Program: Oral and Poster Abstracts
Session: 653. Myeloma: Therapy, excluding Transplantation: Poster II
In the current study, we used in vitro chemosensitivity data to develop a gene expression-based model to predict therapeutic response to proteasome inhibitors that were cross-validated in other cell-based MM models and clinical trials on MM patients. A panel of 50 human multiple myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM was used to generate single-agent cytotoxicity profiles for 4 the PIs Bz, Cz, Ix and Opz based on half maximal inhibitory concentration (IC50) values. Gene expression profiling (GEP) was performed using llumina’s HiSeq 2000 next-generation high-throughput sequencing technology (RNAseq) for 50 paired-end reads with depth of >20 million reads per sample.
Subsequently, GEP data was used to perform relevant gene selection and eventually develop a gene-based prognostic prediction model associated with PI response. In terms of statistical methodology, we used a locally additive polynomial regression model, which is a nonparametric regression model, on multivariate responses generated by our data. This was coupled with a sparsity-driven statistical model for dimension reduction and grouping of GEP data corresponding to drug response of the different cell-lines.
Our GEP-based prediction model could successfully distinguish between good or poor PI response with high level of significance (p<0.005) and stratify survival in the PI-treatment arms of two independent sets of clinical trials on MM patients, APEX (Hazards ratio (HR) = 2.185; p=0.001) and UAMS-MMTT3a (HR= 2.091; p=0.009) but not for the non-PI arms suggesting PI-specificity of our prediction model.
Therefore, we could successfully generate a gene expression signature-based prediction model qualitatively and quantitatively associated with PI response that could be effectively cross-validated on in vitro tumor models and human MM clinical trials. Our research will benefit clinical decision-making through the pre-identification of non-responders to PI treatment prior to initiating MM therapy and the development of novel treatment strategies specifically targeted at PI-resistant MM patients.
Disclosures: No relevant conflicts of interest to declare.
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