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Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms

Abstract

Cancer-induced immune responses affect tumour progression and therapeutic response. In multiple murine models and clinical datasets, we identified large variations of neutrophils and macrophages that define ‘immune subtypes’ of triple-negative breast cancer (TNBC), including neutrophil-enriched (NES) and macrophage-enriched subtypes (MES). Different tumour-intrinsic pathways and mutual regulation between macrophages (or monocytes) and neutrophils contribute to the development of a dichotomous myeloid compartment. MES contains predominantly macrophages that are CCR2-dependent and exhibit variable responses to immune checkpoint blockade (ICB). NES exhibits systemic and local accumulation of immunosuppressive neutrophils (or granulocytic myeloid-derived suppressor cells), is resistant to ICB, and contains a minority of macrophages that seem to be unaffected by CCR2 knockout. A MES-to-NES conversion mediated acquired ICB resistance of initially sensitive MES models. Our results demonstrate diverse myeloid cell frequencies, functionality and potential roles in immunotherapies, and highlight the need to better understand the inter-patient heterogeneity of the myeloid compartment.

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Fig. 1: Diverse immune cell profiles in murine mammary tumour models.
Fig. 2: Myeloid cell profiles in human TNBC.
Fig. 3: The TIN/TIM frequencies are relatively stable for individual tumour models.
Fig. 4: Perturbation of EMT tilts the balance between TIM and TIN.
Fig. 5: Inter-tumoural heterogeneity of TIMs and inverse change of TINs following TIM depletion.
Fig. 6: TINs in NES tumours express multiple immunosuppressive pathways, and negatively regulate Ly6C+ monocyte recruitment.
Fig. 7: Heightened accumulation of immunosuppressive TINs or gMDSCs is associated with de novo resistance to ICB.
Fig. 8: TINs mediate acquired resistance to ICB.

Data availability

The RNA-seq data for cancer cells, tumour-infiltrating macrophages and tumour-infiltrating neutrophils have been submitted to the Gene Expression Omnibus under accession number GSE104765. The normalized RNA-seq data for human TNBC nanostring datasets are provided in Supplementary Table 2c.

Other secondary datasets used in this study include

1. TCGA dataset, available from https://portal.gdc.cancer.gov/. The sample IDs used in this study are provided in Supplementary Table 2d.

2. METABRIC dataset, available from https://ega-archive.org/datasets/EGAD00010000266.

3. BioGPS Primary Cell Atlas, available from http://biogps.org/dataset/BDS_00013/primary-cell-atlas/. The specific samples used in this study are listed in Supplementary Table 2a.

4. Gene expression profiles of TAN, GMDSC and normal neutrophils. Data available from GEO, dataset GSE43254.

5. Metastatic melanoma dataset66: https://github.com/riazn/bms038_analysis

6. Metastatic melanoma dataset67 available from GEO: GSE78220.

Code availability

Key codes for data analyses and major intermediate data are available at Github: https://github.com/Xiang-HF-Zhang/Dichotomous-of-innate-immune-landscape.

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Acknowledgements

We thank D. Weiss for critically editing the manuscript, and A. Muscarella, S. Kurley, S. Kim, J. Kim, B. Ton, L. Ma and S. I. Abrams for providing various tumour models. X.H.-F.Z. is supported by Breast Cancer Research Foundation, NCI CA151293, US Department of Defense DAMD W81XWH-16-1-0073 and W81XWH-18-1-0574, Susan G. Komen CCR14298445, and McNair Medical Institute. J.M.R. is supported by NCI-CA16303. Flow cytometry and cell sorting was performed at the Cytometry and Cell Sorting Core at Baylor College of Medicine with funding from the NIH (P30 AI036211, P30 CA125123 and S10 RR024574) and the expert assistance of J. M. Sederstrom.

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Authors and Affiliations

Authors

Contributions

Conception and design: X.H.-F.Z., I.S.K. and J.M.R. Development of methodology: I.S.K., X.H.-F.Z., T.W., M.J.T., H.W., J.L., K.S., Y.L., Q.M., T.F.W., C.Z., A.R., and A.S. Acquisition of data: I.S.K., Y.G., T.W., M.J., N.Z., A.G., Y.N.,, H-C.L., I.B., T.N., W.B., W.J., J.A., F.G., J.H., D.J., K.W., and X.H.-F.Z. Analysis and interpretation of data: I.S.K., X.H.-F.Z., and J.M.R. Writing and review of manuscript: X.H.-F.Z., I.S.K., and J.M.R. Study supervision: X.H.-F.Z.

