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Mobilizing antigen-presenting mast cells in anti-PD-1-refractory triple-negative breast cancer: a phase 2 trial

Nature Medicine volume 31, pages 2405–2415 (2025)Cite this article

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Abstract

The central challenge in triple-negative breast cancer (TNBC) immunotherapy is to identify novel mechanism-derived strategies for anti-programmed death-1 (PD-1) resistance and efficiently assess their efficacy and safety in humans. Understanding the intricate heterogeneity of the tumor microenvironment and its impact on treatment could guide the initiation of proof-of-concept clinical trials. Here, integrating single-cell transcriptome of 44 treatment-naive patients with TNBC, we unveiled an association between intrapatient mast cell heterogeneity and clinical benefit of PD-1 blockade. Upon independent parallel validation in 484 patients with TNBC, high levels of breast tissue antigen-presenting mast cells (apMCs) were associated with enhanced anti-PD-1 efficacy. Mechanistically, apMCs largely located within tertiary lymphoid structures and were efficient in performing presentation and cross-presentation of antigens and expressed co-stimulatory molecules. Conditional deletion of antigen-presenting machinery in mast cells dampened tumor-reactive T cells. A widely prescribed allergy medication, cromolyn, was identified to mobilize apMC-mediated T cell immunity and sensitize tumors to PD-1 blockade. We subsequently initiated a phase 2 clinical trial in female patients with anti-PD-1-refractory metastatic TNBC. Here we report the results of the cromolyn arm (cromolyn plus anti-PD-1 backbone). The prespecified primary endpoint of this arm was met, with a confirmed objective response rate of 50.0%. Our study defines a crucial role of mast cells in cancer immune control, identifies an apMC-directed approach to overcome anti-PD-1 resistance and highlights a reverse-translational framework that offers conceptual advances in precision immuno-oncology with direct implications for clinical therapy. ClinicalTrials.gov identifier: NCT05076682.

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Fig. 1: apMCs are conserved and associated with clinical benefit of PD-1 blockade in human TNBC.
Fig. 2: apMC-enriched tumors exhibit immunotherapy-responsive immune contexture.
Fig. 3: Cromolyn mobilizes antigen-presenting machinery of mast cells to inhibit tumor growth.
Fig. 4: Efficacy and safety of cromolyn plus anti-PD-1 backbone in anti-PD-1-refractory metastatic TNBC.

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Data availability

The microarray, single-cell and bulk RNA-seq data that were re-analyzed here are available in the European Genome-phenome Archive (EGAS00001004809), the ArrayExpress database at EMBL–EBI (E-MTAB-8107) and cBioPortal (http://www.cbioportal.org). The transcriptomic data of the FUSCC dataset have been deposited in the National Center for Biotechnology Informationʼs Gene Expression Omnibus (GSE118527), the Sequence Read Archive (SRP157974) and the National Omics Data Encyclopedia NODE:OEP000155 (https://www.biosino.org/node/project/detail/OEP000155). scRNA-seq data of mast cells in human TNBCs in the FUSCC-ICI cohort are in NODE:OEP003394 (https://www.biosino.org/node/project/detail/OEP003394). Owing to patient confidentiality, access to individual participant clinical data collected in this study should be directed to yizhoujiang@fudan.edu.cn with a detailed proposal and a signed data access agreement with the sponsor for approval. The requests will be reviewed by the ethics committee of FUSCC and responded to within 8 weeks. The Trial Protocol and the Statistical Analysis Plan are provided in the Supplementary Information. Source data are provided with this paper.

Code availability

No custom code or mathematical algorithm was developed for this study.

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Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (82425044, Y.J.; 82272822, Y.J.; 82341003, Z.S.), the Postdoctoral Fellowship Program and the China Postdoctoral Science Foundation (BX20240085, S.W.), the Foundation of the Shanghai Municipal Education Commission (24RGZNA03, Y.J.), the Clinical Research Plan of SHDC (SHDC2024CRI025, X.J.), the Shanghai Rising-Star Program (24QA2701400, X.J.) and the Shanghai Science and Technology Innovation Action Plan (24YF2705900, S.W.). Y.J. is a SANS Exploration Scholar. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank all investigators who provided accessible data analyzed in this study. We thank C. Zhan, Q. Wang, Y. Shen, W.-Y. Wu and L.-P. Ge for technical assistance. We thank all the investigators, study nurses and patients and their family members who participated in our clinical trials.

