Purpose:

To characterize changes in the soft-tissue sarcoma (STS) tumor immune microenvironment induced by standard neoadjuvant therapy with the goal of informing neoadjuvant immunotherapy trial design.

Experimental Design:

Paired pre- and postneoadjuvant therapy specimens were retrospectively identified for 32 patients with STSs and analyzed by three modalities: multiplexed IHC, NanoString, and RNA sequencing with ImmunoPrism analysis.

Results:

All 32 patients, representing a variety of STS histologic subtypes, received neoadjuvant radiotherapy and 21 (66%) received chemotherapy prior to radiotherapy. The most prevalent immune cells in the tumor before neoadjuvant therapy were myeloid cells (45% of all immune cells) and B cells (37%), with T (13%) and natural killer (NK) cells (5%) also present. Neoadjuvant therapy significantly increased the total immune cells infiltrating the tumors across all histologic subtypes for patients receiving neoadjuvant radiotherapy with or without chemotherapy. An increase in the percentage of monocytes and macrophages, particularly M2 macrophages, B cells, and CD4+ T cells was observed postneoadjuvant therapy. Upregulation of genes and cytokines associated with antigen presentation was also observed, and a favorable pathologic response (≥90% necrosis postneoadjuvant therapy) was associated with an increase in monocytic infiltrate. Upregulation of the T-cell checkpoint TIM3 and downregulation of OX40 were observed posttreatment.

Conclusions:

Standard neoadjuvant therapy induces both immunostimulatory and immunosuppressive effects within a complex sarcoma microenvironment dominated by myeloid and B cells. This work informs ongoing efforts to incorporate immune checkpoint inhibitors and novel immunotherapies into the neoadjuvant setting for STSs.

Translational Relevance

Defining the immunologic effects of standard cytotoxic radio- and chemotherapies is of critical importance for integrating immunotherapies into current cytotoxic treatments. We have used multiple, complementary methodologies to characterize the changes in the tumor immune microenvironment pre- versus postneoadjuvant therapy, in a cohort of 32 soft-tissue sarcoma (STS) patients spanning multiple histologic subtypes. The total immune cell infiltration of tumors increased after neoadjuvant therapy and was dominated by professional antigen-presenting cells, namely myeloid and B cells. Neoadjuvant therapy upregulated genes and cytokines associated with antigen presentation, increased the prevalence of CD4+ T cells, and upregulated the T-cell inhibitory checkpoint receptor TIM3. Our findings are relevant to interpret ongoing trials that combine immunotherapies with neoadjuvant cytotoxic therapies and serves to focus future clinical trial designs on the most relevant immune checkpoints, cell types, and pathways to target in combination with neoadjuvant cytotoxic therapy.

Following curative-intent resection, up to half of patients with large, high-grade soft-tissue sarcomas (STS) will recur, often with metastatic disease, after which survival is severely limited (1–4). Radiotherapy decreases the risk of local recurrence after surgical resection for localized STS of the extremity and trunk (5, 6). However, neither peri-operative radiotherapy nor chemotherapy have been convincingly demonstrated to improve overall survival (6, 7). Therefore, development of more effective therapies is needed. Immune checkpoint inhibitors (ICI) have transformed the treatment of selected inflamed solid tumors. For example, targeting the programmed cell death protein 1 (PD-1) pathway has markedly improved long-term survival for a subset of patients with advanced melanoma and non–small cell lung cancer (8, 9). However, most metastatic cancer patients, including those with STS, do not significantly benefit (10–14). Response rate varies among STS subtypes, with undifferentiated pleomorphic sarcoma (UPS), liposarcoma, and synovial sarcoma representing the highest response to immunotherapy (10).

The impact of neoadjuvant therapy on antitumor immunity is under active investigation for multiple cancer types including STS (15–21), colorectal (22, 23), esophageal (17, 18), and non–small cell lung (24) cancers. Defining the immune landscape of STS and the immunologic effects of standard cytotoxic radio- and chemotherapies is of critical importance for integrating immunotherapies into the STS treatment paradigm. In vitro and in vivo experiments in animal models have established the mechanisms by which radiation induces activation of the innate and adaptive immune system (25). Ionizing radiation may activate the cGAS/STING pathway to promote type-I IFN-dependent antigen uptake and cross-presentation of antigens by dendritic cells to drive adaptive antitumor T-cell immunity (26). Neoadjuvant therapy combinations that optimally induce an inflamed tumor microenvironment (TME) with type-I immunity features and predominance of CD8+, CD4+ Th1 T cells and M1-type macrophages may improve response rates to immune checkpoint inhibition and favorably impact survival. Developing a granular understanding of which specific immune cell populations in the TME are modified by radiotherapy or chemotherapies is needed to rationally select combinations of cytotoxic- and immune-therapies that are likely to synergize in the neoadjuvant setting on future clinical trials (27–29).

Sarcomas present a unique opportunity to assess changes to the TME induced by chemo- and radiotherapy using paired patient tissue samples obtained at the time of diagnostic biopsy (preneoadjuvant therapy) and surgical resection (postneoadjuvant therapy). In this project, we sought to better define the immune response to STS and determine how it is modified by neoadjuvant therapy. In a cohort of 32 patients with STS spanning multiple histologic subtypes, with paired pre- and posttreatment tissue, we used multiple orthogonal methods to assay immune markers at both the transcriptional and protein expression level.

Case selection

Retrospective review of an institutional database was performed to identify patients who underwent curative intent resection of histologically confirmed STS following neoadjuvant radiotherapy, with or without neoadjuvant chemotherapy, at the University of Washington (UW; Seattle, WA)/Seattle Cancer Care Alliance (SCCA) from 2007 to 2014. All patients (N = 32), both female and male, received conventionally fractionated radiotherapy (median dose: 50 Gray; range: 48–50.4 Gy). For those receiving chemotherapy (n = 21), 16 (76%) received anthracycline (doxorubicin), ifosfamide, and mesna (AIM); 3 (14%) received vincristine, doxorubicin and cyclophosphamide/ifosfamide and etoposide (VDC/IE); 1 (5%) received ifosfamide alone; and 1 (5%) received gemcitabine and docetaxel. Information regarding chemotherapy administration by histologic subtype can be found in Supplementary Table S1.

