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Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

Nature Cancer volume 2, pages 835–852 (2021)Cite this article

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Abstract

Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We therefore investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population, with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of the cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

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Fig. 1: Defining the clonal architecture of BCP-ALL at single-cell resolution.
Fig. 2: Genotype and cell states associated with resistance do not co-segregate in BCP-ALL.
Fig. 3: Treatment in a mouse model of childhood BCP-ALL does not affect the overall extent of intratumor genetic heterogeneity.
Fig. 4: ScWGS confirms limited selection of genetic subclones in response to chemotherapy.
Fig. 5: Transcriptionally driven phenotypes contribute to treatment resistance in BCP-ALL and DNA methylation analysis identifies epigenetically deregulated hotspots in BCP-ALL-resistant cells.
Fig. 6: Transcriptional and epigenetic variability provide a substrate for genotype-independent selection over treatment.
Fig. 7: Resistant BCP-ALL cells represent a small subset of G0 cells with MLP-like transcriptional features.
Fig. 8: Treatment selects a rare pre-existing cell state.

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Data availability

Bulk and single-cell DNA- and RNA-seq and methylation array data (Supplementary Table 9) that support the findings of the present study have been deposited in the European Genome–phenome Archive (EGA), which is hosted by the European Bioinformatics Institute and the Centre for Genomic Regulation, under accession no. EGAS00001004407. Previously published WGS data from refs. 25,29 are available under accession identifiers EGAD00001000076 and EGAD00001000636. Source data are provided with this paper.

Code availability

Code used for this analysis is available at https://github.com/UCL-BLIC-analysis/Turati_NatCancer_2021.

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Acknowledgements

We thank all clinicians and the Blood Cancer UK Leukaemia Cell Bank for samples, Y. Guo for the assistance with the in vivo work, B. Gaal for sharing her MATLAB expertise, I. Titley and A. Ford for cell sorting and Ki67 analysis of single quiescent cells, and Fluidigm for technical support. We also thank the UCL-CI CRUK-funded flow cytometry, microscopy and genomics facility for access to instrumentation and for practical help with FACS analysis (W. Day, Y. Guo, B. Wilbourne and G. Morrow). We thank the UCL Legion and Myriad High Performance Computing Facility (Legion@UCL; Myriad@UCL), and associated support services, for the use of their services in completion of this work. The present study was supported by Blood Cancer UK project grants (to T.E., M.G. and S.E.W.J.) and partially supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (grant no. FC001202), the UK Medical Research Council (grant no. FC001202) and the Wellcome Trust (grant no. FC001202). V.A.T. was funded by grants from Children with Cancer UK and Blood Cancer UK, N.P was founded by Gabriel’s Angels, and J.A.G.-A. and L.C. were funded by the Cancer Research UK–UCL Centre Award. M.T. is a postdoctoral fellow supported by the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie grant agreement no. 747852-SIOMICS), P.V.L. is a Winton Group Leader in recognition of the Winton Charitable Foundation’s support toward the establishment of the Francis Crick Institute and M.G. was supported by the Wellcome Trust. L.J.R was funded by the Kay Kendall Leukaemia Fund and Leuka, G.C. was funded by the Italian Association for Cancer Research grants (IG2017 no. 20564 to A.B. and IG2015 no. 17593 to G.C.). A.B. and G.C. were funded by the Comitato Maria Letizia Verga and S.E.W.J. by the UK Medical Research Council (grant no. MC_UU_12009/5).

Author information

Author notes

  1. These authors contributed equally: José Afonso Guerra-Assunção, Nicola E. Potter.

Authors and Affiliations

  1. UCL Cancer Institute, University College London, London, UK

    Virginia A. Turati, José Afonso Guerra-Assunção, Rajeev Gupta, Simone Ecker, Agne Daneviciute, Amy P. Webster, Chuling Ding, Gillian May, Chela James, John Brown, Lucia Conde, Stephan Beck, Javier Herrero & Tariq Enver

  2. Institute of Cancer Research, Sutton, UK

    Nicola E. Potter, Mike Hubank & Mel Greaves

  3. The Francis Crick Institute, London, UK

    Maxime Tarabichi & Peter Van Loo

  4. Translational and Clinical Research Institute, Newcastle University Centre for Cancer, Newcastle University, Newcastle, UK

