Abstract

Transplantation of hematopoietic cells from a healthy individual (allogeneic hematopoietic cell transplantation (allo-HCT)) demonstrates that adoptive immunotherapy can cure blood cancers: still, post-transplantation relapses remain frequent. To explain their drivers, we analyzed the genomic and gene expression profiles of acute myeloid leukemia (AML) blasts purified from patients at serial time-points during their disease history. We identified a transcriptional signature specific for post-transplantation relapses and highly enriched in immune-related processes, including T cell costimulation and antigen presentation. In two independent patient cohorts we confirmed the deregulation of multiple costimulatory ligands on AML blasts at post-transplantation relapse (PD-L1, B7-H3, CD80, PVRL2), mirrored by concomitant changes in circulating donor T cells. Likewise, we documented the frequent loss of surface expression of HLA-DR, -DQ and -DP on leukemia cells, due to downregulation of the HLA class II regulator CIITA. We show that loss of HLA class II expression and upregulation of inhibitory checkpoint molecules represent alternative modalities to abolish AML recognition from donor-derived T cells, and can be counteracted by interferon-γ or checkpoint blockade, respectively. Our results demonstrate that the deregulation of pathways involved in T cell-mediated allorecognition is a distinctive feature and driver of AML relapses after allo-HCT, which can be rapidly translated into personalized therapies.

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Data availability

SNP array, microarray and RNA-seq data generated and analysed during the current study are available through ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) with accession numbers E-MTAB-7631, E-MTAB-7628, E-MTAB-7630 and E-MTAB-7456.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

This work was supported by the Italian Ministry of Health (no. RF-FSR-2008-1202648 to K.F., F.C. and C.Barlassina, no. RF-2011-02351998 to F.C. and L.V., no. RF-2011-02348034 to L.V. and TRANSCAN HLALOSS to L.V. and K.F.), by the Italian Ministry of University and Research (no. MIUR-2015NZWSEC-001 to C.Bonini), by the CARIPLO Foundation (no. 2009-2665 to K.F., A.R. and C.Barlassina), by the Associazione Italiana per la Ricerca sul Cancro (IG no. 18458 to C.Bonini, IG no. 12042 to K.F. and F.C. and Start-Up Grant no. 14162 to L.V.), by the ASCO Conquer Cancer Foundation (2014 Young Investigator Award to L.V.), EU-FP7 (SUPERSIST to C.Bonini), the Deutsche José Carreras Leukämie Stiftung (grant nos. DJCLS R 15-02 and DJCLS 01 R/2017 to K.F.) and by the DKMS Mechtild Harf Foundation (DKMS Mechtild Harf Research Grant 2015 to L.V.). C.T. was supported by an Associazione Italiana per la Ricerca sul Cancro post-doctoral fellowship. G.O. was supported by a Fondazione Matarelli fellowship from the Associazione Italiana Leucemie and by a Fondazione Umberto Veronesi fellowship.

Author information

Author notes

    • Giacomo Oliveira
    •  & Elia Stupka

    Present address: Dana-Farber Cancer Institute, Boston, MA, USA

    • Lara Crucitti
    •  & Nicoletta Cieri

    Present address: University of Milano, Milano, Italy

  1. These authors contributed equally: Laura Zito, Valentina Gambacorta and Michela Riba.

  2. These authors jointly directed this work: Fabio Ciceri and Luca Vago.

Affiliations

  1. Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Cristina Toffalori
    • , Laura Zito
    • , Valentina Gambacorta
    • , Giacomo Oliveira
    • , Gabriele Bucci
    • , Katharina Fleischhauer
    •  & Luca Vago
  2. Unit of Senescence in Stem Cell Aging, Differentiation and Cancer, San Raffaele Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Valentina Gambacorta
  3. Center for Translational Genomics and Bioinformatics, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Michela Riba
    • , Gabriele Bucci
    • , Elia Stupka
    • , Dejan Lazarevic
    • , Giovanni Tonon
    •  & Davide Cittaro
  4. Experimental Hematology Unit, Division of Immunology, Transplantation and Infectious Disease, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Giacomo Oliveira
    • , Nicoletta Cieri
    • , Maddalena Noviello
    • , Francesco Manfredi
    •  & Chiara Bonini
  5. Genomic and Bioinformatics Unit, Department of Health Sciences, University of Milano, Milano, Italy

