Immune signature drives leukemia escape and relapse after hematopoietic cell transplantation

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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Fig. 1: Immune-related changes in leukemia relapsing after allo-HCT.
Fig. 2: Impaired T cell costimulation by leukemia blasts at post-transplantation relapse.
Fig. 3: Loss of HLA class II expression in leukemia cells at post-transplantation relapse.
Fig. 4: Validation, frequency and reciprocal interplay of the two newly identified modalities of post-transplantation leukemia relapse.

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.

References

  1. 1.

    Copelan, E. A. Hematopoietic stem-cell transplantation. N. Engl. J. Med. 354, 1813–1826 (2006).

    CAS  Article  Google Scholar 

  2. 2.

    Kolb, H.-J. Graft-versus-leukemia effects of transplantation and donor lymphocytes. Blood 112, 4371–4383 (2008).

    CAS  Article  Google Scholar 

  3. 3.

    Horowitz M et al. Epidemiology and biology of relapse after stem cell transplantation.Bone Marrow Trans. 53, 1379–1389 (2018).

    CAS  Article  Google Scholar 

  4. 4.

    Vago, L. et al. Loss of mismatched HLA in leukemia after stem-cell transplantation. N. Engl. J. Med. 361, 478–488 (2009).

    CAS  Article  Google Scholar 

  5. 5.

    Waterhouse, M. et al. Genome-wide profiling in AML patients relapsing after allogeneic hematopoietic cell transplantation. Biol. Blood Marrow Trans. 17, 1450–1459.e1 (2011).

    CAS  Article  Google Scholar 

  6. 6.

    Crucitti, L. et al. Incidence, risk factors and clinical outcome of leukemia relapses with loss of the mismatched HLA after partially incompatible hematopoietic stem cell transplantation. Leukemia 29, 1143–1152 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Vago, L., Toffalori, C., Ciceri, F. & Fleischhauer, K. Genomic loss of mismatched human leukocyte antigen and leukemia immune escape from haploidentical graft-versus-leukemia. Semin. Oncol. 39, 707–715 (2012).

    Article  Google Scholar 

  8. 8.

    Gupta, M. et al. Novel regions of acquired uniparental disomy discovered in acute myeloid leukemia. Genes Chromosom. Cancer 47, 729–739 (2008).

    CAS  Article  Google Scholar 

  9. 9.

    Raghavan, M. et al. Segmental uniparental disomy is a commonly acquired genetic event in relapsed acute myeloid leukemia. Blood 112, 814–821 (2008).

    CAS  Article  Google Scholar 

  10. 10.

    Yu, G. et al. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26, 976–978 (2010).

    CAS  Article  Google Scholar 

  11. 11.

    Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    CAS  Article  Google Scholar 

  12. 12.

    Fabregat, A. et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    CAS  Article  Google Scholar 

  14. 14.

    Shlush, L. I. et al. Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature 547, 104–108 (2017).

    CAS  Article  Google Scholar 

  15. 15.

    Li, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat. Med. 22, 792–799 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Goodyear, O. et al. Induction of a CD8+ T-cell response to the MAGE cancer testis antigen by combined treatment with azacitidine and sodium valproate in patients with acute myeloid leukemia and myelodysplasia. Blood 116, 1908–1918 (2010).

    CAS  Article  Google Scholar 

  17. 17.

    Goodyear, O. C. et al. Azacitidine augments expansion of regulatory T cells after allogeneic stem cell transplantation in patients with acute myeloid leukemia (AML). Blood 119, 3361–3369 (2012).

    CAS  Article  Google Scholar 

  18. 18.

    de Lima, M. et al. Maintenance therapy with low-dose azacitidine after allogeneic hematopoietic stem cell transplantation for recurrent acute myelogenous leukemia or myelodysplastic syndrome: a dose and schedule finding study. Cancer 116, 5420–5431 (2010).

    Article  Google Scholar 

  19. 19.