Author information

Author notes

  1. These authors contributed equally: Song-Yang Wu, Xi Jin, Yin Liu.

Authors and Affiliations

  1. Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Shanghai, China

    Song-Yang Wu, Xi Jin, Yin Liu, Zi-Yu Wang, Wen-Jia Zuo, Ding Ma, Yi Xiao, Tong Fu, Yu-Ling Xiao, Li Chen, Xi-Yu Liu, Lei Fan, Zhong-Hua Wang, Minhong Shen, Zhi-Ming Shao & Yi-Zhou Jiang

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Song-Yang Wu, Xi Jin, Yin Liu, Zi-Yu Wang, Wen-Jia Zuo, Ding Ma, Yi Xiao, Tong Fu, Yu-Ling Xiao, Li Chen, Xi-Yu Liu, Lei Fan, Zhong-Hua Wang, Minhong Shen, Zhi-Ming Shao & Yi-Zhou Jiang

  3. Department of Immunology, Shanghai Key Laboratory of Medical Epigenetics, Institutes of Biomedical Sciences, Fudan University, Shanghai, China

    Ronghua Liu

  4. Laboratory Animal Center, Fudan University Shanghai Cancer Center, Shanghai, China

    Wen-Jun Chai

  5. Shanghai Academy of Natural Sciences (SANS), Fudan University, Shanghai, China

    Yi-Zhou Jiang

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Contributions

S.W., X.J. and Y.J. conceptualized the study. X.J., Z.S. and Y.J. oversaw the project. S.W. and Y.L. performed statistical analyses. S.W., X.J., Y.L., T.F. and W.C. performed the experiments. Y.L., L.C., X.L. and L.M. contributed to sample collection. S.W. and X.J. prepared the first draft of the paper. S.W., X.J., Y.L., Y.X., R.L., Z.S. and Y.J. reviewed and edited the paper. S.W., X.J., Z.S. and Y.J. had unrestricted access to all data. All authors agreed to submit the paper for publication, read and approved the final draft and take full responsibility for its content.

Corresponding authors

Correspondence to Zhi-Ming Shao or Yi-Zhou Jiang.

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Nature Medicine thanks Jared Foster, Ignacio Melero and Max Wattenberg for their contribution to the peer review of this work. Primary Handling Editor: Ulrike Harjes, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Cohort characteristics.

The enrolled criteria and conducted analyses of main patient cohorts included in this study (a–d). scRNA-seq, single-cell RNA sequencing; MIBI-TOF, multiplexed ion beam imaging by time of flight; apMC, antigen-presenting mast cell; R, responsive; NR, not responsive; ICI, immune-checkpoint inhibitor; Pembro, pembrolizumab; Camre, camrelizumab; Chemo, chemotherapy; ORR, objective response rate; pCR, pathologic complete response; PFS, progression-free survival; OS, overall survival; DFS, disease-free survival; mIHC, multiplex immunohistochemistry; n/a, not available. # One patient had available ORR evaluation but missed PFS data.

Extended Data Fig. 2 Additional clarification of mast cell functional states via single-cell profiling.

(a) Uniform Manifold Approximation and Projection (UMAP) map of 125,543 immune and stromal cells color-coded for the indicated cell type (left) and signature genes of main clusters (right). (b) Differentially expressed genes in each mast cell cluster compared with others. (c) The fraction of mast cell clusters (left) and absolute numbers of total mast cells (right) in each patient. (d) Pseudotime trajectories for the three major mast cell clusters. (e–h) Analytical workflow to confirm the presence of mast cell clusters in two external validation cohorts, with our data as the reference. Only treatment-naive TNBC was included, aiming to avoid potential interference by previous therapies on tumor microenvironment. (i, j) Multiplex immunofluorescence of apMCs in TNBC FFPE sections (n = 173 biological replicates in Fig. 1g). Tryptase+CD74+ apMC infiltration was analyzed in four random regions in the full-face scanning image (i) and representative high-performance field of tumors with high or low apMC infiltration was shown (j, indicated by dashed circle in the enlarged panel).

