Mentors: Carolina Livi, Ph.D.
Sunil Sudarshan, M.D.
Duration of Program: June 4 through August 3, 2012 (with some flexibility)
Title: Epigenetic changes in response to oncometabolites
Project description: The University of Texas Health Science Center at San Antonio (UTHSCSA) and Indiana University (IU) Integrative Cancer Biology Program (ICBP) is focused on the development of experimental and computational methods to investigate the role of aberrant epigenetic modifications in carcinogenesis. DNA methylation and histone methylation are common epigenetic marks that regulate cellular differentiation and play a role in the development of cancer. Functionally, there is much in common in the two processes both leading to heterochromatin formation and silencing of gene transcription. In addition, it is possible that mechanistically the regulatory steps leading to each process are also tightly connected. It is likely that cancer cells use these epigenetic processes interchangeably, and thus a more integrated approach to the study of epigenetic regulation is warranted.
The effects of oncometabolites on epigenetic alterations have been previously suggested. We propose to study the effects of changes in cancer metabolism on DNA and histone methylation and gene expression. Metabolomic analysis of tumor/normal adjacent pairs using tandem mass spectrometry has yielded a list of putative oncometabolites over expressed in cancer. Similar gene expression profiling analysis confirmed a strong Warburg effect at the transcriptional level with TCA cycle enzymes strongly down-regulated and glycolytic enzymes up-regulated. We propose studying the interaction between these different components and their possible regulatory or causal roles in carcinogenesis.
Student will participate in global DNA and histone (e.g., H3K9me2) methylation profiling from kidney cancer cell lines treated with oncometabolites. Specific genomic regions surrounding TCA cycle enzyme genes will be probed using PCR-based or western blotting methods. Gene expression profiling using next generation sequencing (NGS, RNA-seq) data will be acquired and provided to student to be integrated into the analysis. The student will (1) use bioinformatics tools to analyze global and specific DNA and histone methylation patterns, (2) describe effects of oncometabolite treatments on epigenetic modifications, and (3) develop a model which integrates gene expression, metabolite, cellular phenotype and epigenomic changes observed in these cells.
Primary field of study: Wet lab including genomics and cell culture techniques, practical bioinformatics using existing tools including pathway analysis.
Requirements: Prerequisite coursework: one course in statistics or calculus, two or more upper division courses in cell biology, molecular biology, genetics, or biochemistry; prior experience in a molecular biology lab (or practical lab course); programming or bioinformatics experience are strongly encouraged and may substitute for laboratory experience.
Key words: Epigenetics, DNA methylation, histone methylation, next generation sequencing, RNA-seq, gene expression profiling, cancer, Warburg effect, oncometabolites
Project type: Experimental biology and computational
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Vanderbilt University Medical Center
Nashville, TN
Website: http://vicbc.vanderbilt.edu/ccsb/
Principal Investigator: Vito Quaranta, M.D.
Mentors: Darren Tyson, Ph.D.
Shawn Garbett, M.S.
Duration of Program: Wednesday May 30 through Wednesday August 1, 2012
Title: Dynamic response of cancer cells to anti-proliferative drugs
Project description: Cancer is primarily a disease of unrestrained cellular proliferation. It is now understood that even cells with the same genetic background can respond differently to the same stimulus yet little is known about how individual cells make decisions to progress through the cell cycle. Recent technological advances have now made it possible to obtain information about cell cycle progression at the single cell level and these rich data sets are providing a wealth of new information about how benign and cancerous cells make these decisions. We will use fluorescent time-lapse microscopic imaging of human cell lines to investigate population dynamics of cell cycle progression in response to drug treatment. Cells with fluorescent tags will be imaged and cell age and cell cycle states will be manually or automatically extracted. The data will then be fit to one or more mathematical models to help understand the underlying biology.
Primary field of study: The student may be involved in any or all aspects of this project, depending on background and interest, including: cell culture, live cell fluorescent microscopic imaging, image processing, statistical data analysis, and mathematical modeling.
Requirements: Useful experience would include: cell culture; fluorescence microscopy; image processing using Matlab or ImageJ; familiarity with the statistical analysis program, R; an understanding of the mammalian cell cycle.
Project type: Combination of computational and experimental biology
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Note: this is the end of the list of Project Descriptions.