    Platzbecker, U. et al. Azacitidine for treatment of imminent relapse in MDS or AML patients after allogeneic HSCT: results of the RELAZA trial. Leukemia 26, 381–389 (2012).

    CAS  Article  Google Scholar 

  20. 20.

    Zhou, Q. et al. Program death-1 signaling and regulatory T cells collaborate to resist the function of adoptively transferred cytotoxic T lymphocytes in advanced acute myeloid leukemia. Blood 116, 2484–2493 (2010).

    CAS  Article  Google Scholar 

  21. 21.

    Norde, W. J. et al. PD-1/PD-L1 interactions contribute to functional T-cell impairment in patients who relapse with cancer after allogeneic stem cell transplantation. Cancer Res. 71, 5111–5122 (2011).

    CAS  Article  Google Scholar 

  22. 22.

    Hobo, W., Hutten, T. J. A., Schaap, N. P. M. & Dolstra, H. Immune checkpoint molecules in acute myeloid leukaemia: managing the double-edged sword. Br. J. Haematol. 181, 38–53 (2018).

    Article  Google Scholar 

  23. 23.

    Knaus, H. A., Kanakry, C. G., Luznik, L. & Gojo, I. Immunomodulatory drugs: immune checkpoint agents in acute leukemia. Curr. Drug Targets 18, 315–331 (2017).

    CAS  Article  Google Scholar 

  24. 24.

    Bashey, A. et al. CTLA4 blockade with ipilimumab to treat relapse of malignancy after allogeneic hematopoietic cell transplantation. Blood 113, 1581–1588 (2009).

    CAS  Article  Google Scholar 

  25. 25.

    Davids, M. S. et al. Ipilimumab for patients with relapse after allogeneic transplantation. N. Engl. J. Med. 375, 143–153 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Blazar, B. R. et al. Blockade of programmed death-1 engagement accelerates graft-versus-host disease lethality by an IFN-gamma-dependent mechanism. J. Immunol. 171, 1272–1277 (2003).

    CAS  Article  Google Scholar 

  27. 27.

    Haverkos, B. M. et al. PD-1 blockade for relapsed lymphoma post-allogeneic hematopoietic cell transplant: high response rate but frequent GVHD. Blood 130, 221–228 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Stevanović, S., van Schie, M. L. J., Griffioen, M. & Falkenburg, J. H. HLA-class II disparity is necessary for effective T cell mediated graft-versus-leukemia effects in NOD/scid mice engrafted with human acute lymphoblastic leukemia. Leukemia 27, 985–987 (2013).

    Article  Google Scholar 

  29. 29.

    Fleischhauer, K. & Shaw, B. E. HLA-DP in unrelated hematopoietic cell transplantation revisited: challenges and opportunities. Blood 130, 1089–1096 (2017).

    CAS  Article  Google Scholar 

  30. 30.

    Abiko, K. et al. IFN-γ from lymphocytes induces PD-L1 expression and promotes progression of ovarian cancer. Br. J. Cancer 112, 1501–1509 (2015).

    CAS  Article  Google Scholar 

  31. 31.

    Garcia-Diaz, A. et al. Interferon receptor signaling pathways regulating PD-L1 and PD-L2 expression. Cell Rep. 19, 1189–1201 (2017).

    CAS  Article  Google Scholar 

  32. 32.

    Berthon, C. et al. In acute myeloid leukemia, B7-H1 (PD-L1) protection of blasts from cytotoxic T cells is induced by TLR ligands and interferon-gamma and can be reversed using MEK inhibitors. Cancer Immunol. Immunother. 59, 1839–1849 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    ImusP. H et al. Major histocompatibility mismatch and donor choice for second allogeneic bone marrow transplantation. Biol. Blood Marrow Trans. 23, 1887–1894 (2017).

    Article  Google Scholar 

  34. 34.