Extended Data Fig. 3 Association between intratumoral apMCs and patient survival.

(a) Kaplan-Meier analyses of apMC status (median as cutoff) with PFS (left panel) and OS (right panel) for metastatic TNBC. A total of 197 patients were included and one patient without available PFS/OS data was not included in this analysis. (b) Multivariate cox analyses of apMCs concerning PFS and OS in metastatic TNBC. (c) Kaplan-Meier analyses of apMC status (median as cutoff) with DFS (left panel) and OS (right panel) for primary TNBC. A total of 360 patients were included (with RNA-seq, and n = 186 with tissue sections for staining) and all patients had available OS data. Four patients without available DFS data were not included in the DFS analysis. (d) Multivariate cox analyses of apMCs concerning DFS and OS in primary TNBC. Two-sided log-rank test (a, c) and multivariate Cox regression model (b, d).

Extended Data Fig. 4 Profiling of apMCs in patient-derived breast tumors.

(a, b) Panel design (a) and association of apMC infiltration with other cells in tumor microenvironment (b, n = 9) using MIBI-TOF. Tryptase+HLA-DR+ cells were identified as apMCs. (c) Correlation of multiplex immunohistochemistry versus RNA-seq quantified apMC level (n = 186). (d) Receiver operating characteristic (ROC) curve and area under curve (AUC) of the apMC signature level for staining-confirmed apMC infiltration. (e) Correlation between macrophage (stained by CD68) and apMC infiltration in TNBC. (f) Scaled deconvolution values for apMC and TLS overlay onto tissue spots using spatially resolved transcriptomics. (g) Spatial pattern of apMC and TLS localization in TNBC. Data are mean ± s.e.m. (b) and error bands of the line plot indicate 95% CI (g). Two-sided unpaired Student’s t-test (b), Spearman’s correlation method (c, e).

Source data

Extended Data Fig. 5 apMCs spatially localize with and potentiate T cell immunity in mouse TNBC.

(a, b) A representative image of CD74 and tryptase multiplex immunofluorescence (a), and mast cell subtypes in mouse TNBC (b, n = 6). (c) A representative enlarged image of CD4, CD8, CD74, and tryptase multiplex immunofluorescence in mouse TNBC (n = 6 biological replicates in Fig. 3c). (d) Scaled deconvolution values for apMC and T cell overlay onto tissue spots using spatially resolved transcriptomics. (e) Spatial pattern of apMC and T cell localization. (f) Mast cells other than apMCs were enriched to greater than 95% purity. Numbers indicate percentages of cells in the gates. (g) Composite data of six experiments representing activation marker CD69 expression by OT-II CD4+ T cells primed by relative mast cells cocultured with OVA-expressing or control AT3 cells. (h) Composite data of six experiments representing proliferation (Ki67), activation (CD69), and effector function (IFN-γ, IL-2) of OT-I CD8+ T cells primed by relative mast cells pulsed with tumor lysates of OVA-expressing or control AT3 cells. (i) Distribution of apMC signature in mouse TNBCs responsive (n = 22) or resistant (n = 25) to ICI therapy. (j, k) Tumor cells (j, E0771, n = 5; k, AT3, n = 6) alone or mixed with indicated mast cells were orthotopically injected into B6.Cg-KitW-sh mice, which then received 10 mg/kg anti-PD-1 antibody or IgG isotype. Tumor weights at the endpoint in each group were shown. (l) Flow cytometry of infiltration and functional states of T cells in tumors from each treatment group in (k). (m) Representative flow cytometry plots of surface activation markers in tumor-infiltrating T cells from different treatment groups in (l). (n, o) Growth curves and tumor weights at the endpoint in mice induced with apMC-enriched tumor contexture or in combination with 10 mg/kg CD8 (n, n = 6) or CD4 (o, n = 5) neutralizing antibody in the 4T1 orthotopic tumor model (IgG as isotype control). Data are mean ± s.e.m. (g, h, j–l, n, o) and error bands of the line plot indicate 95% CI (e). Box plots show the median (center line) and interquartile range (bounds of box) as well as the minima and maxima (whiskers) (i). Two-sided unpaired Student’s t-test (g–l, n, o for tumor weight), two-way ANOVA (n, o for tumor volume).