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Correspondence to Xiang H.-F. Zhang.

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Integrated supplementary information

Supplementary Figure 1 Dichotomy of myeloid cell infiltration is observed across murine breast tumour models.

a. Summary of eight breast tumour models. Characteristics are colour-coded as indicated by annotations above table. “Spontaneous” means spontaneously developed tumours without engineered expression of oncogenes (for example, PyMT) or loss of tumour suppressors (for example, P53). b. Representative immunohistochemistry (IHC) staining of ER, PR, and ErbB2 in PyMT-M and PyMT-N tumours. A ER+/PR+ murine tumour, 9809, was used as positive control for ER and PR, and MMTV-Neu tumour was used as positive control for ErbB2. Experiment performed once. Results representative of three independent animals. Scale bar, 100 µm. c. Quantification of ER+, PR+, or ErbB2 + cells in PyMT-M and PyMT-N tumour tissues as compared to positive controls as shown in (b). The dotted line indicate clinically used cutoff: 10%. Data are shown as mean ± S.D of three biological replicates. d. Heatmap shows the expression level of key genes characteristic of indicated molecular subtypes of TNBC across eight syngeneic murine tumour models (each in technical triplicate).e. Frequency of pulmonary metastasis of the eight murine models in syngeneic wild-type hosts. Macroscopic metastases were examined and counted. Number of metastasis-bearing animals over total animals are shown in pie charts.f. Flow cytometry gating strategy for identification of tumour-infiltrating leukocytes in murine breast tumours. Starting from upper left. Markers are indicated left and below each polychromatic dot plot. Identified cell types are marked by pink text. g. Dot plot shows tumour volume upon harvest across eight mammary tumour models. Size-matched tumours (around 1 gram) were used for immune profiling. Data are shown as mean ± S.D. h. Heatmap shows the degree of various tumour-infiltrating myeloid cells (neutrophil, Ly6C high monocyte and macrophage, CD11b+ dendritic cell [DC], CD103+ dendritic cell [DC]) and tumour-infiltrating lymphoid cells (B cell, γδ T cell, CD8+ T cell, CD4+ T cell, regulatory T cell) across eight mammary tumour models quantified by flow cytometry. Number in parentheses show the specific n values of biologically independent mice. Cell frequency was normalized to total cells and log2-transformed. The colour scale shows z-scores across all models and biological replicates. The accompanying box plots (defined in Methods) show the non-scaled cell frequency of each cell type in each model. i. Representative images of co-immunofluorescence staining of neutrophils (Ly6G) and macrophages (CD68) in the indicated tumours. Experiment performed once. Results representative of three independent animals. Green, Ly6G; red, CD68; blue, DAPI (nucleus). Scale bar, 100 μm. j. Boxplots (defined in Methods) show the quantification of tumour-infiltrating T cells (both CD4+ and CD8+ cells) by flow cytometry in syngeneic hosts of murine tumours (not including SCID/Beige hosts for PDXs) across the four clusters shown in Fig. 1e (n=52 biologically independent animals). The P value was computed based on one-way ANOVA. k. Dot plot shows spleen weight of tumour-bearing animals subjected to immune profiling. Splenomegaly was observed in NES tumour-bearing animals. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. l. Bar graphs represent the absolute number of various immune cells in bone marrow from tumour-bearing animals. Systemic immune alteration was observed in NES tumour-bearing animals. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D.

Supplementary Figure 2 TIMER-predicted immune cell infiltration in TCGA (non-TNBC) and METABRIC (TNBC) dataset.

a. Unsupervised hierarchical clustering of TIMER scores of six indicated immune cells in non-TNBC (n=986) of the TCGA dataset after z-transformation. Yellow or blue rectangle indicates potential NES or MES tumours, respectively. TIN: Tumour-infiltrating neutrophils, TIM: Tumour-infiltrating macrophages, DC: Dendritic cells. b. tSNE analyses of the same tumours as examined in (a). Clusters potentially representing “cold”, “MES”, and “NES” subtypes are indicated by grey, blue, and yellow circles, respectively. The colour of each tumour (dot) is determined first by if a tumour is considered “cold” (see Methods), and then by degree of enrichment of TIN or TIM as gauged by the difference of their Z-scores. c. Same as (b), except in TNBC of the METABRIC dataset (n=320).

