Despite decades of conventional chemotherapy, the prognosis for patients with acute myeloid leukemia (AML) remains dismal, with 75% of patients succumbing to their disease within 5 years of diagnosis. Recent advances in cancer therapy have turned to the immune system for specific targeting and clearance of tumors. However, to date, there is not a comprehensive understanding of the neoepitope landscape in AML that could be used in the next generation of immunotherapies. In this study, genomic data from 562 patients (cohort: Beat AML program, Oregon Health & Science University) were analyzed computationally to identify tumor variants, altered mRNA sequences of variants, and HLA-type. Using a computational pipeline and algorithm (neoepiscope), we were able to predict 8-11 amino acid peptide sequences (aka: epitopes) from DNA-seq of complementary tumor and normal patient samples which consider germline context and the potential for co-occurrence of two or more somatic variants on the same mRNA transcript.