11-13 May 2012 Helsingør - Denmark www.networkbio.org

DR JANINE T ERLER,
BRIC, UNIVERSITY Of COPENhAGEN
COPENhAGEN, DENMARK
abStract For SPeakerS
Elucidating the molecular networks associated with metastasis
Metastasis is responsible for over 90% of cancer patient deaths due to a
lack of effective therapies against metastatic disease. We require a better
understanding of the underlying molecular processes in order to identify and
develop novel effective therapeutic strategies. We aim to identify the signaling
networks associated with metastasis through several different approaches.
Our goal is to identify key nodes in the network and target these to prevent
disease progression, and translate these findings into the clinic.
One approach we are taking is to identify and compare the signaling networks
and associated genetic mutations in metastatic versus non-metastatic samples derived from patients. In
our model, we have used non-metastatic and metastatic matched human cancer cell lines isolated from the
same patient at different stages. We have performed quantitative mass spectrometry to compare the global
proteome and phosphoproteome, and next generation sequencing to both identify and compare mutations,
and investigate their impact on the signaling networks. We are deploying the NetPhorest and NetworKIN
algorithms to predict phospho-binding modules likely to interact with the identified sites. Additionally, computational
integration of the molecular data with quantitative phenotypic data acquired from high throughput
invasion assays allows us to build predictive models of cancer progression. We are using RNAi to perturb
these networks and refine our models to identify the core network predicted to drive metastasis. We will additionally
integrate data collected from fresh frozen human patient samples. Finally, we will test our predictions
using an in vivo model of metastasis.
The results of this study will aid the development of a network-based therapeutic strategy for the treatment
and prevention of metastatic disease.
abStract For SPeakerS
NEVAN KROGAN
CELLULAR AND MOLECULAR PhARMACOLOGY/CALIfORNIA INSTITUTE fOR
qUANTITATIVE BIOMEDICAL SCIENCES, UNIVERSITY Of CALIfORNIA, SAN
fRANCISCO, CA, 94158
Dr. Krogan is an Associate Professor in the Department of Cellular and Molecular
Pharmacology at the University of California-San Francisco and is
an expert in the fields of functional genomics and systems biology. He was
born and raised in Regina, Saskatchewan, Canada and obtained his undergraduate
degree from the University of Regina. As a graduate student at the
University of Toronto, Dr. Krogan led a project that systematically identified
protein complexes in the model organism, Saccharomyces cerevisiae, through
an affinity tagging-purification/mass spectrometry strategy. This work led to
the characterization of 547 complexes, comprising over 4000 proteins, and represents the most comprehensive
protein-protein interaction map to date in any organism. To complement this physical interaction
data, Dr. Krogan developed an approach, termed E-MAP (or epistatic miniarray profile), which allows for
high-throughput generation and quantitative analysis of genetic interaction data. Dr. Krogan’s lab at UCSF
focuses on applying these global proteomic and genomic approaches to formulate hypotheses about various
biological processes, including transcriptional regulation, DNA repair/ replication and RNA processing.
He is now developing and applying methodologies to create genetic and physical interactions between
pathogenic organisms, including HIV and TB, and their hosts, which is providing insight into the human
pathways and complexes that are being hijacked during the course of infection.
Functional Insights from Protein-Protein and Genetic Interaction Maps
Pathways and complexes can be considered fundamental units of cell biology, but their relationship to each
other is difficult to define. Comprehensive tagging and purification experiments have generated networks
of interactions that represent most stable protein complexes. We describe this work in various organisms,
including budding yeast and in infectious organisms like HIV and TB, and show how the analysis of pairwise
epistatic relationships between genes complements the physical interaction data, and furthermore can be
used to classify gene products into parallel and linear pathways.
22 / INB 2012 • 11-13 May 2012 www.networkbio.org / 23