Supplementary Figure 3 The frequency of neutrophils and macrophages is stable for each individual tumour model.

a. Box plots (defined in Methods) show the variation of tumour-infiltrating regulatory T cell (left), CD11b+ dendritic cell (middle), and CD103+ dendritic cell (right) frequencies (relative to total cells) within individual model and across eight tumour models. Number in parentheses show the specific n values of biologically independent mice. b. Quantification of PBM and PBN in tumour-bearing animals that were either transplanted with one or two tumours of different immune subtypes. Experimental schematic is shown below dot plot. PBM: peripheral blood Ly6C high monocyte, PBN: peripheral blood neutrophil. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test. c. FACS analyses show dichotomous infiltration of Ly6G+ Ly6Cmed-low cells (neutrophils) and CD11b+ F4/80+ cells (macrophages) in orthotopic tumour of MMTV-PyMT sub-lines (PyMT-M and PyMT-N). Plots are gated on CD45+ CD11b+ to display neutrophils, and on CD45+ to display macrophages. Data representative of five independent experiments. d. PyMT-N line induces profound systemic neutrophil accumulation like other NES tumours. Quantification of PBN (left), and measurement of spleen (middle) and tumour (right) weight of tumour-bearing animals. PBN: peripheral blood neutrophil. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test. e. Quantification of TIM and TIN in spontaneous vs. transplanted MMTV-Wnt1 tumours when they reach similar size. Number in parentheses show the specific n values of biologically independent mice. One animal in “spontaneous group” carried two tumours. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test.f. Relative expression (log2-transformed) of cytokines and chemokines in in vitro cultured tumour cell lines as determined by quantitative polymerase chain reaction. Data are shown as mean ± S.D of four biological replicates. g. Correlation analysis between in vitro monocyte migration and in vivo tumour macrophage infiltration across eight tumour models. The neutrophil/macrophage profile of each model is indicated as a mini pie chart as described in Fig. 3d. n=8 biologically independent samples (one tumour model as one independent sample). Smoothed trend line is shown. Pearson correlation coefficient and corresponding P value (by two-sided Student’s t-tests) are indicated.

Supplementary Figure 4 EMT tilts the balance between TIM and TIN.

a. Relative expression (log2-transformed) of Zeb1 and Cdh1 mRNA in in vitro cultured tumour cell lines as determined by quantitative polymerase chain reaction. Data are shown as mean ± S.D of four biological replicates. See (b) for cell line colour keys. b. Relative expression (log2-transformed) of miR-200c in in vitro cultured tumour cell lines as determined by quantitative polymerase chain reaction. Data are shown as mean ± S.D of three biological replicates. c. FACS analyses of T11 and MDA-MB-231 cell lines upon miR-200c induction with doxycycline. EMT reporter (Z-cad sensor, see Method) allows quantification of epithelial (MET)- and mesenchymal (EMT)-like cells by expression of RFP and GFP, respectively. Experiment performed once. d. Cell morphology of T11 and MDA-MB-231 cells upon miR-200c induction with doxycycline. Data representative of four independent experiments. Scale bars, 100 µm.e. Relative expression of indicated genes in T11 upon shRNA-mediated ZEB1 knockdown as determined by quantitative polymerase chain reaction. Data are shown as mean ± S.D of four biological replicates. P value was computed by two-sided Student’s t-tests. f. Relative expression of indicated genes in three MES tumour cell lines (T11, E0771, PyMT-M) and in MDA-MB-231 (four independent samples) upon miR-200c induction with doxycycline. Data are shown as mean ± S.D. P value was computed by two-sided Student’s t-tests. g. Quantification of monocyte migration toward tumour-conditioned medium of three MES cell lines (T11, E0771, PyMT-M) upon miR-200c induction with doxycycline. Bar graph represents the mean value of biological replicates. P value was determined by paired two-sided Student’s t-test. h. GSEA analyses using TIMER-predicted TIM scores as continuous phenotypic values, MSigDB Hallmark Pathways as gene sets, and TCGA TNBC as dataset (n=112 patients). NES: normalized enrichment score, FDR: false discovery rate, determined by empirical tests based on random permutations.i. Same as (h) except for the PI3K-AKT-mTOR pathway and TIN scores. j. The Pearson correlations between GSVA scores of indicated pathways shown in Fig. 4g (n=112 patients) and TIMER scores. Up-regulated and down-regulated genes in Taube et al54, are separated to ensure correct directionality. UP: Up-regulated genes, DOWN: Down-regulated genes. The P values were computed based on two-sided Student’s t-tests of Pearson correlation coefficients.