    • Matteo Barcella
    •  & Cristina Barlassina
  6. Hematology and Bone Marrow Transplant Unit, ASST Papa Giovanni XXIII, Bergamo, Italy

    • Orietta Spinelli
    •  & Alessandro Rambaldi
  7. Unit of Hematology and Bone Marrow Transplantation, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Raffaella Greco
    • , Lara Crucitti
    • , Nicoletta Cieri
    • , Bernhard Gentner
    • , Matteo G. Carrabba
    • , Massimo Bernardi
    • , Jacopo Peccatori
    • , Fabio Ciceri
    •  & Luca Vago
  8. Genomics of the Innate Immune System Unit, San Raffaele Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Elisa Montaldo
    •  & Renato Ostuni
  9. Translational Stem Cell and Leukemia Unit, San Raffaele Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milano, Italy

    • Matteo M. Naldini
    •  & Bernhard Gentner
  10. Department of Hematology, Oncology and Stem Cell Transplantation, Universitätsklinikum Freiburg, Freiburg, Germany

    • Miguel Waterhouse
    • , Robert Zeiser
    •  & Jurgen Finke
  11. Department of Bone Marrow Transplantation, Universitätsklinikum Essen, Essen, Germany

    • Maher Hanoun
    •  & Dietrich W. Beelen
  12. Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Ivana Gojo
    •  & Leo Luznik
  13. Department of Hematology, Hokkaido University Faculty of Medicine, Graduate School of Medicine, Sapporo, Japan

    • Masahiro Onozawa
    •  & Takanori Teshima
  14. Department of Haematology, Institut Paoli Calmettes, Marseille, France

    • Raynier Devillier
    •  & Didier Blaise
  15. Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands

    • Constantijn J. M. Halkes
    •  & Marieke Griffioen
  16. Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy

    • Alessandro Rambaldi
  17. San Raffaele Vita-Salute University, Milano, Italy

    • Chiara Bonini
    •  & Fabio Ciceri
  18. Institute for Experimental Cellular Therapy, Universitätsklinikum Essen, Essen, Germany

    • Katharina Fleischhauer

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Contributions

C.T., C.Bonini, K.F., F.C. and L.V. designed the study, analyzed the data and wrote the paper. C.T., L.Z. and V.G. performed the ex vivo and in vitro experiments. C.T. and G.O. performed the in vivo experiments. M.R., G.B., M.Barcella and D.C. performed the bioinformatic analyses. N.C., O.S., R.G., L.C., M.W., R.Z., J.F., M.H., D.W.B., I.G., L.L., M.O., T.T., R.D., D.B., C.J.M.H., M.G., M.G.C., M.Bernardi, J.P., A.R. and F.C. collected and analyzed patient samples and clinical data. C.Barlassina, E.S., D.L., G.T. and D.C. supervised the high-throughput studies. N.C., M.N., F.M. and C.Bonini provided reagents and scientific advice on the analysis of T cell dynamics. E.M., R.O., M.M.N. and B.G. provided reagents and scientific advice on the analysis of the immature and mature myeloid compartments.

Competing interests

C.Bonini received research support from Molmed s.p.a and Intellia Therapeutics. K.F. received research support from GenDx. L.V. received research support from GenDx and Moderna Therapeutics. None of the other authors has any relevant conflicts of interest to disclose.

Corresponding author

Correspondence to Luca Vago.

Extended data

  1. Extended Data Fig. 1 Genomic alterations detected in sorted AML blasts of paired samples at diagnosis and relapse after allo-HCT.

    SNP array analyses were performed on high-quality DNA extracted from AML blasts sorted according to the patient-specific LAIP, collected at diagnosis or relapse after allo-HCT from patients from the discovery cohort (n = 12). The Circos plot summarizes the duplications (red bars), deletions (green bars) and CN-LOH (blue bars) detected in leukemic blasts at diagnosis (white corona) or relapse after allo-HCT (gray corona) for each patient (UPN) in each chromosome.

