Report on EU-USA workshop: how systems biology can advance cancer research (27 October 2008).
The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer-related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5-20 years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine).Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ-line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer-relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression.A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer-relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data.Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high-quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects.Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas.A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.
Aebersold
Ruedi
R
Auffray
Charles
C
Baney
Erin
E
Barillot
Emmanuel
E
Brazma
Alvis
A
Brett
Catherine
C
Brunak
Søren
S
Butte
Atul
A
Califano
Andrea
A
Celis
Julio
J
Cufer
Tanja
T
Ferrell
James
J
Galas
David
D
Gallahan
Daniel
D
Gatenby
Robert
R
Goldbeter
Albert
A
Hace
Natasa
N
Henney
Adriano
A
Hood
Lee
L
Iyengar
Ravi
R
Jackson
Vicky
V
Kallioniemi
Ollie
O
Klingmüller
Ursula
U
Kolar
Patrik
P
Kolch
Walter
W
Kyriakopoulou
Christina
C
Laplace
Frank
F
Lehrach
Hans
H
Marcus
Frederick
F
Matrisian
Lynn
L
Nolan
Garry
G
Pelkmans
Lucas
L
Potti
Anil
A
Sander
Chris
C
Seljak
Marija
M
Singer
Dinah
D
Sorger
Peter
P
Stunnenberg
Hendrik
H
Superti-Furga
Giulio
G
Uhlen
Mathias
M
Vidal
Marc
M
Weinstein
John
J
Wigle
Dennis
D
Williams
Michael
M
Wolkenhauer
Olaf
O
Zhivotovsky
Boris
B
Zinovyev
Andrei
A
Zupan
Blaz
B
D015415:Biomarkers; D035843:Biomedical Research / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D004493:Education; D005060:Europe; D006801:Humans; D009369:Neoplasms; D049490:Systems Biology / Q000379:methods*; D014481:United States
D015415:Biomarkers