Immune-Based Subgroups Uncover Diverse Tumor Immunogenicity and Implications for Prognosis and Precision Therapy in Acute Myeloid Leukemia

Abstract

Abstract Background Although a considerable proportion of acute myeloid leukemia (AML) patients achieve remission through chemotherapy, relapse remains a recurring and significant event leading to treatment failure. This study aims to investigate the immune landscape in AML and its potential implications for prognosis and chemo-/immune-therapy.Methods Integrated analyses based on multiple sequencing datasets of AML were performed. Various algorithms estimated immune infiltration in AML samples. A subgroup prediction model was developed, and comprehensive bioinformatics and machine learning algorithms were applied to compare immune-based subgroups in relation to clinical features, mutational landscapes, immune characterizations, drug sensitivities, and cellular hierarchies at the single-cell level.Results Two immune-based AML subgroups, G1 and G2, were identified. G1 demonstrated higher immune infiltration, a more monocytic phenotype, increased proportions of monocytes/macrophages, and higher FLT3, DNMT3A, and NPM1 mutation frequencies. It was associated with a poorer prognosis, lower proportions of various immune cell types and a lower T cell infiltration score (TIS).