Patients included in the study had accessible pre- and posttreatment tumor tissue (i.e., preneoadjuvant treatment core needle biopsy and surgical resection specimen, respectively) obtained at our institution. Patients with nonmetastasizing histologies (e.g., desmoid fibromatosis, chordoma), Kaposi sarcomas, and primary bone sarcomas were excluded. The Ewing sarcoma included were soft-tissue tumors and all analyses were completed on soft-tissue samples. Eligible patients with sufficient tissue archived for analysis were then identified for further histologic and transcriptomic analyses as detailed below. Clinical and pathologic information was collected from patient charts. Tumor size was defined as the largest single dimension of the primary tumor and was extracted from radiology reports. Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) tumor grade and microscopic margin status were extracted from pathology reports. Histologic diagnoses were categorized as undifferentiated pleomorphic sarcoma (UPS), synovial sarcoma, Ewing or Ewing-like sarcoma, leiomyosarcoma, well/dedifferentiated liposarcoma, myxoid round cell liposarcoma (MRCL), or other. Pathologic response was assessed by board-certified anatomic pathologists with subspecialty training in bone and soft-tissue pathology. We defined a favorable or major pathologic response as ≥90% tumor necrosis or hyalinization on surgical pathology following neoadjuvant therapy. All analyses were performed on de-identified data. The study was approved as a minimal-risk protocol by the Cancer Consortium Institutional Review Board at Fred Hutchinson Cancer Research Center (Seattle, WA). All investigations were performed according to the principles expressed in the Declaration of Helsinki.

Multiplex IHC and image cytometry

Hematoxylin and eosin (H&E)-stained tissue sections and formalin-fixed, paraffin-embedded (FFPE) blocks were retrieved from the Pathology archives at UW. H&E slides from the entire resection specimen of each eligible case were reviewed by a board-certified bone and soft-tissue pathologist. Cases deemed to have adequate viable tissue before and after neoadjuvant therapy were then selected for further analysis. Necrotic areas in the sections were excluded from the multiplex IHC (mIHC) analysis.

mIHC was performed as described previously (30). Briefly, FFPE tissues were stained on a Leica BOND Rx autostainer (Leica) using the Akoya Opal Multiplex IHC assay (Akoya Biosciences) with high stringency washes after the secondary antibody and Opal fluor applications using TBST (0.05M Tris, 0.3M NaCl, and 0.1% Tween-20, pH 7.2–7.6). Primary antibodies were incubated at room temperature for 1 hour followed by the OPAL Polymer HRP Mouse plus Rabbit (PerkinElmer). Two different panels were used for mIHC, each of them including six different primary antibodies. Panel 1 includes: CD3, FOXP3, CD68/CD163, CD206, PD1, and VISTA. Panel 2 includes: CD4, CD8, CD33, CD66b, HLA-DR, and Ki67 (Supplementary Table S2). Slides were mounted with ProLong Gold and images were acquired with the Akoya Vectra 3.0 Automated or Polaris Automated Imaging System. Images were spectrally unmixed using Akoya Phenoptics inForm software and explored as multi-image TIFFs for analysis in HALO software (Indica Labs). Nuclear staining in HALO was used to detect individual cells for the multi-spectral image analysis using automated cell counting. Review of tissue staining, selection of regions of interest, segmentation for cell definition, and training algorithm for analysis was supervised by a clinically trained pathologist. Microsoft Excel (RRID:SCR_016137) was used to convert the exported object data csv files into a format compatible with FlowJo 10 (RRID:SCR_008520, Becton Dickinson), by multiplying the fluorescent intensity of each marker on each object by a factor of 106 to convert decimals to integer form. The processed csv files were transformed into fcs files and analyzed in FlowJo according to standard methods, hence allowing for quantitative multiplex analysis.

NanoString transcriptomic analysis

Bulk tumor RNA was extracted from FFPE tissue as described previously (31). Briefly, 5-μm-thick FFPE sections were deparaffinized in xylene, rehydrated in ethanol, and then lysed on the slide by adding 10 to 50 mL PKD buffer (Qiagen Inc.). Tissue was then transferred to a 1.5-mL Eppendorf tube and incubated with Proteinase K (Roche Molecular Systems, Inc.) for 15 minutes at 55°C followed by incubation at 80°C for 15 minutes. Final RNA was quantified within the tissue lysate using a Qubit Fluorometer (Thermo Fisher Scientific) and then stored at −80°C until gene expression profiling was performed using the NanoString nCounter. A custom 800-gene NanoString codeset Panel, including 789 test genes and 11 housekeeping genes, was used for the analysis. NanoString nCounter (NanoString Technologies) analysis was then performed as previously described (31). Briefly, 50-ng RNA per sample was mixed with a 3′ biotinylated capture probe and 5′ reporter probe tagged with a fluorescent barcode from the desired gene expression code set following the manufacturer's instructions. Hybridized samples were run using the high-sensitivity protocol for the NanoString nCounter, and samples were scanned at maximum scan resolution using the NanoString nCounter Digital Analyzer. Data was quantile normalized and nSolver software (NanoString Technologies) was used to calculate raw counts prior to quantile normalization. Exploratory pathway analysis was conducted with KEGG Mapper Search tool (RRID:SCR_012773, https://www.genome.jp/kegg/tool/map_pathway1.html) in May 2021. Significant genes by Bonferroni (P < 0.05/778) up- or downregulated by at least 1.5-fold change when comparing post- with preneoadjuvant therapy were used in the analysis.

Cofactor transcriptomic analysis

Unstained, unmounted FFPE sections cut sequentially from the same specimen block were processed for RNA extraction using the Prism Extraction Kit (Cofactor Genomics), following the manufacturer's suggested protocol. Bioanalyzer or TapeStation assay (Agilent) and Qubit RNA HS or BR Assay (Thermo Fisher Scientific) were used to evaluate total RNA quality and quantity. RNA concentration and quantity (in ng/μL and total ng) and quality (DV200, percentage of fragments above 200 bp) were evaluated. Total RNA was processed by Cofactor Genomics' laboratory using the TruSeq RNA Exome Library Prep Kit (Illumina) following the standard protocol for FFPE material to generate whole-exome libraries; 40 to 100 ng of RNA was used as input depending on sample quality. Libraries were sequenced as single-end 75 bp reads on a NextSeq500 (Illumina) following the manufacturer's protocols. Samples were analyzed using the ImmunoPrism analysis pipeline (Version 1.0) which delivers a standardized report including expression characterization and immune-cell quantification of 18 analytes (30, 32). Using the ImmunoPrism algorithm, RNA sequencing (RNA-seq) data is compared with a database of gene expression models of specific immune cells. These models were built using purified immune cell populations, isolated based on canonical cell-surface markers, followed by machine-learning methods to identify multigenic expression patterns from whole-transcriptome data associated with specific immune cells (32, 33).

