Response Prediction to Isocitrate Dehydrogenase (IDH) Inhibitors in Patients with IDH1- or IDH2-Mutated Acute Myeloid Leukemia Using Clinical and Genomic Data

Background: Ivosidenib (Servier Pharmaceuticals LLC) and enasidenib (Celgene) are inhibitors of mutant IDH1 and IDH2, respectively, approved for the treatment of relapsed/refractory (R/R) AML with an IDH1 or IDH2 mutation (ivosidenib is also approved for front-line use in patients ≥ 75 years or with comorbidities). While these drugs were approved based on durable complete remission (CR) + CR with partial hematologic recovery (CRh) rate, only 23-43% of patients responded. Thus, there is a need for a biomarker to better predict response. We previously presented a genomic model of response to enasidenib using linear discriminate analysis (Ghazanchyan et al., ASH 2018). In the two-part study presented here, we improved this modeling by adding clinical data, using machine learning-based approaches to model response to both enasidenib and ivosidenib, and generated an independent validation cohort using clinical samples.