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Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition in metastatic TNBC

Nature Cancer volume 7, pages 435–450 (2026)Cite this article

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Abstract

Immune checkpoint inhibition (ICI) benefits only a subset of patients with metastatic triple-negative breast cancer and determinants of response remain unclear. We assembled a longitudinal cohort of 103 female patients from the phase 2 TONIC trial, with samples spanning primary tumors, pretreatment metastases and on-treatment metastases during nivolumab therapy. We profiled 37 proteins in 270 tumors using highly multiplexed imaging and developed SpaceCat, an open-source pipeline that extracts more than 800 imaging features per sample, including cell density, diversity, spatial interactions and functional marker expression. Metastatic but not primary tumors contained features predictive of outcome. Spatial metrics such as immune diversity and T cell infiltration at tumor borders were most informative, while ratios of T cells to cancer cells and PDL1 on myeloid cells were also associated with response. Multivariate models stratified patients with the highest performance on treatment (area under the curve = 0.90). Bulk RNA-seq confirmed the predictive value of on-treatment samples. These findings highlight the value of longitudinal profiling to resolve evolving tumor microenvironment dynamics driving ICI response.

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Fig. 1: Study design, workflow and feature extraction.
Fig. 2: SpaceCat feature extraction pipeline.
Fig. 3: Extracted microenvironmental characteristics associated with patient response.
Fig. 4: Evolution of features associated with response.
Fig. 5: Multivariate modeling to predict response.

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Data availability

All processed imaging data from this study, including antibody staining, cell segmentation masks, and SpaceCat outputs, are publicly available online (https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1288). All image analysis files are available from Zenodo (https://doi.org/10.5281/zenodo.14112852)86. The DNA-seq and RNA-seq data, as well as the response information, are available for academic use subject to the limitations of the provided informed consent. The DNA-seq and RNA-seq data from tumor biopsies of the patients with TNBC treated in the TONIC-1 trial stage 1 were deposited to the European Genome–Phenome Archive under accession number EGAS0001003535. The RNA-seq and DNA-seq data from the patients with TNBC treated in TONIC-1 trial stage 2 are not yet deposited in a public repository pending ongoing work. Both the already-deposited and the not-yet-deposited sequencing data, as well as source data supporting the findings of this study, will be made available from the corresponding author (m.kok@nki.nl) for academic use, within the limitations of the provided informed consent. Data will not be made available for commercial use. A first response to the request will be sent in <4 weeks. Data requests will be reviewed by the corresponding author and IRB of the Netherlands Cancer Institute and, after approval, applying researchers will have to sign a data transfer agreement with the Netherlands Cancer Institute. Source data are provided with this paper.

Code availability

The code to generate the figures in this paper (https://github.com/angelolab/publications/tree/main/2024-Greenwald_Nederlof_etal_TONIC), the low-level processing code (https://github.com/angelolab/toffy), the segmentation and cell assignment pipelines (https://github.com/angelolab/ark) and SpaceCat (https://github.com/angelolab/SpaceCat) are available from GitHub.

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Acknowledgements

We thank the patients and their families for taking part in the TONIC trials. TONIC trial costs were supported by Bristol Myers Squibb. We thank all supporting clinical trial staff, particularly nurse specialists and the Departments of Medical Oncology, Surgery, Radiology and Pathology of the participating centers. We thank the Netherlands Cancer Institute Core Facility of Molecular Pathology and Biobanking for their support in processing of samples. Support with title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute (NCI). Figures and graphics were created with BioRender.com. We thank T. Murty for constant support. N.F.G. was supported by NCI CA246880, NCI CA264307 and the Stanford Graduate Fellowship. L.M. was supported by the Stanford School of Medicine Dean’s Fellowship. Collection and processing of samples was made possible by the Dutch Cancer Foundation (grant 2016-10510). Research in the laboratory of M.K. is funded by the Netherlands Organization for Scientific Research and Victoria’s Secret Global Fund for Women’s Cancers Rising Innovator Research Grant, in partnership with Pelotonia and the American Association for Cancer Research. M.A. was supported by National Institutes of Health grants 5U54CA20997105, 5DP5OD01982205, 1R01CA24063801A1, 5R01AG06827902, 5UH3CA24663303, 5R01CA22952904, 1U24CA22430901, 5R01AG05791504, 5R01AG05628705, 5U24CA22430903, 3U54HL165445-03S1, 5R01AG05628705 and 5R01AG05791505, the Department of Defense (W81XWH2110143) and other funding from the Wellcome Trust, the Bill and Melinda Gates Foundation, the Cancer Research Institute, the Parker Institute for Cancer Immunotherapy and the Breast Cancer Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Noah F. Greenwald, Iris Nederlof.