Statistical analyses

Comparison of matched pre- and posttreatment samples was performed using two-tailed paired Student t test. Data were analyzed using GraphPad Prism 9 for Windows 64-bit, Version 9.0.0 (RRID:SCR_002798, GraphPad Software, Inc.). A P value < 0.05 was considered significant. Quantile normalized NanoString data were log2 transformed using R version 4.0.2. Seven hundred and seventy-eight genes were obtained for analysis after filtering out genes with small variation by SD < 0.5. Fold change (FC) of mean value of posttreatment over mean value of pretreatment, and paired t test P value were output. To adjust for multiple comparisons, we used the Benjamini–Hochberg or Bonferroni correction as indicated, defining significance as either a Benjamini–Hochberg q value < 0.05, or a P value < 6.43e-5, i.e., the Bonferroni cut-off of 0.05 divided by 778 test genes.

Data availability statement

The data generated in this study are available within the article and its supplementary data files. Raw data for this study were generated at Fred Hutchinson Cancer Research Center (mIHC), Merck & Co., Inc., Kenilworth (NanoString), and Cofactor Genomics (RNA-seq with ImmunoPrism analysis). Derived data supporting the findings of this study are available from the corresponding author upon request.

Patient clinicopathologic characteristics and experimental design

Patients with localized STS treated with neoadjuvant radiation (with or without preceding chemotherapy) and curative intent surgical resection were retrospectively identified in an institutional database. Of these, 32 patients had adequate matched pre- and posttreatment tissue for analysis with mIHC (n = 19), NanoString (n = 32), and/or RNA-seq with Cofactor Genomics’ ImmunoPrism analysis (n = 26). The patients’ clinical and pathologic characteristics are presented in Table 1 and organized by the assays that were performed. Median age was 49 years (range 24–77), and 59% of the cohort was male. Most tumors were in the extremity (81%), more than 5 cm (81%) in size, and intermediate-to-high grade. UPS was the most common histology (44%). Most patients had a negative microscopic surgical margin, i.e., R0 resection (n = 27; 84%). After obtaining a diagnostic biopsy (pretreatment specimen), 66% of patients received (n = 21) neoadjuvant chemotherapy followed by radiotherapy, while 34% (n = 11) received neoadjuvant radiotherapy alone before proceeding to definitive surgical resection (posttreatment specimen). Cases with sufficient matched pre- and posttreatment tissue samples were selected for analysis with mIHC, NanoString, and RNA-seq followed by ImmunoPrism analysis as schematized in Fig. 1.

Table 1.

Clinicopathologic characteristics of patients undergoing neoadjuvant therapy followed by curative intent resection of localized STS.

Multiplex IHCNanoStringImmunoPrism
(N = 19)(N = 32)(N = 26)
Variablen (%)n (%)n (%)
Agea 52.8 48.5 48.5 
 (25.3–76.7) (23.7–76.7) (23.7–76.7) 
Sex 
 Male 12 (63) 19 (59) 17 (65) 
 Female 7 (37) 13 (41) 9 (35) 
Location 
 Extremity 17 (90) 26 (81) 21 (81) 
 Central 1 (5) 3 (9) 3 (12) 
 Retroperitoneal 1 (5) 3 (9) 2 (8) 
Tumor size 
 ≤5 cm 2 (11) 6 (19) 4 (15) 
 >5 cm 17 (89) 26 (81) 22 (85) 
Grade (FNCLCC system) 
 1 1 (5) 5 (16) 4 (15) 
 2 8 (42) 13 (41) 10 (39) 
 3 9 (47) 13 (41) 11 (42) 
 Unknown 1 (5) 1 (3) 1 (4) 
Histologic type 
 UPS 12 (63) 14 (44) 12 (46) 
 Synovial sarcoma 2 (11) 3 (9) 2 (8) 
 Ewing sarcoma — 3 (9) 2 (8) 
 Leiomyosarcoma 2 (11) 2 (6) 2 (8) 
 MRCL 3 (16) 3 (9) 3 (12) 
 Otherb — 7 (22) 5 (19) 
Preoperative chemotherapy 
 Yes 15 (79) 21 (66) 18 (69) 
 No 4 (21) 11 (34) 8 (31) 
Multiplex IHCNanoStringImmunoPrism
(N = 19)(N = 32)(N = 26)
Variablen (%)n (%)n (%)
Agea 52.8 48.5 48.5 
 (25.3–76.7) (23.7–76.7) (23.7–76.7) 
Sex 
 Male 12 (63) 19 (59) 17 (65) 
 Female 7 (37) 13 (41) 9 (35) 
Location 
 Extremity 17 (90) 26 (81) 21 (81) 
 Central 1 (5) 3 (9) 3 (12) 
 Retroperitoneal 1 (5) 3 (9) 2 (8) 
Tumor size 
 ≤5 cm 2 (11) 6 (19) 4 (15) 
 >5 cm 17 (89) 26 (81) 22 (85) 
Grade (FNCLCC system) 
 1 1 (5) 5 (16) 4 (15) 
 2 8 (42) 13 (41) 10 (39) 
 3 9 (47) 13 (41) 11 (42) 
 Unknown 1 (5) 1 (3) 1 (4) 
Histologic type 
 UPS 12 (63) 14 (44) 12 (46) 
 Synovial sarcoma 2 (11) 3 (9) 2 (8) 
 Ewing sarcoma — 3 (9) 2 (8) 
 Leiomyosarcoma 2 (11) 2 (6) 2 (8) 
 MRCL 3 (16) 3 (9) 3 (12) 
 Otherb — 7 (22) 5 (19) 
Preoperative chemotherapy 
 Yes 15 (79) 21 (66) 18 (69) 
 No 4 (21) 11 (34) 8 (31) 

Abbreviation: FNCLCC, French Federation of Comprehensive Cancer Centers.

aAge at diagnosis is displayed as median (minimum–maximum) in years.

bOther histologies: 2 myxofibrosarcomas; 1 each of fibroblastic-myofibroblastic sarcoma, sclerosing epithelioid fibrosarcoma, solitary fibrous tumor, sarcoma with epithelioid features suggestive of MPNST, and low grade fibromyxoid sarcoma.

Figure 1.

Experimental design. mIHC, NanoString (800 Gene Immune Panel), and RNA-seq with analysis by ImmunoPrism platform were performed on paired STS tissue samples before and after neoadjuvant therapy. Schema indicates the number of paired samples analyzed by each technology. Two mIHC panels were performed with the indicated markers above (in addition to VISTA which is not shown in panel 1). Example mIHC of UPS tumors paired pre- (A–D) and postneoadjuvant (E–H) chemoradiation at low (A, C, E, G) and high (B, D, F, H) magnification, with an example of the image cytometric analysis shown below. Representative images including one tumor with low (A–D) and one tumor with high (E–G) immune infiltration were selected.

