Abstract
Acute myeloid leukemia (AML) is a hematopoietic malignancy with poor prognosis and limited treatment options. Here we provide a comprehensive census of the bone marrow immune microenvironment in adult and pediatric patients with AML. We characterize unique inflammation signatures in a subset of AML patients, associated with inferior outcomes. We identify atypical B cells, a dysfunctional B-cell subtype enriched in patients with high-inflammation AML, as well as an increase in CD8+GZMK+ and regulatory T cells, accompanied by a reduction in T-cell clonal expansion. We derive an inflammation-associated gene score (iScore) that associates with poor survival outcomes in patients with AML. Addition of the iScore refines current risk stratifications for patients with AML and may enable identification of patients in need of more aggressive treatment. This work provides a framework for classifying patients with AML based on their immune microenvironment and a rationale for consideration of the inflammatory state in clinical settings.
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Data availability
Human scRNA-seq, CITE-seq and TCR-seq data were submitted to the Gene Expression Omnibus (GEO) repository and can be accessed under GEO accession no. GSE185381. The RNA expression data can be interactively explored and downloaded from the Single Cell Portal at https://singlecell.broadinstitute.org/single_cell/study/SCP1987. Newly generated RNA-seq data from the Alliance cohort can be accessed on GEO using accession no. GSE216738. Previously published AML cohorts and mouse scRNA-seq data that were re-analyzed here are available under GSE137851, GSE63646 and GSE182615. Human AML bulk RNA-seq data were derived from TCGA Research Network at http://cancergenome.nih.gov/. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.
Code availability
All code used to generate and analyze data in this study is available on GitHub at https://github.com/BettinaNa/inflammation-immune-microenvironment-adult-pediatric-AML.
Change history
19 January 2023
A Correction to this paper has been published: https://doi.org/10.1038/s43018-023-00518-x
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Acknowledgements
We thank all members of the Aifantis laboratory for discussions throughout this project. A. Heguy and the NYU Genome Technology Center (supported in part by National Institutes of Health (NIH)/National Cancer Institute (NCI) grant P30CA016087-30) for expertise with sequencing experiments; the NYU Flow Cytometry facility for expert cell sorting and the Applied Bioinformatics Laboratory for computational assistance. I.A. is supported by the NIH (NCI and NHLBI) (R01CA271455, R01CA173636, 1R01CA228135, R01CA242020 and 1R01HL159175), the Vogelstein Foundation and the EvansMDS Foundation. T.A.G. was supported by the American Lebanese Syrian Associated Charities of St. Jude Children’s Research Hospital. A.L. was supported by the Aplastic Anemia and MDS International Foundation. A.-K.E. was supported by the NCI 1R01CA262496, Pelotonia, the American Society of Hematology, the Leukemia & Lymphoma Society (LLS), and the Leukemia Research Foundation. U10CA180821, U10CA180882, and U24CA196171 have supported the Alliance for Clinical Trials in Oncology. The authors are grateful to Christopher Manring and the Leukemia Tissue Bank at The Ohio State University Comprehensive Cancer Center, Columbus, OH, for sample processing and storage services.
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Authors and Affiliations
Contributions
A.L., B.N., T.A.G., A.K.E. and I.A. conceived and designed the study. A.L., Z.S., M.T.W., A.N.T. and G.R. processed human BM samples. B.N., M.F., D.N., H.W., A.Y., A.T., C.J.W. and S.P. performed computational and statistical analysis. A.L. and B.N. analyzed all data. M.G.-R. and G.C. processed Tet2HR murine BM samples. E.A.O., R.M.S. and J.C.B. provided clinical information and access to bulk AML patient cohorts. W.L.C. and T.A.G. provided pediatric AML patient samples. A.K.E. provided adult AML patient samples. A.L. and B.N. wrote the manuscript with help from T.A.G., A.K.E. and I.A.
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Competing interests
A.L., B.N., S.P., A.K.E., T.A.G. and A.I. submitted a patent application for the iScore patient risk stratification. I.A. is a consultant for Forsite Labs. T.A.G. is a consultant for Kura Oncology and Janssen. A.T. is a scientific advisor to Intelligencia AI.
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Extended data
Extended Data Fig. 1 Cell populations in the bone marrow.
a. Heatmap of average expression of top RNA cluster markers for different cell subsets in the BM (left), heatmap of average expression of surface protein markers for different cell subsets in the BM (right). HSC – hematopoietic stem cells, MPP – multipotent progenitors, GMP – granulocyte-monocyte progenitors, MEP – megakaryocyte progenitors, LymP – lymphoid progenitors, DC – dendritic cells, Ery – erythrocytes. b. Quantification of HSPC subsets in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5 × IQR. c. Quantification of myeloid subsets in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5 × IQR. d. Quantification of B cell subsets in the BM. e. Quantification of conventional (CD4+, CD8+), non-conventional (MAIT, γδ) and NK cells in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. All statistical tests shown in this figure are two-sided. Pair-wise comparisons were evaluated using Wilcoxon test, multi-group comparisons were evaluated using Kruskal-Wallis test. For panels B-E, HD_Y – Healthy donors 19-26 years old (n = 5), HD_O – healthy donors 39-55 years old (n = 5), PED – pediatric patients with AML (n = 22), AD – adult AML patients (n = 20).
