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A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer

Abstract

Immune-checkpoint blockade (ICB) combined with neoadjuvant chemotherapy improves pathological complete response in breast cancer. To understand why only a subset of tumors respond to ICB, patients with hormone receptor-positive or triple-negative breast cancer were treated with anti-PD1 before surgery. Paired pre- versus on-treatment biopsies from treatment-naive patients receiving anti-PD1 (n = 29) or patients receiving neoadjuvant chemotherapy before anti-PD1 (n = 11) were subjected to single-cell transcriptome, T cell receptor and proteome profiling. One-third of tumors contained PD1-expressing T cells, which clonally expanded upon anti-PD1 treatment, irrespective of tumor subtype. Expansion mainly involved CD8+ T cells with pronounced expression of cytotoxic-activity (PRF1, GZMB), immune-cell homing (CXCL13) and exhaustion markers (HAVCR2, LAG3), and CD4+ T cells characterized by expression of T-helper-1 (IFNG) and follicular-helper (BCL6, CXCR5) markers. In pre-treatment biopsies, the relative frequency of immunoregulatory dendritic cells (PD-L1+), specific macrophage phenotypes (CCR2+ or MMP9+) and cancer cells exhibiting major histocompatibility complex class I/II expression correlated positively with T cell expansion. Conversely, undifferentiated pre-effector/memory T cells (TCF7+, GZMK+) or inhibitory macrophages (CX3CR1+, C3+) were inversely correlated with T cell expansion. Collectively, our data identify various immunophenotypes and associated gene sets that are positively or negatively correlated with T cell expansion following anti-PD1 treatment. We shed light on the heterogeneity in treatment response to anti-PD1 in breast cancer.

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Fig. 1: BioKey study design and annotation of cell and T cell phenotypes by scRNA-seq.
Fig. 2: Trajectory analysis of CD8+ and CD4+ T cells according to T cell expansion.
Fig. 3: Differentially expressed genes in CD8+ TEX and CD4+ TH1 trajectories.
Fig. 4: Expanding versus non-expanding T cells in BC and in BC subtypes.
Fig. 5: Dendritic cells and macrophages expressing PD-L1 correlate with T cell expansion.
Fig. 6: An interactive immune environment positively correlated with T cell expansion.

Data availability

Raw sequencing reads of all single-cell experiments (scRNA-seq, scTCR-seq and CITE-seq) have been deposited in the European Genome-phenome Archive (EGA) under study no. EGAS00001004809 (with a summary of the BioKey study and patient characteristics) and with data accession no. EGAD00001006608 (to access the data itself under restricted access). Requests for accessing raw sequencing reads will be reviewed by the UZLeuven-VIB data access committee. Any data shared will be released via a Data Transfer Agreement that will include the necessary conditions to guarantee protection of personal data (according to European GDPR law). Alternately, a download of the read count data per individual patient is publicly available at http://biokey.lambrechtslab.org. The publicly available gnomAD database (https://gnomad.broadinstitute.org) was used to filter tumor exome-seq data for somatic mutations and calculate tumor mutation burden. Raw sequencing reads of all exome and low-coverage whole-genome sequencing experiments are also provided under EGAS00001004809.

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Acknowledgements

The BioKey study was supported by an MSD grant to A.S., by Fonds Nadine De Beauffort to A.S., by a ‘Kom op Tegen Kanker’ to A.S. and H.W., by the Stichting Tegen Kanker and the Flemish Fund for Scientific Research (FWO; project G0B6120N) Belgium, by Agilent Technologies (Thought Leader award) to D.L. This VIB Grand Challenges project also received support from the Flemish Government under Management Agreement 2017–2021 (VR 2016 2312 doc.1521/4), from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 847912 (RESCUER) and from KU Leuven grant (C14/18/092) Symbiosys3. A.D.G. acknowledges financial support from the Research Foundation Flanders (FWO; Fundamental Research grant, G0B4620N; Excellence of Science/EOS grant, 30837538, for ‘DECODE’ consortium), KU Leuven (C1 grant, C14/19/098; POR award funds, POR/16/040) and Kom op Tegen Kanker/KOTK (KOTK/2018/11509/1). L.V.D. was supported by an aspirant FWO grant. G.F. is recipient of a post-doctoral mandate from the Klinsche Onderzoek en OpleidingsRaad (KOOR) of the University Hospitals Leuven. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government (department EWI). None of these funders had a role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank T. Van Brussel and R. Schepers for technical assistance.

