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Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels

Nature Cell Biology volume 22, pages 896–906 (2020)Cite this article

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Abstract

Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumour heterogeneity. We addressed this issue by studying the heterogeneity of nodal B-cell lymphomas by single-cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug-response and genomic profiles. Malignant subpopulations from the same patient responded strikingly differently to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene-expression signatures were similar across all donors, whereas the frequencies of T-cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B-cell lymphomas and highlight the relevance of intratumour heterogeneity for personalized cancer therapy.

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Fig. 1: Identification of cell types using scRNA-seq.
Fig. 2: Transcriptional heterogeneity of lymph node-derived T cells.
Fig. 3: Gene-expression signatures driving B-cell heterogeneity.
Fig. 4: Cellular crosstalk in B-cell lymphomas in the lymph node microenvironment.
Fig. 5: In-depth analysis of the sample tFL1.
Fig. 6: In-depth analysis of the sample DLBCL1.

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Data availability

The WGS and WES data have been deposited at the European Genome-phenome Archive (EGA). The EGA study accession ID is EGAS00001004335. The scRNA-seq data that support the findings of this study have been deposited in heiDATA under accession code VRJUNV. The single-cell expression data of merged B- and T-cell UMAP plots (Fig. 2a,b and Fig. 3a,b) are available for easy-to-use interactive browsing at https://www.zmbh.uni-heidelberg.de/Anders/scLN-index.html. Genes that were differentially expressed between B-cell clusters can be browsed in an interactive html file (Supplementary Data 1). All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The R codes used for data analysis are available at our GitHub repository without further restriction (www.github.com/DietrichLab/scLymphomaExplorer).

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Acknowledgements

T.R. was supported by a physician scientist fellowship from the Medical Faculty of University Heidelberg. M.Seiffert was supported by a grant from the Deutsche Forschungsgemeinschaft (DFG). S.D. was supported by a grant from the Hairy Cell Leukemia Foundation, Heidelberg Research Centre for Molecular Medicine (HRCMM) and an e:med BMBF junior group grant and DFG through the SFB873 (project B7). The Anders laboratory was supported by the DFG via Collaborative Research Centre (CRC) 1366 (project number 394046768). For the data management, we thank the Scientific Data Storage Heidelberg (SDS@hd), which is funded by the state of Baden-Württemberg and a DFG grant (grant no. INST 35/1314-1 FUGG). We thank C. Kolb (University Hospital Heidelberg), M. Knoll (University Hospital Heidelberg), A. Lenze (University Hospital Heidelberg), the EMBL flow core facility and the EMBL gene core facility for their excellent technical assistance. We also thank the DKFZ Single-Cell Open Lab (scOpenLab) for the experimental assistance and J. Apel (heiDATA, University of Heidelberg) for assistance with data management. This study was also supported by the Heidelberg Centre for Personalized Oncology (DKFZ-HIPO). We thank the DKFZ Omics IT and Data Management Core Facility (ODCF) and the DKFZ Genomics and Proteomics Core Facility (GPCF) for their technical support.

Author information

Author notes

  1. These authors contributed equally: Simon Anders, Sascha Dietrich.

Authors and Affiliations

  1. Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany

    Tobias Roider, Marta Stolarczyk, Sophie A. Herbst, Peter-Martin Bruch, Michael Hundemer, Carsten Müller-Tidow & Sascha Dietrich

  2. Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany

    Tobias Roider, Sophie A. Herbst, Peter-Martin Bruch, Carsten Müller-Tidow, Wolfgang Huber & Sascha Dietrich

  3. European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    Tobias Roider, Sophie A. Herbst, Peter-Martin Bruch, Carsten Müller-Tidow, Wolfgang Huber & Sascha Dietrich

  4. Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Julian Seufert, Marie Bordas & Matthias Schlesner

  5. Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany

    Julian Seufert & Sophie A. Herbst

  6. Center for Molecular Biology of the University of Heidelberg (ZMBH), Heidelberg, Germany

    Alexey Uvarovskii, Felix Frauhammer & Simon Anders

  7. Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Marie Bordas & Martina Seiffert

  8. Department for Translational Genomics, University of Cologne, Cologne, Germany

    Nima Abedpour, Hyatt Balke-Want & Martin Peifer

  9. Division of Chromatin Networks, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany

    Jan-Philipp Mallm & Karsten Rippe

  10. Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Sophie A. Herbst, Stefan Fröhling & Sascha Dietrich

