Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels.
Antineoplastic Agents
/ pharmacology
Biomarkers, Tumor
/ genetics
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Lymphoma, B-Cell
/ drug therapy
Male
Middle Aged
Sequence Analysis, RNA
Single-Cell Analysis
T-Lymphocytes
/ drug effects
Transcriptome
/ drug effects
Tumor Microenvironment
/ immunology
Journal
Nature cell biology
ISSN: 1476-4679
Titre abrégé: Nat Cell Biol
Pays: England
ID NLM: 100890575
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
02
07
2019
accepted:
10
05
2020
pubmed:
17
6
2020
medline:
21
10
2020
entrez:
17
6
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32541878
doi: 10.1038/s41556-020-0532-x
pii: 10.1038/s41556-020-0532-x
doi:
Substances chimiques
Antineoplastic Agents
0
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
896-906Références
Burrell, R. A., McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).
pubmed: 24048066
Chapuy, B. et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690 (2018).
pubmed: 29713087
pmcid: 6613387
Swanton, C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 72, 4875–4882 (2012).
pubmed: 23002210
pmcid: 3712191
McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).
pubmed: 28187284
Kim, I. S. & Zhang, X. H. One microenvironment does not fit all: heterogeneity beyond cancer cells. Cancer Metastasis Rev. 35, 601–629 (2016).
pubmed: 27858305
pmcid: 5215976
Fridman, W. H., Pages, F., Sautes-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat. Rev. Cancer 12, 298–306 (2012).
pubmed: 22419253
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
pubmed: 21399628
pmcid: 4504184
Wang, Y. & Navin, N. E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).
pubmed: 26000845
pmcid: 4441954
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452
pmcid: 4944528
Gao, R. et al. Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer. Nat. Commun. 8, 228 (2017).
pubmed: 28794488
pmcid: 5550415
Yuan, J. & Sims, P. A. An automated microwell platform for large-scale single cell RNA-seq. Sci. Rep. 6, 33883 (2016).
pubmed: 27670648
pmcid: 5037380
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).
pubmed: 24363023
Milpied, P. et al. Human germinal center transcriptional programs are de-synchronized in B cell lymphoma. Nat. Immunol. 19, 1013–1024 (2018).
pubmed: 30104629
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
pubmed: 24925914
pmcid: 4123637
Ramskold, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).
pubmed: 22820318
pmcid: 3467340
Powell, A. A. et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7, e33788 (2012).
pubmed: 22586443
pmcid: 3346739
Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893 (2018).
pubmed: 29681456
pmcid: 6132060
Teras L. R. et al. 2016 US lymphoid malignancy statistics by World Health Organization subtypes. CA Cancer J. Clin. 66, 443–459 (2016).
Wagner-Johnston, N. D. et al. Outcomes of transformed follicular lymphoma in the modern era: a report from the National LymphoCare Study (NLCS). Blood 126, 851–857 (2015).
pubmed: 26105149
pmcid: 4543911
Crump, M. et al. Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study. Blood 130, 1800–1808 (2017).
pubmed: 28774879
pmcid: 5649550
Philip, T. et al. High-dose therapy and autologous bone marrow transplantation after failure of conventional chemotherapy in adults with intermediate-grade or high-grade non-Hodgkin’s lymphoma. N. Engl. J. Med. 316, 1493–1498 (1987).
pubmed: 3295541
Bartlett, N. L. et al. Single-agent ibrutinib in relapsed or refractory follicular lymphoma: a phase 2 consortium trial. Blood 131, 182–190 (2018).
pubmed: 29074501
pmcid: 5757691
Winter A. M., et al. A multi-institutional outcomes analysis of patients with relapsed or refractory DLBCL treated with ibrutinib. Blood 130, 1676–1679 (2017).
Horna, P., Olteanu, H., Kroft, S. H. & Harrington, A. M. Flow cytometric analysis of surface light chain expression patterns in B-cell lymphomas using monoclonal and polyclonal antibodies. Am. J. Clin. Pathol. 136, 954–959 (2011).
pubmed: 22095382
Challa-Malladi, M. et al. Combined genetic inactivation of β2-Microglobulin and CD58 reveals frequent escape from immune recognition in diffuse large B cell lymphoma. Cancer Cell 20, 728–740 (2011).
pubmed: 22137796
pmcid: 3660995
Schreiber, R. D., Old, L. J. & Smyth, M. J. Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science 331, 1565–1570 (2011).
pubmed: 21436444
Xu-Monette, Z. Y., Zhou, J. & Young, K. H. PD-1 expression and clinical PD-1 blockade in B-cell lymphomas. Blood 131, 68–83 (2018).
pubmed: 29118007
pmcid: 5755041
Sun, L. L. et al. Anti-CD20/CD3 T cell-dependent bispecific antibody for the treatment of B cell malignancies. Sci. Transl. Med. 7, 287ra270 (2015).
