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A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia

Nature Medicine volume 28, pages 1212–1223 (2022)Cite this article

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Abstract

The treatment landscape of acute myeloid leukemia (AML) is evolving, with promising therapies entering clinical translation, yet patient responses remain heterogeneous, and biomarkers for tailoring treatment are lacking. To understand how disease heterogeneity links with therapy response, we determined the leukemia cell hierarchy makeup from bulk transcriptomes of more than 1,000 patients through deconvolution using single-cell reference profiles of leukemia stem, progenitor and mature cell types. Leukemia hierarchy composition was associated with functional, genomic and clinical properties and converged into four overall classes, spanning Primitive, Mature, GMP and Intermediate. Critically, variation in hierarchy composition along the Primitive versus GMP or Primitive versus Mature axes were associated with response to chemotherapy or drug sensitivity profiles of targeted therapies, respectively. A seven-gene biomarker derived from the Primitive versus Mature axis was associated with response to 105 investigational drugs. Cellular hierarchy composition constitutes a novel framework for understanding disease biology and advancing precision medicine in AML.

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Fig. 1: Functional significance of LSPC populations from scRNA-seq.
Fig. 2: AML hierarchy composition correlates with genomics and survival.
Fig. 3: Transitions in hierarchy composition from diagnosis to relapse.
Fig. 4: AML hierarchy composition as a determinant of targeted therapy response.
Fig. 5: Changes in cellular composition after drug treatment.
Fig. 6: Hierarchy-based stratification predicts in vivo response to fedratinib and CC-90009.

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Data availability

Processed and raw RNA-seq data generated in this study are available in the GEO under Superseries GSE199336. Citations and links of re-analyzed data from all clinical datasets analyzed in this study are provided in Supplementary Table 4. Citations and links of re-analyzed data from the literature screen are provided in Supplementary Table 13. Pathways and signatures used for relapse benchmarking were obtained from the MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/). All deconvolution results are available with the analysis code on GitHub (https://github.com/andygxzeng/AMLHierarchies).

Code availability

Analysis notebooks for the main figures, as well as instructions for applying AML deconvolution, are available at: https://github.com/andygxzeng/AMLHierarchies.

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Acknowledgements

We thank C. Jones, A. Tikhonova, N. Iscove, S. Chan, S. Abelson, B. Haibe-Kains, M. Anders and all members of the Dick Laboratory for valuable feedback on the manuscript. We thank P. Valk and the HOVON-SAKK trial group for providing the clinical annotations associated with GSE6891. A.G.X.Z. is supported by a CIHR Vanier scholarship. J.E.D. is supported by funds from the Ontario Institute for Cancer Research through funding provided by the Government of Ontario; the Canadian Institutes for Health Research (RN380110 - 409786); the International Development Research Centre Ottawa Canada; the Canadian Cancer Society (grant 703212 (end date 2019) and grant 706662 (end date 2025)); the Terry Fox New Frontiers Program Project Grant (Project 1106); University of Toronto’s Medicine by Design initiative with funding from the Canada First Research Excellence Fund; a Canada Research Chair; Princess Margaret Cancer Centre; The Princess Margaret Cancer Foundation; and the Ontario Ministry of Health. The Leukemia Tissue Bank is supported by funds to M.D.M. from the Wendy Eisen Princess Margaret Hospital Foundation Account and the Philip Orsino Chair in Leukemia Research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Weihsu Claire Chen

    Present address: Biologics Discovery, Amgen British Columbia, Burnaby, BC, Canada

  2. These authors contributed equally: Suraj Bansal, Liqing Jin.

Authors and Affiliations

  1. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    Andy G. X. Zeng, Suraj Bansal, Liqing Jin, Amanda Mitchell, Weihsu Claire Chen, Michelle Chan-Seng-Yue, Mark D. Minden, James A. Kennedy, Jean C. Y. Wang & John E. Dick

  2. Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada

    Andy G. X. Zeng & John E. Dick

  3. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Hussein A. Abbas & Naval Daver

  4. The Donnelly Centre, University of Toronto, Toronto, ON, Canada

    Veronique Voisin

  5. Division of Hematology, Brigham and Women’s Hospital, Boston, MA, USA

    Peter van Galen

  6. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Peter van Galen

  7. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Peter van Galen

  8. Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA

    Peter van Galen

  9. Laboratory Medicine Program, Hematopathology, University Health Network, Toronto, ON, Canada

    Anne Tierens

  10. University of Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER – Cancer Heterogeneity Plasticity and Resistance to Therapies, Lille, France

