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Using Synthetic Lethality to Identify Predictive Biomarkers of Targeted Therapies
Using synthetic lethality to ID biomarkers for targeted anti-cancer drugs.
Precision medicine – the promise of using the right targeted drug to treat the right patient based on the power of genomics –is still evolving. One of the current challenges is the limitations of current computational tools that can identify patient populations that are likely to respond to targeted therapies. SRI researchers are working on a new, computational way to identify predictive genetic biomarkers of targeted therapies that can be used to identify responders. The method will speed clinical development of an innovative anti-cancer treatments and can ultimately be used to create a precision medicine tool for the wider research community.
SRI researchers hypothesize synthetic lethality can help identify predictive biomarkers of targeted anti-cancer therapies. In synthetic lethal (SL) interactions, a genetic alteration in one gene leads to dependency on a second gene. Neither alteration by itself is essential for survival, but together they lead to cancer cell death. These researchers believe genetic alterations in the cancer cells can make them susceptible to targeted drugs through synthetic lethality. In other words, the inhibition of the drug targets (due to drug action) combined with the presence of the genetic alteration, leads to cancer cell death. The cell-specific genetic alterations will serve as predictive biomarkers of response to the drugs.
Utilizing synthetic lethal interactions, SRI researchers are developing a computational platform to identify predictive biomarkers in lung cancer for efficacy of Sudemycin-D6 (SD6), a novel splicing modulator developed by Thomas Webb that has been shown to have potent anti-tumor activity. They will use MiSL (“mining synthetic lethals,” pronounced missile), a computational tool developed by researcher Subarna Sinha while she was at Stanford University, to mine SL interactions from large-scale primary tumor genomic and transcriptomic datasets.
Once predictive biomarkers are identified computationally, SRI researchers will experimentally validate them in two steps. First, they will validate the top candidate biomarkers using genetic knockdown of the biomarker with shRNA and pharmacologic knockdown with SD6 in isogenic lung cancer cell lines in vitro and in vivo. They also will confirm that the mechanism of SD6 sensitivity is via synthetic lethality between the biomarker and splicing factors.
SRI researchers expect identifying predictive biomarkers will accelerate SD6’s clinical development in lung cancer. The long-term objective is to create a tool for identifying predictive biomarkers that the wider research community can use, ultimately unlocking the clinical benefit of the available drug arsenal, furthering the clinical development of new targeted anti-cancer agents, and matching patients to treatment options that are likely to be effective.
This work is supported by the National Institutes of Health grant R21CA218778.