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The interaction of CD4+ helper T cells with dendritic cells shapes the tumor microenvironment and immune checkpoint blockade response

Abstract

Despite their key regulatory role and therapeutic potency, the molecular signatures of interactions between T cells and antigen-presenting myeloid cells within the tumor microenvironment remain poorly characterized. Here, we systematically characterize these interactions using RNA sequencing of physically interacting cells (PIC-seq) and find that CD4+PD-1+CXCL13+ T cells are a major interacting hub with antigen-presenting cells in the tumor microenvironment of human non-small cell lung carcinoma. We define this clonally expanded, tumor-specific and conserved T-cell subset as T-helper tumor (Tht) cells. Reconstitution of Tht cells in vitro and in an ovalbumin-specific αβ TCR CD4+ T-cell mouse model, shows that the Tht program is primed in tumor-draining lymph nodes by dendritic cells presenting tumor antigens, and that their function is important for harnessing the antitumor response of anti-PD-1 treatment. Our molecular and functional findings support the modulation of Tht–dendritic cell interaction checkpoints as a major interventional strategy in immunotherapy.

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Fig. 1: PIC-seq application on tumor and adjacent healthy tissues derived from stage-I biopsies of patients with NSCLC.
Fig. 2: Interaction preferences of T-cell and myeloid cell subsets revealed by PIC-seq.
Fig. 3: CD4+PD-1+CXCL13+ T cells present unique gene expression and interactive profile in TME.
Fig. 4: Differentiation to murine Tht cell state requires tumor antigen presentation by DCs.
Fig. 5: Differentiation to mTht cell state is restricted to tdLNs and tumor site.
Fig. 6: mTht cell role in response to anti-PD-1 immunotherapy.

Data availability

scRNA-seq data that support the findings of this study (including MARS-seq and PIC-seq of human NSCLC biopsies and mouse experiments), are deposited in the Gene Expression Omnibus under accession code GSE160903. Previously published scRNA-seq data and TCR-sequencing data that were reanalyzed here are available under accession codes GSE123139 (ref. 11) and EGAD00001006608 (ref. 28).

Source data are provided with this paper. All other materials and data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The PIC-seq algorithm is available in the GitHub repository: https://github.com/aygoldberg/PIC-seq. All algorithms and auxiliary scripts used to analyze data and generate scripts are provided as supplementary software and will be deposited in the GitHub repository https://github.com/aygoldberg/NSCLS-PIC.

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Acknowledgements

We thank T. Wiesel for artwork and the Dean’s Flow Cytometry CORE and Biorepository and Pathology CoRE Laboratory of the Icahn School of Medicine at Mount Sinai. The research of I.A. and A.T. is supported by the Seed Networks for the Human Cell Atlas of the Chan Zuckerberg Initiative and by Merck KGaA, Darmstadt. I.A. is an Eden and Steven Romick Professorial Chair, supported by the HHMI International Scholar Award, the European Research Council Consolidator grant (no. 724471-HemTree2.0), an MRA Established Investigator award (no. 509044), DFG (no. SFB/TRR167), the Ernest and Bonnie Beutler Research Program for Excellence in Genomic Medicine, the Helen and Martin Kimmel awards for innovative investigation and the SCA award of the Wolfson Foundation and Family Charitable Trust. The Thompson Family Foundation Alzheimer’s Research Fund and the Adelis Foundation also provided support. The laboratory of A.T. is supported by the European Research Council (no. 724824), the I-CORE for chromatin and RNA regulation, a grant from the Israel Science Foundation and a grant from the Kahn Foundation. A.T. is a Kimmel investigator. The laboratory of M.M. is supported by R01 CA257195, R01 CA254104 and Samuel Waxman Cancer Research Foundation. A.G. is funded by the Rothschild Postdoctoral Fellowship of the Yad Hanadiv Foundation.