Supplementary Figure 5 Diverse effects of CCR2 KO in different tumour contexts.

a. The same heatmap as shown in Fig. 5b but with complete list of Hallmark pathway names and dendrograms. b. Box plots (defined in Methods) show the impact of CCR2 KO-mediated Ly6Chigh monocyte depletion on tumour weights in indicated tumour models. X axes indicate monocyte frequency of individual tumours quantified by flow cytometry. Number in parentheses show the specific n values of biologically independent mice per group denoted by different colour. Two-sided Student’s t-test was performed to assess statistical significance for tumour weights between two groups. c. Heatmaps show the impact of CCR2 KO-mediated Ly6Chigh monocyte (Mono) depletion on frequency of tumour-infiltrating CD4+ and CD8+ T cells. Number in parentheses show the specific n values of biologically independent mice per group denoted by different colour. The absolute cell numbers were quantified by flow cytometry. Arrows to the right of the heatmaps show the direction of changes. Numbers beside the arrows indicate fold changes of immune cell infiltration in CCR2 KO compared to wild type (WT). Numbers in parentheses indicate P value computed by two-sided Student’s t-tests. d. Quantification of IHC staining of proliferation (Ki67), apoptosis (cleaved caspase 3), and angiogenesis (CD31) in indicated tumour models. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. P value was computed by two-sided Student’s t-tests. e. Quantification of peripheral blood neutrophils (PBN) and monocytes (PBM) in WT and CCR2 KO hosts carrying MES tumours (T11, E0771). Number in parentheses show the specific n values of biologically independent mice per group denoted by different colour. Data are shown as mean ± S.D. P value was computed by two-sided Student’s t-tests. f. Quantification of neutrophils and monocytes in blood and bone marrow of tumour-free WT and CCR2 KO hosts. Number in parentheses show the specific n values of biologically independent mice per group denoted by different colour. Data are shown as mean ± S.D. P value was computed by two-sided Student’s t-tests.

Supplementary Figure 6 Neutrophils in NES overexpress multiple immunosuppressive pathways.

a. The same heatmap as shown in Fig. 6b but with complete list of Hallmark pathway names and dendrograms. b. GSEAs of indicated pathways that are differentially expressed between TINs of NES (n=3 biologically independent samples) and those of MES (n=3 biologically independent samples). Net enrichment scores were determined by GSEA algorithms. Two-sided P values and false discovery rate (FDR) were computed based on random simulations. FDR is adjusted for multiple comparisons.

Supplementary Figure 7 The impact of TIMs and TINs on ICB therapy.