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Contributions

D.L. and J.Q. designed and supervised the single-cell experiments and wrote the manuscript. A.S. designed the clinical study and supervised sample collection and clinical annotation, with important help from H.V., as well as I.N., K.P., P.N. and H.W. G.F. and K.L. performed and interpreted all histopathological data including TIL scoring. Data analysis was performed by A.B. and J.Q., with substantial contributions from L.v.D., B.B. and M.v.B. C.D., I.A. and A.D.G. contributed critical data interpretation. All authors read or provided comments on the manuscript.

Corresponding authors

Correspondence to Junbin Qian or Ann Smeets or Diether Lambrechts.

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The authors declare no competing interests.

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Peer review information Nature Medicine thanks Matthew Spitzer, Justin Balko and Aditya Bardia for their contribution to the peer review of this work. Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Cell types detected based on scRNA-seq profiling of 175,942 cells.

a,b, Heatmap (a) and UMAP (b) showing the expression of 3 marker genes for each cell type. c, CNV profile in cancer versus stromal cells assessed using InferCNV based on scRNA-seq. d-f, UMAP and barplots color-coded for individual patients (d), pre- versus on-treatment biopsies (e) and BC subtype (f) showing their distribution across cell types. pDC: plasmacytoid dendritic cell; CNV: copy number variation.

Extended Data Fig. 2 T-cell expansion versus contraction, TIL-scores and PD1 expression.

a, Number of clonotypes that contract (yellow) compared to the number of clonotypes that expand (purple) per patient. A clonotype was considered to undergo T-cell expansion when it increased in proportion on- versus pre-treatment and the frequency on-treatment was >2 (purple). A clonotype was considered to undergo T-cell contraction when it decreased in proportion on- versus pre-treatment and the frequency on-treatment was < 2 (yellow). b, Boxplot showing the difference in number of expanded versus contracted clonotypes, comparing Es (n = 9) versus NEs (n = 20). c, Percentage of T-cells in the pre-treatment biopsy (gray) per patient compared to the number of expanded clonotypes after anti-PD1 (proportion data from Fig. 1a are shown). Horizontal dotted lines indicate the thresholds used to define tumors with a high % of T-cells (gray) or with T-cell expansion (purple). d, Number of expanding T-cells per patient with clonotypes not present (novel clonotypes; green) or present (pre-existing clonotypes; gray) pre-treatment. Up to 61% (range: 27-85%) of expanded T-cells in Es on-treatment had clonotypes already present pre-treatment. e, Percentage of stromal TILs (sTILs) by histopathology on a hematoxylin and eosin stained slide, comparing Es (n = 9) versus NEs (n = 19). f, Percentage of TILs based on scRNA-seq (scTILs) determined by the fraction of B- and T-cells in each sample, comparing Es (n = 9) versus NEs (n = 20). g-h, Tumor mutation burden (TMB) (g) and chromosomal instability (h) in Es (n = 9 for TMB, n = 8 for chromosomal instability) versus NEs (n = 20 for TMB, n = 19 for chromosomal instability) and for each BC subtype; TNBC (n = 13 for TMB, n = 11 for chromosomal instability), ER/HER2+ (n = 3) and ER+/HER2± (n = 15). i, Heatmap of normalized PD1 and UMAP color-coded for PD1. j, Normalized PD1 expression in T-cells. Panels b, e-h: exact P values by Mann-Whitney test or Wilcoxon matched-pairs signed rank test for paired samples (pre- versus on-treatment); two-sided (b, e, g-h) or one-sided (f); *P < 0.05, *P < 0.01, ***P < 0.001. Panel j: ***P < 0.001 by MAST test and Bonferonni-corrected (Seurat). Panels b, e-h: box, median ± interquartile range; whiskers, minimum and maximum.

