CGEP Project Overview
Cancer development is a multi-step process that leads to uncontrolled tumour cell growth. The CONCORD study estimates that across 1.9 million patients, from 31 countries and 5 continents, current treatments achieve a 5-year survival rate for less than 50% of diagnosed cancers (Coleman et al, 2008). Despite extensive research on resistance in cancer therapy to data, only limited insight has been gained and translated into more effective therapies. Over the past decade, it has become increasingly clear that varying combinations of factors underlie cancer treatment resistance in each individual. These include the interaction of multiple pathways causing treatment resistance, specific gene mutations leading to cancer predisposition (Ding et al. 2008) as well as mutations correlated with treatment response (Tsao et al. 2005; Yun et al. 2008; Zhu et al. 2008).
The maturation of high-throughput technologies in the post-genomics era has moved science from the characterization of single genes and proteins to the investigation of entire interactomes and pathways. Given the myriad of dynamic changes occurring in the cancer milieu, efforts to characterize and analyze these changes in a systematic and thus more effective manner are still in their relative infancy. The use of predictive methods (e.g., optimized association mining algorithms such as FPClass (Kotlyar and Jurisica 2006) that base predictions on sequence, domains, gene expression and derived attributes) and higher quality data sources can improve experimental validation, as demonstrated by a number of key Ontario-led initiatives, including the Interologous Interaction Database (I2D), Cancer Data Integration Portal (CDIP), GeneMANIA, Pathway Commons and the Reactome knowledge base (Vastrik, D'Eustachio et al. 2007).
This project brings together an international research team leveraging tools and resources from 20 top cancer centers and research institutions in eight countries. Our team is compiling the world's first Cancer Gene Encyclopedia. This encyclopedia will allow scientists to identify new cancer targets. At the same time, it will help transform cancer research and ultimately, cancer care by improving the outcome for patients with lung, prostate, breast, ovarian and head and neck cancers, which have some of the highest mortality rates.
Coleman, M. P., M. Quaresma, et al. (2008). "Cancer survival in five continents: a worldwide population-based study (CONCORD)." Lancet Oncol 9(8): 730-56. [Pubmed]
Ding, L., G. Getz, et al. (2008). "Somatic mutations affect key pathways in lung adenocarcinoma." Nature 455(7216): 1069-75. [Pubmed]
Kotlyar, M. and I. Jurisica (2006). "Predicting protein-protein interactions by association mining." Information Systems Frontiers 8: 37-47.
Tsao, M. S., A. Sakurada, et al. (2005). "Erlotinib in lung cancer - molecular and clinical predictors of outcome." N Engl J Med 353(2): 133-44. [Pubmed]
Vastrik, I., P. D'Eustachio, et al. (2007). "Reactome: a knowledge base of biologic pathways and processes." Genome Biol 8(3): R39. [Pubmed]
Yun, C. H., K. E. Mengwasser, et al. (2008). "The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP." Proc Natl Acad Sci U S A 105(6): 2070-5. [Pubmed]
Zhu, C. Q., G. da Cunha Santos, et al. (2008). "Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21." J Clin Oncol 26(26): 4268-75. [Pubmed]