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Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy

Nature Cancer volume 5, pages 642–658 (2024)Cite this article

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Abstract

Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.

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Fig. 1: A machine-learning framework for large-scale identification and validation of sarcoma cell states and ecosystems.
Fig. 2: Discovery and characterization of sarcoma-specific cell states.
Fig. 3: Association of cell-state abundances with patient outcomes across cohorts.
Fig. 4: Discovery of sarcoma multicellular communities.
Fig. 5: Characterization of SEs.
Fig. 6: Association of SEs with patient outcomes.
Fig. 7: Predicting STS response to ICI with SEs.
Fig. 8: Validation of SE3 as a predictor of response to ICI in STSs.

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Data availability

The bulk RNA-seq data, scRNA-seq data and spatial transcriptomics data new to this paper have been deposited at the GEO under accession codes GSE213065, GSE212527 and GSE212526, respectively. Previously published RNA-seq, microarray and scRNA-seq data that were reanalyzed here are available through the TCGA Research Network (http://cancergenome.nih.gov/), through GEO under accession codes GSE21050 and GSE131309, through the European Genome–Phenome Archive under dataset ID EGAD00001004439, through the ‘Imvigor210CoreBiologies’ R package66 and in the supplementary material of the published papers41,67,68,69. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The original EcoTyper code is available for nonprofit academic use at https://github.com/ejmoding/sarcomaecotyper and https://github.com/digitalcytometry/ecotyper. Any additional information is available from the corresponding author upon request.

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Acknowledgements

We thank the patients and families who participated in this study. We acknowledge N. Pillay and the University College London for providing sequencing data included in the analyses of this paper. This work used the Stanford Genomics Shared Resource, which is supported by National Institutes of Health grants S10OD025212 and 1S10OD021763. This work was supported by the Department of Defense (W81XWH-22-1-0161, E.J.M.), the National Cancer Institute (K08CA25542501, E.J.M.), the Stanford Cancer Institute, a National Cancer Institute-designated Comprehensive Cancer Center (N.Q.B. and E.J.M.), the My Blue Dots organization (E.J.M.) and the Tad and Diane Taube Family Foundation (D.G.M., M.v.R. and E.J.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The schematic in Fig. 1 was created with BioRender.com.

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Author notes

  1. These authors contributed equally: Ajay Subramanian, Neda Nemat-Gorgani, Timothy J. Ellis-Caleo.

Authors and Affiliations

  1. Department of Radiation Oncology, Stanford University, Stanford, CA, USA

    Ajay Subramanian, Neda Nemat-Gorgani, Timothy J. Sears, Anish Somani, Eniola Oladipo, Michael S. Binkley & Everett J. Moding

  2. Department of Medicine, Stanford University, Stanford, CA, USA

    Timothy J. Ellis-Caleo

  3. Department of Pathology, Stanford University, Stanford, CA, USA

    David G. P. van IJzendoorn, Matt van de Rijn & Gregory W. Charville

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

    Bogdan A. Luca & Aaron M. Newman

  5. Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA

    Maggie Y. Zhou, Ileana A. Torres, Kristen N. Ganjoo & Nam Q. Bui

  6. Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Martina Bradic

  7. Department of Orthopedic Surgery, Stanford University, Stanford, CA, USA

    Christin New, Deborah E. Kenney, Raffi S. Avedian, Robert J. Steffner & David G. Mohler

  8. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    William D. Tap & Sandra P. D’Angelo

  9. Weill Cornell Medical Center, New York, NY, USA

    William D. Tap & Sandra P. D’Angelo

  10. Stanford Cancer Institute, Stanford University, Stanford, CA, USA

    Everett J. Moding

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  1. Ajay Subramanian
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Contributions