Extended Data Fig. 2 Separation of malignant and microenvironment cells in AML samples.
a. InferCNV heatmaps for patients with clinically annotated chromosome gains or losses. b. InferCNV heatmaps for healthy donor BM samples. c. UMAP projection of Healthy donors, CNV+ and CNV- cells from patients with annotated chromosome gains or losses (left), quantification of malignant and microenvironment cells for each sample (right).
Extended Data Fig. 3 Validation of occupancy score method.
a. UMAP projection depicting cell clustering for calculation of occupancy scores. b. UMAP projection of occupancy score. c. UMAP projection of malignant and microenvironment cells based on occupancy scores (left) or single cell genotyping (right).
Extended Data Fig. 4 Non-annotated karyotype aberrations detected by InferCNV.
a. InferCNV heatmaps for patient samples with non-annotated karyotype aberrations. b. Patient-by-patient quantification of broad cell types in malignant cells.
Extended Data Fig. 5 Pathogenic programs in AML.
a. UMAP projections of cells expressing different gene expression programs identified by NMF.
Extended Data Fig. 6 Inflammatory signatures in AML.
a. Pathway analysis for genes in the adult (left) and pediatric (right) inflammation signatures. b. Overlap between genes in the adult and pediatric inflammation signatures. c. Pearson correlation between age and inflammation score in the adult AML cohort. d. Inflammation score in older controls (n = 5) and adult AML patients (n = 20) in the single cell cohort. Dashed line represents cutoff for high or low inflammation. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. e. Inflammation score in younger controls (n = 5) and pediatric AML patients (n = 22). Dashed line represents cutoff for high or low inflammation. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. f. Heatmap of average expression of the pediatric inflammation signature in malignant cells from pediatric patients. Max CT – maximum cell count. Infants – 0–3 years old (n = 6), children – 3–12 years old (n = 9), teens – 12–21 years old (n = 7). g. Heatmap of average expression of the adult inflammation signature in malignant cells from adult patients. Max CT – maximum cell type.
Extended Data Fig. 7 Inflammatory B cells in AML.
a. Heatmap of average expression of surface protein markers in different B cell subsets. CLP – common lymphoid progenitor. b. Heatmap of average expression of RNA markers in different B cell subsets. c. Quantification of Atypical B cells split by young healthy donor (n = 5), older healthy donors (n = 5) adult (n = 14) and pediatric (n = 19) AML patients. Note the reduction in patient numbers due to exclusion of patients with less than 50 B cells in the BM. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5 × IQR. d. Pearson correlation between the atypical B cell gene signature and the adult inflammation signature in the TCGA cohort (n = 152). e. UMAP representation of B cells from wild type (WT, n = 7) and Tet2 mutant (n = 11) mouse BM. f. UMAP representation of wild type (WT, n = 7) and Tet2 mutant (n = 11) cell distribution in B cell clusters. g. Heatmap showing expression of the mouse atypical B cell gene signature in B cell clusters in wild type (WT, n = 7) and Tet2 (n = 11) mutant mouse BM. h. Quantification of atypical B cells in aged wild type (WT, n = 3) or Tet2 mutant mice (n = 7). Mild – mild disease (n = 2), severe – severe disease (n = 5). Statistical tests in this panel are two-sided. Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5 × IQR. i. Inflammation scores of samples used for FACS validation of atypical B cell expansion in high inflammation AML BM (high inflammation n = 4, low inflammation n = 4). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. All pair-wise comparisons were evaluated using Wilcoxon test, multi-group comparisons were evaluated using Kruskal-Wallis test.
Extended Data Fig. 8 T cell responses in AML.
a. Heatmap of average expression of surface protein markers in different T cell subsets. TCM – central memory T cells; TReg – regulatory T cells; TRM – resident memory T cells. b. Heatmap of average expression of RNA markers in different T cell subsets. c. Quantification of T cell subsets in high and low inflammation AML patients. d. Gating strategy for sorting of T cells from AML or healthy donor BM aspirates. e. Pie charts representing the fraction of small (0-1%), large (1-10%) and hyperexpanded (10–100%) clones in individual samples. f. Quantification of CD8+ subsets from expanded clones in AML patients. g. Clonal diversity in infants (0-3 years old, n = 37), children (3-12 years old, n = 59) and teens (12-21 years old, n = 49) from the TARGET-AML bulk RNA-Seq cohort. All statistical tests shown in this figure are two-sided. All box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. All pair-wise comparisons were evaluated using Wilcoxon test.
