Escape from T-cell-targeting immunotherapies in acute myeloid leukemia

Abstract

Single-cell and spatial multimodal technologies have propelled discoveries of the solid tumor microenvironment (TME) molecular features and their correlation with clinical response and resistance to immunotherapy. Computational tools are incessantly being developed to characterize tumor-infiltrating immune cells and to model tumor immune escape. These advances have led to substantial research into T-cell hypofunctional states in the TME and their reinvigoration with T-cell-targeting approaches, including checkpoint inhibitors (CPIs). Until recently, we lacked a high-dimensional picture of the acute myeloid leukemia (AML) TME, including compositional and functional differences in immune cells between disease onset and postchemotherapy or posttransplantation relapse, and the dynamic interplay between immune cells and AML blasts at various maturation stages. AML subgroups with heightened interferon gamma (IFN-γ) signaling were shown to derive clinical benefit from CD123×CD3-bispecific dual-affinity retargeting molecules and CPIs, while being less likely to respond to standard-of-care cytotoxic chemotherapy. In this review, we first highlight recent progress into deciphering immune effector states in AML (including T-cell exhaustion and senescence), oncogenic signaling mechanisms that could reduce the susceptibility of AML cells to T-cell-mediated killing, and the dichotomous roles of type I and II IFN in antitumor immunity. In the second part, we discuss how this knowledge could be translated into opportunities to manipulate the AML TME with the aim to overcome resistance to CPIs and other T-cell immunotherapies, building on recent success stories in the solid tumor field, and we provide an outlook for the future.

Graphical abstract

Figure 1.

AML immune interplay and potential avenues for clinical translation. Current understanding of T-cell functional states and their effect on AML response to chemotherapy, bispecific molecules, and CPIs. Immune gene expression profiling (bulk and/or scRNA-seq) should be integrated with clinically validated prognosticators, including ELN risk category, LSC17 score, and molecular lesions (TP53, RUNX1, IDH1/2, and TET2 mutational status), to accurately stratify patients with AML into subgroups with substantially different survival probabilities. Patients with an IFN-γ–dominant, immune-enriched TME could be allocated immunotherapies that target AML-induced T-cell dysfunctional states, including T-cell engagers and CPIs. TP53-mutated AML have been shown to respond to a CD123-targeting bispecific molecule. Conversely, patients with a “cold,” immune-depleted profile could benefit from increasing T-cell trafficking to the TME and/or from priming therapies such as vaccines, adoptive T-cell transfer, or allogeneic HSCT. Interventions that balance type I (tumor-cell intrinsic) and type II (immune-cell intrinsic) IFN signaling could be instrumental to overcoming resistance to CPIs and other T-cell–based immunotherapies. In this respect, IFN-I hyporesponsiveness in tumor cells before anti-PD1 treatment has been correlated with long-term survival, as discussed in the main text. Furthermore, abrogating cancer cell IFN-I signaling increases IFN-II signaling in immune cells, thereby expanding T cells toward effector-like functional states. Red arrows denote inhibition; green arrows denote stimulation. GEP, gene expression profiling; LSC17, 17-gene leukemia stem cell; mAbs, monoclonal antibodies; TAM, tumor-associated macrophage.