    Vago, L. & Ciceri, F. Choosing the alternative. Biol. Blood Marrow Trans. 23, 1813–1814 (2017).

    Article  Google Scholar 

  35. 35.

    Mathew, N. R. et al. Sorafenib promotes graft-versus-leukemia activity in mice and humans through IL-15 production in FLT3-ITD-mutant leukemia cells. Nat. Med. 24, 282–291 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

    Article  Google Scholar 

  37. 37.

    Robinson, J. T. et al. Integrative genomics viewer. Nat Biotechnol 29, 24–26 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Paulsson, K., Lindgren, D. & Johansson, B. SNP array analysis of leukemic relapse samples after allogeneic hematopoietic stem cell transplantation with a sibling donor identifies meiotic recombination spots and reveals possible correlation with the breakpoints of acquired genetic aberrations. Leukemia 25, 1358–1361 (2011).

    CAS  Article  Google Scholar 

  39. 39.

    Van Gelder, R. N. et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl Acad. Sci. USA 87, 1663–1667 (1990).

    Article  Google Scholar 

  40. 40.

    Smyth, G. K., Michaud, J. & Scott, H. S. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21, 2067–2075 (2005).

    CAS  Article  Google Scholar 

  41. 41.

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Article  Google Scholar 

  42. 42.

    Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  Google Scholar 

  43. 43.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  Article  Google Scholar 

  44. 44.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  Article  Google Scholar 

  45. 45.

    Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).

    Article  Google Scholar 

  46. 46.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  47. 47.

    García-Ruano, A. B. et al. Analysis of HLA-ABC locus-specific transcription in normal tissues. Immunogenetics 62, 711–719 (2010).

    Article  Google Scholar 

  48. 48.

    Ulbricht, T. et al. PML promotes MHC class II gene expression by stabilizing the class II transactivator. J. Cell Biol. 199, 49–63 (2012).

    CAS  Article  Google Scholar 

  49. 49.

    Wang, J., Roderiquez, G., Jones, T., McPhie, P. & Norcross, M. A. Control of in vitro immune responses by regulatory oligodeoxynucleotides through inhibition of pIII promoter directed expression of MHC class II transactivator in human primary monocytes. J. Immunol. 179, 45–52 (2007).

    CAS  Article  Google Scholar 

  50. 50.

    Kolde, R. Pheatmap: Pretty heatmaps. R package version 0.6.1. https://cran.r-project.org/web/packages/pheatmap/index.html (2012).

  51. 51.

    Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    CAS  Article  Google Scholar 

  52. 52.

    Wolfl, M. et al. Activation-induced expression of CD137 permits detection, isolation, and expansion of the full repertoire of CD8+T cells responding to antigen without requiring knowledge of epitope specificities. Blood 110, 201–210 (2007).

    CAS  Article  Google Scholar 

  53. 53.

    Cieri, N. et al. IL-7 and IL-15 instruct the generation of human memory stem T cells from naive precursors. Blood 121, 573–584 (2013).

    CAS  Article  Google Scholar 

  54. 54.

    Jilani, I. et al. Better detection of FLT3 internal tandem duplication using peripheral blood plasma DNA. Leukemia 17, 114–119 (2003).

    CAS  Article  Google Scholar 

  55. 55.

    Brambati, C. et al. Droplet digital polymerase chain reaction for DNMT3A and IDH1/2 mutations to improve early detection of acute myeloid leukemia relapse after allogeneic hematopoietic stem cell transplantation. Haematologica 101, e157–e161 (2016).

    CAS  Article  Google Scholar 

Download references

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.

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Authors

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.

Corresponding author

Correspondence to Luca Vago.

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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.

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Extended data

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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Toffalori, C., Zito, L., Gambacorta, V. et al. Immune signature drives leukemia escape and relapse after hematopoietic cell transplantation. Nat Med 25, 603–611 (2019). https://doi.org/10.1038/s41591-019-0400-z

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