Predictive Gene Signatures Determine Tumor Sensitivity to MDM2 Inhibition.

Early clinical trials using murine double minute 2 (MDM2) inhibitors demonstrated proof-of-concept of p53-induced apoptosis by MDM2 inhibition in cancer cells; however, not all wild-type tumors are sensitive to MDM2 inhibition. Therefore, more potent inhibitors and biomarkers predictive of tumor sensitivity are needed. The novel MDM2 inhibitor DS-3032b is 10-fold more potent than the first-generation inhibitor nutlin-3a. mutations were predictive of resistance to DS-3032b, and allele frequencies of mutations were negatively correlated with sensitivity to DS-3032b. However, sensitivity to DS-3032b of wild-type tumors varied greatly. We thus used two methods to create predictive gene signatures. First, by comparing sensitivity to MDM2 inhibition with basal mRNA expression profiles in 240 cancer cell lines, a 175-gene signature was defined and validated in patient-derived tumor xenograft models and human acute myeloid leukemia (AML) cells. Second, an AML-specific 1,532-gene signature was defined by performing random forest analysis with cross-validation using gene expression profiles of 41 primary AML samples. The combination of mutation status with the two gene signatures provided the best positive predictive values (81% and 82%, compared with 62% for mutation status alone). In addition, the top-ranked 50 genes selected from the AML-specific 1,532-gene signature conserved high predictive performance, suggesting that a more feasible size of gene signature can be generated through this method for clinical implementation. Our model is being tested in ongoing clinical trials of MDM2 inhibitors. This study demonstrates that gene expression profiling combined with mutational status predicts antitumor effects of MDM2 inhibitors and .