Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy

Abstract

In contrast to the curative effect of allogenic stem cell transplantation in acute myeloid leukemia via T cell activity, only modest responses are achieved with checkpoint-blockade therapy, which might be explained by T cell phenotypes and T cell receptor (TCR) repertoires. Here, we show by paired single-cell RNA analysis and TCR repertoire profiling of bone marrow cells in relapsed/refractory acute myeloid leukemia patients pre/post azacytidine+nivolumab treatment that the disease-related T cell subsets are highly heterogeneous, and their abundance changes following PD-1 blockade-based treatment. TCR repertoires expand and primarily emerge from CD8⁺ cells in patients responding to treatment or having a stable disease, while TCR repertoires contract in therapy^-resistant patients. Trajectory analysis reveals a continuum of CD8⁺ T cell phenotypes, characterized by differential expression of granzyme B and a bone marrow-residing memory CD8⁺ T cell subset, in which a population with stem-like properties expressing granzyme K is enriched in responders. Chromosome 7/7q loss, on the other hand, is a cancer-intrinsic genomic marker of PD-1 blockade resistance in AML. In summary, our study reveals that adaptive T cell plasticity and genomic alterations determine responses to PD-1 blockade in acute myeloid leukemia.

Fig. 1. Study design and single cell assessment of AML and healthy bone marrows.

A Clinical design summarizing the age, response type per ELN, time to response, treatment frequency and the number of cells analyzed per patient. B Canonical gene expression markers to define the healthy bone marrow (BM) and tumor microenvironment (TME) cellular subsets. C UMAP-based analysis of healthy BM cellular components with frequency of each cell type. D Representative output of combining patient BM sample (represented by PT1, post-treatment timepoint C) with healthy BM donor cells demonstrating distinct clustering of tumor cells with concordant expression of CD34 confirmed by flow cytometry and immunohistochemistry, when available (Supplementary Fig. 3A–D). E Representative Inferring copy number variation of malignant cells compared to monocytes demonstrate concordant cytogenetic profiling, further confirming AML cell identity. F Spearman correlation between the number of cells detected by scRNA versus flow cytometry and histopathology. G UMAP clustering of AML cells. H UMAP clustering of TME components. I Distribution of TME components in AML patients at different timepoints (A is pre-treatment, B and C are post-treatment samples). PR partial response, CR complete response, NR no response, SD stable disease, HSC hematopoietic stem cell, GMP granulocyte–monocytic progenitor, cDC conventional dendritic cell, pDC plasmacytoid dendritic cell, unconv T unconventional T, NK natural killer.

Fig. 2. T-cell receptor clonotype assessment across different patients and timepoints.

A Scatterplot of the correlation between the number of T cell clonotypes and the size of the clonotype i.e. the number of T cells contributing to the clonotype. B Distribution of the most abundant clonotypes by patient. C Simpson’s clonality index of individual patients at each of their respective timepoints. D Scatterplots of clonotypes change of post- versus pre-treatment and E by response groups. F Number of novel, expanded and contracted clonotypes by response group.

Fig. 3. Characterization of T-cell subsets.

A UMAP of T cell subsets. B Distribution of T cell subsets prior to and following treatment. C Heatmap of canonical marker expression of identified T cell subsets. UMAP of the different D CD4⁺ and E CD8⁺ phenotypes with exhaustion and cytotoxicity scores projected onto the UMAP. F Mann–Whitney test for exhaustion scores of CD4⁺ and CD8⁺ T lymphocytes of different response groups prior to treatment. G Mann–Whitney test for exhaustion scores of CD4⁺ and CD8⁺ T lymphocytes at pre and post treatment in responders. H Overall survival of AML patients in TCGA cohort by GZMK expression.

Fig. 4. Trajectory analysis of CD8+ T cells.

A, B Monocle3-based pseudotemporal analysis of CD8⁺ subsets. C–F Expression of GZMA, GZMB, GZMK and GNLY in cells projected onto the trajectory of CD8⁺ continuum. G Pearson correlation between GZMK and other cytotoxic genes in CD8⁺ cells. H Heatmap of differentially expressed genes among CD8⁺ T lymphocyte subsets. I UMAP of MAIT cells with exhaustion and cytotoxicity scores projection. J Distribution of T-cell subsets in PT1. K Heatmap of differentially expressed genes between MAIT subsets.

Fig. 5. T-cell clonotype analysis.

A Distribution of the TCR clonotype frequency by cell type. B Number of novel, expanded and contracted clonotypes by cell type. C Fraction of T cells from top, most abundant 3 clonotypes. D Heatmap of overlapping clonotypes between different cell types at pre- and post- treatment timepoints. E Heatmap for observed phenotype transitions for matched clones at pre- and post- treatment timepoints.

Fig. 6. Correlation of responses with cytogenetics.

A Inferred copy number variation of representative 300 cells per patient at pretreatment (timepoint A). B Inferred copy number variation of the 3 timepoints (A, B and C) for PT3 (responder). C Fish plot of mutational evolution of PT3. D A two-sided Pearson’s Chi-square for correlation analysis for responders to azacitidine/nivolumab and E azacitidine-based therapy based on chr7/7q deletion. F Mann–Whitney, two-sided test for CIBERSORTx analysis of T_reg cell components in AML from TCGA by chr7/7q status (n = 19 for del7/7q and n = 152 for no deletion in chr7/7q). Center line represents the main and lower and upper hingest correspond to the first and third quartiles.