    Meyling Cheok & Claude Preudhomme

  11. Department of Hematology, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France

    Hervé Dombret

  12. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    P. Andrew Futreal

  13. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    Mark D. Minden

  14. Department of Medicine, University of Toronto, Toronto, ON, Canada

    Mark D. Minden & Jean C. Y. Wang

  15. Division of Medical Oncology and Hematology, University Health Network, Toronto, ON, Canada

    Mark D. Minden & Jean C. Y. Wang

  16. Division of Medical Oncology and Hematology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    James A. Kennedy

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  1. Andy G. X. Zeng
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Contributions

A.G.X.Z. and J.E.D. conceived the project. A.G.X.Z. carried out the analysis. S.B. contributed to analyses of post-treatment changes in cell composition. L.J. performed fedratinib and CC-90009 combination treatments. A.M. and W.C.C. performed RNA-seq extraction and library preparation for LSC fractions and fedratinib samples, respectively. M.C.-S.-Y. and V.V. performed alignment and pre-processing for LSC fractions and fredratinib RNA-seq data. H.A.A., N.D. and A.F. provided scRNA-seq data and leukemic cell annotations for relapsed AMLs. P.v.G. provided bulk RNA-seq data for AML samples. A.T. analyzed clinical flow data from the PMH Leukemia cohort. M.C., C.P. and H.D. provided GE and clinical data for the ALFA-0701 trial cohort. M.D.M. provided PMH AML samples. J.A.K. and M.D.M. provided clinical annotations for the PMH AML cohort. A.G.X.Z., J.A.K., J.C.Y.W. and J.E.D. interpreted the data. A.G.X.Z. and J.E.D. wrote the paper. J.A.K. and J.C.Y.W. revised the paper.

Corresponding author

Correspondence to John E. Dick.

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Competing interests

J.E.D. reports research funding from Celgene/Bristol Myers Squibb and advisory board/royalities from Trillium Therapeutics. All other authors declare no competing interests.

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Nature Medicine thanks Craig Jordan, Simon Haas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Editor recognition statement: primary handling editors were Javier Carmona and Michael Basson, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Features of leukemia stem and progenitor cell populations from scRNA-seq.

A) UMAP and PCA embeddings of 4163 AML LSPCs after feature weight derivation with the Self-Assembling Manifolds (SAM) algorithm. B-F) Diffusion map of re-annotated LSPC populations using SAM-derived feature weights, depicting: B) patient identity, C) prior cell type annotation, D) enrichment of LSC-specific genes from Ng et al (2016) and Shannon Diversity Index, E) scaled CDK6 expression and enrichment of the E2F3 regulon, and F) enrichment of E2F1 and CTCF regulons. G) Cell cycle status of Quiescent (n = 1855), Primed (n = 1240), and Cycling LSPCs (n = 1068). H) Enrichment of inflammatory signaling pathways and regulons in LSPCs. I) Transcription factor regulon activity, inferred through pySCENIC, specific to each LSPC. J) Normalized confusion matrix depicting classifier accuracy of prior and new cell type annotations for primitive AML cells. The classifier was built using SingleCellNet, an Ensemble classifier for scRNA-seq data trained from the top pairs of genes unique to each cell type. 800 cells from each cell type were used for training and 250 were used for validation.

Extended Data Fig. 2 Benchmarking gene expression deconvolution approaches for AML.

