A novel platform to identify synergistic drug combination to combat cancer recurrence and prolong clinical efficacy

presentation · 1 month ago
by Stephan Schurer (University of Miami)
JChem PostgreSQL Cartridge

The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national research consortium funded by the NIH to generate an extensive reference datasets and tools to study how cells respond to a variety of small molecule, genetic and environmental stressors. Cellular responses to a perturbation (such as a small molecule) are described as highly standardized signature across many biological dimensions such as transcriptomics, proteomics, epigenomics, cell phenotypes. All signatures and associated digital research objects, such as chemical compounds are registered and made accessible via the LINCS Data Portal (LDP). Chemical structure search and management in LDP are powered by the ChemAxon JChem PostgreSQL Cartridge. We have used the LINCS drug perturbation transcriptional signatures to address a major challenge in targeted cancer therapies, the almost universal emergence of resistance and concomitant cancer recurrence. Combination therapies offer the potential to combat cancer recurrence through different mechanisms, such as the simultaneous inhibition of multiple oncogenic pathways and the increase of efficacy through synergism. However, the number of possible combinations prohibits their comprehensive in vitro and in vivo testing. To overcome this limitation, we developed an integrative pipeline, called SynergySeq, which computationally scores and prioritizes compound combinations for a disease of interest. As a proof of concept, we applied SynergySeq in the context of Glioblastoma treatment. To identify therapeutic drug combinations for Glioblastoma, we utilized the extensive chemical perturbation signatures from the LINCS project and generated transcriptional-response consensus signatures for each of the compounds used. Moving forward we will further integrate chemical structure information and gene expression signatures to improve predictions and rationalize efficacious drug combinations.

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