T.J.S., N.Q.B., G.W.C., A.M.N. and E.J.M. were responsible for conceptualization. A. Subramanian, A. Somani, D.v.I., B.A.L., G.W.C., A.M.N. and E.J.M. were responsible for the methodology. A. Subramanian, A. Somani, B.A.L. and E.J.M. were responsible for software. A. Subramanian, T.J.E., T.J.S., A. Somani and E.J.M. were responsible for formal analysis. A. Subramanian, N.N., T.J.E., D.v.I., T.J.S., A. Somani, M.Y.Z., N.Q.B., G.W.C. and E.J.M. were responsible for investigation. D.v.I., B.A.L., M.B., I.A.T., E.O., C.N., D.E.K., R.S.A., R.J.S., D.G.M., W.D.T., S.P.D., M.v.R., K.N.G., N.Q.B. and G.W.C. were responsible for resources. A. Subramanian, N.N., T.J.E. and E.J.M. were responsible for data curation. E.J.M. was responsible for writing the original draft. A. Subramanian, N.N., T.J.E., D.v.J., T.J.S., A. Somani, B.A.L., M.Y.Z., M.B., I.A.T., E.O., C.N., D.E.K., R.S.A., R.J.S., M.S.B., D.G.M., W.D.T., S.P.D., M.v.R., K.N.G., N.Q.B., G.W.C., A.N.M. and E.J.M. were responsible for review and editing. A. Subramanian, T.J.E., A Somani and E.J.M. were responsible for visualization. M.v.R., N.Q.B., A.M.N. and E.J.M. were responsible for supervision. D.G.M., N.Q.B. and E.J.M. were responsible for funding acquisition.

Corresponding author

Correspondence to Everett J. Moding.

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W.D.T. has served as a consultant/advisor for Eli Lilly, Daiichi Sankyo, Deciphera, Foghorn Therapeutics, AmMAx Bio, Novo Holdings, Servier, Medpacto, Ayala Pharmaceuticals, Kowa Research Inst., Epizyme (Nexus Global Group), Bayer, Cogent Biosciences, Amgen and PER; has received research funding from Novartis, Eli Lilly, Plexxikon, Daiichi Sankyo, Tracon Pharma, Blueprint Medicines, Immune Design, BioAlta and Deciphera; and has a patent on companion diagnostics for CDK4 inhibitors (4/854,329). S.P.D. has served as a consultant/advisor for EMD Serono, Amgen, Nektar, Immune Design, GlaxoSmithKline, Incyte, Merck, Adaptimmune, Immunocore, Pfizer, Servier, Rain Therapeutics, GI Innovations and Aadi Bioscience and has received research funding from EMD Serono, Amgen, Merck, Incyte, Nektar, Bristol-Meyers Squibb and Deciphera. E.J.M. has served as a paid consultant for Guidepoint. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Summary of patient cohorts and datasets utilized for discovery and validation of sarcoma cell states and ecosystems.

a, Two cohorts of patients with localized soft tissue sarcomas (STSs) were analyzed. Soft tissue sarcomas profiled by the Cancer Genome Atlas (TCGA)16 and a previously published group of patients with undifferentiated pleomorphic sarcoma (UPS)21 profiled by RNA sequencing were combined to form the training cohort. Initial identification of sarcoma cell states and ecotypes was performed in non-leiomyosarcoma (LMS) soft tissue sarcomas from TCGA, then the full training cohort was used to assess associations between cell states and ecotypes with patient outcomes. A previously published cohort of patients with localized STS profiled by microarray was used as the validation cohort for associations between cell states and ecotypes with patient outcomes. b, Cell states were validated using single-cell RNA sequencing from 12 previously published synovial sarcomas (SS)15 and 4 LMS and 3 UPS new to this work. c, The spatial distributions of cell states and ecotypes were validated using spatial transcriptomics analysis of 4 STSs.

Extended Data Fig. 2 Identification of malignant sarcoma cells and validation of sarcoma ecotyper cell states using scRNA-Seq.

a, Representative plot of inferred copy number alterations (CNAs) in single cells clustered into two groups using inferCNV from SRC164. Amplifications and deletions are shown across each chromosome. b-d, t-SNE plots of scRNA-Seq profiles from SRC164 colored by (b) assignment to normal or malignant cells, (c) detection of CNAs, and (d) differential similarity to sarcomas of the same histology profiled by bulk RNA-Seq compared with normal cells profiled by scRNA-Seq. e, Significance of EcoTyper cell state recovery across scRNA-Seq datasets measured by permutation testing and aggregated into a meta z score using Stouffer’s method. Z-scores > 1.65 (one-sided P value < 0.05) are considered significant.

