Purpose: Cytarabine (also known as ara-C) has been the backbone of acute myeloid leukemia (AML) chemotherapy for more than five decades. Recent pharmacogenomics-based 10-SNP ara-C (ACS10) scores showed low ACS10 (≤0) to be associated with poor outcomes in patients with AML treated with standard chemotherapy. Here, we evaluated the ACS10 score in the context of three different induction I regimens in patients with pediatric AML. Experimental Design: ACS10 score groups (low, ≤0, or high, >0) were evaluated for association with event-free survival (EFS) and overall survival (OS) by three randomized treatment arms in patients treated on the AML02 (NCT00136084) and AML08 (NCT00703820) clinical trials: AML02 low-dose ara-C (LDAC arm, n = 91), AML02 + AML08 high-dose ara-C (HDAC arm, n = 194), and AML08 clofarabine + ara-C (Clo/ara-C arm, n = 105) induction I regimens. Results: Within the low-ACS10 score (≤0) group, significantly improved EFS and OS were observed among patients treated with Clo/ara-C as compared with LDAC (EFS, HR = 0.45; 95% CI, 0.23–0.88; P = 0.020; OS, HR = 0.44; 95% CI, 0.19–0.99; P = 0.048). In contrast, within the high-ACS10 score group (score >0), augmentation with Clo/ara-C was not favorable as compared with LDAC (Clo/ara-C vs. LDAC, EFS, HR = 1.95; 95% CI, 1.05–3.63; P = 0.035; OS, HR = 2.10; 95% CI, 0.96–4.59; P = 0.063). Personalization models predicted 9% improvement in the outcome in ACS10 score–based tailored induction (Clo/ara-C for low and LDAC for high-ACS10 score groups) as compared with nonpersonalized approaches (P < 0.002). Conclusions: Our findings suggest that tailoring induction regimens using ACS10 scores can significantly improve outcomes in patients with AML. Given the SNPs are germline, preemptive genotyping can accelerate matching the most effective remission induction regimen.

Purpose:Cytarabine (also known as ara-C) has been the backbone of acute myeloid leukemia (AML) chemotherapy for more than five decades. Recent pharmacogenomics-based 10-SNP ara-C (ACS10) scores showed low ACS10 (≤0) to be associated with poor outcomes in patients with AML treated with standard chemotherapy. Here, we evaluated the ACS10 score in the context of three different induction I regimens in patients with pediatric AML.Experimental Design:ACS10 score groups (low, ≤0, or high, >0) were evaluated for association with event-free survival (EFS) and overall survival (OS) by three randomized treatment arms in patients treated on the AML02 (NCT00136084) and AML08 (NCT00703820) clinical trials: AML02 low-dose ara-C (LDAC arm, n = 91), AML02 + AML08 high-dose ara-C (HDAC arm, n = 194), and AML08 clofarabine + ara-C (Clo/ara-C arm, n = 105) induction I regimens.Results:Within the low-ACS10 score (≤0) group, significantly improved EFS and OS were observed among patients treated with Clo/ara-C as compared with LDAC (EFS, HR = 0.45; 95% CI, 0.23–0.88; P = 0.020; OS, HR = 0.44; 95% CI, 0.19–0.99; P = 0.048). In contrast, within the high-ACS10 score group (score >0), augmentation with Clo/ara-C was not favorable as compared with LDAC (Clo/ara-C vs. LDAC, EFS, HR = 1.95; 95% CI, 1.05–3.63; P = 0.035; OS, HR = 2.10; 95% CI, 0.96–4.59; P = 0.063). Personalization models predicted 9% improvement in the outcome in ACS10 score–based tailored induction (Clo/ara-C for low and LDAC for high-ACS10 score groups) as compared with nonpersonalized approaches (P < 0.002).Conclusions:Our findings suggest that tailoring induction regimens using ACS10 scores can significantly improve outcomes in patients with AML. Given the SNPs are germline, preemptive genotyping can accelerate matching the most effective remission induction regimen.

Supplementary Tables and Figures

Background: Rapid advances in biotechnology provide researchers with the opportunity to integrate omics profiles (genomics, epigenomics, transcriptomics, proteomics, etc.) with multiple phenotypes or experimental conditions. In cancers such as acute myeloid leukemia (AML), where combination therapies are standard of care, identifying genetic drivers of drug resistance requires evaluating how genes are associated with multiple drug response phenotypes. Statistical analyses associating omics profiles with multiple phenotypes yield multiple significance values and rankings for each of many genes. There is a great need to consolidate these multiple rankings into a consensus ranking to prioritize specific genes for detailed follow-up wet-lab or clinical studies. Methods/Results: Here, we evaluate the well-known Fisher’s method, the sum of squared z-statistics (SSz), and the recently published cellMCD method as tools for gene prioritization. In simulation studies, cellMCD showed very similar or highly superior performance to the widely used Fisher’s and SSz methods. These advantages were also observed in an example application involving a CRISPR drug screen of an acute myeloid leukemia cell line. Conclusions: In summary, our results indicate that cellMCD should be more widely used for prioritizing discoveries from multiple omic association studies. These methods are available as an R package on github.