Extended Data Fig. 3 T-cell phenotypes during anti-PD1 treatment.

a, Heatmap showing expression of marker genes for the 14 T-/NK-cell phenotypes. b, UMAP color-coded for marker gene expression of T-/NK-cell phenotypes. c, Heatmap showing expression of functional genes in the 14 T-/NK-cell phenotypes. d, UMAP depicting the T-cell phenotypes detected in the proliferative T-cell subcluster. e, Heatmap showing marker gene expression for TREG, CD4+ and CD8+ TEX-cells in the proliferative T-cell subcluster. f, T-/NK-cell phenotypes after assigning proliferating T-cells to their respective subtype (CD4+, CD8+ TEX-cells and CD4+ TREG). g, Relative contribution (in %) of each T-cell phenotype in on-treatment biopsies, comparing Es (n = 9) versus NEs (n = 20). h, UMAP showing all T-cell clonotypes expanding on-treatment (left panel) and pie charts showing the distribution of expanding T-cells across T-cell phenotypes (right panel) in Es. Each clonotype on the UMAP has a specific color and clonotypes in pre- and on-treatment tissues are shown separately. i, T-cell clonality, Gini index and T-cell clonality of each T-cell phenotype both pre- (upper panel) and on-treatment (lower panel), comparing Es (n = 9) versus NEs (n = 20). j, ROC curves for the indicated parameter pre-treatment to predict T-cell expansion. AUC values and 95% confidence intervals are shown. Panels g and i: exact P values by two-sided Mann-Whitney test or Wilcoxon matched-pairs signed rank test for paired samples (pre- versus on-treatment); *P < 0.05, *P < 0.01, ***P < 0.001. Panels g and i: box, median ± interquartile range; whiskers, minimum and maximum.

Extended Data Fig. 4 Analysis of expanding T-cells trajectories.

a, Barplots showing TCR richness for the indicated CD8+ T-cell phenotype. b, Clonotype sharing (thickness indicates proportion of sharing) between CD8+ T-cell phenotypes. c, Expression dynamics of transcription factors (TFs) along the CD8+ TEX-trajectory. d, Density plots reflecting the relative number of T-cells combined. e, Violin plots showing expression of T-cell effector, cytotoxicity and exhaustion markers in CD8+ TEX-cells on-treatment. ***P < 0.001 by two-sided Wilcoxon rank sum test per gene. f, LAMP1 expression by CITE-seq. ***P < 0.001 by two-sided Wilcoxon rank sum test and Bonferroni-corrected (Seurat). g, Cell cycle scores along the CD8+-trajectories pre- and on-treatment in E versus NE. h, Heatmap (left panel) and 2D density UMAP (right panel) showing that the major CD4+ T-cell phenotypes (CD4+ TN, TEM, TEX) can be split into subclusters with corresponding marker genes. i, Barplots showing TCR richness for the indicated CD4+ T-cell phenotype. j, Clonotype sharing (thickness indicates proportion of sharing) between the CD4+ T-cell phenotypes. k, Density plots reflecting the relative number of T-cells combined. l, Violin plots showing expression of T-cell effector, cytotoxicity and exhaustion markers in CD4+ TH1- and TFH-cells on-treatment. ***P < 0.001 by two-sided Wilcoxon rank sum test per gene. m, Cell cycle scores along the CD4+-trajectories. Gray shades in panel c, g and m represent the 95% confidence interval at any given pseudotime.