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Extended Data Fig. 3 Recovery of malignant cells across soft tissue sarcomas using CIBERSORTx.

a-b, Stacked bar plots displaying the percentage of patients with each soft tissue sarcoma histology from the training cohort with (a) fibroblast-like cells and (b) epithelial-like cells identified by CIBERSORTx. Synovial sarcoma = SS, leiomyosarcoma = LMS, malignant peripheral nerve sheath tumor = MPNST, undifferentiated pleomorphic sarcoma = UPS, liposarcoma = LPS. c, Scatter plots showing the correlation between tumor purity and the combined abundance of epithelial-like cells and fibroblast-like cells by CIBERSORTx. Spearman’s correlation coefficient and two-sided P value are displayed on the graph.

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Extended Data Fig. 4 Characterization of epithelial-like malignant cells within soft tissue sarcomas.

a, t-SNE plots displaying expression of epithelial marker genes in malignant sarcoma cells from LMS and UPS tumors profiled by scRNA-Seq. b, t-SNE plots of epithelial vs. mesenchymal differentiation in malignant LMS and UPS cells using three previously described signatures of epithelial to mesenchymal transition34,35,36. Sample and histology for each cell are displayed in Fig. 2c. The gene sets are displayed in Supplementary Table 11.

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Extended Data Fig. 5 Association of cell state abundances with patient outcomes in the validation cohort.

a, Association of sarcoma-specific cell states identified by EcoTyper with distant metastasis-free survival in the validation cohort. Significance was assess using multivariable Cox proportional hazards models including cell state abundance as a continuous variable along with sarcoma histology. P values were calculated using two-sided Wald tests without correction for multiple comparisons. Marker genes are displayed for significantly associated cell states. Patient survival, histologies, and cell state abundances used in the analysis are shown in Supplementary Table 3. Only patients with survival data available were included in the analysis.

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Extended Data Fig. 6 Correlation of sarcoma ecotypes across time and association with genomic alterations.

a, Scatter plots showing the correlation between ecotype abundance across different timepoints from the same patient. Spearman’s correlation coefficients and two-sided P values are displayed. The lines of best fit by linear regression and 95% confidence intervals for the lines of best fit are shown on the graph. The data used for this analysis are shown in Supplementary Table 20. b, c, Box plots displaying (b) the total number of high impact SNVs/Indels and (c) normalized contribution of COSMIC mutational signatures 1, 5, and 13 by sarcoma ecotype (n=79 SE1, 78 SE2, and 33 SE3 sarcomas for both panels). P values were calculated using Kruskal–Wallis tests followed by Dunn’s tests for pairwise comparisons. Boxes show median and quartiles, and whiskers extend to the minimum and maximum value.

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Extended Data Fig. 7 Validation of sarcoma ecotypes using spatial transcriptomics.

a, Distribution of cell states from sarcoma ecotype 3 (SE3) in SCR93, a sarcoma profiled by spatial transcriptomics. Abundance of the cell states that make up SE3 within each spatial transcriptomics spot are shown, and fibroblast-like cell abundance is plotted to show the tumor outline. b, Heat maps displaying Spearman correlation of cell state abundances within spatial transcriptomics spots. c, Distribution of sarcoma ecotypes in three sarcomas profiled by spatial transcriptomics. H&E staining along with the abundance of SEs within each spatial transcriptomics spot are shown. Fibroblast-like cell abundance is plotted to show the tumor outline. Scale bars show 1000 μm. A total of four sarcomas were profiled, and SRC93 is displayed in Fig. 5f.