Figure 1.

Experimental design. mIHC, NanoString (800 Gene Immune Panel), and RNA-seq with analysis by ImmunoPrism platform were performed on paired STS tissue samples before and after neoadjuvant therapy. Schema indicates the number of paired samples analyzed by each technology. Two mIHC panels were performed with the indicated markers above (in addition to VISTA which is not shown in panel 1). Example mIHC of UPS tumors paired pre- (A–D) and postneoadjuvant (E–H) chemoradiation at low (A, C, E, G) and high (B, D, F, H) magnification, with an example of the image cytometric analysis shown below. Representative images including one tumor with low (A–D) and one tumor with high (E–G) immune infiltration were selected.

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Neoadjuvant therapy increases immune cell infiltration in sarcomas

Prior to treatment, myeloid cells (monocytes and macrophages) and B cells were the most common immune cells in the TME, constituting respectively 45.3% and 36.5% of the total immune cells; T cells constituted 13.4% of immune cells, while natural killer (NK) cells represented 4.9% of total immune cells by Cofactor's ImmunoPrism analysis. The percentage of all ImmunoPrism-defined cell populations are presented as a percentage of total cells in Supplementary Table S3.

RNA-seq followed by ImmunoPrism analysis demonstrated a statistically significant (P = 0.0002) 1.7-fold increase in the percentage of total immune cell infiltrate pre- (mean percentage of total cells ± SD: 24.04 ± 12.37) versus post- (mean percentage of total cells ± SD: 40.19 ± 17.92) neoadjuvant treatment (Fig. 2A). This statistically significant difference in the percentage of total immune infiltrate pre- versus postneoadjuvant treatment was observed for both patients receiving radiotherapy alone (n = 8; mean percentage of total cells ± SD, pre: 29.50 ± 9.94 versus post: 37.25 ± 10.90; P = 0.029) and chemotherapy and radiotherapy (n = 18; mean percentage of total cells ± SD, pre: 21.61 ± 12.81 versus post: 41.50 ± 20.43; P = 0.0009), and therefore data from patients receiving radiotherapy and chemotherapy and radiotherapy alone has been combined in subsequent analysis. No statistically significant difference in the total immune infiltrates postneoadjuvant therapy (P = 0.587) was observed between those patients receiving radiotherapy alone and those receiving chemotherapy and radiotherapy.

Figure 2.

Tumor-infiltrating immune cells pre- and postneoadjuvant therapy identified by RNA-seq with analysis by Cofactor ImmunoPrism. A, Percentage of immune-infiltrating cells for all pooled sarcoma subtypes. Individual values, mean, and SDs are shown. B, Percentage of immune-infiltrating cells by sarcoma subtype. UPS (n = 12); LMS (n = 2); MRCL (n = 3); SS (n = 2); EW (n = 2); and other (n = 5). Comparisons for statistical differences in means were tested using two-tailed paired t tests: *, P < 0.05; ***, P < 0.001. EW, Ewing sarcoma; LMS, leiomyosarcoma; Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy; SS, synovial sarcoma.

Figure 2.

Tumor-infiltrating immune cells pre- and postneoadjuvant therapy identified by RNA-seq with analysis by Cofactor ImmunoPrism. A, Percentage of immune-infiltrating cells for all pooled sarcoma subtypes. Individual values, mean, and SDs are shown. B, Percentage of immune-infiltrating cells by sarcoma subtype. UPS (n = 12); LMS (n = 2); MRCL (n = 3); SS (n = 2); EW (n = 2); and other (n = 5). Comparisons for statistical differences in means were tested using two-tailed paired t tests: *, P < 0.05; ***, P < 0.001. EW, Ewing sarcoma; LMS, leiomyosarcoma; Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy; SS, synovial sarcoma.

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Next, we examined changes induced by neoadjuvant therapy in total immune cell infiltrate by histologic subtype. A significant increase in the percentage of total immune cell infiltrate was observed in the UPS cohort (mean percentage of total cells ± SD, pre: 27.25 ± 12.53; post: 44.92 ± 20.86; P = 0.021; Fig 2B). Most other sarcoma subtypes, which represented small sample sizes, showed an increased total immune cell infiltration posttreatment, though small samples sizes limit the power to detect statistically significant differences. Confirming previous reports (31, 34, 35), translocation-associated tumors demonstrated a significantly lower percentage of total immune cell infiltrate prior to neoadjuvant therapy (mean percentage of total cells ± SD, translocation-driven sarcomas: 16.75 ± 8.36; not translocation-driven: 27.28 ± 12.65; P = 0.043). Following neoadjuvant therapy, total immune cell infiltrate increased in both nontranslocation and translocation-associated histologies, and no significant difference in the percentage of total immune cell infiltrate was observed relative to translocation status (mean percentage of total cells ± SD, translocation-driven sarcomas: 38.88 ± 14.60; not translocation-driven: 40.78 ± 19.57; P = 0.808). To increase the power of subsequent analyses, all sarcoma histologic subtypes were pooled to further characterize the changes in the TME induced by neoadjuvant therapy.

Neoadjuvant therapy induces an increase in monocyte-lineage cells including CD206+ macrophages in the sarcoma TME

The myeloid compartment of the TME is described in Fig. 3. Macrophages, defined as CD68+/CD163+ cells in mIHC, showed a 1.8-fold increase from pre- to posttreatment (mean ± SD % of total cells, pre: 7.05 ± 11.56; post: 12.87 ± 13.92; P = 0.055; Fig. 3A). A significant increase in the percentage of CD206+ M2 macrophages was observed after neoadjuvant therapy (mean % of total cells ± SD, pre: 4.85 ± 10.46; post: 9.84 ± 13.51; P = 0.031; Fig. 3B). CD33 and CD66b markers were also assayed for monocytic myeloid-derived suppressor cells (mMDSC), (CD33+/CD66) and polymorphonuclear MDSC (pmnMDSC)/neutrophils (CD33+/CD66b+), and no statistically significant changes were observed before and after neoadjuvant therapy (Supplementary Table S4). Comprehensive mIHC data are presented by sarcoma subtype in Supplementary Fig. S1 and in Supplementary Tables S4 and S5.

Figure 3.