Author information

Authors and Affiliations

Authors

Contributions

M.C. and I.A. conceived and designed the project, led all analyses and interpreted all experiments. A.G. conceived and designed the project, interpreted all experiments and performed computational analysis. M.C. and A.G. prepared the figures. M.C., A.G. and O.B. designed and performed experiments and analyzed the data. P.H., B.L., M.Z., A.G.-S., B.M., M.B., A.D. and J.L.B. performed experiments and analyzed the data. I.K. (under the supervision of J.B.) and C.G.B. (under the supervision of M.I.) performed experiments and analyzed data. M.C., A.G., A.T., M.M. and I.A. wrote the manuscript. A.T., M.M. and I.A. supervised the study.

Corresponding authors

Correspondence to Merav Cohen, Amos Tanay, Miriam Merad or Ido Amit.

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Nature Cancer thanks Sean Bendall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Summary of samples and RNA-sequencing data.

(a-d) Summary of all experimental samples, plates and cells, processed by MARS-seq and PIC-seq. “nrep” indicates number of biological replicates (patients, co-cultures or mice), “nbatches” indicates number of technical replicates, “ncells” indicates number of analyzed cells (Supplementary Table 2). Shown are (b) total number of Illumina reads, (c) total number of Unique Molecular Identifiers (UMIs) per cell, and (d) fraction of QC-positive cells retained for further analysis per technical replicate. (e) Patient contribution to each of the 112 metacells derived from TCRβ+ T cells and CD11c+CD64+ myeloid cells. Patients are ordered from top (brown) to least (white) contribution per metacell. (f) The confusion matrix of the MetaCell model shown in Fig. 1c. Entries denote for each pair of metacells the propensity of cells from both metacells to be clustered together in a bootstrap analysis. Bottom and left panels indicate metacell annotation to 22 T and myeloid subtypes.

Source data

Extended Data Fig. 2 NSCLC PIC-seq quality controls.

(a) Performance of the linear regression model estimating the mixing factor (α) of synthetic T-myeloid (left) and T-NK (right) PICs. (b) Performance of the T (left) and myeloid (right) metacell assignments of PIC-seq over 5,000 synthetic PICs. Each row summarizes all synthetic PICs originating from one metacell and their assignments to all metacells (columns). Data is row-normalized. (c) The cumulative distribution of the lldoublets-llsinglets score, for PICs (orange) and for the T (green) and myeloid (red) singlet populations. The score indicates the gain in likelihood when each PIC is modeled as a doublet compared to its most likely singlet assignment (Methods). PICs whose scores were not positive were suspected as singlets and discarded from further analysis. (d) Fraction of retained PICs for each profiled patient (Supplementary Table 1). Numbers on top indicate absolute number of retained PICs per patient. (e) Flow cytometry dotplots analysis (top) and quantification (bottom) of in situ and in vitro PICs formed before or after tissue dissociation. Values in brackets indicate the estimated spurious PIC frequency out of the PIC population, defined as PICs combining fluorophores from parallel samples. (f) Gene expression profiles of 10,762 single cells grouped into 22 transcriptional subsets. Top panel indicates whether a cell is derived from healthy tissue or TME. (g) Myeloid and T-cell subset identities of single cells in (f) (Fig. 1c). (h) Gene expression profiles of 839 QC-positive PICs, grouped by their contributing T-cell and myeloid identities, as determined by PIC-seq algorithm. Top panel as in (f). (i) Myeloid and T cell subset identity of PIC contributing cells in (g), as determined by PIC-seq algorithm. (j) Estimation of the relative UMI count from T cells (green), and myeloid (red) contributing to each PIC in (h), as inferred by PIC-seq algorithm (mixing factor, α).

Source data

Extended Data Fig. 3 Characterization of singlet and PIC-derived CD4+PD-1+CXCL13+ T cells in human TME.

