Research ArticleCancer

Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia

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Science Translational Medicine  03 Jun 2020:
Vol. 12, Issue 546, eaaz0463
DOI: 10.1126/scitranslmed.aaz0463

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Informing immunotherapy decisions

Predicting which patients with acute myeloid leukemia (AML) are likely to respond to immunotherapy is currently difficult. To better understand the immune heterogeneous landscape in AML, Vadakekolathu et al. studied gene expression in bone marrow biopsies and clinical outcomes of multiple AML cohorts. They observed variable T cell infiltration and interferon-driven signatures that could be associated with outcomes such as chemotherapy resistance. Matched samples from patients before or after flotetuzumab indicated that an IFN-γ signature could identify patients likely to respond to this therapy. These data lead to a better understanding of the immune environment of AML and could help inform immunotherapy treatment decisions in the future.

Abstract

Acute myeloid leukemia (AML) is a molecularly and clinically heterogeneous hematological malignancy. Although immunotherapy may be an attractive modality to exploit in patients with AML, the ability to predict the groups of patients and the types of cancer that will respond to immune targeting remains limited. This study dissected the complexity of the immune architecture of AML at high resolution and assessed its influence on therapeutic response. Using 442 primary bone marrow samples from three independent cohorts of children and adults with AML, we defined immune-infiltrated and immune-depleted disease classes and revealed critical differences in immune gene expression across age groups and molecular disease subtypes. Interferon (IFN)–γ–related mRNA profiles were predictive for both chemotherapy resistance and response of primary refractory/relapsed AML to flotetuzumab immunotherapy. Our compendium of microenvironmental gene and protein profiles provides insights into the immuno-biology of AML and could inform the delivery of personalized immunotherapies to IFN-γ–dominant AML subtypes.

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