Myeloid cells pre- and postneoadjuvant therapy identified by multiplex (mIHC; top), NanoString quantile normalized mRNA expression (middle), and RNA-seq with analysis by ImmunoPrism (bottom). mIHC: percentage of CD68+/CD163+ cells (A) and CD206+ as percentage of total cells (B). NanoString: quantile normalized expression values for CD163 (C) and CD206 (MRC1; D) genes. ImmunoPrism: percentage of monocytes (E) and M2 macrophages (F). Individual values, mean, and SDs are shown. mIHC and ImmunoPrism statistical differences were tested using two-tailed paired t tests: *, P < 0.05; **, P < 0.01. NanoString: differences were tested with paired t tests with Benjamini–Hochberg adjusted P values (q values). ***, q < 0.001; ****, q < 0.0001. Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy.

Figure 3.

Myeloid cells pre- and postneoadjuvant therapy identified by multiplex (mIHC; top), NanoString quantile normalized mRNA expression (middle), and RNA-seq with analysis by ImmunoPrism (bottom). mIHC: percentage of CD68+/CD163+ cells (A) and CD206+ as percentage of total cells (B). NanoString: quantile normalized expression values for CD163 (C) and CD206 (MRC1; D) genes. ImmunoPrism: percentage of monocytes (E) and M2 macrophages (F). Individual values, mean, and SDs are shown. mIHC and ImmunoPrism statistical differences were tested using two-tailed paired t tests: *, P < 0.05; **, P < 0.01. NanoString: differences were tested with paired t tests with Benjamini–Hochberg adjusted P values (q values). ***, q < 0.001; ****, q < 0.0001. Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy.

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In agreement with mIHC findings, macrophage marker gene expression was significantly increased in NanoString analysis for M2 gene markers CD163 (mean quantile normalized values ± SD, pre: 159.16 ± 114.89; post: 364.38 ± 241.23; q < 0.0001; Fig. 3C), and CD206 (MRC1; pre: 62.00 ± 56.12; post: 139.00 ± 87.37; q = 0.0002; Fig. 3D). Analysis with ImmunoPrism showed a significant (P = 0.002) increase in the monocyte population from pre- to postneoadjuvant therapy (mean percentage of total cells ± SD, pre: 6.74 ± 5.01; post: 14.06 ± 11.48; Fig. 3E) with no significant changes in the populations defined as M1 (P = 0.504; not shown) or M2 (P = 0.426; Fig. 3F) macrophages. M2 macrophages were prevalent in the TME and constituted on average 3.18% (±3.6) of total cell population, while M1 macrophages constituted 0.65% (±0.87) of total cells (Supplementary Table S3). Comprehensive ImmunoPrism data are presented by sarcoma subtype in Supplementary Fig. S2 and Supplementary Table S3. Additional transcriptional changes are discussed below.

Neoadjuvant therapy increases CD4+ T cells while decreasing CD3+FOXP3+ regulatory T cells in the sarcoma TME

Tumor-infiltrating lymphocytes (TIL) in the sarcoma TME are characterized in Fig. 4. A nonstatistically significant increase in the percentage of CD3+ cells was observed pre- to posttreatment (mean percentage of total cells ± SD, pre: 3.33 ± 3.90; post: 5.63 ± 4.49; P = 0.069; Fig. 4A) by mIHC. Further characterization demonstrated a significant 2.9-fold increase in CD4+ cells (mean percentage of total cells ± SD, pre: 1.68 ± 1.92; post: 4.80 ± 4.97; P = 0.015; Fig. 4B). We observed a significant 2.3-fold decrease in CD3+FOXP3+ regulatory T cells (Treg; mean percentage of CD3+ cells ± SD, pre: 9.63 ± 12.46; post: 4.17 ± 5.12; P = 0.023; Fig. 4C). No significant change was observed in the percentage of CD8+ cells before and after treatment (P = 0.175). Comprehensive mIHC data are presented by sarcoma subtype in Supplementary Fig. S1 and in Supplementary Tables S4 and S5.

Figure 4.

Tumor-infiltrating T cells pre- and postneoadjuvant therapy identified by mIHC (top), NanoString quantile normalized mRNA expression (middle), and RNA-seq with analysis by ImmunoPrism (bottom). mIHC: percentage of CD3+ cells (A), CD4+ cells (B), and FOXP3+ T cells (C). NanoString: quantile normalized expression values for CD3G (D), CD4 (E), and FOXP3 (F) genes. ImmunoPrism: percentage of T cells (CD3; G), CD4 cells (H), and Tregs (I). Individual values, mean, and SDs are shown. mIHC and ImmunoPrism statistical differences were tested using two-tailed paired t tests: *, P < 0.05. Nanostring: differences were tested with paired t tests with Benjamini–Hochberg adjusted P values (q values). **, q < 0.01; ****, q < 0.0001. Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy.

Figure 4.

Tumor-infiltrating T cells pre- and postneoadjuvant therapy identified by mIHC (top), NanoString quantile normalized mRNA expression (middle), and RNA-seq with analysis by ImmunoPrism (bottom). mIHC: percentage of CD3+ cells (A), CD4+ cells (B), and FOXP3+ T cells (C). NanoString: quantile normalized expression values for CD3G (D), CD4 (E), and FOXP3 (F) genes. ImmunoPrism: percentage of T cells (CD3; G), CD4 cells (H), and Tregs (I). Individual values, mean, and SDs are shown. mIHC and ImmunoPrism statistical differences were tested using two-tailed paired t tests: *, P < 0.05. Nanostring: differences were tested with paired t tests with Benjamini–Hochberg adjusted P values (q values). **, q < 0.01; ****, q < 0.0001. Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy.

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Gene expression analyses were performed with the NanoString and ImmunoPrism platforms. Supporting the mIHC data, NanoString showed a significant increase in CD4 expression (mean quantile normalized values ± SD, pre: 28.44 ± 26.36; post: 53.88 ± 23.30; q = 0.0001; Fig. 4E), accompanied by significantly decreased FOXP3 expression (pre: 10.56 ± 4.71; post: 7.78 ± 3.84; q = 0.009; Fig. 4F). No statistically significant changes were observed in CD3G (q = 0.262; Fig. 4D) or CD8A (q = 0.982) expression. ImmunoPrism did not show a significant change in the percentage of T cells (P = 0.979; Fig. 4G) or CD4+ T cells (P = 0.605; Fig. 4H). Limited detection of Treg (Fig. 4I) and CD8+ T cells was consistent with the low percentage of CD3+FOXP3+ and CD8+ cells observed in most mIHC samples. Comprehensive ImmunoPrism data are presented by sarcoma subtype in Supplementary Fig. S2 and Supplementary Table S3.