  11. Institute of Pathology, University of Heidelberg, Heidelberg, Germany

    Benjamin Goeppert & Gunhild Mechtersheimer

  12. Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Benedikt Brors

  13. Department of Medical Oncology and Hematology, University of Zürich, Zürich, Switzerland

    Thorsten Zenz

  14. Clinic for Hematology and Medical Oncology, University Medicine Göttingen, Göttingen, Germany

    Björn Chapuy

  15. German Cancer Consortium (DKTK), Heidelberg, Germany

    Stefan Fröhling

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  1. Tobias Roider
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Contributions

T.R., M.B., M. Stolarczyk, J.-P.M., SA.H. and P.-M.B. performed experiments. T.R., J.S., A.U., F.F., M.B., N.A., H.B.-W., M.P., M. Schlesner and S.A. analysed the data. T.R., M.H., K.R., B.G., M. Seiffert, B.B., G.M., C.M.-T., S.F., W.H., S.A. and S.D. interpreted the data. T.R., T.Z., S.F. and S.D. designed the study. T.R., J.S., K.R., B.C., M. Schlesner, W.H., S.A. and S.D. wrote the paper.

Corresponding author

Correspondence to Sascha Dietrich.

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Extended data

Extended Data Fig. 1 Classification of B and T cells by scRNA-seq.

a) Representative lymph node sample demonstrating how lymph node-derived B and T cells were classified by scRNA-seq (CD79B vs. CD3), flow cytometry (CD19 vs. CD3), or immunohistochemistry (PAX5 vs. CD3). Frequencies of B and T cells were determined for each approach in 12 (scRNA-seq, flow cytometry) or 7 (immunohistochemistry) biologically independent samples and correlated pairwise with each other (see main Fig. 1b and Fig. 1c). b) ScRNA-seq data of 12 biologically independent lymph node samples were subjected to SNN-based clustering and visualized by t-SNE. Each t-SNE represents one individual sample as indicated. Different B cell or T cell clusters are illustrated by different shades of green or blue, respectively. SNN: Shared-nearest-neighbour. See Source Data Extended Data Fig. 1.

Source data

Extended Data Fig. 2 Classification of malignant versus healthy B cells by means of kappa and lambda light chain expression at the scRNA level.

scRNA-seq data of 12 independent lymph node samples were subjected to SNN-based clustering and visualized by t-SNE. Each t-SNE represents one individual sample as indicated. Cells are coloured by light chain kappa fraction IGKC/(IGKC + IGLC2) of each single cell to demonstrate light chain restriction of each cluster. Non-B cells are coloured in grey. The sample DLBCL1 showed only marginal light chain expression on single cell RNA level. Therefore, these cells were regarded as malignant cells (see Method section for details). The same is true for the larger cluster of sample FL4. SNN: Shared-nearest-neighbour. See Source Data Extended Data Fig. 2.

Source data

Extended Data Fig. 3 Frequencies of T cells, healthy B cells and malignant B cells perfectly correlate between flow cytometry and scRNA-seq.

a) Stacked bar graph of the relative frequencies of malignant B cells (B), T cells (T), healthy B cells (hB) and myeloid cells (Other) calculated on the basis of scRNA expression profiles. Shown are N = 12 biologically independent samples. b) Lymph node derived cells from those samples passed to scRNA-seq (A) were stained for viability, CD19, CD3, kappa light chain and lambda light chain. The proportion of healthy (hB) and malignant B cells (B) were estimated based on the ratio of light chain restricted CD19+ tumour cells and CD19+ non-tumour cells (malignant B cells = light chain restricted B cells – non-restricted B cells). T cells (T) refer to CD3+CD19− cells, whereas Other refers to CD19−CD3− cells. Pearson’s correlation coefficients (r) are given. c) Lymph node derived cells were stained and analysed as described in B. Shown are the frequencies for each subpopulation for n = 9 (rLN), N = 4 (MCL), N = 11 (FL), N = 9 (DLBCL) and N = 7 (CLL) biologically independent samples. Box plots show the minimum, first quartile, median, second quartile and maximum. Outliers defined as values higher or lower than 1.5 interquartile ranges from median are shown as individual dots. rLN: Reactive lymph node. MCL: Mantle cell lymphoma. FL: Follicular lymphoma. DLBCL: Diffuse large B cell lymphoma, CLL: Chronic lymphocytic leukaemia. See Source Data Extended Data Fig. 3.

Source data

Extended Data Fig. 4 Proliferative capacity is preserved at the scRNA level and correlates with immunohistochemical and flow cytometry-based staining of Ki67.

a) Dot plots show G2M and S score for the B cells of four representative samples. Cells with positive G2M or S score were marked as proliferating (please see method section for details). b) The proportion of proliferating cells based on scRNA-seq (A) was correlated with the percentage of Ki67+ cells determined either by flow cytometry (orange) or immunohistochemistry (green). R values represent Pearson correlation coefficients. N = 12 biologically independent samples are shown. See Source Data Extended Data Fig. 4.