Extended Data Fig. 5 Expression signatures and phenotypes predictive of T-cell expansion.

a,b, Volcano plot showing DEGs between CD8+ (a) and CD4+ (b) T-cells that expand versus those that not expand pre-treatment. Black dots: P=ns; gray: P < 0.05; red: P < 0.05 and absolute log2FC≥0.5. P values by MAST test and Bonferroni-corrected (Seurat). Genes in bold are discussed in the manuscript. c-d, GSEA (hypeR) on genes up- or down-regulated in pre-treatment T-cells that expand (c) versus T-cells that do not expand upon treatment (d) on REACTOME (upper panel) and GO (lower panel) pathways. e, Scatterplots showing Spearman correlations between the number of expanded clonotypes versus the abundance of phenotypes, or average expression of marker genes and signature module scores per patient. Signature module scores were calculated using the Seurat function AddModuleScore per cell and then averaged per patient. P values by two-sided Spearman’s rank correlation test. R values are Spearman’s rank correlation coefficients (rho). f, ROC curve based on the average module expression of our 50-gene signature in CD4+ T-cells (excluding TREG) pre-treatment to predict T-cell expansion. AUC-value and 95% confidence interval are shown. g, Heatmap showing protein expression of 7 immune stimulation-induced immune-checkpoints in all T-cells, CD8+ and CD4+ T-cells.

Extended Data Fig. 6 T-cell expansion according to BC subtype and in patients receiving neoadjuvant chemotherapy followed by anti-PD1.

a, UMAP pre- and on-treatment color-coded for expanded clonotypes in TNBC (n = 5; upper panel) and ER+/HER2± (n = 3; lower panel) Es. b, Relative contribution of T-cells pre-treatment in Es, comparing TNBC (n = 5) and ER+/HER2± (n = 3). c, Volcano plots showing DEGs for CD8+ expanding T-cells in TNBC versus ER+/HER2± Es pre-treatment (left panel) and on-treatment (right panel). d, Volcano plots showing DEGs for CD4+ expanding T-cells in TNBC versus ER+/HER2± Es pre-treatment (left panel) and on-treatment (right panel). e, UMAP of 50,693 cells from n = 11 BC patients with neoadjuvant chemotherapy followed by anti-PD1 (replication cohort or cohort 2) by scRNA-seq color-coded for the indicated cell type. f, Relative contribution of each cell type (in %) pre-treatment in the replication cohort, comparing Es (n = 3) versus NEs (n = 8). g, Normalized PD1 expression in T-cells (cohort 2). h, Relative contribution of each T-cell phenotype (in %) on-treatment in the replication cohort, comparing Es (n = 3) versus NEs (n = 8). i, Volcano plot showing DEGs between pre-treatment T-cells that expand (n = 347 cells) versus those that do not expand (n = 3159 cells) in the replication cohort. j, ROC curve based on the average module expression of TH1-activity and our 50-gene signature in CD4+ T-cells (excluding TREG) pre-treatment to predict T-cell expansion. AUC values and 95% confidence intervals are shown. Black dots in Volcano plots (panels c, d and i): P=ns; gray: P < 0.05; red: P < 0.05 and absolute log2FC≥0.5. P values Volcano plots by MAST test and Bonferroni-corrected (Seurat). Genes in bold are discussed in the manuscript. Panels b, f and h: exact P values by two-sided (b) or one-sided (f,h) Mann-Whitney test; *P < 0.05, *P < 0.01, ***P < 0.001. Panels b, f and h: box, median ± interquartile range; whiskers, minimum and maximum. Panel g: **P < 0.01, ***P < 0.001 by MAST test (Seurat).