Neoadjuvant therapy induces changes in immune activation status and checkpoint molecule expression

Changes in CD4+ and CD8+ T-cell activation pre- versus posttreatment were evaluated by mIHC quantification of activation and immune checkpoint markers, including HLA-DR, Ki67, VISTA, and PD-1. No significant changes in the expression of any of these proteins was observed in either CD4+ or CD8+ T cells (Supplementary Table S5). The percentage of PD-1+CD3+ cells was not significantly changed from pre- to postneoadjuvant therapy by mIHC (Supplementary Fig. S3A).

The following activation and/or checkpoint control genes were included in the ImmunoPrism analysis: PD1, PD-L1, CTLA4, OX40, TIM3, BTLA, ICOS, CD47, IDO, and ARG1. A significant increase pre- to posttreatment was observed in the expression of the inhibitory T-cell coreceptor TIM3 [P = 0.0002; mean ± SD: pre: 15.92 ± 13.62 transcripts per million (TPM); post: 38.15 ± 27.68 TPM], together with a significant decrease in the activating T-cell coreceptor OX40 (P = 0.032; pre: 4.62 ± 4.85 TPM; post: 2.15 ± 2.13 TPM; Supplementary Fig. S3B). No statistically significant changes were observed in other genes. NanoString analysis supported the ImmunoPrism expression data with a significant increase in TIM3 (HAVCR2) expression [fold change (FC) = 1.34, q = 0.005; Supplementary Fig. S3C) and decrease in OX40 (TNFRSF4; FC = 0.45, q = 0.003; Supplementary Fig. S3D). NanoString analysis also demonstrated mixed changes in other immune-stimulating and inhibitory markers, with significant decreases in the coinhibitory ligand CD276 (B7-H3) and inhibitory receptor ADORA2A (adenosine A2A receptor), but increased expression of the coinhibitory ligand PDCD1LG2 (PD-L2; Supplementary Fig. S3C).

Neoadjuvant therapy increases expression of antigen presentation-related pathways in the sarcoma TME

Next, we investigated how changes observed in the TME associated with outcome utilizing pathologic response to neoadjuvant treatment as a surrogate outcome. Patients with favorable pathologic response (n = 9, 35%) demonstrated a significantly greater increase in tumor-infiltrating monocytes (P = 0.034), measured by ImmunoPrism, than their counterparts without such a pathologic response (n = 17, 65%; Fig. 5A). No significant difference in the ImmunoPrism-defined monocyte population was observed pretreatment between patients with favorable and unfavorable pathologic responses to neoadjuvant treatment (P = 0.93). Favorable pathologic response rates were not significantly different (P = 0.19; Fisher exact test), for patients receiving chemotherapy and radiotherapy compared with radiotherapy alone. An association between pathologic response and pretreatment percentages or posttreatment changes was not observed in any other immune cell types, including T cells, B cells, M1 macrophages, M2 macrophages, and NK cells. In concordance with the cytotoxic effects of neoadjuvant treatment, genes associated with cell-cycle progression were downregulated (Fig. 5B). A significant decrease in Ki67+ nonimmune cells (CD4 CD8 HLA-DR CD33 CD66b percentage of total cells, pre: 10.08 ± 12.19; post: 2.30 ± 2.50; P = 0.010; Supplementary Table S5) was observed in mIHC, likely indicating that neoadjuvant therapy killed proliferating tumor cells as intended.

Figure 5.

Changes in gene expression pathways after neoadjuvant therapy. A, Difference in the percentage of monocytes (post- and preneoadjuvant therapy) identified by ImmunoPrism in tumors with a favorable pathologic response (≥90% necrosis) versus < 90% necrosis. B, Heatmap with differentially regulated genes pre- versus postneoadjuvant therapy analyzed by NanoString followed by clustering with KEGG pathway analysis. Genes significantly up- or downregulated (FC > 1.5 for upregulation, <0.67 for downregulation with a Bonferroni corrected P < 6.43e-5) pre- versus postneoadjuvant therapy were analyzed in KEGG to identify gene pathways. Immune-oncology relevant pathways with more than 3 genes identified are shown. C, Percentage of HLA-DRHi non-T cells (CD4 CD8 cells) pre- and postneoadjuvant therapy identified by mIHC. D, Percentage of B cells pre- and postneoadjuvant therapy identified by ImmunoPrism. E, Percentage of NK cells pre- and postneoadjuvant therapy identified by ImmunoPrism. Testing for differences in mean levels was performed using two-tailed paired t tests: *, P < 0.05; **, P < 0.01. Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy.

Figure 5.

Changes in gene expression pathways after neoadjuvant therapy. A, Difference in the percentage of monocytes (post- and preneoadjuvant therapy) identified by ImmunoPrism in tumors with a favorable pathologic response (≥90% necrosis) versus < 90% necrosis. B, Heatmap with differentially regulated genes pre- versus postneoadjuvant therapy analyzed by NanoString followed by clustering with KEGG pathway analysis. Genes significantly up- or downregulated (FC > 1.5 for upregulation, <0.67 for downregulation with a Bonferroni corrected P < 6.43e-5) pre- versus postneoadjuvant therapy were analyzed in KEGG to identify gene pathways. Immune-oncology relevant pathways with more than 3 genes identified are shown. C, Percentage of HLA-DRHi non-T cells (CD4 CD8 cells) pre- and postneoadjuvant therapy identified by mIHC. D, Percentage of B cells pre- and postneoadjuvant therapy identified by ImmunoPrism. E, Percentage of NK cells pre- and postneoadjuvant therapy identified by ImmunoPrism. Testing for differences in mean levels was performed using two-tailed paired t tests: *, P < 0.05; **, P < 0.01. Pre, preneoadjuvant therapy; Post, postneoadjuvant therapy.

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NanoString data were used to investigate immunologic pathways altered by therapy. Sixty-five genes from the custom, immune-oncology 800-gene NanoString panel had significantly altered expression pre- versus postneoadjuvant therapy, using Bonferroni correction for multiple comparisons. Forty-three genes were upregulated (FC > 1.5) and 22 genes were downregulated (FC < 0.67; Supplementary Fig. S4). These 65 genes were evaluated in Kyoto Encyclopedia of Genes and Genomes (KEGG) Gene Ontology enrichment analysis to identify pathways of interest (Fig. 5B). Several genes and cytokines related to antigen presentation and the phagosome were enriched and upregulated posttreatment, including HLA-II genes, the costimulatory ligand CD86, and cathepsin proteases (CTSB, CTSS). Increased expression of monocyte- (CD14) and macrophage- (MRC1, MARCO) related genes was observed. Similarly, a significant increase in the frequency of HLA-DRhi CD4/CD8 cells (percentage of total cells, pre: 5.95 ± 6.56; post: 14.54 ± 11.39; P = 0.003; Fig. 5C) by mIHC is presumed to be from antigen-presenting cells (APC) such as myeloid or B cells.