(a) Gene expression profiles of T cells from melanoma (left) and breast cancer (right). 36,341 cells from 21 melanoma patients grouped into 245 metacells, and 62,909 cells from 42 breast cancer patients grouped into 243 metacells are shown. Values indicate enrichment (log2 fold change) of a gene in a metacell over its median value across metacells. Annotation to T subsets is indicated below. (b) Joint K-mean clustering (K = 38) of 763 genes differentially expressed in CD4+PD-1+CXCL13+ cells across different T subsets derived from NSCLC, melanoma and breast tumors (Supplementary Table 3). (c) Mean normalized expression of genes upregulated by CD4+PD-1+CXCL13+ T derived from NSCLC TME across all T cell states. Error bars indicate binomial 95% confidence intervals of the estimated mean. n = 3371 TME T cells. (d) Gene expression profiles of single Tht cells divided into the Tht-I and Tht-II subsets. Shown are Tht cells from melanoma, breast cancer, NSCLC and NSCLC PICs assigned to the Tht identity. Tht PICs were further dissected to Tht-I and Tht-II using breast cancer Tht metacells as the T cell reference model (Methods). (e) Enrichment of Tht-I and Tht-II in the NSCLC PIC population compared to their frequency in the single cell TCRβ+ population. Two-tailed paired Mann–Whitney test; n = 10 NSCLC patients.

Source data

Extended Data Fig. 4 Spatial and clonal properties of Tht cells.

(a) Representative confocal microscopy images of tumor sections derived from one NSCLC patient stained for CD4, DC-LAMP and CD272 (BTLA) or PRDM1 proteins. (b) Representative confocal microscopy images of tumor sections derived from four additional NSCLC patients stained for CD4, DC-LAMP and PD-1 proteins. (a-b) Scale bar=30μm, and 15 μm in (a) top left panel; arrows indicate T-DC conjugates; images are representative of seven scanned patients. (c) Left – nuclear segmentation of the image depicted in Fig. 3e. Yellow markings outline the Voronoi diagram, enclosing all pixels sharing a nearest nucleus. Right – Cell type annotation of each segmented nuclei by expression and co-expression of marker intensity in the area of each nucleus’ Voronoi structure (Methods). (d) Colocalization analysis. For each pair of cell types, we counted the number of occurrences the two cell types co-exist in a 2-cell radius community. Colors indicate log2 enrichment over the expected values. n = 5 TLS from the same patient. (e) Fraction of cells related to T cell clones across different melanoma T cell subsets (Extended Data Fig. 3a) in melanoma patients for whom sufficient TCR-seq data was available. A cell is considered part of a clone if it shares a TCR sequence with at least one other T cell from the same patient. FDR-adjusted two-tailed unpaired Mann–Whitney test comparing Tht-I and II to other T subsets. n = 10 melanoma patients. (f) A heat map depicting the propensity of two cells from two melanoma T subsets to belong to the same clone (clone sharing). Data was calculated by sampling 10,000 pairs of cells, and comparing clonal sharing characteristics to 10,000 pairs of cells sampled after shuffling clone identities, while preserving the number of clones and clone sizes per patient (Methods). *P < 0.05, **P < 0.001, ***P < 10−5.

Source data

Extended Data Fig. 5 Dynamic and spatial characterization of OT-II CD45.1+ T cells in murine TME niche and cLN.

