Cancer Research - Europa

ESBIC-D
European Systems Biology Initiative
combating complex diseases
Summary
It is the goal of this coordination action (CA) to establish
a European framework for a systems biology approach to
combat complex diseases using cancer as a prototypical
problem. The coordination action will be fundamentally
based on existing resources of leading research groups in
Europe. It unites groups with a strong clinical focus, with
experience in high throughput functional genomics as well
as with computational and systems biology resources. Moreover,
it brings together groups from some of the largest
European cancer research organisations and centres.
Problem
Keywords | Systems biology | computational biology | cancer research | modelling | signalling pathways |
bioinformatics and patient information |
Primary targets of the Sixth Framework programme are
activities for the combat of multigenic complex diseases
such as cancer, diabetes, obesity, heart diseases and diseases
of the nervous system. In particular, cancer is, after decades
of research, still a devastating disease, responsible for
roughly one quarter of the death in Europe.
Essentially, the three main causes for cancer are infection,
environmental infl uence and genetic predisposition. However,
on a more analytical and molecular level the ontogeny of
cancer is less evident and both clinical as well as basic
research suggests that cancer is the result of an accumulation
of many factors that promote tumour growth and
metastasis. Consequently, it is not clear, if much of current
cancer research, typically focussed on analysing subprocesses
involving at most a few genes or gene products at
a time, will ever be able to ‘understand’ such a complex phenomenon,
and to form the basis for dramatic improvements
in cancer treatment. It is also clear, that the current research
approaches, in spite of all successes in some areas, have not
resulted in any dramatic increase in the rates of cure for
most common cancers.
With this CA we expect to improve this situation by
addressing the problem with a systems biology approach.
In particular, the strong interaction of clinical and experimental
data with theoretical computer modelling will be
applied in an interdisciplinary and international approach.
This goal will be achieved via a series of steps:
• designing the protocols needed for rapid data and information
exchange for the diff erent levels of cellular
information;
• connecting leading European research groups in a consortium
that contributes existing data and computational
resources and links clinical and experimental groups
with computational groups;
• providing standards and protocols to combine the data
resources with theoretical models;
• providing documentation and a series of workshops to
achieve the largest possible benefi t for European cancer
research.
These interaction points between the diff erent expertises
build the basis for measurable and verifi able targets of
the project that will have a high impact on future planning
and design of systems biology approaches for all complex
genetic diseases.
Aim
Exchange and dissemination of information – combining
leading EU wide resources. A particular goal of this CA will
be the agglomeration and integration of relevant information
from all three components. Existing resources of the
partners/partner institutions will be incorporated and an
immediate added-value is achieved on the European level
by the correlation and integration of those components.
Performance of joined studies and analyses – bridging
experiment and model. There is a fundamental discrepancy
in current cancer research. Much of the analysis carried out
up to now has been focussed on the eff ect of single genes,
resulting in models that are far too simple to explain the
complex biological processes acting in cancer development.
Thus, modelling at the state of the art is in most
cases not very helpful for e.g. prediction of the response of
patients to diff erent types of treatment or the development
of new drugs. On the other hand there is the tendency
to generate more and more data in an uncoordinated and
non-standardized fashion. This not only increases costs but
also leads to heterogeneous and often confl icting results
for the relevant biological processes. Thus, experimental
data generation at the state of the art is not very helpful to
guide the development of theoretical models. With this CA
we aim at identifying critical parameters in the course of
such model development and the identifi cation of experimental
protocols and strategies to measure these critical
parameters in coordinated experiments that target diff erent
levels of cellular information.
244 CANCER RESEARCH PROJECTS FUNDED UNDER THE SIXTH FRAMEWORK PROGRAMME