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Extended Data Fig. 8 Summary of advanced soft tissue sarcoma cohorts.

a, Two cohorts of patients with advanced soft tissue sarcoma treated at Stanford were analyzed based on the type of systemic therapy received. Patients treated with both chemotherapy and ipilimumab/nivolumab were included in both cohorts. The ICI validation cohort consisted of patients with advanced soft tissue sarcomas treated with anti-PD-1 antibodies (pembrolizumab or nivolumab) in combination with experimental immunotherapies (talimogene laherparepvec = T-VEC, bempegaldesleukin = NKTR-214, or epacadostat) as part of 3 clinical trials.

Extended Data Fig. 9 Association of sarcoma ecotypes with response to ICI and chemotherapy.

a, Plot of SE3 abundance in patients with and without 6-month non-progression after starting ipilimumab and nivolumab (n = 8 patients with and 30 patients without 6-month non-progression). P value was calculated using a two-sided Mann–Whitney U-test. b, Receiver operating characteristic curves for prediction of 6-month non-progression on ipilimumab and nivolumab by SE3 abundance, PD-L1 expression, and the presence of tertiary lymphoid structures (TLS). Area under the curve (AUC) and 95% confidence intervals (95% CI) are displayed on the graph. c, Box plot of PD-L1 combined positive score across sarcoma ecotypes (n = 10 SE1, 14 SE2, and 4 SE3 sarcomas). P values were calculated using Kruskal–Wallis tests followed by Dunn’s tests for pairwise comparisons. Boxes show median and quartiles, and whiskers extend to the minimum and maximum value. d, Stacked bar plot of the presence of tertiary lymphoid structures across sarcoma ecotypes. P values were calculated using two-sided Fisher’s exact tests. e, Waterfall plot showing the best response by RECIST criteria for patients with advanced STSs treated with chemotherapy based on sarcoma ecotype assignment. Horizontal dotted lines represent the criteria for progressive disease (20% increase) and partial response (30% decrease). Patients with only non-target disease are plotted at 0%. f, Plot of SE3 abundance in patients with and without a response to chemotherapy (n=6 responders and 31 nonresponders). P value was calculated using a two-sided Mann–Whitney U-test. g, Receiver operating characteristic curves for prediction of response to chemotherapy by SE3 abundance. AUC and 95% CI are displayed on the graph. Patient survival, treatment response, tumor characteristics, ecotype assignments, and ecotype abundances used in the analysis of the chemotherapy and ipilimumab/nivolumab cohorts are shown in Supplementary Tables 18–20. For panels c, d, and e, patients were analyzed based on ecotype assignment, and patients not assigned to an ecotype were not included. For panels a, b, f, and g, patients were analyzed based on ecotype abundance, and all patients were included in the analysis.

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Extended Data Fig. 10 Validation of ICI response prediction by SE3 abundance and changes in sarcoma cell states and ecotypes during treatment with ICI.

a, Receiver operating characteristic curve and area under the curve (AUC) with 95% confidence intervals (95% CI) for prediction of treatment response in the ICI validation cohort. b, Areas under the curve for prediction of response to immune checkpoint inhibition based on SE3 abundance in patients with metastatic bladder cancer and melanoma (n = 348 bladder anti-PD-L1, 172 melanoma anti-PD-1, and 51 melanoma anti-CTLA-4). Error bars display the 95% confidence interval. c, Heatmap of fold change in cell state and sarcoma ecotype abundances on-treatment in the ICI validation cohort. Adjusted P values comparing pre-treatment and on-treatment samples are displayed. P values were calculated using two-sided Wilcoxon signed-rank tests and corrected for multiple hypothesis testing. Patient treatment response and ecotype abundances used in analysis of the ICI validation cohort and bladder/melanoma cohorts are shown in Supplementary Table 21 and Supplementary Table 22, respectively.

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Subramanian, A., Nemat-Gorgani, N., Ellis-Caleo, T.J. et al. Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy. Nat Cancer 5, 642–658 (2024). https://doi.org/10.1038/s43018-024-00743-y

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