A) Pearson correlation between observed relative abundance of 14 cell types from scRNA-seq and predicted abundance of each cell type from deconvolution of matched bulk RNA-seq data, analyzed by patient. Gene expression deconvolution using CIBERSORTx (S-mode or No Batch Correction), DWLS, Bisque, or MuSIC (direct or recursive) were benchmarked across these samples. B) scRNA-seq of 1389 ProMono-like cells across 10 patients, demonstrating separation between AML556 and other patients. C) Pearson correlation depicting deconvolution performance of CIBERSORTx for AML556’s bulk RNA-seq profile using patient-specific reference signatures derived from scRNA-seq data from AML556. D-E) Correlation between deconvolution and clinical flow cytometry for 7 AML patients from the Toronto PMH cohort. Deconvolution using scRNA-seq reference profiles was performed on RNA-seq data and matched with clinical flow cytometry data, both obtained from peripheral blood. D) Pearson correlation between total mature myeloid abundance (ProMono-like + Mono-like + cDC-like) from deconvolution with pan-myeloid surface marker CD64. E) Pearson correlation between mono-like abundance from deconvolution with monocyte-specific surface marker CD14. F) Dendrogram depicting associations between leukemic cell-types across scRNA-seq samples from 12 diagnostic AML patients. G) Observed associations between leukemic cell types from deconvolution analysis of 173 patients within the TCGA cohort, depicted for each deconvolution tool. MuSIC Direct was excluded due to multiple cell types having a detection rate of zero in bulk RNA-seq. H-I) Correlation between observed transcriptomic profiles and synthetic transcriptomic profiles reconstructed based on predicted cell-type abundance from CIBERSORTx. Higher correlation suggests greater deconvolution confidence. Box plots indicate the range of the central 50% of the data, with the central line marking the median. Whiskers extend from each box to 1.5*(interquartile range). Comparisons were performed through two-sided Wilcoxon signed-rank tests. These correlations are depicted for H) Deconvolution of 864 patients across three AML cohorts using reference signatures from leukemic populations compared to deconvolution with reference signatures from matched healthy populations, and I) RNA-seq compared to microarray from 158 matched TCGA patient samples. Prior to deconvolution, microarray data was normalized through either chip-based (RMA) or single-sample (SCAN) normalization approaches. J) Pearson correlation of estimated LSPC abundances between RNA-seq deconvolution and Microarray deconvolution, normalized with either RMA or SCAN, among 158 matched patient samples from the TCGA cohort.

Extended Data Fig. 3 Comparison of new and prior leukemia stem and progenitor cell annotations for discerning biological phenotypes.

A) Workflow to compare prior (HSC-like and Prog-like) annotations and new (Quiescent LSPC, Primed LSPC, and Cycling LSPC) annotations with regard to their utility in predicting important biological phenotypes in AML. This was measured through the performance of logistic regression and random forest models trained on the relative abundance of these populations. Models were trained using nested cross-validation wherein samples were subject to a 5-fold split, in which 80% of samples (white boxes) were used to train a model and 20% of samples (orange boxes) were used to evaluate the model. Within each training set, hyperparameter optimization was performed by grid search with 5-fold internal cross validation. The model AUC from each outer cross-validation split was averaged to estimate overall classifier performance. This nested cross-validation process was repeated over 1000 iterations, with samples being shuffled between each iteration, to generate a distribution of AUC metrics. B-F) Model performance for predicting key biological and clinical phenotypes from either HSC-like and Prog-like abundance or Quiescent, Primed, and Cycling LSPC abundance. Performance metrics are paired by iteration, wherein sample order and cross validation splits were identical for each model. Box plots indicate the range of the central 50% of the data, with the central line marking the median. Whiskers extend from each box to 1.5*(interquartile range). Statistical significance was evaluated through a two-sided Wilcoxon signed-rank test. B) Prediction of functional LSC activity measured by leukemic engraftment from 72 LSC+ fractions and 38 LSC- fractions. C) Prediction of overall survival in the TCGA and BEAT-AML cohorts, evaluated through the likelihood ratio statistic from stratified cox regression (combined n = 454). In this case LASSO and Ridge regression was performed and these models were trained on splits of the TCGA and BEAT-AML cohorts, stratified by cohort. The repeated nested cross validation approach remained the same. D) Prediction of diagnosis vs relapse status from 44 relapsed and 44 diagnostic samples. E) Prediction of Adverse cytogenetic status in TCGA from 37 patients with Adverse cytogenetics and 131 patients with Intermediate or Favorable cytogenetics. F) Prediction of Adverse cytogenetic status in BEAT-AML from 53 patients with Adverse cytogenetics and 175 patients with Intermediate or Favorable cytogenetics.

Extended Data Fig. 4 Biological and genomic correlates of AML hierarchies.

A) Correlation between deconvoluted abundance of leukemic and immune cell types with clinical features in TCGA (n = 173). Only correlations with P < 0.05 are depicted, and correlations with FDR < 0.05 are noted with an asterisk. B) Cellular hierarchy projections of patient samples classified as FAB M0 (n = 30), M1 (n = 122), M2 (n = 77), M3 (n = 30), M4 (n = 58), M4Eo (n = 22), M5A (n = 28), or M5B (n = 11) C) Density plots depicting all mutation combinations along the Primitive versus Mature axis (PC2). D) Density plots depicting all mutation combinations along the Primitive versus GMP axis (PC1). E) Density plots depicting all cytogenetic alterations along the Primitive versus Mature axis (PC2). F-G) Impact of DNMT3A R882 mutations compared to other DNMT3A mutations on leukemic hierarchy organization along the Primitive versus Mature axis (PC2). F) Boxplot comparing PC2 of AMLs with DNMT3A R882 mutations (n = 84) compared to other DNMT3A (n = 96) mutations, split by mutational partner. Box plots indicate the range of the central 50% of the data, with the central line marking the median. Whiskers extend from each box to 1.5*(interquartile range). Statistical significance was evaluated through a two-sided Wilcoxon rank sum test. G) Density plot depicting PC2 of mutational combinations with either DNMT3A R882 or other DNMT3A mutations.