    Lisa J. Russell

  5. Great Ormond Street Hospital, London, UK

    Phil Ancliff & Sarah Inglott

  6. Centro Ricerca M. Tettamanti, University of Milano-Bicocca, Monza, Italy

    Giovanni Cazzaniga

  7. Department of Pediatrics, University of Milano-Bicocca, Fondazione MBBM/Ospedale San Gerardo, Monza, Italy

    Andrea Biondi

  8. John Radcliffe Hospital, Oxford, UK

    Georgina W. Hall

  9. Fluidigm Corporation, San Francisco, CA, USA

    Mark Lynch

  10. Royal Marsden Hospital, Sutton, UK

    Mike Hubank

  11. The Earlham Institute, Norwich, UK

    Iain Macaulay

  12. MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK

    Sten E. Jacobsen

  13. Center for Hematology and Regenerative Medicine, Department of Medicine and Department of Cell and Molecular Biology, Karolinska Institutet and Karolinska University Hospital Huddinge, Stockholm, Sweden

    Sten E. Jacobsen

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  1. Virginia A. Turati
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  2. José Afonso Guerra-Assunção
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Contributions

V.A.T., T.E., M.G. and S.E.J. conceived the study. V.A.T., M.L., J.B., A.W. and M.H. conducted the methodology. V.A.T., N.P., I.M. and A.D. did the investigations. J.A.G.-A., M.T., P.V.L., S.B. and J.H. carried out the formal analysis. J.A.G-A., A.W., S.E., C.D., C.J., C.L. and J.H. curated the data. S.E.J., G.W.H., A.B., L.R., S.I., P.A., G.C. and L.R. provided the resources. T.E., S.E.J and M.G. supervised the project. V.A.T., R.G., G.M., T.E. and M.G. wrote the manuscript. T.E. and M.G. acquired the funding.

Corresponding authors

Correspondence to Virginia A. Turati or Tariq Enver.

Ethics declarations

Competing interests

M.L. was an employee of rgw Fluidigm Corporation at the time of the present study. The other authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Single cell whole genome sequencing analysis.

a, Representative traces of two whole-genome sequenced cells. Mapped reads are plotted across all chromosomes to highlight variations in copy number status as changes in the ploidy of the cells (n = 2). b, Diagram explaining the data processing pipeline. c, Scatterplots comparing the average normalized, GC-corrected and ploidy-adjusted counts per bins for pairs of copy-number events, where each dot represents an individual cell (n = 420 cells) harvested from Pt1 PDXs (across all treatment conditions and timepoints). Red lines divide the plots in 4 quadrants based on the nominal thresholds for calling each CNA.

Extended Data Fig. 2 Single cell gene expression analysis and mFISH analysis.

a, Representative plots describing the flow cytometry sorting approach used to collect CD34 + /CD38-/CD19 + cells. DAPI and forward scatter were used to identify viable cells (gate p1). To ensure that no dying cells were sorted, the p1 gate was further refined based on cells staining with the cell viability dye carboxyfluorescein succinimidyl ester (CFSE - FITC channel) (gate p2 – used to sort bulk cells). Positive vs negative combinations of specific antibodies were used to identify the populations of interest: CD34 + /CD38-/CD19 + were collected from gate p4. b, Representative heat map from a FLEXsix single-cell Q-PCR transcription panel showing the expression level of selected relevant transcripts in 11 single-cells sorted from the same patient as negative for CD38 expression. UBC is polyubiquitin-C used as a housekeeping gene control, CB8 is a single normal cord blood control cell. c and d, Phylogenetic trees showing the subclonal architectures of Pt10 and Pt11 bulk and CD34+/CD19+/CD38-/low sorted cells.

Source data

Extended Data Fig. 3 mFISH analysis.