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Extended Data Fig. 6 The role of CD74 on antigen-presenting machinery of mast cells.

(a, b) Negative (a) and positive controls (b) of the CD74 flow cytometry staining. (c) Flow cytometric gating scheme showing CD74hi, CD74int, and CD74neg mast cells. (d) Quantification of surface CD74 expression on CD74hi, CD74int, and CD74neg mast cells. One representative quantification of mean fluorescence intensity (MFI) from six independent experiments was shown. (e) Composite data of activation marker CD69 expression by OT-II CD4+ T cells primed by CD74hi, CD74int, and CD74neg mast cells pulsed with OVA peptide or vehicle (n = 6). (f) Activation marker CD69 expression by OT-I CD8+ T cells primed by CD74hi, CD74int, and CD74neg mast cells pulsed with OVA protein or vehicle (n = 6). (g–i) OT-II CD4+ (g, n = 6), OT-I CD8+ T cell activation (h, n = 6), and IL-2 production measured in the culture supernatant of T cells (i, n = 6) after stimulation with CD74+ or CD74− mast cells transferred with Cd74-siRNA (Si-Cd74) or negative-control siRNA (Si-NC) pulsed with relative OVA. (j) Growth curves and tumor weights at endpoint in orthotopic AT3 tumors treated as indicated (mice receiving the same treatment in siRNA replicates were merged, n = 6). Si-NC apMCs were transfected with negative-control siRNA. Si-Cd74 apMCs were transfected with Cd74-siRNA, and two Cd74-siRNAs were used. (k) Quantification of CD4+ and CD8+ T cell densities by IHC staining from FFPE tumor sections in (j, n = 6). (l) Representative images of CD4 and CD8 staining in tumors from different treatment groups in (k). (m) Flow cytometry of infiltration and functional states of CD8+ T cells in tumors from each treatment group in (j, n = 6). (n) Representative flow cytometry plots of CD8+ T cell cytotoxic molecules in tumors from different treatment groups in (m). Data are mean ± s.e.m. (e, f, g–m). Two-sided unpaired Student’s t-test (e–i, j for tumor weight, k, m), two-way ANOVA (j for tumor volume).

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Extended Data Fig. 7 Antigen presentation by mast cells is crucial for priming antitumor T cell immunity.

(a, b) Interference efficiency of Cd74 (Si-Cd74) by siRNA and cell proliferation (shown by Ki67) after 48 h interference in C57BL/6-derived apMCs (a) and mast cell status in tumors at the end of treatment in B6.Cg-KitW-sh mice (b). Three samples were used in each test to ensure reliability. (c, d) The generation (c) and examples of PCR gel identification (d, n = 3 biological replicates) of Cpa3CreERT2Cd74fl/fl mice. In this experiment, the No. 33 mouse was identified positive for both genes. (e) Analysis of Cpa3CreERT2Cd74fl/fl and Cd74fl/fl mice tumors by microscopy (n = 6 biological replicates). Representative staining image of a tumor with a five-color overlay of mast cells (tryptase, green) immune (CD45, white), stromal (vimentin, red), and tumoral (EpCAM, yellow) regions was shown (scale bar 100μm). (f) Mast cell distribution in the stained regions across tumors (n = 6). (g) Distance analysis of mast cells from tumoral, stromal, and immune cells (n = 6). (h) Relative mRNA expression on sorted mast cells in tumors from Cpa3CreERT2Cd74fl/fl and Cd74fl/fl mice. Three samples were used in each test to ensure reliability. (i) GSVA-calculated RNA-seq-derived signature scores of mast cells sorted from Cpa3CreERT2Cd74fl/fl conditional knockout or Cd74fl/fl control mice (n = 6). (j) E0771 tumor cells were orthotopically injected into Cd74fl/fl and Cpa3CreERT2Cd74fl/fl mice, which received tamoxifen for a total of 5 consecutive days. Then, anti-PD-1 antibody or IgG isotype was used beginning on day 10. Growth curves and tumor weights at the endpoint in each group were shown (n = 5). (k) Granzyme B+ and perforin+ cell densities quantified by IHC staining in FFPE tumor sections from Cd74fl/fl and Cpa3CreERT2Cd74fl/fl mice in (j, n = 5). Data are mean ± s.e.m. (a, b, f–k). Two-sided unpaired Student’s t-test (a, b, g–i, j for tumor weight, k), two-way ANOVA (f, j for tumor volume).

