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Breast cancer cells survive chemotherapy by activating targetable immune-modulatory programs characterized by PD-L1 or CD80

Abstract

Breast cancer cells must avoid intrinsic and extrinsic cell death to relapse following chemotherapy. Entering senescence enables survival from mitotic catastrophe, apoptosis and nutrient deprivation, but mechanisms of immune evasion are poorly understood. Here we show that breast tumors surviving chemotherapy activate complex programs of immune modulation. Characterization of residual disease revealed distinct tumor cell populations. The first population was characterized by interferon response genes, typified by Cd274, whose expression required chemotherapy to enhance chromatin accessibility, enabling recruitment of IRF1 transcription factor. A second population was characterized by p53 signaling, typified by CD80 expression. Treating mammary tumors with chemotherapy followed by targeting the PD-L1 and/or CD80 axes resulted in marked accumulation of T cells and improved response; however, even combination strategies failed to fully eradicate tumors in the majority of cases. Our findings reveal the challenge of eliminating residual disease populated by senescent cells expressing redundant immune inhibitory pathways and highlight the need for rational immune targeting strategies.

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Fig. 1: Human and mouse mammary tumors that become senescent-like following chemotherapy treatment are highly enriched for immune-modulatory pathways that include PD-L1 and CD80.
Fig. 2: scRNA-seq identifies unique clusters within the doxorubicin-treated senescent tumor.
Fig. 3: IFNγ strongly induces PD-L1 expression in senescent but not proliferating breast cancer cells.
Fig. 4: Doxorubicin treatment increases accessibility of chromatin and IRF1 TF binding at many IFNγ response genes including Cd274.
Fig. 5: p53 activation induces expression of CD80 and other checkpoint genes in breast cancer cells.
Fig. 6: Immune response to chemotherapy treatment in the tumor.
Fig. 7: Complete mammary tumor eradication and durable response in one-third of mice treated with chemotherapy followed by anti-PD-L1.
Fig. 8: Tumors treated with chemotherapy followed by ICi are characterized by a massive accumulation of T cells and pathological changes.

Data availability

RNA-seq, ATAC-seq and scRNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE189302. Requests for resources or reagents can be directed to and will be fulfilled by the lead contact J.G.J. Source data are provided with this paper.

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Acknowledgements

We acknowledge flow cytometry support from D. Wyczechowska; multi-IHC staining support from A. Minic, University of Colorado Denver; scRNA-seq support from K. Song and the CTRII NextGen Sequencing Core; ATAC-seq support from S. Hilliard; thoughtful scientific discussion from H. Chun and technical support from J. Nguyen, E. Cowles, M. Xiao, M. G. Mayer and A. I. Sugi. BioRender.com was used to create schemata. This study was supported by the Department of Defense Breast Cancer Research Program (W81XWH-14-1-0216 to J.G.J.), the National Cancer Institute of the National Institutes of Health (R01CA259001 to J.G.J. and R01CA212518 to H.L.M.), the National Institute of Environmental Health Sciences (R01ES032036 to J.G.J. and R01ES028271 to Z.F.P.), a Tulane Office of Research Bridge Funding Program Award (to J.G.J.), a Tulane Lavin-Bernick Faculty Grant (to Z.F.P.), a fellowship from the Leukemia & Lymphoma Society (to N.A.U.) and a Center of Clinical and Translational Science Ruth L. Kirschstein National Service Award (NRSA) TL1 Predoctoral Research Training Program TL1 (award TL1TR003106 to A.S.). The Louisiana Clinical and Translational Science Center is supported in part by U54 GM104940 from the National Institute of General Medical Sciences. The Cellular Immunology and Immune Metabolism Core at the Louisiana Cancer Research Consortium is supported by a grant from the NIN/NIGMS (1P30GM114732-01). The Human Immune Monitoring Shared Resource (University of Colorado Denver) is supported by a Cancer Center Support Grant (P30CA046934). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

Authors

Contributions

A.S. and J.G.J. conceived the study, designed the experiments and wrote the manuscript. A.S. performed all IHC and IF analyses. A.S., F.-Y.C., A.Y.A., S.G.R. and J.G.J. performed all in vivo transplant experiments. A.S., R.K., Z.M. and J.G.J. performed protein expression analyses. A.S., R.K., Z.M. and F.-Y.C. performed mRNA analyses. A.S. and N.A.U. performed scRNA-seq analyses. A.S., N.A.U. and J.G.J. performed human breast cancer data analyses. N.A.U. provided bioinformatics and heat map support. D.T. provided pathology support. D.A.W. performed fluorescence-activated cell sorting analysis and supported immune analyses. M.J.S.S. generated the Trp53-knockout cell lines. H.L.M. provided reagents, and H.L.M. and Z.F.P. helped supervise the study and provided critical scientific discussion. All authors edited the manuscript. J.G.J. was the principal investigator for this study.

