Sequential azacitidine and lenalidomide for patients with relapsed and refractory acute myeloid leukemia: Clinical results and predictive modeling using computational analysis. (June 2019)

• Record Type: Journal Article
• Authors: Stevens, Brett; Winters, Amanda; Gutman, Jonathan A.; Fullerton, Aaron; Hemenway, Gregory; Schatz, Derek; Miltgen, Nicholas; Wei, Qi; Abbasi, Taher; Vali, Shireen; Singh, Neeraj K.; Drusbosky, Leylah; Cogle, Christopher R.; Hammes, Andrew; Abbott, Diana; Jordan, Craig T.; Smith, Clayton; Pollyea, Daniel A.
• Is Part Of: Leukemia research. Volume 81(2019)
• Page Start: 43
• Page End: 49
• Publication Date: 2019-06
• Subjects: Acute myeloid leukemia -- Relapsed -- Lenalidomide -- Azacitidine -- Computational modeling -- Prediction; Leukemia -- Periodicals; Leukemia -- Periodicals; Leucémie -- Périodiques; Leukemia; Periodicals; Electronic journals; Electronic journals; 616.9941905
• DOI: 10.1016/j.leukres.2019.04.005
• Languages: English
• ISSNs: 0145-2126

Abstract

Highlights: Azacitidine + lenalidomide is active in some relapsed/refractory AML patients. Tolerability of this regimen was in some cases poor. A computational biological modeling approach predicted responders to this regimen. Toxic, low response rate therapies have a role if response prediction is robust. Abstract: Background: Patients with relapsed and refractory (R/R) acute myeloid leukemia (AML) have limited treatment options. Genomically-defined personalized therapies are only applicable for a minority of patients. Therapies without identifiable targets can be effective but patient selection is challenging. The sequential combination of azacitidine with high-dose lenalidomide has shown activity; we aimed to determine the efficacy of this genomically-agnostic regimen in patients with R/R AML, with the intention of applying sophisticated methods to predict responders. Methods: Thirty-seven R/R AML/myelodysplastic syndrome patients were enrolled in a phase 2 study of azacitidine with lenalidomide. The primary endpoint was complete remission (CR) and CR with incomplete blood count recovery (CRi) rate. A computational biological modeling (CBM) approach was applied retrospectively to predict outcomes based on the understood mechanisms of azacitidine and lenalidomide in the setting of each patients' disease. Findings: Four of 37 patients (11%) had a CR/CRi; the study failed to meet the alternative hypothesis. Significant toxicity was observed in some cases, with three treatment-related deaths and a 30-day mortality rate of 14%. However, the CBM method predicted responses in 83% of evaluable patients, with a positive and negative predictive value of 80% and 89%, respectively. Interpretation: Sequential azacitidine and high-dose lenalidomide is effective in a minority of R/R AML patients; it may be possible to predict responders at the time of diagnosis using a CBM approach. More efforts to predict responses in non-targeted therapies should be made, to spare toxicity in patients unlikely to respond and maximize treatments for those with limited options.