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Extended Data Fig. 8 Cromolyn mobilizes antigen-presenting machinery of mast cells.

(a) Comparison of activity scores for indicated gene signatures between apMCs and other functional states in human TNBC. A total of 1,064 mast cells were included. (b, c) In vitro assay to identify clinically available medications that could facilitate antigen-presenting machinery of mast cells. Human (b, n = 5) and mouse (c, n = 3) mast cells were cultured in individual medium with or without indicated medications for 5 days. Flow cytometric analyses for the percentage of MHC-II+, CD40+, CD80+, and CD86+ cells are shown (compared with vehicle). ns, P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001. (d) OT-II CD4+ cell activation after stimulation with cromolyn or vehicle-pretreated mast cells via direct or indirect coculture system (n = 6). (e) OT-II CD4+ cell activation after stimulation with cromolyn or vehicle-pretreated mast cells (n = 6). (f) OT-II CD4+ cell activation after stimulation with cromolyn or vehicle-pretreated mast cells isolated from Cpa3CreERT2Cd74fl/fl conditional knockout or Cd74fl/fl control mice (n = 6). (g) Hepatic and renal function levels measured from 4T1 tumor-bearing mice at the endpoint in Fig. 3n (n = 6). ALT, alanine aminotransferase; AST, aspartate aminotransferase. (h) Flow cytometry and Immunohistochemistry staining analyses in 4T1 tumors from each treatment group in Fig. 3n (n = 6). (i) Flow cytometry and Immunohistochemistry staining analyses in AT3 tumors from each treatment group in Fig. 3o (n = 6). (j) AT3 tumor cells were orthotopically injected into wild-type and B6.Cg-KitW-sh mice, which then received cromolyn or vehicle (n = 6). Growth curves and tumor weights at the endpoint in each group were shown. (k) Growth curves and tumor weights at the endpoint in mice treated with cromolyn or in combination with ACK2 neutralization or IgG isotype control in 4T1 orthotopic tumors (n = 6). (l) Functional markers of CD8+ T cells measured using flow cytometry from tumors in Fig. 3p (n = 5). Relative MFI of markers expressed on CD8+ T cells in each group compared with that in the Cd74fl/fl + control group was shown. MFI, mean fluorescence intensity. Data are mean ± s.e.m. Two-sided Mann-Whitney test (a), unpaired Student’s t-test (b–i, j, k for tumor weight, l), two-way ANOVA (j, k for tumor volume).

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Extended Data Fig. 9 The antitumor profile of cromolyn is dependent on its effect on mast cells.