Os, A. et al. Chronic lymphocytic leukemia cells are activated and proliferate in response to specific T helper cells. Cell Rep. 4, 566–577 (2013).
pubmed: 23933259
Becht E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Schaerli, P. et al. CXC chemokine receptor 5 expression defines follicular homing T cells with B cell helper function. J. Exp. Med. 192, 1553–1562 (2000).
pubmed: 11104798
pmcid: 2193097
Breitfeld, D. et al. Follicular B helper T cells express CXC chemokine receptor 5, localize to B cell follicles, and support immunoglobulin production. J. Exp. Med. 192, 1545–1552 (2000).
pubmed: 11104797
pmcid: 2193094
Dorfman, D. M. & Shahsafaei, A. CD200 (OX-2 membrane glycoprotein) is expressed by follicular T helper cells and in angioimmunoblastic T-cell lymphoma. Am. J. Surg. Pathol. 35, 76–83 (2011).
pubmed: 21164290
Weber, J. P. et al. ICOS maintains the T follicular helper cell phenotype by down-regulating Kruppel-like factor 2. J. Exp. Med. 212, 217–233 (2015).
pubmed: 25646266
pmcid: 4322049
Yang, Z. Z. et al. PD-1 expression defines two distinct T-cell sub-populations in follicular lymphoma that differentially impact patient survival. Blood Cancer J. 5, e281 (2015).
pubmed: 25700246
pmcid: 4349259
Byford, E. T., Carr, M., Ladikou, E., Ahearne, M. J. & Wagner, S. D. Circulating Tfh1 (cTfh1) cell numbers and PD1 expression are elevated in low-grade B-cell non-Hodgkin’s lymphoma and cTfh gene expression is perturbed in marginal zone lymphoma. PLoS ONE 13, e0190468 (2018).
pubmed: 29293620
pmcid: 5749831
Yamazaki, T., Nagumo, H., Hayashi, T., Sugane, K. & Agematsu, K. CD72-mediated suppression of human naive B cell differentiation by down-regulating X-box binding protein 1. Eur. J. Immunol. 35, 2325–2334 (2005).
pubmed: 16047337
Klein, U., Rajewsky, K. & Kuppers, R. Human immunoglobulin (Ig)M
pubmed: 9802980
pmcid: 2212515
Hans, C. P. et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood 103, 275–282 (2004).
pubmed: 14504078
Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353 (2018).
pubmed: 30429548
Shirota, H. et al. IL4 from T follicular helper cells downregulates antitumor immunity. Cancer Immunol. Res. 5, 61–71 (2017).
pubmed: 27920023
Aguilar-Hernandez, M. M. et al. IL-4 enhances expression and function of surface IgM in CLL cells. Blood 127, 3015–3025 (2016).
pubmed: 27002119
Peter-Martin, B. et al. Systematic investigation of microenvironmental drug resistance mechanisms in chronic lymphocytic leukemia. Blood 134, 3363 (2019).
Spolski, R. & Leonard, W. J. IL-21 and T follicular helper cells. Int. Immunol. 22, 7–12 (2010).
pubmed: 19933709
Gu-Trantien C. et al. CXCL13-producing TFH cells link immune suppression and adaptive memory in human breast cancer. JCI Insight 2, e91487 (2017).
DiToro D. et al. Differential IL-2 expression defines developmental fates of follicular versus nonfollicular helper T cells. Science 361, eaao2933 (2018).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
pubmed: 26771021
pmcid: 4707969
Dietrich, S. et al. Drug-perturbation-based stratification of blood cancer. J. Clin. Invest. 128, 427–445 (2018).
pubmed: 29227286
Delmore, J. E. et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146, 904–917 (2011).
pubmed: 21889194
pmcid: 3187920
Chapuy, B. et al. Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma. Cancer Cell 24, 777–790 (2013).
pubmed: 24332044
pmcid: 4018722
Andor, N. et al. Single-cell RNA-seq of follicular lymphoma reveals malignant B-cell types and coexpression of T-cell immune checkpoints. Blood 133, 1119–1129 (2019).
pubmed: 30591526
pmcid: 6405336
Ledergor, G. et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 24, 1867–1876 (2018).
pubmed: 30523328
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck. Cancer Cell 171, 1611–1624 (2017).
de Boer, B. et al. Prospective isolation and characterization of genetically and functionally distinct AML subclones. Cancer Cell 34, 674–689 (2018).
pubmed: 30245083
Li, J. et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity 49, 178–193 (2018).
pubmed: 29958801
pmcid: 6707727
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
pubmed: 29203879
pmcid: 5715110
Ratech, H. & Litwin, S. Surface immunoglobulin light chain restriction in B-cell non-Hodgkin’s malignant lymphomas. Am. J. Clin. Pathol. 91, 583–586 (1989).
pubmed: 2497637
Kaleem, Z., Zehnbauer, B. A., White, G. & Zutter, M. M. Lack of expression of surface immunoglobulin light chains in B-cell non-Hodgkin lymphomas. Am. J. Clin. Pathol. 113, 399–405 (2000).
pubmed: 10705821
Heining, C. et al. NRG1 fusions in KRAS wild-type pancreatic cancer. Cancer Discov. 8, 1087–1095 (2018).
pubmed: 29802158
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179
pmcid: 6700744
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
pubmed: 29608177
pmcid: 6152897
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517
pmcid: 1239896
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
pubmed: 12808457
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393
pmcid: 3106198
López, C. et al. Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma. Nat. Commun. 10, 1459 (2019).
pubmed: 30926794
pmcid: 6440956
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
pubmed: 20601685
pmcid: 2938201
Kleinheinz K. et al. ACEseq—allele specific copy number estimation from whole genome sequencing. Preprint at bioRxiv https://doi.org/10.1101/210807 (2017).
Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).
pubmed: 27100738
pmcid: 4839673