  2. Extended Data Fig. 2 ClueGO networks of biologic processes deregulated at post-transplantation relapse.

    a, Network representing the mutual relationship between gene ontology, KEGG and BioCarta terms related to the 110-gene signature identified from the pairwise comparison of AML blasts collected at disease diagnosis and at relapse after allo-HCT from discovery cohort patients (n = 9), as evidenced by the ClueGO package of Cytoscape. Dots and colored circles represent genes and biological terms, respectively. The amplitude of the circles represents the adjusted P value (calculated by a two-sided Fisher’s exact test, Bonferroni step-down correction) of each term enrichment. The thickness of each line correlating genes with biological terms represents the strength of interaction as defined by the kappa score. The enriched terms are clustered by function according to their gene content similarity: ‘positive regulation of T cell activation’ (in dark green), ‘peptide antigen assembly with MHC class II protein complex’ (in red), ‘negative regulation of protein import in the nucleus’ (in blue) and ‘regulation of protein tyrosine kinase activity’ (in light blue). b, The gray-scale image represents the same network of panel a, with the direction of deregulation of genes: red represents upregulation at relapse, green represents downregulation.

  3. Extended Data Fig. 3 Expression of HLA class I molecules at post-transplantation relapse.

    a, Heatmap representing fold expression changes in HLA class I gene transcripts (fuchsia markers), their regulators (purple markers) and accessory molecules involved in HLA class I presentation (teal markers). Transcript levels were assessed by microarrays, comparing leukemia at diagnosis with relapses after chemotherapy (CT, n = 3) or allo-HCT (allo-HCT, n = 9). Red and green indicate transcript upregulation and downregulation at relapse, respectively. Bars on the right side of the heatmap summarize mean fold changes at post-transplantation relapse. b, mRNA expression levels of HLA-A and -C measured by locus-specific qPCR in leukemia blasts pairwise collected and purified from patients at diagnosis (red dots) and at post-transplantation relapse (blue dots) (n = 7). Dots indicate values from single patients, lines indicate mean ± s.e.m. P values were calculated by a two-sided Wilcoxon matched-pairs signed rank test at 95% CI. c, HLA class I cell surface expression by leukemia blasts, assessed by immunophenotypic analysis in samples pairwise collected from discovery series patients before allo-HCT (red dots) and at post-transplantation relapse (blue dots) (n = 33). Dots indicate values from single patients, lines indicate mean ± s.e.m. P values were calculated by a two-sided Wilcoxon matched-pairs signed rank test at 95% CI.

  4. Extended Data Fig. 4 In vivo rescue of HLA class II expression on exposure of relapsed leukemia to IFN-γ.

    a, HLA-DR expression on primary leukemia cells collected from UPN 17 at diagnosis (in red) and at relapse after allo-HCT (in blue), re-assessed before infusion in NSG mice (left side panel) and on the respective patient-derived xenografts (PDXs). The gray histograms represent the FMO control of AML blasts at diagnosis. For each histogram, the percentage displayed refers to the comparison with the relevant FMO control. Shown are results representative for two independent experiments with primary leukemias and with PDXs originated from UPN 17 leukemia at diagnosis (n = 3 per experiment) and at post-transplantation relapse (n = 4 per experiment). b, Layout of the in vivo experiment: AML blasts purified from UPN 17 at diagnosis (D-AML) or at relapse after allo-HCT (R-AML) were infused by tail vein injection into 4-week-old NSG mice. Mice were monitored weekly for leukemia engraftment and, on appearance in their peripheral blood of human leukemic cells, received the infusion of T cells collected and ex vivo expanded from UPN 17 donor. c, From left to right are shown results obtained in mice receiving only leukemia cells gathered from UPN 17 at diagnosis (n = 3), mice receiving only leukemia cells gathered from UPN 17 at relapse after allo-HCT (n = 4), mice receiving leukemia cells gathered from UPN 17 at diagnosis followed by donor T cell infusion (green arrow, n = 4) and mice receiving leukemia cells gathered from UPN 17 at relapse after allo-HCT followed by donor T cell infusion (green arrow, n = 4). The top panel row displays the absolute counts of circulating human T cells (in green) and leukemia cells in diagnosis (in red) or relapse (in blue) PDXs. The middle panel row displays the expression on the surface of PDXs of HLA class I (black dots) and HLA-DR (white dots) molecules. The lower panel row displays the concentration of human IFN-γ (in orange), TNF-α (in fuchsia), IL-6 (in green), IL-10 (in light blue) and IL-2 (in violet) measured in the peripheral blood of the mice during the experiment. All the data are displayed as mean ± s.e.m. Shown are results representative for two independent experiments.