(a) Representative FACS plot showing gating strategy for isolation of adoptively transferred CD45.1+ OT-II cells and bystander polyclonal TCRβ+ T cells from tdLN. (b) Quantification of the percentage of CD45.1+ OT-II T cell out of the entire TCRβ+ population in matched cLN and tdLN tissues. Two-tailed paired Mann–Whitney test; n = 8 mice from two independent experiments (c) Gene module analysis. Shown are seven correlated gene modules derived from CD45.1+TCRβ+ OT-II T cells isolated from cLN, dLN and TME, 10- and 17 days post tumor cell injection. Left – pairwise Pearson correlation. Right – normalized pooled expression across different conditions. (d) Pooled expression of the seven gene modules from (c) across OT-II T cells derived from all conditions. Dots represent single cells and dot colors indicate time points. Representative genes from each module are depicted. (e) Fraction of proliferating cells in each quartile of the mTht activation signature as in Fig. 5b in dLN OT-II T cells. A cell was determined proliferating if it exhibited above-threshold expression of the cell-cycle module in (d). (f) Representative confocal microscopy images of cLN sections extracted 10- and 17 days following tumor cell injection, stained for CD45.1+ (OT-II) T cells, PD-1 and CD11c proteins. Scale bar=30μm; asterisks indicate OT-II mTht; images are representative of two independent experiments.

Source data

Extended Data Fig. 6 PIC-seq in mouse model of tumor antigen specificity.

(a) A representative FACS plot of CD11c+ myeloid (purple), TCRβ+ T (blue) singlets, and CD11c+TCRβ+ PICs (orange) purified from the tdLN (n = 6 independent experiments); Population frequencies represent mean ± s.e.m. (b) Gene expression of 7541 tdLN- and cLN-derived TCRβ+ and CD45.1+TCRβ+ T cells isolated 10 days following tumor injection, grouped into 86 metacells. Bottom panel indicates cell annotations. (c) Performance of the linear regression model used to estimate the mixing factor (α) of synthetic T-myeloid PICs. (d) Performance of the T metacell assignments of PIC-seq over 5,000 synthetic PIC. Each row summarizes all synthetic PICs originating from one metacell and their assignments to metacells by the PIC-seq algorithm (columns). Data is row-normalized. (e) Distribution of T subsets in TCRβ+ singlet T and CD11c+TCRβ+ PICs in cLN and tdLN 10 days following tumor cells injection. Cells are downsampled so that T and PIC numbers are equal per replicate and then pooled from all profiled patients. FDR-adjusted Two-tailed Fisher’s exact test. (f) Comparison of different T subset frequencies in TCRβ+ singlets and PICs isolated from cLN and tdLN 10 days following tumor cells injection, across all biological replicates. Two-tailed paired Mann–Whitney test. (g) Mean observed (gray) and expected (colored) gene expression levels in PICs of OT-II mTht and mTht+ subsets. Each connected pair of dots signifies a biological replicate; Dot colors relate to their specificity in the T (green) or myeloid (red) cell expected contributions. Groups with less than 10 cells were discarded. Median value is marked for each category. Data summarizes two independent experiments; (e-g) n = 4 day 10 cLN and 9 day 10 tdLN. *P < 0.05, **P < 0.001, ***P < 10−5.

Source data

Extended Data Fig. 7 Tht cells are involved in the anti-tumor effect of aPD-1 treatment.

(a-b) K-means analysis of TCRβ+ and CD45.1+ TCRβ+ OT-II cells derived from tdLN of mice with/without αPD-1 treatment, at day 17 following tumor injection. Day 10 tdLN and cLN T cells from Fig. 5b were included for comparison. Shown are cluster centers (a) and all genes from cluster 4 (enriched for type I Interferon response genes; (b)). Values represent log2 fold change over the median. (c) Mean normalized expression of key mTht-related genes from cluster 11 across the different samples. *P < 0.05, **P < 0.001, ***P < 10−5.

Source data

Supplementary information

Reporting Summary.

Supplementary Tables

Supplementary Tables 1–6.

Supplementary Software 1

Scripts used to analyze MARS-seq and PIC-seq data and generate all main and Extended Data Figures.

Source data

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Raw MICSSS images.

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Cohen, M., Giladi, A., Barboy, O. et al. The interaction of CD4+ helper T cells with dendritic cells shapes the tumor microenvironment and immune checkpoint blockade response. Nat Cancer 3, 303–317 (2022). https://doi.org/10.1038/s43018-022-00338-5

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