(a) Growth curves and tumor weights at the endpoint in mice treated with 30 mg/kg fexofenadine (histamine antagonist) alone or in combination with 10 mg/kg anti-ACK2 neutralizing antibody (to deplete mast cells), anti-CSF1R neutralizing antibody (to deplete macrophages), or isotype control in the AT3 orthotopic tumor model (n = 5). (b, c) Growth curves and tumor weights at the endpoint in B6.Cg-KitW-sh mice (b, n = 5) or Cpa3CreERT2Cd74fl/fl mice (c, n = 5) treated with 30 mg/kg fexofenadine and 10 mg/kg anti-PD-1. (d) Growth curves and tumor weights at the endpoint in mice transferred with mast cells (CD45+CD117+), macrophages (CD45+F4/80+), dendritic cells (CD45+CD11c+), or other cells (CD45+CD117−F4/80−CD11c−, excluding mast cells, macrophages, and dendritic cells) pretreated with vehicle (Veh) or cromolyn (Cro). After cultured ex vivo for 3 days, cells were washed and transferred into mice bearing AT3 orthotopic tumors (n = 5). (e) Individual cells were sorted via flow cytometry according to classic surface markers and treated as indicated (under the condition of ± cromolyn and ± tumor lysate) for 12 h. After washing out, we cocultured these cells with healthy donor-derived T cells at 1:5 ratio for 24 h, which were then used for tumor-killing assay (1:5 ratio for 48 h). (f) Experimental groups of the study design. To avoid potential bias, we compared each cromolyn-treatment group versus vehicle-treatment group under the same condition (as per tumor/normal lysate and cell type). (g, h) Quantification of relative tumor viability from MDA-MB-231 (g) and Hs578T (h) cell lines, where MCF10A normal breast epithelial cells were used as normal control (n = 6). (i) Quantification of relative tumor viability from patient-derived organoids. In this study, each tumor was cut into small pieces and enzymatically digested. Then, digested tissues were equally mixed and separated into two parts for generating PDO and tumor lysate, respectively, and paired peritumoral tissues (≥2 cm from tumor region) were used as normal control (n = 10 per group, every point represents the mean value of 3 replicates). Data were normally distributed (Shapiro-Wilk test P > 0.05) and compared using two-sided paired t test. (j) Proliferation of indicated cells measured by CCK-8 reagent (n = 3). Two-way ANOVA was used for comparison. Data are mean ± s.e.m. (a–d, g–j). Two-sided unpaired Student’s t-test (a–d for tumor weight), paired Student’s t-test (g–i), two-way ANOVA (a–d for tumor volume, j).

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Extended Data Fig. 10 Additional information of combining cromolyn and PD-1 blockade in anti-PD-1-refractory TNBC.

(a–c) CD8+ and granzyme B+ cell densities quantified by IHC staining from FFPE tumor sections from 4T1 (a, n = 12), 66c14 (b, n = 8), and AT3 orthotopic models (c, n = 8). (d) Schematic diagram of the cromolyn arm in the Renaissance trial. (e) Representative images from one responding patient with confirmed partial response. CT scans from patient Cro-002 at four separate time points depicted rapid tumor reduction following combination treatment. Scale bar, 10 cm. (f, g) Kaplan-Meier estimate of progression-free survival as assessed per RECIST v1.1 by radiologists (f) and overall survival (g). Analysis was performed on 31 August 2023. (h) Flow cytometry gating strategy for peripheral blood phenotyping. Pseudocolor dot plots of PBMC from a representative patient showing the gating strategy used to identify the different immune cell populations by flow cytometry. After gating live immune cells (Zombie−CD45+), individual subsets were defined as follows: CD4+ T cells (CD3+CD4+CD8−), CD8+ T cells (CD3+CD4−CD8+), γδT cells (CD3+γδ-TCR+), naive CD8+ T cells (CD3+CD4−CD8+CCR7+), activated CD8+ T cells (CD3+CD4−CD8+PD-1+), regulatory CD4+ T cells (CD3+CD4+CD8−CD25+Foxp3+), B cells (CD3−CD19+), NK cells (CD3−CD56+), cytotoxic NK cells (CD3−CD56+CD16+), NKT cells (CD3+CD56+), and neutrophils (CD15+CD16+). Data are mean ± s.e.m. (a–c). Two-sided unpaired Student’s t-test (a–c).

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Wu, SY., Jin, X., Liu, Y. et al. Mobilizing antigen-presenting mast cells in anti-PD-1-refractory triple-negative breast cancer: a phase 2 trial. Nat Med 31, 2405–2415 (2025). https://doi.org/10.1038/s41591-025-03776-7

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