The welcome address of the conference was provided by the Dean of the School of Medicine, Dentistry and Biomedical Sciences, the newly elected President and Vice-Chancellor of the Queen’s University Belfast, Professor Patrick Johnston. Professor Johnston emphasized the general importance of computational & systems biology approaches to understand causal disease mechanisms on a genomic scale. Furthermore, he reminded at the beginnings of the conference that was initially funded by the Department for Employment and Learning (DEL, UK) to support the establishment of a Computational Biology infrastructure in Belfast.
It is important to emphasize that Genomic Medicine sets all boundary conditions that allows a systems analysis of medicine. However, many approaches still do not utilize available data up to their full potential but use them in a traditional, reductionist manner, e.g., by neglecting correlation structures among proteins or SNPs. Instead, the talks presented in this session showed original and creative approaches for Genomic Medicine on the systems level.
The first talk of the conference with the title ‘Modeling Endocrine Resistance in Breast Cancer’, which was also the keynote lecture, was presented by Robert Clarke (Biomedical Graduate Research Organization, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center (USA)). The talk advocated the general perspective that a systems biology approach is required in order to integrate knowledge from cancer biology with computational and mathematical methods. As a specific case study, endocrine resistance in breast cancer was discussed adopting a network medicine approach[2]. A thought provoking conclusion from the presented analysis was that endocrine resistance may not require many new genes for its explanation, but just a few changes in the usage of existing interactions among known genes.
Francesca Ciccarelli (Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus (Italy)) presented a talk about ‘Genomics and Network Biology to Identify Systems level properties of cancer genes’. In her talk common mutations in cancer genes were discussed with the goal to identify driver genes and novel therapeutic targets. As a result it was found that cancer genes form interconnected hubs in the human protein-protein interaction (PPI) network and are broadly expressed, in particular in the cancer tissue where they mutate. Furthermore, recessive cancer genes are old on an evolutionary scale and form singleton hubs, whereas dominant cancer genes are fairly recent present in the PPI network as duplicated hubs[3].
Christos Hatzis (Yale Comprehensive Cancer Center, Yale School of Medicine (USA)) gave a talk about ‘Complexity and limits of predictability in breast cancer’. Christos’ presentation started by emphasizing different types of heterogeneity, e.g., inter-tumor and intra-tumor heterogeneity, in breast cancer and methods for their characterization. In the following, different breast cancer subtypes were studied quantitatively and basal-like tumors were found to be more heterogeneous than HER2 or Luminal A and B. On a general note, it was suggested to view the heterogeneity of tumors more as a feature rather than a nuisance in order to exploit this information.