Methods for High-throughput Drug Combination Screening and Synergy Scoring

Abstract

Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

PRMT5 Regulates DNA Repair by Controlling the Alternative Splicing of Histone-Modifying Enzymes

“…Except for MV4-11 cells, the cell lines tested exhibited a low sensitivity to PARPi, with an IC50 in the micromolar range (Figure S7A), an order of magnitude higher than what is reported for other cancer cells in vitro, such as a BRCA1/2-deficient breast cancer cell line (Farmer et al, 2005). We examined cell viability after four days of treatment, and used two different methods to assess possible synergistic effects between PRMT5i and PARPi: the Bliss synergy method (He et al, 2018; Iniguez et al, 2018) (Figure 7B), and the Chou-Talalay method (Chou, 2010) (Figure S7B). We observed positive Bliss scores for all cell lines tested (Figure 7B), indicating a synergistic effect.…”

“…Combination index values were generated using CalcuSyn v2.11 (http://www.biosoft.com/w/calcusyn.htm) and the Chou-Chou plots (Chou, 2010) were generated from the Dose Reduction Indexes (DRI −1 ) of the experimental values. Bliss synergy was calculated using the bioconductor package synergyfinder v1.0.0 using default parameters for calculating bliss independence (He et al, 2018). Excess over bliss was calculated as the difference between the observed response and the expected bliss independence (Iniguez et al, 2018).…”

Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

“…Viability data were normalized to the negative control condition (i.e., DMSO treated cells). We fitted a four-parameter log-logistic model using the "drm" R package (Ritz et al, 2015) and computed the synergy score with Bliss independence model as implemented in the "synergyfinder" R package (He et al, 2018).…”

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

“…DMSO treated cells). We fitted a four-parameter loglogistic model using the 'drm' R package (Ritz et al, 2015) and computed the synergy score with Bliss independence model as implemented in the 'synergyfinder' R package (He et al, 2018).…”