Immune dysfunction signatures predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia
Sergio Rutella, Jayakumar Vadakekolathu, Francesco Mazziotta, Stephen Reeder, Tung-On Yau, Rupkatha Mukhopadhyay, Benjamin Dickins, Heidi Altmann, Michael Kramer, Hanna A Knaus, Bruce R Blazar, Vedran Radojcic, Joshua F Zeidner, Andrea Arruda, Bofei Wang, Hussein A Abbas, Mark D Minden, Sarah K Tasian, Martin Bornhäuser, Ivana Gojo, Leo Luznik, Sergio Rutella, Jayakumar Vadakekolathu, Francesco Mazziotta, Stephen Reeder, Tung-On Yau, Rupkatha Mukhopadhyay, Benjamin Dickins, Heidi Altmann, Michael Kramer, Hanna A Knaus, Bruce R Blazar, Vedran Radojcic, Joshua F Zeidner, Andrea Arruda, Bofei Wang, Hussein A Abbas, Mark D Minden, Sarah K Tasian, Martin Bornhäuser, Ivana Gojo, Leo Luznik
Abstract
BackgroundImmune exhaustion and senescence are dominant dysfunctional states of effector T cells and major hurdles for the success of cancer immunotherapy. In the current study, we characterized how acute myeloid leukemia (AML) promotes the generation of senescent-like CD8+ T cells and whether they have prognostic relevance.METHODSWe analyzed NanoString, bulk RNA-Seq and single-cell RNA-Seq data from independent clinical cohorts comprising 1,896 patients treated with chemotherapy and/or immune checkpoint blockade (ICB).ResultsWe show that senescent-like bone marrow CD8+ T cells were impaired in killing autologous AML blasts and that their proportion negatively correlated with overall survival (OS). We defined what we believe to be new immune effector dysfunction (IED) signatures using 2 gene expression profiling platforms and reported that IED scores correlated with adverse-risk molecular lesions, stemness, and poor outcomes; these scores were a more powerful predictor of OS than 2017-ELN risk or leukemia stem cell (LSC17) scores. IED expression signatures also identified an ICB-unresponsive tumor microenvironment and predicted significantly shorter OS.ConclusionThe IED scores provided improved AML-risk stratification and could facilitate the delivery of personalized immunotherapies to patients who are most likely to benefit.TRIAL REGISTRATIONClinicalTrials.gov; NCT02845297.FUNDINGJohn and Lucille van Geest Foundation, Nottingham Trent University's Health & Wellbeing Strategic Research Theme, NIH/NCI P01CA225618, Genentech-imCORE ML40354, Qatar National Research Fund (NPRP8-2297-3-494).
Keywords: Cancer immunotherapy; Cellular senescence; Hematology; Leukemias.
Figures
![Figure 1. Study workflow.](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g163.jpg)
![Figure 2. Markers of T cell senescence…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g165.jpg)
![Figure 3. Signatures of immune effector dysfunction…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g166.jpg)
![Figure 4. Covarying gene programs of immune…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g167.jpg)
![Figure 5. Predictive ability of immune effector…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g168.jpg)
![Figure 6. Immune effector dysfunction scores correlate…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g169.jpg)
![Figure 7. Immune effector dysfunction scores correlate…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g170.jpg)
![Figure 8. Immune effector dysfunction scores increase…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g171.jpg)
![Figure 9. Immune effector dysfunction scores predict…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g172.jpg)
![Figure 10. Immune effector dysfunction (IED) scores…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9621145/bin/jci-132-159579-g164.jpg)
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