Gene networks constructed through simulated treatment learning can predict proteasome inhibitor benefit in multiple myeloma

Abstract

Purpose: Proteasome inhibitors are widely used in treating multiple myeloma, but can cause serious side effects and response varies among patients. It is, therefore, important to gain more insight into which patients will benefit from proteasome inhibitors. Experimental Design: We introduce simulated treatment learned signatures (STLsig), a machine learning method to identify predictive gene expression signatures. STLsig uses genetically similar patients who have received an alternative treatment to model which patients will benefit more from proteasome inhibitors than from an alternative treatment. STLsig constructs gene networks by linking genes that are synergistic in their ability to predict benefit. Results: In a dataset of 910 patients with multiple myeloma, STLsig identified two gene networks that together can predict benefit to the proteasome inhibitor, bortezomib. In class “benefit,” we found an HR of 0.47 (P ¼ 0.04) in favor of bortezomib, while in class “no benefit,” the HR was 0.91 (P ¼ 0.68). Importantly, we observed a similar performance (HR class benefit, 0.46; P ¼ 0.04) in an independent patient cohort. Moreover, this signature also predicts benefit for the proteasome inhibitor, carfilzomib, indicating it is not specific to bortezomib. No equivalent signature can be found when the genes in the signature are excluded from the analysis, indicating that they are essential. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or multiple myeloma disease progression. Conclusions: STLsig can identify gene signatures that could aid in treatment decisions for patients with multiple myeloma and provide insight into the biological mechanism behind treatment benefit.