Corresponding author

Correspondence to James G. Jackson.

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The authors declare no competing interests.

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Nature Cancer thanks Paula Bos, Jennifer Guerriero 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 Human and mouse breast cancers that become senescent-like post chemotherapy enrich the same immune modulatory programs.

a) Summary of breast cancer patient treatment scheme and RNA-seq analysis. b) Hierarchical clustering of an RNA seq dataset18 was performed on NAC post/pre fold-change values for each tumor, using cell cycle genes (PCNA, CDC7, MCM6, CCNE2, BUB1, CCNA2, CCNB1), senescence/ SASP genes (LMNB1, CCL2, TNFRSF10B, CSF1, CCL5, CCL22, MMP2) and p53 targets (CDKN1A, CCNG1 POLK, ZNF365, EDA2R) as justified in Supplementary Table 1. The leftmost column represents treated/untreated fold change in expression of each gene, derived from RNA seq of spontaneous MMTV-Wnt1 tumors. These tumors are well characterized to undergo senescence post treatment3,4,5 but were not used in the clustering. c, d) Two additional p53 wild-type MMTV-Wnt1 tumors (5581, 5560) were each transplanted and mice were treated and harvested as in Fig. 1d. Indicated immunoblots were performed. D) Formalin fixed paraffin embedded (FFPE) tumor sections from treated or untreated mice as in Fig. 1e were IHC stained for PD-L1 or CD80 and counter stained with nuclear fast red. Scale bar=50 µm. Western blots and IHC were repeated at least twice and representative data are shown.

Source data

Extended Data Fig. 2 Single-cell RNA sequencing of doxorubicin-treated and untreated mouse mammary tumors identifies distinct clusters with markers and pathways indicative of senescence.

10X Genomics scRNA-seq was performed on MMTV-Wnt1 mammary tumor transplants (3642 from Fig. 1c) from doxorubicin treated and untreated mice. Sequenced cells from untreated and treated tumors were pooled, standard quality control and data processing performed, and clusters and cells with reads for Ptprc (CD45) were removed to deplete immune cells. a) Heat map depicting the top 25 upregulated genes between the doxorubicin treated and untreated cells (elements represented in heatmaps are normalized average log2 counts). b) UMAP dimensional reduction analysis was performed, revealing nine distinct clusters (left); original identities of cells (Doxo or Unt) are shown on right. c) Dot plot was generated showing enriched HALLMARK pathways using the top 100 upregulated genes in each cluster. P-values for upregulated genes were determined using a Wilcoxon Rank Sum test. d) Feature plots depicting expression levels of indicated genes were generated. e) Feature plots depicting enrichment of indicated genesets/signatures were generated.

Extended Data Fig. 3 Analysis of single-cell sequenced doxorubicin-treated tumors reveals clusters enriched for distinct pathways.

a) Feature plot showing expression levels of HALLMARK_TNFA_SIGNALING_VIA_NFKB in doxorubicin-treated tumor cells (left); heatmap was generated depicting top 25 most upregulated genes in cluster 0 vs. all other clusters from Fig. 2c. Cluster 0 is indicated by orange dashed line. b) Feature plot showing expression levels of mesenchymal genes in cluster 3 (Acta2, Fn1, Cdh2, S100a4, Snai1, Snai2, Vim) (left); heatmap of top 25 most upregulated genes in cluster 3 vs. all other clusters. Cluster 3 is indicated by turquoise dashed line. c) Feature plot showing expression levels of HALLMARK_G2M_CHECKPOINT in doxorubicin-treated tumor cells (left); heatmap of top 25 most upregulated genes in cluster 4 vs. all other clusters. Cluster 4 is indicated by dark blue dashed line.

