How leukemia evades the immune system

Immune therapies, such as chimeric antigen receptor T cells, bispecific antibodies, and immune checkpoint inhibitors, have emerged as promising modalities in multiple hematologic malignancies. Despite the excitement surrounding these therapies, we are currently unable to predict which patients will respond, and which patients are at the highest risk for the unique toxicities associated with these therapies. Within solid tumors, the status of the immune microenvironment provides valuable insight regarding potential responses to immune therapies. Much less is known about the immune microenvironment within hematologic malignancies, but the characteristics of this environment are likely to serve a similar predictive role.

Acutemyeloid leukemia (AML) is the most common hematologic malignancy in adults, and only 25% of patients are alive at 5 years. Our lab uses both patient samples and mouse models of AML to study the effect of AML on the immune system.  Of particular interest to our lab is to study the mechanism of action of combined targeted drug and immune therapies on hematological malignancies with a focus on AML. We make extensive use of mass-spec based flow cytometry (aka CyTOF) to develop a “road map” of the cell types and functional status of cells in the bone marrow and blood of leukemia patients. 

Figure comparing activation markers in bone marrow samples from healthy donors to those in AML patients.
Demonstration of immune checkpoints and activation markers distributed over 10 healthy (left) and 10 AML (right) bone marrow samples. Heat maps are distributed by first gating on CD4 (blue), CD8 (orange), and Treg (green) populations.
Figure with three sets of plots, labeled A, B and C, showing examples of T cell differentiation identification by mass cytometry.
Examples of T cell differentiation identification by Mass cytometry. A. Plots identifying T cell subtypes of a healthy donor (left) and patient with AML (right). Differentiation of CD4 T cells (top row) and CD8 T cells (middle row) can be further delineated into the following groups: Naïve (CCR7+ CD45RA+), Effector (Eff CCR7- CD45RA+), Central memory (CM CCR7- CD45RA+) and Effector memory (CCR7- CD45RA-). Treg (bottom row) gating scheme of CD4 T cells by expression of CD25+ CD127-. B. Population Sunburst plots showing relative distributions of T cell subtypes based on gating shown in A (CD4 top, CD8 bottom). C. viSNE plots showing populations of CD4 and CD8 T cells and Tregs. All plots generated in Cytobank.

Micro-RNA (miRNA) in adaptive and innate immune responses

The discovery of miRNAs has revolutionized our understanding of protein regulation. The study of how these molecules regulate immune responses has recently become a field of intense interest. Our lab studies how miRNAs modulate both innate and adaptive immune responses. We are specifically testing the impact of specific miRNAs on the ability of DCs to promote protective T cell responses using a variety of bacterial and viral pathogens. Better understanding of how DCs prime T cell responses will allow us to develop more efficient DC-based cancer vaccine strategies in the future.

Figure showing four illustrations, labeled A: T-Cell, B: Autoimmunity, C: Tumor and D: Infection.

Figure on right: The effects of mir-155 on T cells in the context of autoimmunity, antitumor responses, and infection. (A) T-cell receptor engagement results in increased mir-155 levels in the T cell. The resulting translational repression of target mRNA molecules, several of which with identified T-cell function (listed), leads to increased functional activity of the T cell. (B) In the context of autoimmunity, mir-155 has an impact on skewing to the Th-17 lineage though the regulation of ETS1 expression and IL-23 signaling. (C) Antitumor T-cell responses require mir-155 to allow vigorous cellular expansion and IFN-γ production. (D) CD8+ T cells require mir-155 to promote clonal expansion, survival and memory generation as part of their antiviral and antibacterial responses.

Three figures, labeled A, B and C, presenting data analysis.
Analysis of miRNA upregulation after CpG-induced DC maturation. Expression levels of miRNA were quantified from unstimulated or CpG-stimulated DCs. (A) Hierarchical clustering of miRNA signatures revealed a cluster of 19 miRNAs that were increased after CpG stimulation. (B) The 19 miRNAs that increased between unstimulated and CpG-treated are listed in descending order of fold change from highest to lowest. Each data point represents normalized data from four individual biological replicates. (C) Expression of miR-155 in DC stimulated with the indicated TLR ligands. Graphs display the fold increase in miR-155 between TLR-treated and untreated BMDCs. The experiment was repeated five times, and data shown are mean values of five experiments (± SEM). FLA, flagellin from S. typhimurium; HKLM, heat-killed L. monocytogenes; HMW, high m.w.; LMW, low m.w.