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

STEPhAN M. fELLER, WIMM,
OxfORD UNIVERSITY, OxfORD, UK
abStract For SPeakerS
How are complex signal computations in cells accomplished by multi-protein complexes assembled on
‘intrinsically disordered’ platform proteins? The N terminal folding nucleation (NFN) hypothesis
Proteins with little recognisable secondary structure -according to current prediction programs -comprise
one third of the human proteome. Some of these are large and serve as docking platforms for many proteins
involved in the processing of cell signals, thereby creating large signalosomes in which signal computations
are performed. It was always difficult to conceptualise how an intrinsically disordered, i.e. ‘chaotic’ signal
computation platform protein should be able to mediate effective pathway crosstalk in cells. However, we
have now first evidence that the cancer-relevant Gab family proteins display a previously unrecognised
organisation (Simister et al. 2011, PLoS Biol; Simister & Feller 2012, Mol BioSystems). Gab proteins contain
N terminal PH domains followed by long ‘tail regions’ supposedly lacking any structure. Our data point to
several specific interactions of the Gab1 tail with is structured N-terminus. These generate, around the PH
domain, tail loop structures, in which docking sites for pathway-specific signaling proteins are clustered.
Functionally dedicated, distinct sub-complexes can assembly in these loops. If this model is correct, it is
easy to see how effective signaling pathway crosstalk occurs: simply by interactions of distinct sub-complexes
bound to specialised loops, which are held in place by their interactions with the PH domain. We believe
that proving this hypothesis, which is also relevant to multiple similarly structured signaling protein families
(p130Cas, IRS/Dok, Frs etc.), will have a profound impact on the understanding of molecular signal computing
in cells and we will report on our first results.
abStract For SPeakerS
ThEO KNIJNENBURG
NEThERLANDS CANCER INSTITUTE, DIVISION Of MOLECULAR CARCINOGENESIS
PLESMANLAAN 121, 1066Cx AMSTERDAM, ThE NEThERLANDS
MAThEW J. GARNETT
CANCER GENOME PROJECT , WELLCOME TRUST SANGER INSTITUTE
hINxTON CAMBRIDGE CB10 1SA, UNITED KINGDOM
GUNNAR W. KLAU
LIfE SCIENCES GROUP, CENTRUM WISKUNDE & INfORMATICA
SCIENCE PARK 123, 1098 xG AMSTERDAM, ThE NEThERLANDS
fRANCESCO IORIO
EUROPEAN BIOINfORMATICS INSTITUTE, WELLCOME TRUST GENOME CAMPUS
hINxTON CAMBRIDGE CB10 1SD, UNITED KINGDOM
JULIO SAEz-RODRIGUEz
EUROPEAN BIOINfORMATICS INSTITUTE, WELLCOME TRUST GENOME CAMPUS
hINxTON CAMBRIDGE CB10 1SD, UNITED KINGDOM
ULTAN MCDERMOTT
CANCER GENOME PROJECT , WELLCOME TRUST SANGER INSTITUTE
hINxTON CAMBRIDGE CB10 1SA, UNITED KINGDOM
LODEWYK WESSELS
NEThERLANDS CANCER INSTITUTE, DIVISION Of MOLECULAR CARCINOGENESIS
PLESMANLAAN 121, 1066Cx AMSTERDAM, ThE NEThERLANDS
Drug sensitivity of cancer cell lines explained as a logic combination of mutations
Cancer arises as a result of the acquisition of DNA mutations. It is still unclear which and how combinations
of mutations are involved in tumor initiation and development. Two major challenges need to be addressed in
order to systematically unravel these genetic interactions.
First, large amounts of high quality data are necessary in order to ensure the statistical power required
to uncover these genotype-to-phenotype relationships. Current advances in high-throughput biology are
enabling the generation of very large datasets that should facilitate the detection of higher order genetic
interactions. Here, we report on the analysis of a panel of 1000 cancer cell lines. The mutation status of 66
known cancer genes has been characterized for each cell line in this panel. Additionally, these cell lines
have been screened to model drug response with a large number (250+) of anti-cancer therapeutics. This
dataset is part of the Cancer Genome Project at the Wellcome Trust Sanger Institute.
The second challenge is to design a computational framework that employs a mathematical formalism rich
enough to capture the underlying biological complexity, while limiting the computational complexity that
would otherwise prohibit finding optimal (or even good) solutions in the vast combinatorial search space.
We contribute to addressing this second challenge by proposing a novel computational approach based on
integer programming that infers logic combinations of mutations that predict the observed drug response.
The use of a logic formalism enables the formulation of intelligible models, from which the cancer biologists
can generate a deeper understanding based on domain knowledge and easily formulate novel hypotheses
and experiments.
Our models show that for most drugs, combinations of mutations explain the drug response better than
single mutations. For example, of the 8 BRAF inhibitors in the panel, the drug sensitivity to 3 of them is better
explained using a logic combination of a BRAF mutation and one or more mutations in other genes, such
as TET2. These results immediately suggest putative drug combination therapies.
Additionally, cancer signaling pathways as annotated in e.g. the Pathway Interaction Database can be
employed to dramatically reduce the search space and offer a mechanistic explanation of the uncovered
gene combinations.
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