Authors and Affiliations

  1. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Noah F. Greenwald, Cameron Sowers, Alex Kong, Sricharan Reddy Varra, Candace C. Liu, Jolene S. Ranek, Adam Kagel, Erin McCaffrey, Christine Camacho Fullaway, Zumana Khair, Yunhao Bai, Hadeesha Piyadasa, Tyler Risom, Alea Delmastro, Felix J. Hartmann, Sean C. Bendall, Marc Bosse & Michael Angelo

  2. Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA

    Noah F. Greenwald & Brennan G. Simon

  3. Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Iris Nederlof, Manon de Graaf, Veerle Geurts & Marleen Kok

  4. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA

    Daisy Yi Ding, Christine Yiwen Yeh, Robert Tibshirani & Christina Curtis

  5. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Seongyeol Park, Kathleen E. Houlahan, Aziz Khan, Lise Mangiante, Cristina Sotomayor-Vivas, Zhicheng Ma & Christina Curtis

  6. Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Leonie Voorwerk

  7. Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Michiel de Maaker

  8. Department of Computational Biology, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE

    Aziz Khan

  9. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Christine Yiwen Yeh & Christina Curtis

  10. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Christine Yiwen Yeh & Christina Curtis

  11. Department of Chemistry, Stanford University School of Humanities and Sciences, Stanford, CA, USA

    Yunhao Bai

  12. German Cancer Research Center (DKFZ), Systems Immunology & Single-Cell Biology, Heidelberg, Germany

    Felix J. Hartmann

  13. Division of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Ton N. Schumacher

  14. Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands

    Ton N. Schumacher

  15. Department of Pathology, University of Amsterdam, Amsterdam, the Netherlands

    Marc J. van de Vijver

  16. Department of Statistics, Stanford University, Stanford, CA, USA

    Robert Tibshirani

  17. Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Hugo M. Horlings

  18. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Christina Curtis

  19. Division of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    Marleen Kok

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  1. Noah F. Greenwald
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  2. Iris Nederlof
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Contributions

N.F.G., I.N., C.C., M.A. and M.K. conceptualized the project. I.N. collected the longitudinal samples and clinical data. M.d.G. collaborated on the clinical data collection. L.V. and V.G. coordinated the TONIC trial procedures. M.d.M. processed the FFPE tissues for IHC, isolated the DNA and RNA from tissue biopsies and constructed the TMAs. M.J.v.d.V. and H.M.H. annotated the FFPE tissue for TMA construction and provided a pathology review of each tissue slide. M.J.v.d.V. funded the longitudinal sampling and logistics of the project. T.N.S. and S.C.B. provided guidance. N.F.G., C.C.F., Z.K., Y.B., E.M., H.P., T.R., A.D., F.J.H. and M.B. generated the MIBI data. N.F.G., C.S., D.Y.D., A. Kong, I.N., S.R.V., E.M., C.C.L., J.S.R., A. Kagel, B.G.S. and R.T. processed and analyzed the MIBI data. S.P., K.E.H., A. Khan, N.F.G., D.Y.D., C.C.L., C.Y.Y., L.M., C.S.-V. and Z.M. processed and analyzed the DNA-seq and RNA-seq data. N.F.G., I.N., C.S., D.Y.D., S.P., A. Kong and S.R.V. generated the figures. N.F.G., I.N., C.C., M.K. and M.A. wrote the paper. C.C., M.K. and M.A. supervised the project. All authors provided feedback on the paper.