Extended Data Fig. 9 Clinical implications of inflammation in AML.
a. Overall survival of high and low inflammation adult AML patients in the Alliance cohort (n = 686 < 60 years old, n = 184 > =60 years old). Log rank test was used to evaluate significance. b. Overall survival of high and low inflammation pediatric AML patients in the TARGET-AML cohort (n = 336). Log rank test was used to evaluate significance. c. Distribution of the iScore in adult AML patients in the Alliance cohort, by risk (Adverse risk - n = 274, Intermediate risk - n = 176, Favorable - n = 359). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. d. Distribution of the iScore in pediatric AML patients in the TARGET cohort, by risk (High risk – n = 105, intermediate risk – n = 95, low risk – n = 136). Box plots represent the median with the box bounding the interquartile range (IQR) and whiskers showing the most extreme points within 1.5× IQR. e. Overall survival association of iScore and LSC17 in adult AML patients (n = 686 < 60 years old, n = 184 > =60 years old) assessed by global test. f. Overall survival association of iScore and other prognostic predictors in pediatric AML patients (n = 336) assessed by global test. g. Overall survival in high and low iScore patients in the TCGA AML cohort (<60 yrs, n = 90). h. 8-year predicted overall survival (OS) in favorable, intermediate and adverse risk adult AML patients in the BeatAML cohort (n = 172 < 60 years old, n = 211 > =60 years old), based on iScore. H. 8-year predicted OS in low, intermediate and high risk pediatric patients in a pediatric microarray cohort (n = 329), based on iScore.
Extended Data Fig. 10 Effect of iScore on event free survival in AML.
a. Event free survival in high and low iScore Favorable risk patients in adult patients in the TCGA AML cohort (<60 yrs, n = 89). Log rank test was used to evaluate significance. b. Event free survival in pediatric patients in a microarray cohort (n = 372). Log rank test was used to evaluate significance. c. Event Free survival in high and low iScore favorable risk patients in the Alliance AML cohort (n = 323 < 60 years old). Log rank test was used to evaluate significance. d. Event free survival in high and low iScore intermediate risk patients in the Alliance AML cohort (n = 140 < 60 years old, n = 33 > =60 years old). Log rank test was used to evaluate significance. e. Event free survival in high and low iScore adverse risk patients in the Alliance AML cohort (n = 182 < 60 years old, n = 92 > =60 years old). Log rank test was used to evaluate significance.
Supplementary information
Supplementary Table 1
Supplementary Tables 1–13
Source data
Source Data Fig. 1
UMAP coordinates for cells.
Source Data Fig. 2
Cell-type annotation, NMF score and differential expression.
Source Data Fig. 3
B-cell UMAP coordinates, inflammation and B-cell correlations, flow data, differential expression and CD72 quantification.
Source Data Fig. 4
T-cell UMAP coordinates, quantifications, clonotype information and distribution, bulk RNA-seq TCR data.
Source Data Fig. 5
Adult and pediatric inflammation scores, t-SNE coordinates.
Source Data Extended Data Fig. 1
ADT and RNA expression for cluster markers, quantifications of cell subsets.
Source Data Extended Data Fig. 2
InferCNV output, CNV status for each cell.
Source Data Extended Data Fig. 3
Occupancy score for each cell, malignant/ME classification and occupancy score for data from van Galen et al.
Source Data Extended Data Fig. 4
InferCNV output and malignant/ME status for each cell.
Source Data Extended Data Fig. 5
NMF values for each cell.
Source Data Extended Data Fig. 6
Inflammation GO pathways, inflammation gene signatures, inflammation scores and inflammation gene expression data.
Source Data Extended Data Fig. 7
B-cell ADT/RNA marker expression, B-cell quantification data, B-cell/inflammation correlation in TCGA, mouse B-cell data and B-cell flow validation inflammation scores.
Source Data Extended Data Fig. 8
T-cell ADT/RNA marker expression, T-cell quantifications, clonotype information, pediatric bulk TCR data.
Source Data Extended Data Fig. 9
Global test results.
Source Data Extended Data Fig. 10
Adult bulk cohorts iScore data.
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Lasry, A., Nadorp, B., Fornerod, M. et al. An inflammatory state remodels the immune microenvironment and improves risk stratification in acute myeloid leukemia. Nat Cancer 4, 27–42 (2023). https://doi.org/10.1038/s43018-022-00480-0
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DOI: https://doi.org/10.1038/s43018-022-00480-0
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