Extended Data Fig. 4 Interferon gamma (IFNγ) signaling is responsible for PD-L1 induction in senescent breast cancer cells.

a) FPKM values for Ifnb1 and 14 Ifna genes from bulk RNA-sequencing of spontaneous MMTV-Wnt1 treated tumors or untreated tumors (n = 6 biologically independent tumors for each group). b) 3642 tumor cells were transplanted into multiple wild-type or Ifng−/− C57BL/6j mice and treated or not with doxorubicin. Tumors were harvested 48 h following the final treatment, mRNA prepared, and qPCR was performed for indicated checkpoint genes. Shown are individual data points representative of biologically independent tumors (Ifng−/− untreated: n = 4, Ifng−/− doxorubicin-treated: n = 6, WT untreated: n = 4, WT doxorubicin-treated: n = 6), mean, and SEM. c) FFPE tumor sections from untreated or doxorubicin treated mice were IF co-stained for PD-L1 (green) and IRF1 (red). Closed arrows indicate PD-L1+ cells with nuclear IRF1 staining d) FFPE tumor sections as in (C) were IF co-stained for PD-L1 (green) and Galectin-9 (Gal-9, red). Closed arrows indicate PD-L1 + /Gal-9+ cells; open arrows indicate PD-L1/Gal-9 negative cells. Statistical significance was determined using one-way ANOVA with Tukey’s posttest for comparisons of three or more groups. qPCR and IF were repeated at least twice and representative data are shown.

Source data

Extended Data Fig. 5 Interferon gamma (IFNγ) strongly induces PD-L1 expression in senescent but not proliferating human breast cancer cells and ex vivo plated mouse mammary tumor cells.

a) Mammary tumor cells from a 3642 transplant were isolated and plated at 5% O2, and the next day untreated (Unt) or made senescent over 7 days by treatment with doxorubicin (Doxo, 0.75 µM for 24 hr) as indicated. These cultures were then exposed to rIFNγ (10 ng/ml) for 24 h (IFNγ) or not (Control). RNA was extracted and qPCR for indicated genes was performed. Representative images of the treated, plated cells are shown before harvest. Shown are individual data points representative of biologically independent cell harvests (n = 3 for each group), with mean and SEM. B) MCF-7 cells were similarly plated and either untreated or made senescent by doxorubicin treatment (250 nM for 24 hr). These cultures were then exposed to rIFNγ (10 ng/ml) for 24 h (IFNγ) or not (Control). RNA was extracted and qPCR for indicated genes was performed. Shown are individual data points representative of biologically independent cell harvests (n = 3 for each group), with mean and SEM. Statistical significance was determined using one-way ANOVA with Tukey’s posttest for comparisons of three or more groups. qPCR was repeated at least twice and representative data are shown.

Source data

Extended Data Fig. 6 ATAC-Comprehensive. Chromatin accessibility at IFNγ response genes.

a) RNA-seq and ATAC-seq data from Fig. 4 were analyzed for A) genes that cooperate with doxorubicin (‘Oasl2-like genes’); and b) genes that do not require doxorubicin treatment for maximal induction (‘Socs1-like genes’). Shown on the left of each column is a log2 fold change bar graph of RNA expression for Doxo+IFNγ/IFNγ alone. At the right, corresponding overlayed ATAC-seq tracks from control (green) and doxorubicin treated (yellow) cells, for the same gene (as in Figure ATAC).

Extended Data Fig. 7 p53 signaling induces a subset of immune checkpoint genes in chemotherapy-treated breast cancer cells.

a) GSEA analysis of sequenced single cells from clusters 0, 3, and 4 vs. cluster 1 from Fig. 2c was performed, and enrichment plots for indicated pathways are shown. b) 3642 tumor cells were transplanted into multiple wild-type or Ifng−/− C57Bl/6j mice and treated or not with doxorubicin. Tumors were harvested 48 h following the final treatment, mRNA prepared, and qPCR was performed for indicated checkpoint genes. Shown are individual data points representative of biologically independent tumors (Ifng−/− untreated: n = 4, Ifng−/− doxorubicin-treated: n = 6, WT untreated: n = 4, WT doxorubicin-treated: n = 6), mean, and SEM. c) FFPE tumor sections from untreated or doxorubicin treated mice were IF co-stained for PD-L1 (green) and p21 (red). Closed arrows indicate PD-L1+ cells but with weak nuclear p21 staining; open arrows indicate cells negative for PD-L1 but with strong, nuclear p21 staining. d) IF co-staining for CD80 (green) and p21 (red) on sections from Fig. Fig. 5. Closed arrows indicate CD80+ cells with strong nuclear p21 staining. Statistical significance was determined using one-way ANOVA with Tukey’s posttest for comparisons of three or more groups. e, f) As for Fig. 5j, k, multi-IHC staining was performed for PD-L1 (red), IRF1 (magenta), CD80 (green), p21 (yellow), γH2AX (orange), pStat3 (white) and DAPI (blue). e) Boxes show three individual areas that have been and expanded, with indicated channels separated. Arrowheads mark p21 + /CD80 + (closed yellow arrow) and IRF1 + /PD-L1+ cells (closed magenta arrow); γH2AX + /CD80+ cells (closed orange arrow) and γH2AX/PD-L1 negative cells (open arrow); p-Stat3+/PD-L1+ (closed arrow) and p-Stat3 + /CD80+ cells (open arrow). f) Untreated tumor stained and imaged in parallel. Scale bar=40 µm. qPCR and IF were repeated at least twice and representative data are shown.