Corresponding authors

Correspondence to Christina Curtis, Marleen Kok or Michael Angelo.

Ethics declarations

Competing interests

N.F.G. is an advisor for Cellformatica. T.N.S. is advisor for Allogene Therapeutics, Asher Bio, Merus, Neogene Therapeutics and Scenic Biotech, is a stockholder in Allogene Therapeutics, Asher Bio, Cell Control, Celsius, Merus and Scenic Biotech and is venture partner at Third Rock Ventures, all outside of the current work. M.K. reports research funding to the institute from BMS, Roche and AstraZeneca/MedImmune and an advisory role/speakers fee (all compensated to the institute) for Alderaan, BMS, Domain Therapeutics, Medscape, Roche, MSD and Daiichi Sankyo, outside the submitted work. M.A. is a named inventor on patent US20150287578A1, which covers the mass spectrometry approach used by MIBI to detect elemental reporters in tissue using secondary-ion mass spectrometry. M.A. is a board member and shareholder in IonPath, which develops and manufactures the commercial MIBI platform. The other authors declare no competing interests.

Peer review

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Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 ROI selection and sample summary.

a Workflow for selecting cores to be included in the study. Hematoxylin and eosin (H&E) slides were digitized in slidescore.com, annotated by a dedicated breast cancer pathologist, and demarcated with areas of interest. Each identified region was punched with a 1.5 mm core and placed onto a tissue microarray (TMA). b Illustrative examples of the H&E image of the entire core, the cropped inset of the portion of the core selected for MIBI analysis, and the corresponding MIBI image generated from the selected region. c Histogram showing the number of fields-of-view (x-axis) acquired on MIBI across all timepoints from all patients (y-axis) d Upset plot showing the overlap of distinct timepoints of MIBI data across the patients in the cohort. e Venn diagram showing the overlap between DNA, RNA, and MIBI data for baseline samples (N = 93 patients) f Venn diagram showing the overlap between RNA and MIBI data for pre-nivo samples (N = 77 patients) g Same as f) for on-nivo (N = 66 patients).

Source data

Extended Data Fig. 2 Cell clustering scheme and cell type prevalence.

a Diagram illustrating the three levels of cell clustering (broad, intermediate, detailed), and the relationship between clusters in each level b Number of cells of each cell type in the broad clusters c Number of cells of each cell type in the intermediate clusters d Number of cells of each cell type in the detailed clusterings. b, c, d are all based on all primary and metastatic tumors in the sample set. e Mean count of cell types (broad clusters) for each timepoint f Mean count of cell types (intermediate clusters) for each timepoint g Average proportion of each intermediate cancer subpopulation in metastatic samples (N = 101 patients) h Average proportion of each intermediate structural subpopulation in metastatic samples (N = 101 patients) i Average proportion of each intermediate immune subpopulation in metastatic samples (N = 101 patients) j PD1 expression in Wang et al. Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound.

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Extended Data Fig. 3 Feature extraction pipeline schematics.

a Cartoon illustrating how compartment masks are defined b Table illustrating how functional marker thresholds are used to binarize cells into positive or negative for each marker; these assignments are then used to generate positivity proportion statistics per image c Schematic showcasing how diversity scores are calculated d Cartoon showing how the radius around a cell is used for neighborhood diversity e Cartoon showing how cell distances are used to compute the linear distance feature f Cartoon showing how the mixing score is calculated, along with examples of high and low mixing g Schematic showing how the fiber segmentation pipeline is used to generate features h Schematic showing how the extracellular matrix pipeline is used to generate features.

