In Silico Prediction of Novel Drug Combinations to Combat Bortezomib-Resistant Multiple Myeloma

Abstract 1344

Bortezomib/VELCADE® (Bz) is a proteasome inhibitor that has been used successfully in the treatment of multiple myeloma (MM) patients. However, acquired resistance to Bz is an emerging problem. Thus, there is a need for novel therapeutic combinations that enhance Bz sensitivity or re-sensitize Bz resistant MM cells to Bz. The Connectivity Map (CMAP; Broad Institute) database contains treatment-induced transcriptional signatures from 1,309 bioactive compounds in 4 human cancer cell lines. An input signature can be used to query the database for correlated drug signatures, a technique that has been used previously to identify drugs that combat chemoresistance in cancer (Wei, et al. Cancer Cell (2006) 10:331). In this study we used in silico bioinformatic screening of gene expression profiles from isogenic pairs of Bz sensitive and resistant mouse cell lines derived from the iMycCα/Bcl-x_L mouse model of plasma cell malignancy to identify compounds that combat Bz resistance.

We established Bz-induced kinetic gene expression profiles (GEPs) in 3 pairs of Bz sensitive and resistant mouse cell lines over the course of 24 hours. GEPs were collected in the absence of large-scale cell death. The 16 and 24 hour time points were averaged and compared between each Bz sensitive and resistant pair. Genes in the sensitive cell line with a fold change greater than 2, relative to the resistant line, were given the binary distinction of “up” or “down” depending on the direction of change. Genes that met these criteria were assembled into signatures, and then used as inputs for CMAP queries to identify compounds that induce similar transcriptional responses. In all pairs, treatment of the Bz sensitive line correlated with GEPs of drugs that target the proteasome, NF-κB, HSP90 and microtubules, as indicated by positive connectivity scores. However eight compounds, all classified as Topoisomerase (Topo) I and/or II inhibitors, were negatively correlated to our input signature. A negative connectivity score could have two interpretations: (1) this could indicate simply that Topos are upregulated by Bz treatment in Bz sensitive lines, which has been previously reported (Congdan, et al. Biochem. Pharmacol. (2008) 74: 883); or (2) this score could be interpreted as Topos are inhibited in Bz resistant cells upon Bz treatment. This led us to ask whether Topo inhibitors could target Bz resistant MM cells and re-sensitize them to Bz. Indeed, we found that multiple Topo inhibitors were significantly more active against Bz resistant cells as single agents and restored sensitivity to Bz when combined with Bz as a cocktail regimen. This work demonstrates the potential of this in silico bioinformatic approach for identifying novel therapeutic combinations that overcome Bz resistance in MM. Furthermore, it identifies Topo inhibitors – drugs that are already approved for clinical use – as agents that may have utility in combating Bz resistance in refractory MM patients.