Extended Data Fig. 5 The Primitive versus GMP axis governs AML prognosis.

A) 495 adult AML patients from GSE6891, projected by hierarchy composition and classified based on the reference cohorts. B) Overall survival outcomes of 495 AML patients from GSE6891, stratified by hierarchy subtype. C) 287 pediatric AML patients from the TARGET-AML cohort, projected by hierarchy composition and classified based on the reference cohorts. D) Overall survival outcomes 287 pediatric AML patients from TARGET-AML, stratified by hierarchy subtype. E) Correlation between a prognostic score trained by regularized cox regression using leukemic cell type abundances with the Primitive versus GMP axis (PC1) within the TCGA and BEAT-AML cohorts (combined n = 454). F) Overall survival outcomes of patients stratified by PC1 within the TCGA (n = 173), BEAT-AML (n=281), and GSE6891 (n = 495) cohorts. G) Association between cell-type abundance and induction failure in four independent studies: Bolouri et al 2018 (pediatric AML; n = 257 remission, n = 30 induction failure), Chiu et al 2019 (adult AML; n = 18 remission, n = 18 induction failure), Herold et al 2018 (adult AML; n = 164 remission, n = 86 induction failure), and Tyner et al 2018 (adult AML; n = 140 remission, n = 63 induction failure), represented through the test statistic from a two-sided Wilcoxon rank sum test. Green denotes higher relative abundance in induction failure patients compare to patients who achieved complete remission, while purple denotes lower relative abundance in induction failure patients. Differences with an uncorrected P < 0.10 are noted with an asterisk. H) Correlation between four prognostic AML scores with the relative abundance of each leukemic cell type across the TCGA, BEAT-AML, and Leucegene cohorts (combined n = 864). I) Relative abundance of Quiescent LSPC and GMP-like blasts among 864 AML patients split into high and low risk categories by median split for four prognostic scores. Significance was evaluated through two-sided Wilcoxon rank-sum tests. Box plots indicate the range of the central 50% of the data, with the central line marking the median. Whiskers extend from each box to 1.5*(interquartile range). J) GSEA analysis with gene signatures derived from the Primitive versus GMP axis in normal and malignant hematopoiesis, performed on genes ranked by univariate associations with overall survival within the TCGA and BEAT-AML cohorts.

Extended Data Fig. 6 Changes in hierarchy composition at AML relapse.

A) Hierarchy composition of 44 matched diagnosis and relapse pairs. Top row depicts hierarchy composition at diagnosis while the bottom row depicts hierarchy composition at relapse. Samples from the same patient are aligned vertically. B) Relative abundance of each leukemic cell population from scRNA-seq of 12 diagnostic AMLs (van Galen et al, 2019) compared with 8 relapsed AMLs (Abbas et al, 2021). Statistical significance was evaluated through two-sided Wilcoxon rank-sum tests. Box plots indicate the range of the central 50% of the data, with the central line marking the median. Whiskers extend from each box to 1.5*(interquartile range). C-F) Evolution of paired diagnosis and relapse AML samples depicted through shifts in cellular hierarchies, evolution of genetic subclones, and changes in cell-type composition. C) Patient 303642, in which significant genetic evolution is accompanied by a dramatic shift in cellular hierarchy from GMP to primitive. D) Patient 1019, in which replacement of an NRAS and IDH2 positive clone with an IDH1 positive clone is associated with a modest shift in cellular hierarchy. E) Patient 4, in which a loss of monocytic blasts is accompanied by a modest decrease in the the size of an NRAS bearing clone. F) Patient 150288, in which extensive linear genetic evolution is not associated with any appreciable change in cell type composition.

Extended Data Fig. 7 The Primitive versus Mature axis governs ex vivo drug sensitivity.