a. Tumour growth curves show responses of two tumour models to ICB therapy (anti-PD1+anti-CTLA4) with escalated dose. Treatment was initiated Day 1 post-tumour implantation and continued every other day till end-point. Number in parentheses show the specific n values of biologically independent mice. Dotted lines indicate the time point at which tumour sizes were compared between control and treatment group. P value was computed by two-sided Student’s t-test. b. Tumour growth curves show responses of tumour tissue-derived cell lines to ICB therapy. PyMT-M exhibited enhanced response to ICB (compared to Fig. 7a). Number in parentheses show the specific n values of biologically independent mice. Dotted lines indicate the time point at which tumour sizes were compared between control and treatment group. P value was computed by two-sided Student’s t-test. c. Flow cytometric quantification of tumour-infiltrating CD8+ cytotoxic T cells across eight untreated breast tumour models. Data are shown as mean ± S.D. d. Flow cytometric quantification of PDL1+ cells assorted by total leukocytes (identified by CD45+) and non-immune cells (identified by CD45-) across eight untreated breast tumour models. Data are shown as mean ± S.D. e. Dot plot shows the quantification of total (left) and PD1+ (right) tumour-infiltrating CD8+ cytotoxic T cells upon ICB therapy (anti-PD1+anti-CTLA4). Number in parentheses show the specific n values of biologically independent mice per group denoted by different colour. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test. f. Kaplan-Meier curves show the progression-free survival of CCR2 KO animals bearing E0771 or PyMT-M tumour cell line treated with ICB therapy (anti-PD1+anti-CTLA4). Number in parentheses show the specific n values of biologically independent mice. P value was computed by two-sided log rank test. g. Tumour growth kinetics of NES tumours (PyMT-N and 2208L) treated with combined anti-CXCR2+anti-Ly6G and ICB therapy (anti-PD1+anti-CTLA4). Data are shown as mean ± S.D at each measured time point. Number in parentheses show the specific n values of biologically independent mice. h. In vitro immunosuppression assay by co-culturing splenic Ly6c+ monocytes harvested from neutrophil-depleted (treated with anti-CXCR2+anti-Ly6G) PyMT-N tumour-bearing animals with T cells. Proliferation of T cells was determined based on CFSE intensity as measured by FACS. A left-shift of CFSE intensity histogram indicates dilution of signals by proliferation. Data are shown as mean ± S.D of three biological replicates (monocytes from three different mice). P value was determined by two-sided Student’s t-test. i. Flow cytometric quantification of total and PD1+ tumour-infiltrating CD4+ and CD8+ T cells in PyMT-N tumours treated with anti-CXCR2+anti-Ly6G. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test.

Supplementary Figure 8 Acquisition of TINs mediated acquired resistance to ICB therapy in MES tumours.

a. Ly6G intensity of peripheral blood neutrophils in parental (yellow) and recurrent (ICBR, pink) E0771 tumour-bearing animals. Data representative of two independent experiments with similar results. b. Bar graph represents the absolute number of various immune cells in bone marrow from animals that are tumour-free, carry parental or recurrent (ICBR) E0771 tumours. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. c. Representative image of femurs and tibias of parental and recurrent (ICBR) E0771 tumour-bearing animals. Experiment performed once. d. Data show weights of tumour and spleen from animals carrying parental or recurrent (ICBR) E0771 tumour. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test. e. Quantification of peripheral blood neutrophils (PBN) and tumour-infiltrating neutrophils (TIN) in parental and ICBR-PyMT-M tumour-bearing animals. Weights of tumour and spleen from these animals are also shown. Number in parentheses show the specific n values of biologically independent mice. Data are shown as mean ± S.D. P value was determined by two-sided Student’s t-test. f. Relative expression of indicated genes in parental and ICBR PyMT-M cell lines by quantitative polymerase chain reaction. Data are shown as mean ± S.D of three biological replicates. P value was determined by two-sided Student’s t-test. g. The Q-Q plot of TIMER-TIN scores of Riaz et al. dataset (n=56 patients). Sample quantiles are plotted against expected quantiles based on normal distribution. Points that deviate from the straight line are highlighted in red, and represent samples that do not follow normal distribution based, and therefore, may represent a distinct group. h. Histogram shows the distribution of melanoma from Riaz et al. dataset (n=56 patients) based on z transformed TIMER TIN scores. Dotted line marks the top 20% of the population, representing potential NES-like tumours. P value was determined by Shapiro-Wilk test and Skew test to evaluate the normality of the data. i. Tabulation of patients with different TIN status and therapeutic responses. Shown are combined data from two datasets used in (Fig. 8f) and (Fig. 8h). The numbers (n) of corresponding patients from Riaz et al., and Hugo et al. dataset are shown in parenthesis by indicated order. P value was determined by two-sided Fisher’s Exact Test.

Supplementary Information

Supplementary Information

Supplementary Figures 1–8, Supplementary Table titles/legends

Reporting Summary

Supplementary Table 1

Statistics of intermodal variations and median frequency of major immune cell populations across the 8 murine TNBC models.

Supplementary Table 2

Information relevant for TIM/TIN analyses in human datasets in Fig. 2.

Supplementary Table 3

Information related to analyses of neutrophil gene expression profiles in Fig. 6.

Supplementary Table 4

Sequence of primers used for qPCR.

Supplementary Table 5

Statistical source data for Figs. 1–8 and Supplementary Figures 1–8.

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Kim, I.S., Gao, Y., Welte, T. et al. Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms. Nat Cell Biol 21, 1113–1126 (2019). https://doi.org/10.1038/s41556-019-0373-7

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