  5. Extended Data Fig. 5 Expression of HLA class II molecules and inhibitory ligands on leukemia blasts at relapse after sole chemotherapy.

    Surface expression of HLA-DR, HLA-DP, PD-L1, B7-H3 and Vista was assessed by immunophenotypic analysis in samples pairwise collected from patients at diagnosis (red dots) and at relapse after sole chemotherapy (purple dots) (n = 7). Dots indicate values from single patients, lines indicate mean ± s.e.m. by a two-sided Wilcoxon matched-pairs signed rank test at 95% CI.

  6. Extended Data Fig. 6 Expression of HLA class II molecules and inhibitory ligands on hematopoietic progenitors and monocytes from healthy individuals and transplanted patients.

    Using multiparametric flow cytometry and the gating strategy summarized in Supplementary Fig. 2, we analyzed the surface expression profile of bone marrow myeloid progenitors and peripheral blood monocytes in samples from healthy individuals (n = 5, in white, HD), patients from the discovery cohort who subsequently relapsed with one of the two newly described relapse modalities (n = 10, in cyan, REL) and transplanted patients who achieved long-term disease remission (n = 10, in yellow, CR). Dots indicate values from single patients, lines indicate mean ± s.e.m.

  7. Extended Data Fig. 7 Changes in the transcript levels of genes related to HLA class II antigen presentation and T cell costimulation in the validation cohort.

    a,b, Heatmaps mirroring those shown in Figs. 2a and 3a for the discovery cohort, representing fold expression changes in transcripts for molecules involved in HLA class II presentation (a) and T cell costimulation (b) assessed by RNA-seq of leukemia sample pairwise collected and purified from validation cohort patients (n = 15). Red and green indicate transcript upregulation and downregulation at relapse, respectively. Bars on the right side of the heatmap summarize mean fold changes at post-transplantation relapse.

  8. Extended Data Fig. 8 High-dimensional analysis of immunophenotypic data obtained from the validation cohort.

    a, Color maps obtained using the BH-SNE bioinformatic algorithm for single-cell analysis, allowing the visualization in a two-dimensional map of complex datasets of high-dimensional objects (in this case, single cells stained with 16 different fluorochromes), plotted in the map on the basis of their reciprocal similarity. Shown are maps obtained from the full dataset of immunophenotypic analyses performed in our validation cohort, encompassing all the events registered in the analysis of paired diagnosis-relapse samples from the validation cohort (n = 36). The BH–SNE map relative to expression of HLA-DR, HLA-DP, PD-L1, B7-H3 and Vista was colored to evidence the differential positioning (and consequently phenotypic dissimilarity) of events originating from diagnosis samples (in red, left panel) or relapse samples (in blue, right panel). b, On the basis of K-means analysis of the BH-SNE map, meta-clusters of events unique for diagnoses (n = 19) and relapses (n = 4) were identified, and the mean fluorescence intensity of the markers characterizing them are plotted in red and blue, respectively. P values were calculated by a two-sided unpaired t-test at 95% CI.

  9. Extended Data Fig. 9 Clinical and immunogenetic correlates of HLA class II downregulation at post-transplantation relapse.

    Forest plot represents the odds ratio (diamonds) and 95% CI (error bars) of belonging to the ‘HLA class II downregulation’ clusters identified in Fig. 4c, d, calculated in the entire study population (n = 69) using an univariate logistic regression model for demographic, disease-related, immunogenetic and transplant-related variables. *These variables were considered as continuous in the model. §Considering allelic mismatches in the graft-versus-host direction in HLA-A, -B, -C and -DRB1.

Supplementary information

  1. Supplementary Information

    Supplementary Tables 1, 2, 3, 5 and 6, Supplementary Figures 1 and 2

  2. Reporting Summary

  3. Supplementary Table 4

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https://doi.org/10.1038/s41591-019-0400-z