a, Flow-sorting strategy used to assess engraftment and isolate human leukemic cells (hCD45+) from mouse cells (mCD45+). b, Representative Multicolor FISH image. The subclonal variegation within each sample was assessed using fluorochrome-labelled custom- made probes detecting AML1 (RUNX1), TEL (ETV6), p16 and PAX5 genes. c and d, Tables showing the copy number status associated with each of the subclones identified within Pt2 and Pt3 engrafted leukemias. e-f, BM engraftment data before and after chemotherapy for Pt2 (n = 11 control mice and n = 8 treated mice) and Pt3 (n = 10 control mice and n = 13 treated mice) at d0 and d28 as assessed by flow cytometry. Note engraftment is expressed here in the conventional way as hCD45 + /(hCD45+ +mCD45+; the >10 log cytoreduction achieved by chemotherapy is obscured in this representation due to the accompanying decrease in mouse CD45+ cells. Data are presented as mean values +/− SD. The statistical significance of each comparison (Paired t-test, two-tailed) is shown (p-value). g, Absolute numbers of human white blood cells (WBC) retrieved at d28 from both control (n = 5 mice) and treated mice (n = 8 mice) as quantified by the XP-300TM Automated Haematology Analyser (SYSMEX Co., Japan). Data are presented as mean values +/− SD. h, Dot plot showing treatment responses across different metastatic sites (brain, liver, spleen), with representative images of the reduction in spleen size following treatment (n = 2 control mice and n = 5 treated mice). Data are presented as mean values +/− SD. Error bars are shown for treated samples where n≥3. i, Treatment response kinetics of xenografts derived from high and low risk patients. Response was assessed by flow cytometry comparing human chimerism at d7 and d28 to d0 engraftment. In the plot each line represents a diffent patient (including some of the patients used for downstream analysis (pt1, pt2, pt3)). Data are presented as mean values+/− SD (n = 3 mice). j, Graph tracking the size of individual subclones from Pt2 xenografted leukemias before (d0 BM aspiration) and after treatment (d28 BM and d28 spleen). Multiple lines of the same color highlight the kinetic of the same subclone across different treated xenografts.

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Extended Data Fig. 4 scWGS analysis.

a-b, TimeScape plots visualizing the clonal composition of two untreated (Ctrl) and two treated mice at 3 timepoints: i) in the primary diagnostic leukemia ii) at d0 in the xenograft (BM aspirate) and ii) at d28 in the xenograft (total BM harvest). c, Hierarchical classification of the main clones identified across all analyzed Pt1 specimens (primary diagnosis and xenografts d0-d28); provides a legend for the color coding used for the TimeScape plots in panels a and b (left) as well as Fig. 4d-e.

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Extended Data Fig. 5 Population RNAseq analysis.

a-b, Graphical representations of the standard and multi-arm treatment models and resulting PCA analysis. In the standard model (a) matching d0 and d28 cells harvested from untreated (n = 37) and treated (n = 17) mice were analyzed. Untreated samples, shown in light blue, represent d0 cells from untreated mice and treated mice as well as d28 samples from untreated mice. In the multi-arm model (b), two consecutive rounds of transplantation and treatment (7 days) allowed the analysis of ‘acutely treated’ (n = 8 samples; green), ‘treatment withdrawn’ (n = 6 samples; dark yellow), ‘chronically treated’ (n = 5 samples; red), and ‘untreated control’ (n = 5 samples; dark blue) cells harvested from n = 3 mice each (see main text). ‘Relapse-like’ cells were also obtained through a separate arm of the experiment, whereby n = 3 mice were treated for 28 days and then let to relapse in situ in the absence of further treatment (6–8 weeks). c, Untreated and treated cells from primary recipients of Pt2 cells were transplanted into secondary recipients at three different limiting doses (100, 1*103, 1*104 cells). Engraftment (%) of BM, spleen and brain was assessed by flow cytometry and is shown in the dot plot (n = 4 mice for control; n = 5 mice for treated 100 and 1*103 cells; n = 4 mice for treated 1*104 cells). d, Estimated LIC frequencies for treated and untreated cell populations from each analyzed tissue (n = 4 mice for control; n = 5 mice for treated 100 and 1*103 cells; n = 4 mice for treated 1*104 cells) as calculated using the ELDA software69. In the top plot the number of initially injected cells (x-axis) is plotted against the log fraction of non-responders, corresponding to mice with no engraftment (y-axis).