Extended Data Fig. 7 DC and monocyte/macrophage subclustering.

a, Heatmap showing 3 marker genes for each DC phenotype. b, UMAP of DCs color-coded for one marker gene per DC phenotype. c, Heatmap of genes specific for cDC2 and pDC showing that ASDCs share characteristics of cDC2s and pDCs. d, UMAP of DCs color-coded for PD-L1/L2 and CCL19. e, Heatmap showing expression of the mregDC signature described by Maier and colleagues26 across DC phenotypes. f, Relative contribution of each DC phenotype (in %) to all cells pre-treatment, comparing Es (n = 9) versus NEs (n = 20). P>0.05 for all by two-sided Mann-Whitney test. Box, median ± interquartile range; whiskers, minimum and maximum. g, Volcano plot showing DEGs in DCs comparing Es versus NEs pre-treatment. Black dots: P=ns; gray: P < 0.05; red: P < 0.05 and absolute log2FC ≥ 0.5. P values by MAST test and Bonferroni-corrected (Seurat). Genes in bold are discussed in the manuscript. h, Heatmap showing marker genes for each myeloid cell phenotype. i, UMAP of myeloid cells color-coded for one marker gene per myeloid cell phenotype. j, Heatmap showing expression of functional genes in the 10 myeloid cell phenotypes. k, Boxplot showing relative percentage of DCs and macrophages detected in each patient (n = 31) by scRNA-seq. Box, median ± interquartile range; whiskers, minimum and maximum. FC: fold change. l, GSEA (hypeR) based on REACTOME for DEGs in macrophages comparing Es versus NEs pre-treatment.

Extended Data Fig. 8 Pathway analysis in cancer cells.

a, Change in Ki67 positivity (by immunohistochemistry) comparing paired on- versus pre-treatment biopsies in Es (n = 8) versus NEs (n = 19). Box, median ± interquartile range; whiskers, minimum and maximum. b-c, Differences in pathway activities scored per cell by GSVA pre-treatment in Es versus NEs (b) and on- versus pre-treatment in Es (c). Shown are the top 35 pathways based on absolute t-values obtained by a linear model. d, Violin plots showing GSVA scores for the indicated pathways. Stripes indicate median values. ***P < 0.001 (Benjamini-Hochberg adjusted) and absolute t-value>10 by a linear model. e-f, GSEA of DEGs upregulated on-treatment in cancer cells of Es using hypeR on GO (e) and hallmark gene sets (f). Genes driving enrichment included CD74, HLA-DQA1 (antigen presentation), IDO1 (antigen-dependent T-cell activation), A2M (tumour migration and growth), CXCL10 and CXCL14 (chemoattractant of immune cells), CHI3L1 (IFN production).

Extended Data Fig. 9 Immune context analysis.

Spearman correlation between the number of expanded clonotypes and other key functional marker genes or signature modules pre-treatment. Average expression of genes was calculated in the indicated (sub)cell type pre-treatment per patient. In each heatmap two clusters positively or negatively correlating with expansion were identified, as indicated by the squared boxes.

Extended Data Fig. 10 Cell-to-cell type interactions by CellPhoneDB.

a, Heatmap showing the number of interactions in NEs pre-treatment, in Es pre-treatment, NEs on-treatment and Es on-treatment. b, Barplot showing for each cell-to-cell type interaction, the number of interactions shared and specific pre-treatment comparing Es versus NEs. c, Dotplot showing the significance (-log10 P value) and strength (mean value) of specific interactions between cancer cells and CD8+ T-cells comparing Es versus NEs. The aLb2 complex refers to ITGAL and ITGB2, which together form LFA-1 d, Difference in the number of significant CellPhoneDB interactions in Es comparing pre- versus on-treatment. e, Barplot showing per cell-to-cell type interactions, the number of interactions shared and specific in pre- versus on-treatment biopsies from Es. f, Dotplot showing the significance (-log10 P value) and strength (mean value) of specific interactions in Es between CD8+ T-cells and myeloid cells (either DCs, macrophages or monocytes) pre- versus on-treatment.

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Bassez, A., Vos, H., Van Dyck, L. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat Med 27, 820–832 (2021). https://doi.org/10.1038/s41591-021-01323-8

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