Source data

Extended Data Fig. 5 intratumour heterogeneity of malignant B cells.

a–i) ScRNA expression profiles of malignant and non-malignant B cells only were subjected to SNN-based clustering and visualized by t-SNE. Shown are 9 biologically independent samples, whereby each t-SNE represents one individual sample as indicated. Cells were coloured by cluster. SNN: Shared-nearest-neighbour. See Source Data Extended Data Fig. 5.

Source data

Extended Data Fig. 6 Dissecting four distinct subpopulations of malignant B cells in FL4 sample by means of scRNA-seq-informed flow cytometry.

a) ScRNA expression profiles of B cells from the FL4 sample only (2367 cells) were subjected to SNN-based clustering and visualized by t-SNE. b) Heatmap shows a selection of differentially expressed surface markers used for cluster differentiation. Gene expression values were scaled to the maximum of each row. c) T-SNE plot of panel A coloured by the light chain kappa fraction IGKC/(IGKC + IGLC2) of each single cell for FL4 sample only. C1 contains cells either expressing IGKC or IGLC2 predominantly (benign B cells), C2 contains only cells expressing predominantly IGLC2, whereas C3 to C5 hardly express both IGKC and IGLC2. d–f) Cells derived from sample FL4 were stained for viability, CD3, CD19, kappa, lambda, CD44, CD24, CD22 and CD27. Shown is the stepwise gating strategy to comprehend the scRNA-seq-based clusters of panel A. g) Lymph node derived cells from the FL4 sample were incubated for 48 hours with 58 different drugs and 5 concentrations. Cells were stained for viability, CD3, CD19, kappa, lambda and CD27. Viability was normalized to DMSO controls for each subpopulation separately. Shows are only those drugs with significantly differential drug response between the two subpopulations. SNN: Shared-nearest-neighbour. DMSO: Dimethyl sulfoxide. See Source Data Extended Data Fig. 6.

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Extended Data Fig. 7 Intratumour heterogeneity of tFL1 characterized by differential cell cycle states, gene expression signatures and copy number alterations.

a, b) ScRNA expression profiles of B cells from the tFL1 sample only (492 cells) were subjected to t-SNE and coloured by S-Score (a) or G2M-Score (b, see Methods section for details). c–e) Gene set enrichment analysis was performed between CD32Low and CD32High cluster. Shown are enrichment plots for hallmark MYC targets (c), MTORC1 signalling (d) and hallmark G2M targets (e). Given are the false-positive detection rate (FDR) and the normalized enrichment score (NES). FDR and NES were calculated using GSEA desktop application (see Method section for details). f) T-SNE as shown in panel A/B was coloured by FCGR2B expression. g) Line plot shows cluster-specific total copy number estimation for all chromosomes inferred from whole exome sequencing. See Source Data Extended Data Fig. 7.

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Extended Data Fig. 8 Intratumour heterogeneity of DLBCL1 sample.

a–c) ScRNA profiles of malignant B cells from the DLBCL1 sample only (3114 cells) were subjected to t-SNE and coloured by CD48 (a), SELL (b) and MYC (c) expression. d) DLBCL1 derived lymph node cells were stained for viability, CD19, CD3, CD48, CD62L and MYC or respective isotype control. Histograms show fluorescence intensity of MYC for isotype control, T cells, CD48HighCD62L+ and CD48LowCD62L− subpopulations. Experiment was repeated three times with similar results. E-G) DLBCL1 derived lymph node cells were incubated with DMSO, I-BET-762 (e) at two concentrations (1 µM, 5 µM), OTX015 (f) at two concentrations (1 µM, 5 µM), or ibrutinib (g) at two concentrations (0.2 µM, 1 µM). After 24 hours, cells were harvested and stained as described in panel d. Histograms show fluorescence intensity for CD48HighCD62L+ subclone. Experiment was repeated three times with similar results. H) Line plot shows total cluster-specific copy number estimation of DLBCL1 sample for all chromosomes as indicated using whole genome sequencing. DMSO: Dimethyl sulfoxide. See Source Data Extended Data Fig. 8.

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Supplementary information

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Supplementary Data 1

Interactive html file to browse differentially expressed genes between B-cell clusters in scRNA-seq data.

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Roider, T., Seufert, J., Uvarovskii, A. et al. Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels. Nat Cell Biol 22, 896–906 (2020). https://doi.org/10.1038/s41556-020-0532-x

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