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Extended Data Fig. 6 Single cell RNA sequencing analysis of fresh diagnostic samples.

a-b, Analysis of the biological states, processes and lineages underpinning the intercellular transcriptional heterogeneity amongst diagnostic cells from either Pt1 or Pt2 using the bioinformatics tool scran70 to decompose gene expression heterogeneity into technical noise and a biologically relevant component. A list of genes ranked by their biologically meaningful variance was then used as input for enrichment analysis with the fGSEA R-package using the Hallmark pathway gene sets (MSigDB) and lineage markers defined in71. The importance of each given signature in explaining variation amongst untreated cells is reflected in the normalized enrichment score (NES) reported next to the signature itself. P-values are obtained using 2-sided permutation test52 with 10,000 permutations. All shown processes have a Benjamini-Hochberg FDR-ajusted p-value <0.05. c, Heatmap showing the expression of gene sets associated with different lineages and differentiation stages of the hematopoietic hierarchy. Each line of the map represents an individual diagnostic cell from either Pt1 or Pt2. d, Bar plots visualizing the percentage of individual diagnostic cells from either Pt1 or Pt2 assigned to their closest normal hematopoietic cell lineage genes (signature with the highest Z-score average). The analysis is based on the expression of known markers32,53.

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Extended Data Fig. 7 Single cell RNA sequencing lineage analysis.

Series of t-SNE plots of n = 448 untreated and n = 163 treated cells showing the positive (TRUE) or negative (FALSE) assignment of each lineage across different regions of the plot. Each cell is colored in red if it expresses genes associated with the named lineage (>0.75 average z-score over genes in the signature) or in grey if it does not express the signature or expresses it below the cut-off level. The plots do not show relative expression levels but aim to highlight how the same cell can have concomitantly high expression of gene markers characteristic of different lineages.

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Extended Data Fig. 8 Single cell RNA sequencing clustering analysis.

a, UMAP clustering of the single cell RNA-seq xenograft data. The dimensionality reduction tool identifies 4 main clusters of cells. Clusters 0, 2 and 3 correspond to different states of untreated cells, while cluster 1 mostly comprises chemotherapy-treated cells. b, Heatmap showing the top positively differentially expressed features that characterize each of the UMAP clusters. c, UMAP plots visualizing the expression of selected biomarkers for each of the clusters. Images produced using the Seurat analysis package72.

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Extended Data Fig. 9 Boxplots of scRNAseq gene expression signatures.

Plot showing the expression of relevant MSigDB 50 Hallmark gene sets across single cells grouped by treatment status (untreated (n = 448 cells) and treated (n = 163 cells)). The median expression per class is shown as a solid black line within each box. The box covers the 25th–75th percentiles range; whiskers cover all data points within a 1.5x the inter-quantile ranges from the bounds of the box; remaining data points are plotted as outliers.

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Extended Data Fig. 10 Single cell RNA sequencing cell cycle, lineage and pseudotime analysis.

a, Violin plots showing the expression of E2F1 and MYC genes in n = 448 untreated and n = 163 treated cells. Both genes are on average 10 log2 folds downregulated in treated cells. b, Stacked bar plot comparing the inferred lineage of each n = 448 untreated and n = 163 treated single cell with its cell cycle status as determined by the expression of G1/S, G2/M, MYC targets, and E2F targets signatures. c, TSCAN analysis displaying a PCA analysis of the data (n = 611 cells) and inferred pseudotime. The TSCAN package uses a cluster-based minimum spanning tree (MST) approach to order cells based on the gradual transition of their transcriptomes39. A line on the PCA highlights the constructed pseudo-temporal path. The table shows the number of treated and untreated cells within each cluster. The numbers of treated and untreated cells analysed are shown in the histograms at the top of the figure. d, Plot showing the distribution of deep, shallow quiescent and cycling cells (n = 611 cells), and lineage scores, across the TSCAN pseudotime clusters. e, Plots displaying the dynamic expression along the inferred pseudo-temporal path of representative genes with a known role in B-cell differentiation. Each cell (n = 611 cells) is shown as a circle (treated) or a triangle (untreated), the color of which reflects the cluster it belongs to (see panel c). f, Heatmap showing the top variable genes that contribute to clustering of n = 448 untreated and n = 163 chemotherapy-treated cells. PRS cells (n = 10 cells) as defined by this approach are evidenced by black arrows. g, Violin plots showing the expression of mitochondrial genes in treated, PRS and untreated cells as defined by either pseudotime (n = 163, 30 and 418 cells respectively; left plot) or t-SNE analysis (n = 163, 10 and 428 cells respectively; right plot).

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Supplementary information

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Turati, V.A., Guerra-Assunção, J.A., Potter, N.E. et al. Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia. Nat Cancer 2, 835–852 (2021). https://doi.org/10.1038/s43018-021-00219-3

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