Drug GRADE: An Integrated Analysis of Population Growth and Cell Death Reveals Drug-Specific and Cancer Subtype-Specific Response Profiles
This week we profile a recent publication in Cell Reports from the laboratory
of Dr. Michael Lee (pictured, front row right) at UMass Medical School.
Can you provide a brief overview of your lab’s current research focus?
We are a laboratory of “systems pharmacology”, meaning that we try to understand how drugs or drug combinations function. We apply this interest in the context of anti-cancer therapies with the aim of understanding and improving treatments for various types of cancer. Within this area, we are currently interested in three related topics: 1) mechanisms of regulation for non-apoptotic cell death, 2) mechanisms of action and mechanisms of resistance for common anti-cancer drugs, and 3) design principles in combination drug therapy.
What is the significance of the findings in this publication?
In most cases, drugs are evaluated using a measurement called “relative viability” (live cells in drug treated vs. control conditions). This measurement doesn’t distinguish between responses that are due to inhibition of cell growth versus those that are due to activation of cell death, and it is generally unclear to what extent a drug is inducing these two phenotypes. In this study, we develop a new drug evaluation metric, called drug GRADE (growth rate-adjusted death). GRADE reports in a quantitative way the relative contribution of growth inhibition vs. cell death for a given drug response. Using this method we evaluated a panel of common anti-cancer drugs finding an unexpected range of diverse types of response. Essentially all conceivable responses were observed, including drugs that only activate cell death, drugs that only inhibit growth, and drugs that cause both of these but in varied proportions and with different timing. Importantly, the improved sensitivity and mechanistic resolution provided by our GRADE metric appears to capture cancer subtype-dependent drug sensitivities that are not captured using the traditional relative viability based measurement.
What are the next steps for this research?
Our next step is to use GRADE to improve our understanding of drug mechanisms of action. We found that GRADE varies across drugs, but did not vary between drugs that shared a common mechanism. Using this observation we are beginning to identify compounds that are misclassified and likely function in an unexpected manner. Additionally, we are finding genotypic variations that change GRADE. Based on this, we are using GRADE as a lens to explore how the genetic background of a cancer changes a drugs mechanism of action. We think that drug GRADE will be an important analytical tool for these and other applications in precision medicine and biomarker development.
This work was funded by:
This work was funded by the NIGMS/NIH and the American Cancer Society (ACS).