Genes associated with B and NK cells were also enriched and upregulated per KEGG Gene Ontology enrichment analysis of the NanoString expression data (Fig. 5B). B cells were significantly upregulated by neoadjuvant therapy (Fig. 5D; mean percentage of total cells ± SD, pre: 8.60 ± 6.23; post: 15.41 ± 12.13; P = 0.006) and were the second most common tumor-infiltrating immune cell type (39.8% of all immune cells) postneoadjuvant therapy, as determined by ImmunoPrism assays. An increase in NK cells from pre- to postneoadjuvant therapy was also observed (Fig. 5E; percentage of total cells, pre: 1.06 ± 1.57; post: 2.21 ± 2.07; P = 0.010) by ImmunoPrism analysis. B and NK cell markers were not assessed by mIHC, and putative B (CD19, CD20) and NK-cell (CD56) genes were not included in the NanoString panel.

Standard neoadjuvant therapy induced heterogeneous inflammatory effects in the sarcoma TME, including increased myeloid cells, B cells, and CD4+ cells, with corresponding transcriptional changes reflecting enhanced phagocytosis, immune trafficking, and antigen presentation. These findings were broadly applicable to patients receiving either neoadjuvant radiotherapy alone or chemotherapy preceding radiotherapy for diverse sarcoma subtypes. As previously reported, genomically complex sarcoma subtypes, e.g., leiomyosarcoma and UPS, had greater immune cell infiltration than those driven by oncogenic chromosome translocations, e.g., synovial sarcoma and MRCL (31, 34–36), and neoadjuvant therapy increased immune infiltration significantly in both sarcoma groups. Data were consistent across three different platforms incorporating transcription and protein level expression data. These data contributed to the design of a neoadjuvant study of pembrolizumab and radiation in locally advanced patients with STS (NCT03338959), and are particularly relevant to the ongoing national, randomized SARC032 trial of neoadjuvant radiation with or without neoadjuvant and adjuvant pembrolizumab (20). In establishing real-world changes in the human sarcoma TME following radiation with or without chemotherapy, we have defined baseline TME changes against which the effects of novel combinations of therapies can be compared. Such results also deepen our understanding of how cytotoxic therapies may synergize with immunotherapies and how best to integrate immunotherapy with radiotherapy and chemotherapy in future clinical trials. In our cohort of patients with STS, both antecedent and postneoadjuvant treatment immune infiltrates were dominated by two cell lineages involved in professional antigen presentation: monocytes/macrophages and B cells.

Our findings show that neoadjuvant therapy significantly increases monocyte-lineage cells and CD206+ (M2-type) macrophages in the sarcoma TME, and our baseline findings prior to neoadjuvant therapy are consistent with published reports on myeloid cells in STS (19, 30, 34). A significant increase in the pre- to postneoadjuvant intratumoral monocyte-lineage cell frequency was observed in tumors with a favorable pathologic response (≥90% tumor necrosis or hyalinization) compared to those without such a response. Pathologic complete response has been associated with improved survival in patients with sarcoma following neoadjuvant therapy and resection (37, 38). Our findings suggest that a subset of intratumoral myeloid cells could be stimulating antisarcoma immune responses and contributing to neoadjuvant therapy effects. Tumor-associated macrophages (TAM) and related tumor-infiltrating myeloid cells are functionally diverse and can elicit acute inflammation, stimulating T cells in an MHC class II/antigen-specific manner, or, conversely, terminate immune responses and mediate tissue remodeling and repair (39–41). Multiple ongoing studies are attempting to target macrophages in metastatic STS using either standard treatments such as doxorubicin and trabectedin (NCT03074318) or novel treatments such as anti-CD47 (NCT04886271). Our findings support further preclinical investigation and clinical study of TAM-targeted therapies for STS. Establishing type-I antitumor immunity early in the disease course may lead to improved survival outcomes.

B cells were the second most abundant immune cell within the STS TME at baseline and were significantly enriched by neoadjuvant therapy. Although the role of B cells in antitumor immunity remains controversial (42), recent studies have found that B cells, especially when organized in tertiary lymphoid structures (TLS), played a critical role in the response to ICI (43, 44). In STS, an increased density of B cells and TLS was associated with improved survival and higher response rates to ICI (45). Neoadjuvant therapy also increased the percentage of CD4+ T cells and decreased CD3+FOXP3+ cells, suggesting enrichment of Th cells capable of stimulating antigen-specific CD8+ cytotoxic T cells. Increased CD4+ T-cell infiltration has been previously reported following neoadjuvant treatment for UPS (46). Our study expands these findings to a larger and more diverse cohort of patients with STS.

More broadly surveying immune costimulatory and coinhibitory pathways, we found that neoadjuvant therapy had a net inhibitory effect on T-cell activation pathways, including a transcriptional level increase in the inhibitory receptor TIM3, an increase in PD-L2 expression, and a decrease in the stimulatory receptor OX40. TIM3 blockade is being increasingly studied in patients with anti–PD-1–refractory solid tumors (47), but has not been studied extensively in STS. Our results support further investigation of the role of TIM3 in STS, including verification of TIM3 upregulation by neoadjuvant therapy in larger cohorts, confirmation of TIM3 protein expression in STS, and histology-specific studies. We observed no difference in PD-1 or PD-L1 expression following neoadjuvant therapy. This contrasts with a study by Patel and colleagues, in which they reported an increase in PD-L1 expression (P = 0.056; ref. 48). Taken together, these data emphasize the need for correlative analyses from ongoing trials of PD-1 inhibition and neoadjuvant radiation in STS (NCT03092323, NCT03338959, NCT03307616, NCT03463408, NCT03116529; refs. 21, 49). Higher dimensional profiling (e.g., single-cell RNA-seq analyses, surface proteomics, or spatial transcriptomics) and functional analyses of the myeloid, B-cell, and T-cell components of the STS TME may provide more insight into the phenotype, clonality, and functional state of these populations and improve our understanding of how antigen presentation in sarcomas direct the functionality of CD4 helper and CD8 effector T cells.

