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Publications of the Week

Comprehensive Genomic Profiling of Glioblastoma Tumors, BTICs, and Xenografts Reveals Stability and Adaptation to Growth Environments

By September 16, 2019No Comments

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This week we profile a recent publication in PNAS from the laboratory of Dr. Steven
Jones 
(pictured) at Canada’s Michael Smith Genome Sciences Centre.

Can you provide a brief overview of your lab’s current research focus?

The Jones Lab is located at Canada’s Michael Smith Genome Sciences Centre at BC Cancer (GSC). Its focus is uncovering the complete mutational landscape of cancer using genomics, understanding the diversity of the mutations that drive cancer and promote its progression, and how the mutation profiles of individual cancers can be exploited for the development of novel therapeutic strategies. Toward this end, our laboratory develops novel computational approaches and methodologies to enable high-throughput analyses of next-generation sequencing data. Using approaches such as molecular docking and dynamics, our group is also working to identify, optimize and refine compounds that can be used to target the cancer epigenome and has identified a number of epigenetic modifications that can be targeted in order to reverse the effects of oncogenic mutations.

What is the significance of the findings in this publication?

The prognosis for patients diagnosed with Glioblastoma multiforme (GBM), the deadliest form of brain cancer, is very poor. Even with surgical interventions and chemotherapy, only 10 per cent of patients survive five years following diagnosis.

To better understand the disease and its sensitivity to new treatments, researchers have developed an important laboratory technique to study GBM tumours in mice. To do this, cancer cells are taken from patients, allowed to multiply in the laboratory and are then transplanted into mice. While this is an extremely useful approach for the study of GBM, it is important to keep in mind that humans and mice differ in their biology. To make the most out of this valuable research tool, scientists need a clear picture of the similarities and differences between GBM from patients and those in mice.

In collaboration with scientists at the University of Calgary, the Hospital for Sick Kids, the University of British Columbia, and the University of Toronto, our team at the GSC employed expertise in DNA sequencing and analysis to characterize the genetics of tumour cells in patients, in the laboratory and in mice. Our findings demonstrated that while the GBM mouse model resembles the human disease and may provide valuable insights into GBM, variations in how the cancer cells behave in the human, mouse and laboratory settings do exist. The wealth of information provided by this research is extremely valuable for the future study of GBM, particularly in the accurate interpretation of studies relying on this disease model and understanding its limitations. The study produced an in-depth catalogue comparing human and mouse modelled GBM, enabling researchers to avoid biases or misleading results introduced by the experimental conditions.

What are the next steps for this research?

The mouse xenograft model is essential for GBM research and drug screening. Moving forward, researchers will continue to use this model to improve our understanding of the disease and hopefully, to develop novel therapeutics. Our findings can be used by researchers to improve the outcomes of these studies. Our analyses demonstrate which pathways behave similarly in the models and the human disease. This knowledge can be used by scientists to avoid the disappointing scenarios where a drug looks promising in a cell or mouse models but shows little success in human clinical trials. We hope that the findings in this study will enable improved approaches for drug screening, thus contributing to the search for effective treatments for this devastating disease.

This work was funded by:

The Terry Fox Research Institute and the Canadian Institute for Health Research.

Learn more about brain cancer from BC Cancer

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