Center for Cancer Systems Biology  

Seminar Series

2014 Seminars


Future Speakers:

John Albeck, UC Davis - Friday May 16, 2014
Calvin Kuo, Stanford University - Friday June 20, 2014
Carlos Lopez, Vanderbilt University - Friday July 18, 2014
Adam Margolin, Oregon H&S University - Friday September 19, 2014

Linking the dynamics of kinase, transcriptional, and metabolic networks in single cells

Friday May 16th at 11 am (Li Ka Shing Center, Room 120)
John Albeck, Molecular & Cellular Biology, University of California at Daivs

As single-cell technologies expand, it is becoming clear that many cellular signaling events are very dynamic, necessitating a time-lapse approach.  I will present our work on the single-cell kinetics of two kinases - ERK and AMPK - that play key roles in the response to targeted cancer therapies aimed at disrupting cellular growth, proliferation, and homeostasis.  Induced by growth factor stimulation, ERK activity is a central controller of transcription factors involved in oncogenesis, including Myc, Fra-1, and Egr-1.  We show that at the single-cell level, each of these factors interprets ERK dynamics differently, leading to a diversity of cellular states within a genetically homogeneous population.  ERK pathway inhibitors, now being evaluated for use in multiple cancers, modulate ERK dynamics differentially and redefine the repertoire of cellular states in unique ways.  AMPK responds to cellular energy deprivation, and we show that direct inhibition of glycolysis results in a strikingly regular modulation of AMPK activity and metabolic state.  In contrast, PI3K and mTOR inhibitors, another key class of targeted therapies, lead to highly disordered disruption of metabolic dynamics.  Together these findings underscore the concept that, despite the chemically specific of modern targeted cancer therapies, their usefulness may be limited by the highly variable kinetics that they induce within cellular populations, resulting in sub-optimal, heterogeneous responses.


genomic biomakers of cancer prevention and treatment

Friday April 11th at 11 am (Alway Building, Room M114)
Andrea Bild, Department of Pharmacology and Toxicology, University of Utah

In this presentation, I will discuss the use of genomics to determine optimal strategies for cancer prevention.  The research I will discuss includes the interrogation of genomic data for women who have a family history of breast cancer and who face considerable uncertainty about which aggressive prevention strategies to pursue. Development of more accurate individualized risk-estimation tools may help women choose between standard screening, intensive screening, and prophylactic surgery. In addition, a better understanding of the biological processes that lead to familial breast cancer development may lead to better treatment strategies. Our multi-omic study and functional experiments support the concept that novel oncogenic processes are disrupted in women with a family history of breast cancer and play a role in FBC development. Further, our results represent a novel approach to produce individualized estimates of cancer risk and to identify disease-susceptibility mechanisms.


ADaptive models for assessing drug sensitivity and pathway activation in individual patient samples

Friday March 14th at 11 am (Alway Building, Room M114)
W. Evan Johnson, Computational Biomedicine, Boston University

The development of personalized treatment regimes is an active area of current research in genomics. The focus of our research is to investigate core biological components that contribute to disease prognosis and development, and to develop latent variable models to accurately determine optimal therapeutic regimens for individual patients. To accomplish this aim, we have developed an adaptive Bayesian factor analysis model that integrates in vitro experimental data into our models while still allowing for the refinement and adaptation of drug or pathway profiles within each patient cohort and individual, efficiently accounting for cell-type specific pathway differences or any “rewiring” do to cancer deregulation. Our modeling approach serves an essential role in our attempts to develop a comprehensive and integrated set of relevant, biologically interpretable computational tools for genomic studies in personalized medicine. We are currently working on a variety of applications using data from cancer and pulmonary disease with the potential to be extremely important in treating patients with these diseases.


retrospective and prospective views of non hodgkin lymphoma tumour evolution

Friday February 21st at 11 am (Li Ka Shing Center, Room 101)
Ryan Morin, Bioinformatics, Simon Fraser University

Non-Hodgkin lymphomas (NHLs) are a collection of over 30 cancers deriving from lymphoid cells. Diffuse large B-cell lymphoma (DLBCL) is a the most common NHL type and is clinically and genetically heterogeneous. Exome, whole genome and transcriptome sequencing in this tumour type has uncovered a plethora of common mutation targets and a long tail of infrequently mutated genes with potential relevance. Genome sequencing has also revealed myriad structural rearrangements and focal deletions affecting specific genes relevant to disease. Using these data, we have computationally dissected individual DLBCLs to identify multiple sub-clones and evidence for ongoing acquisition of driver mutations during tumour evolution. In this talk, I will provide an overview of the discoveries we have made into the molecular nature of DLBCL and other common NHLs using next-generation sequencing. I will also discuss the ongoing application of this knowledge to develop a suite of non-invasive assays for monitoring tumour dynamics in NHL using circulating tumour DNA.


reconstructing regulatory ciruits: lessons from immune cells

Wednesday January 22nd at 11 am (Li Ka Shing Center, Room 120)
Aviv Regev, Broad Institute of MIT and Harvard

Abstract will be added.








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