Extended Data Fig. 4 Quantification of features across tumor compartments.

a Heatmap showing features that are consistent across compartments. Each row represents a different feature, and each column is a distinct compartment. Features are normalized to the whole image value b Same as above, but for features that are enriched in specific compartment(s) c Histogram showing threshold used to identify varying vs. non-varying compartment features d Distribution of the CD8 T / CD4 T ratio, a feature that changed across compartments (n = 895 features) e Distribution of Smooth Muscle density, a feature that changed across compartments (n = 1084 features) f Distribution of Ki67 positivity in Cancer 1, a feature that did not change across compartments g Distribution of PD-L1 positivity in CD68 macrophages, a feature that did not change across compartments (n = 956 features) h Heatmap showing expression of markers across distinct tile-based clusters (n = 507 features) i Proportion of TIM3+ Structural cells across ECM neighborhoods (n = 227 FOVs) j Proportion of TIM3 + CD68 Macrophages cells across ECM neighborhoods (n = 200 FOVs) k Proportion of PDL1 + CD68 Macrophages cells across ECM neighborhoods (n = 200 FOVs) Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound.

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Extended Data Fig. 5 Evolution of the TME through treatment.

a Summary of features that changed between baseline and on-nivo samples across all patients b Ratio of Cancer cells to T cells in stroma border at baseline and on-nivo timepoint across all patients c Proportion of all CD45RO+ cells at baseline and on-nivo timepoint across all patients d Diversity of cells surrounding Mac_Other cells at baseline and on-nivo timepoint across all patients e Summary of features that changed between baseline and on-nivo samples across paired samples(3.5) f Ratio of Cancer cells to T cells in stroma border at baseline and on-nivo timepoint across paired samples g Ratio of Cancer cells to unclassified cells in stroma border at baseline and on-nivo timepoint across paired samples h Ratio of NK cells to T cells at baseline and on-nivo timepoint across paired samples For panels b-d and f-h, N = 101 patients.

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Extended Data Fig. 6 Relationship between MIBI- and RNA-based features.

a Distribution of ssGSEA normalized enrichment scores (NES) across MSigDB signatures related to cytokine signaling (N = 210 samples) b Volcano plot showing correlation coefficient (x-axis) and p-value from an unpaired two-sided t-test (y-axis) for pairs RNA- and MIBI-based features c Correlation between HLA1+ cells (MIBI) and MHCI (RNA). Scatter points represent individual data values, the line indicates the linear regression fit, and the shaded area shows the 95% confidence interval of the fit. d Correlation between CD45RO + CD4 + T cells at the border (MIBI) with a TGFb gene score (RNA) e Correlation between HLA1+ Cancer cells (MIBI) and interferon signaling (RNA) f Correlation between diversity surrounding CD8 T cells in the cancer core (MIBI) with a signature for T cell exhaustion (RNA) g Enrichment of MIBI-RNA feature pairs in top correlated features based on the compartment the MIBI feature is defined in Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound.

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Extended Data Fig. 7 Outcomes associations.

a Enumeration of the number of top MIBI features which are ratios compared to the number which are densities b Volcano plot of RNA-seq features associated with response c Top RNA features associated with response, organized by biological category d Comparison of number of DNA and RNA features associated with response e Number of RNA-seq features associated with response by timepoint f The number of times a given MIBI feature was repeated across distinct timepoints among the top 100 features g Same as Fig. 4b, but with all labels included h Types of features associated with response from baseline metastatic timepoint i Top 50 features associated with response from baseline timepoint, colored by compartment and sorted by feature type j Cancer/Immune mixing score stratified by response status across timepoints: primary (N = 51, 7 Yes / 44 No), baseline (N = 50, 10 Yes / 40 No), pre nivo (N = 39, 7 Yes / 32 No), on nivo (N = 37, 7 Yes / 30 No). N=number of patients. k Proportion of Ki67+ Cancer 1 cells stratified by response status across timepoints l Proportion of GLUT1+ Cancer 1 cells stratified by response status across timepoints Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound. For j and k: primary (N = 59, 9 Yes / 50 No), baseline (N = 74, 12 Yes / 62 No), pre nivo (N = 67, 11 Yes / 56 No), on nivo (N = 57, 8 Yes / 49 No). N=number of patients.

