Report on EU–USA Workshop: How Systems Biology Can Advance Cancer Research (27 October 2008)

Abstract

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 proces...

Copy number abnormalities in sporadic canine colorectal cancers

“…This has become both increasingly challenging and urgent in recent years with the launch of high-throughput cancer genome projects, such as the Cancer Genome Atlas (http:// cancergenome.nih.gov/), the International Cancer Genome Consortium (http://www.icgc.org/), and others (e.g., Greenman et al 2007;Jones et al 2008). Researchers have been tackling this challenge by improving experimental conditions (e.g., high-resolution microarray to refine the boundaries of amplicons to narrow down the ''driver'' genes; see Haverty et al 2008) and by developing more sophisticated statistical models and functional analysis strategies to systematically (Aebersold et al 2009) identify significant abnormalities (e.g., Beroukhim et al 2007;Greenman et al 2007;Jones et al 2008).…”

Biochemical network-based drug-target prediction

“…Therefore, future studies must include cell-type specific modeling and also integrate pharmacokinetics and pharmacodynamics. With the increasing speed of high-throughput analyses and rising computer power, it will become possible to integrate network-oriented research with specific, individualized information and thereby bring the dream of personalized medicine within reach [50].…”

Molecular networks and system-level properties

“…One of the promises of systems biology is that to be able to rationalize and make more effective drug discovery for multifactorial diseases such as cancer, diabetes and metabolic syndromes, neurodegenerative diseases that are recognized as network diseases and are in strong medical need (Aebersold et al, 2009;Henney and Superti-Furga, 2008;Kitano, 2004b;Westerhoff, 2007b). It has been proposed that drug (possibly combinatorial) treatments targeting fragile nodes specific for the diseased compared to the healthy network should allow more efficient, even personalized, therapies for cancer (Alberghina et al, 2004a;Auffray et al, 2009;Kitano, 2007a,b).…”