A) Volcano plot depicting associations between Primitive versus GMP axis (PC1) and ex vivo drug sensitivity from the BEAT-AML (Tyner et al 2018) drug screen (n = 202 patients), and between PC1 and PC2 and ex vivo drug sensitivity from Lee et al 2018 (n = 30 patients). B) Unsupervised clustering of 30 primary AML patients from Lee et al on the basis of ex vivo sensitivity to 159 drugs. Drug sensitivity values (scaled negative AUC) are depicted for the top drugs corresponding to each patient cluster. Red denotes sensitivity while blue denotes lower sensitivity. C) GSEA analysis with gene signatures derived from the Primitive vs Mature axis in normal and malignant hematopoiesis, performed on genes ranked by differential expression between the two drug response clusters from (B). D) Correlations of AML gene expression scores with ex vivo drug sensitivities from two drug screens (Tyner et al, n = 202; Lee et al, n = 30) performed on primary AML samples. E) Ex vivo drug sensitivity to Venetoclax (n = 114 from Tyner et al) and Azacytidine (n = 30 from Lee et al) of primary patient samples grouped into ‘High’ or ‘Low’ based on median splits of patient scores for each AML gene expression score. Significance was evaluated through two-sided Wilcoxon rank-sum tests. Box plots indicate the range of the central 50% of the data, with the central line marking the median. Whiskers extend from each box to 1.5*(interquartile range).

Extended Data Fig. 8 The Primitive versus Mature axis predicts clinical benefit from Gemtuzumab-Ozogamicin in both adult and pediatric AML.

A) Subgroup analysis of randomized clinical trial ALFA-0701 (n = 192) after stratification by PC2-34. Event-free survival (EFS) and Relapse-free survival (RFS) curves comparing chemotherapy only (Control arm) against chemotherapy + Gemtuzumab-Ozogamicin (GO arm). B) Lack of correlation between CD33 levels by flow cytometry and Primitive versus Mature axis (PC2 and PC2-34 score), evaluated across 23 Toronto PMH AML patients for which both RNA-seq and clinical flow information was available. C) Stratification of ALFA-0701 patients on the basis of both LSC17 and LinClass-7. EFS and RFS for the LinClass-7 Low (Mature > Primitive) and LSC17 Low subgroup (n = 56) is depicted as this was the only group to derive significant benefit from GO treatment. D) Stratification of ALFA-0701 patients on the basis of both LSC17 and PC2-34. EFS and RFS for the PC2-34 High (Mature > Primitive) and LSC17 Low subgroup (n = 54) is depicted as this was the only group to derive significant benefit from GO treatment. E-F) Subgroup analysis of a retrospective cohort of pediatric AML patients treated with either GO (n = 154) or Chemo (n = 91), stratified by the PC2 Primitive versus Mature axis and related gene expression scores. Outcomes are depicted for both overall survival (E) and event-free survival (F).

Extended Data Fig. 9 LinClass-7 as a companion score for LSC17.

A) LSC17 and LinClass-7 scores of 864 AML patients by RNA-seq. Patients belonging to each hierarchy subtype (Primitive, Intermediate, GMP, Mature) are also depicted. (B) LSC17 and LinClass-7 scores measured through a 17-gene NanoString assay. Normalized NanoString-derived LSC17 and LinClass-7 scores from 306 Toronto PMH patients from Ng et al (2016) are depicted.

Extended Data Fig. 10 Literature screen to identify treatment-induced changes in cellular composition.

A) ComplexHeatmap depicting changes in cell type composition following drug treatment from 43 preclinical studies in human AML, represented as the absolute log(P value) from a two-sided Wilcoxon rank sum test in the direction of the change. Green depicts an increase in cell type abundance and purple depicts a decrease in cell type abundance. Each treatment is labeled with its target(s) in parentheses. Changes in PC2 (Primitive versus Mature) are depicted above the heatmap, and candidate differentiation drugs (increase in PC2 with uncorrected P < 0.05) are denoted with an asterisk. AML sample type and key genomic characteristics are also depicted for each treatment condition. B) UMAP coordinates for each drug treatment condition depicting changes in each cell type after drug treatment compared to control, represented as the absolute log(P value) from a two-sided Wilcoxon rank sum test in the direction of the change. Tissue source (Primary vs Cell Line), MLL translocation status, and drug target are also depicted for each treatment condition.

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Zeng, A.G.X., Bansal, S., Jin, L. et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nat Med 28, 1212–1223 (2022). https://doi.org/10.1038/s41591-022-01819-x

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