Source data

Extended Data Fig. 8 p53 signaling is required for CD80 induction and enhances PD-L1 induction.

a) Two p53 R172H mutant (with LOH) mammary tumors (4515 and 4251) were transplanted into multiple mice, treated with doxorubicin or not, then harvested 48 hr following the final treatment. Tumor powder was lysed and indicated immunoblots were performed. b) RNA was extracted from the same tumors as in (A), and qPCR for indicated genes performed. One p53 wild-type Control and doxorubicin treated tumor (3642, from Fig. 1) was run in parallel and is shown for comparison. Shown are individual data points representative of biologically independent tumors (#1 C: n = 3, #1 Doxo: n = 3, #2 C: n = 3, #2 Doxo: n = 8), with mean and SEM. c) CRISPR-Cas9 was used to generate a p53 knockout clone (clone 2, asterisk) and a non-edited control (clone 3, asterisk) of 4226 cell line. d) p53 knockout clone 2 of the 4226 cell line was untreated (Unt) or treated with doxorubicin (Doxo, 0.75 µM for 24 hr) as indicated. Over 1 to 5 days, these cultures were then exposed to rIFNγ (10 ng/ml) for 24 h (IFNγ, lanes 7-12)) or not (Control, lanes 1-6), followed by immunoblot for CD80, PD-L1, Stat1, and IRF1 on a total of 2 membranes, which were then blotted for actin. p53 WT clone 3, five days post-doxorubicin treatment was either IFN treated (lane 14) or not (lane 13) and is shown for comparison. e) 4226 p53 knockout clone 2 (KO) and non-edited clone 3 (WT) were treated with doxorubicin or Nutlin-3a for 4 h, and then qPCR was performed for indicated checkpoint genes. Shown are individual data points representative of biologically independent cell harvests (n = 3 for each group), mean, and SEM, with proliferating (-) cells in the first lane normalized to 1. Statistical significance was determined using one-way ANOVA with Tukey’s posttest for comparisons of three or more groups. Western blots and qPCR were repeated at least twice and representative data are shown.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–4.

Source data

Source Data Fig. 1

Raw Ct values and fold change values in the heat map for Fig. 1 and mouse number legend.

Source Data Fig. 1

Unprocessed western blots for Fig. 1.

Source Data Fig. 3

Raw Ct values and normalized qPCR data for Fig. 3.

Source Data Fig. 3

Unprocessed western blots for Fig. 3.

Source Data Fig. 4

Raw Ct values and normalized qPCR data for Fig. 4.

Source Data Fig. 5

Raw Ct values and normalized qPCR data for Fig. 5.

Source Data Fig. 5

Unprocessed western blots for Fig. 5.

Source Data Fig. 6

Flow percentages for Fig. 6.

Source Data Fig. 7

Tumor volume measurements for Fig. 7.

Source Data Fig. 8

Flow percentages, raw Ct values and normalized qPCR data for Fig. 8.

Source Data Extended Data Fig. 1

Unprocessed western blots for Extended Data Fig. 1.

Source Data Extended Data Fig. 4

Raw Ct values and normalized qPCR data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Raw Ct values and normalized qPCR data for Extended Data Fig. 5.

Source Data Extended Data Fig. 7

Raw Ct values and normalized qPCR data for Extended Data Fig. 7.

Source Data Extended Data Fig. 8

Raw Ct values and normalized qPCR data for Extended Data Fig. 8.

Source Data Extended Data Fig. 8

Unprocessed western blots for Extended Data Fig. 8.

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Shahbandi, A., Chiu, FY., Ungerleider, N.A. et al. Breast cancer cells survive chemotherapy by activating targetable immune-modulatory programs characterized by PD-L1 or CD80. Nat Cancer 3, 1513–1533 (2022). https://doi.org/10.1038/s43018-022-00466-y

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