In conclusion, valuable matched pre- and postneoadjuvant treatment STS samples were assayed with multiple, complementary genomic and immunohistologic techniques to characterize immunologic effects of neoadjuvant therapy across a broad spectrum of histologic and molecular STS subtypes. Our study is limited by its retrospective nature, sample size, heterogeneity in the STS subtypes enrolled, and heterogeneity in treatment received (radiotherapy alone or chemotherapy preceding radiotherapy), recognizing a selection bias for patients receiving chemotherapy based on STS histology. Nevertheless, these data strongly support further histology-specific clinical and laboratory research to explore future trials of novel myeloid- and B-cell–targeted therapies in combination with cytotoxic neoadjuvant therapy in STS. Importantly, our analyses highlight emerging inhibitory immune checkpoints, such as TIM3, for which there are antibody therapies being tested in clinical trials (47), and blockade of which may bolster antitumor immunity in conjunction with neoadjuvant therapy in patients with STS. Our findings also support the study of novel immunotherapy agents stimulating type-I immunity, e.g., oncolytic viruses (NCT02923778, NCT03069378), vaccines (NCT01803152), or Toll-like receptors (TLR; NCT02180698), in the neoadjuvant setting for STS which may enhance the efficacy of established or novel ICIs. This work serves to focus future clinical trial designs on the most relevant immune checkpoints, cell types, and pathways to target in combination with neoadjuvant cytotoxic therapy in STS. More broadly, our work may also help to inform the investigation of other rare solid tumor types for which preoperative cytotoxic therapy is standard, but immune checkpoint inhibition is not efficacious.

P.H. Goff reports grants from Radiological Society of North America (RSNA) Research and Education Foundation and from Gilead Sciences, Inc. outside the submitted work. B.J. LaFleur reports personal fees from Cofactor Genomics during the conduct of the study. M.B. Spraker reports grants and personal fees from Varian Medical Systems Inc. outside the submitted work. J.S. Campbell is an employee at Sensei Biotherapeutics; the research in this manuscript was completed prior to this employment. L.D. Cranmer reports grants from Aadi Bioscience, Philogen, Tracon, Gradalis, Exelixis, Eli Lilly and Company, Blueprint Medicines, and Merck, as well as personal fees from Daiichi Sankyo during the conduct of the study. M.J. Wagner reports personal fees and other support from Adaptimmune and Deciphera; other support from Incyte, GlaxoSmithKline, Athenex; and personal fees from Epizyme outside the submitted work. R.L. Jones reports personal fees from Adaptimmune, Astex, Athenex, Bayer HealthCare, Boehringer Ingelheim, Blueprint Medicines, Clinigen, Eisai, Epizyme, Daichii Sankyo, Deciphera, Immunedesign, Immunicum, Karma Oncology, Eli Lilly and Company, Merck, Mundipharma, Pharmamar, Springworks, SynOx, Tracon, and UptoDate during the conduct of the study. T.K. McClanahan is a Merck employee and stockholder. J. Earls reports personal fees from Cofactor Genomics during the conduct of the study. K.C. Flanagan is an employee and has common stock interest in Cofactor Genomics, maker of the ImmunoPrism Assay used in this manuscript. N.A. LaFranzo reports personal fees from Cofactor Genomics during the conduct of the study; in addition, N.A. LaFranzo has Patent No. 10636512 issued, and is currently employed by Personalis, Inc. S.M. Pollack reports nonfinancial support from Cofactor Genomics and Merck during the conduct of the study; S.M. Pollack also reports grants from Merck, EMD Serono, and Incyte, as well as personal fees from T-knife, Deciphera, Aadi Bioscience, Epizyme, Obsidian, Bayer HealthCare, and Eli Lilly and Company outside the submitted work. No disclosures were reported by the other authors.

P.H. Goff: Conceptualization, data curation, formal analysis, validation, investigation, visualization, writing–original draft, writing–review and editing. L. Riolobos: Conceptualization, data curation, formal analysis, validation, investigation, visualization, writing–original draft, writing–review and editing. B.J. LaFleur: Data curation, software, formal analysis, validation, investigation, writing–review and editing. M.B. Spraker: Conceptualization, resources, formal analysis, validation, investigation, writing–review and editing. Y.D. Seo: Resources, formal analysis, writing–review and editing. K.S. Smythe: Resources, data curation, formal analysis, validation, visualization, writing–review and editing. J.S. Campbell: Resources, formal analysis, supervision, validation, writing–review and editing. R.H. Pierce: Resources, formal analysis, supervision, validation, writing–review and editing. Y. Zhang: Resources, data curation, formal analysis, validation, visualization, writing–review and editing. Q. He: Resources, supervision, validation, writing–review and editing. E.Y. Kim: Resources, validation, writing–review and editing. S.K. Schaub: Resources, validation, writing–review and editing. G.M. Kane: Resources, validation, writing–review and editing. J.G. Mantilla: Resources, data curation, formal analysis, writing–review and editing. E.Y. Chen: Resources, data curation, formal analysis, writing–review and editing. R. Ricciotti: Resources, data curation, formal analysis, writing–review and editing. M.J. Thompson: Resources, data curation, formal analysis, writing–review and editing. L.D. Cranmer: Resources, validation, writing–review and editing. M.J. Wagner: Resources, validation, writing–review and editing. E.T. Loggers: Resources, validation, writing–review and editing. R.L. Jones: Conceptualization, resources, writing–review and editing. E. Murphy: Resources, data curation, formal analysis, validation, writing–review and editing. W.M. Blumenschein: Resources, data curation, formal analysis, validation, writing–review and editing. T.K. McClanahan: Resources, data curation, formal analysis, validation, writing–review and editing. J. Earls: Data curation, software, formal analysis, methodology, writing–review and editing. K.C. Flanagan: Formal analysis, investigation, methodology, writing–review and editing. N.A. LaFranzo: Data curation, software, formal analysis, methodology, writing–review and editing. T.S. Kim: Conceptualization, resources, data curation, formal analysis, supervision, validation, investigation, visualization, writing–original draft, project administration, writing–review and editing. S.M. Pollack: Conceptualization, resources, supervision, funding acquisition, validation, investigation, writing–original draft, project administration, writing–review and editing.

We thank members of the Fred Hutchinson Cancer Research Center Experimental Histopathology Core Facility for assistance with multiplex IHC and image analysis. We thank Dr. Venu Pillarisetty for helpful discussions, and Ms. Silvia Christian for administrative support. This work was supported by CA180380 and the Sarcoma Foundation of America (to S.M. Pollack). NanoString assays were performed by Merck & Co., Inc. RNA-seq with ImmunoPrism was performed by Cofactor Genomics. S.M. Pollack was also supported by R01CA244872, CA180380, a grant from the V Foundation, and the Seattle Translational Tumor Research (STTR). The Experimental Histopathology Shared Resource at the Fred Hutchinson Cancer Research Center was supported by the NIH P30 CA015704 Cancer Center Support Grant.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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Supplementary data