Using Genomic Signatures to Predict Combination Therapy in GBM

Using Genomic Signatures to Predict Combination Therapy in GBM

Award: $2,452,367 over 3 years

Principal Investigators: John H. Sampson, MD, PhD, MHSc, Joseph R. Nevins, PhD

Background: The molecular heterogeneity of cancer, including GBM, resulting from the acquisition of multiple genetic alterations that contribute to the development of the tumor, underlies the heterogeneity of patient outcomes and response to therapy. Although the development of new agents that target the activities that underlie these GBM phenotypes holds the promise of matching therapy with disease mechanism, three challenges are posed in the implementation of these advances in clinical practice. First, there is a need to increase the number of drugs developed that target activities involved in GBM so as to match the complexity and heterogeneity of the disease process. Second, there is a need to develop biomarkers capable of matching these new therapies with individual patients. Third, given the likelihood that single agents, while showing activity in a small number of patients, are not likely to be effective within the population of patients with GBM as a whole, there is a critical need to develop strategies for combinations of therapeutics that will be effective. As such, the development of rational combination therapy is clearly critical but makes the challenge of matching therapy with the individual patient even more daunting.

Hypothesis: The use of expression signatures predicting activation of oncogenic pathways, previously shown to also predict response to therapeutics that target components of these pathways, coupled with an ability to identify patterns of pathway activation, will provide a unique opportunity to develop rational combinations for the treatment of GBM.

Primary objective: To develop a rational scheme to identify combinations of cancer therapeutics that have the potential to be beneficial to patients with GBM and at the same time, to couple this with tools that can select those patients most likely to benefit from a given combination

Specific Aims:
1. Make use of patterns of pathway activity in GBM to identify opportunities for combination therapy
2. Make use of xenograft models of GBM to evaluate the efficacy and predictability of combination therapy regimens based on pathway signatures
3. Establish a prospective clinical study in GBM that makes use of pathway activation patterns as a basis to assign novel targeted combination therapeutic regimens