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Extended Data Fig. 8 Timepoint-specific features.

a Box plots showing the distribution of the B / NK ratio in the cancer core feature in responders and non-responders, across the four distinct timepoints in the cohort, primary (N = 21, 4 Yes / 17 No), baseline (N = 15, 5 Yes / 10 No), pre nivo (N = 13, 2 Yes / 11 No), on nivo (N = 15, 4 Yes / 11 No). b Same as a), with CD68 Macrophage density as the feature, primary (N = 59, 9 Yes / 50 No), baseline (N = 79, 12 Yes / 67 No), pre nivo (N = 70, 13 Yes / 57 No), on nivo (N = 62, 12 Yes / 50 No) c Same as a), with Vim+ in CD4 T cells as the feature, primary (N = 53, 7 Yes / 46 No), baseline (N = 58, 11 Yes / 47 No), pre nivo (N = 52, 9 Yes / 43 No), on nivo (N = 52, 12 Yes / 40 No) d Same as a), with Structural cell density in the stroma border as the feature, primary (N = 59, 9 Yes / 50 No), baseline (N = 76, 12 Yes / 64 No), pre nivo (N = 66, 12 Yes / 54 No), on nivo (N = 58, 8 Yes / 50 No) e Same as a), with Other / Cancer cell ratio in the stroma core as the feature, primary (N = 59, 9 Yes / 50 No), baseline (N = 78, 12 Yes / 66 No), pre nivo (N = 69, 13 Yes / 56 No), on nivo (N = 62, 12 Yes / 50 No) f Same as a), with Nk / Other cell ratio in the stroma border as the feature, primary (N = 56, 7 Yes / 49 No), baseline (N = 72, 12 Yes / 60 No), pre nivo (N = 65, 12 Yes / 53 No), on nivo (N = 53, 7 Yes / 46 No) Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound. N=number of patients for all.

Source data

Extended Data Fig. 9 Multivariate modeling approach and benchmarking.

a Diagram illustrating the cross validation approach used for model training b Histogram showing the number of times the top features were selected across the 10 different iterations of model training c Comparison of the importance score of the top features identified by the model across distinct timepoints, stratified by MIBI vs. RNA d Overlap across timepoints of top features identified by the MIBI models e Overlap across timepoints of top features identified by the RNA models f AUROC evaluated for the on-nivo MIBI data using separate train, val, test split without cross validation. g Same as above, for AUPRC h Accuracy of multivariate model with decreasing number of retained features i Relative accuracy of combined MIBI + RNA model compared to best performing unimodal model Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound.

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Extended Data Fig. 10 Comparison with previously identified predictive features.

a Overlap in pre-treatment predictive features originally identified in Wang et al. b Overlap in pre-treatment predictive features after running SpaceCat on the data from Wang et al. c Proportion of Ki67+ Cancer cells stratified by response across timepoints in Wang et al., baseline (N = 109, 55 pCR / 54 RD), on treatment (N = 67, 28 pCR / 39 RD) d Proportion of Ki67 + CD8 T cells stratified by response across timepoints in Wang et al., baseline (N = 113, 56 pCR / 57 RD), on treatment (N = 97, 48 pCR / 49 RD) e Density of Cancer cells stratified by response across timepoints in Wang et al., baseline (N = 108, 54 pCR / 54 RD), on treatment (N = 93, 47 pCR / 46 RD) f Comparison of SpaceCat features, original features, and combined features to predict outcome in Wang et al. data g Comparison of SpaceCat features, Wang et al. features, and combined features to predict outcome in TONIC data h Overlap in on-treatment predictive features originally identified in Wang et al. (4.2) i Overlap in on-treatment predictive features after running SpaceCat on the data from Wang et al. j Distribution of top features across timepoints when using only features defined in Wang et al. k Change in top 100 predictive features when including cell subsets defined in Wang et al. l Change in multivariate model specific cell clusters that are predictive, as well as multivariate results Box plot: Lower bound is 1st quartile, center is median, upper bound is 3rd quartile, whiskers extend to 1.5*IQR beyond bound. N=number of patients.

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Greenwald, N.F., Nederlof, I., Sowers, C. et al. Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition in metastatic TNBC. Nat Cancer 7, 435–450 (2026). https://doi.org/10.1038/s43018-026-01114-5

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