Cancer’s Byzantine Architecture – The Plot Thickens
Campbell Family Institute for Cancer Research ♦ Ontario Cancer Institute ♦ University of Toronto
Published in Nature, 20 Jan., 2011
In the mid 90s one of Canada’s foremost stem cell researchers, John Dick, made the rather shocking discovery that not all cancer cells are equivalent. Based on his eloquent work we were able to formulate our current thinking on cancer biology, which has it that only a small sub-population of cells within tumours support malignant expansion, while the majority of cancer cells — although dividing — can only divide so many times before they hit cell cycle arrest and begin to senesce. Therefore, to completely eradicate a population of cancer cells, this minority of “cancer stem cells” must be targeted and destroyed. Our historical view on transformation was that single cells accumulate mutations over time, and in a step-wise fashion their genetic contents slowly mutates to the point that the cell itself loses control over its proliferation. Under this model each clone, or descendant of the original transformant, is linearly related. However, recent genomic work in the area indicates that this view is all too simple. It is now apparent that the architecture, the “framework” upon which cancer supports itself, is in fact a complex and branching network of sub-clones that each have the capacity to support tumour growth. Working with human BCR-ABL lymphoblastic leukemia cell lines, John Dick and his colleagues found that many diagnostic patient samples had several genetically distinct leukemia-initiating clones. DNA copy number alteration (CNA) profiling allowed them to reconstruct an evolutionary map of these clones. Transplantation of clones into xenograft models revealed that the predominant diagnostic clone, sometimes, but not always, was associated with the most aggressive growth properties. In some cases, interestingly, minor subclones proved to be the most potent leukemia-initiating cells. Next generation cancer therapeutics are being targeted to cancer stem cells, but now it appears — for these to be effective — they must target not only the dominant cancer stem cell clone, but all of the minor subclones that may be equally, if not more, vicious.
Freeing Systems Biology Data from the Shackles of the 2D Realm
University of Toronto ♦ Published in PLoS ONE, Jan. 10, 2011
One of the great challenges of systems biology will be to integrate multiple data sets, of widely differing scales, into an interactive and visual interface for human interpretation. Visualization is an important element in elucidating the connections between diverse data sets. Only in recent times have platforms existed that have the capacity to weave together large quantities of data into meaningful 3D representations. Historically, visualizations of biological data have been limited to 2D outputs that fail to do justice to the underlying connections between different biological processes. The need for 3D visualization tools is essential. We as humans have lived and evolved in a 3D environment and as a result have adapted a profound capacity to reason and conceptualize along three axes. In addition, biological processes occur within 3D environments, so carrying out analysis of biological data sets in three dimensions is logical. As a solution to this challenge, a group of researchers at the University of Toronto led by Dr. Nicholas Provart have created an open-source template that integrates and visualizes systems biology data as interactive 3D representations on the world wide web. The group applied their template to the model plant organism Aribidopsis thaliana and have dubbed it ePlant. The platform incorporates proteome-scale protein structure prediction and annotation along with existing -omics scale data, and allows users to evaluate protein structure and function, protein-protein interactions, protein subcellular locations (great visual display here), gene expression patterns, and genetic variation. The result is a program that integrates systems biology data found on the nanoscale with genetic variation found on the kilometer-scale. The open-source nature and flexibility of the ePlant framework circumvents one of the major limitations of current computational systems biology tools — accessibility. ePlant does not require users to download specific data visualization and analysis software to their specific operating system, reducing the learning curve required to grasp the program. Software development on the world wide web also allows for community-driven expansion of systems biology software like that of ePlant, allowing for continued growth and refinement.