Immune checkpoint therapy with anti-CTLA-4 and anti-PD-1/PD-L1 has revolutionized the treatment of many solid tumors. However, the clinical efficacy of immune checkpoint therapy is limited to a subset of patients with specific tumor types1,2. Multiple clinical trials with combinatorial immune checkpoint strategies are ongoing; however, the mechanistic rationale for tumor-specific targeting of immune checkpoints is elusive. To garner an insight into tumor-specific immunomodulatory targets, we analyzed 94 patients representing five different cancer types, including those that respond relatively well to immune checkpoint therapy and those that do not, such as glioblastoma multiforme, prostate cancer and colorectal cancer. Through mass cytometry and single-cell RNA sequencing, we identified a unique population of CD73hi macrophages in glioblastoma multiforme that persists after anti-PD-1 treatment. To test if targeting CD73 would be important for a successful combination strategy in glioblastoma multiforme, we performed reverse translational studies using CD73−/− mice. We found that the absence of CD73 improved survival in a murine model of glioblastoma multiforme treated with anti-CTLA-4 and anti-PD-1. Our data identified CD73 as a specific immunotherapeutic target to improve antitumor immune responses to immune checkpoint therapy in glioblastoma multiforme and demonstrate that comprehensive human and reverse translational studies can be used for rational design of combinatorial immune checkpoint strategies.
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CyTOF data (Figs. 1, 2a,b, 3c–g and 4c,d) have been deposited with the FlowRepository (FR-FCM-Z2B3). scRNA-seq data (Figs. 2c–e and 3a) have been deposited with the Sequence Read Archive with accession number PRJNA588461. All requests for data and materials should be made to the corresponding author, following verification of any intellectual property or confidentiality obligations.
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We thank the entire Immunotherapy Platform team at the MD Anderson Cancer Center for assistance in obtaining patient samples and processing them for CyTOF, immunofluorescence and gene expression analysis. We thank F. Gherardini at The Parker Institute for Cancer Immunotherapy for his expert advice with normalization of CyTOF data and J. Zhang and S. Meena Natarajan for technical assistance with the murine experiments. This work was supported by philanthropic contributions to the University of Texas MD Anderson Cancer Center Glioblastoma Moon Shot and Lung Cancer Moon Shot Program and by the National Institutes of Health/National Cancer Institute (award no. CA1208113 to A.B.H. and no. P30CA016672 to B.S.) and used the Tissue Biospecimen and Pathology Resource. P.S. is a member of the Parker Institute for Cancer Immunotherapy and the codirector of the Parker Institute for Cancer Immunotherapy at the MD Anderson Cancer Center.
P.S. has ownership in Jounce Therapeutics, Neon, Constellation, Oncolytics Biotech, BioAtla, Forty Seven, Apricity, Polaris, Marker Therapeutics, Codiak BioSciences, ImaginAb, Dragonfly, Lytix Biopharma and Hummingbird. P.S. serves as a consultant for Constellation, Jounce Therapeutics, Neon, BioAtla, Pieris Pharmaceuticals, Oncolytics Biotech, Forty Seven, Polaris, Apricity, Marker Therapeutics, Codiak BioSciences, ImaginAb, Dragonfly, Lytix Biopharma and Hummingbird.
Peer review information Joao Monteiro and Saheli Sadanand were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contour plots indicating the gating strategy used to define manually gated CD3, CD4, CD8 and FoxP3 positive populations in Fig. 1a.
A, Scatter plot indicating the absolute number of CD45+ live singlets of mass cytometry samples used for the multi-tumor comparison. Dashed line depicting the 600- cell threshold for sample inclusion. B, Stacked bars (left) depicting the distribution of the identified meta- cluster frequencies within different tumor types in the color code indicated below. t-SNE map of 10,000 randomly selected cells per tumor type colored by tumor type with color legend indicated on the right (right, top panel), or by meta-cluster (right, bottom panel) with color legend indicated in the left panel. C, Box-plots indicating CD45+ immune meta-cluster frequencies across tumor types from the PhenoGraph-based clustering approach in Fig. 1. (Number of patients, GBM =7, NSCLC=11, RCC=11, CRC=11, and PCa=5). In all the box plots depicted, boxes indicate interquartile range with central bar indicating median and whiskers indicating the range. Individual patients are represented with dots. D, Histograms depicting expression of immune markers on the respective meta-clusters indicated on the left related to Fig. 1d.
T cell phenotypes in PBMC suspensions from renal cell carcinoma (RCC) patients undergoing combined ipilimumab and nivolumab ICT were analyzed by mass cytometry and identified using the PhenoGraph algorithm on viable CD45+ cells (n=14). A, Heatmap indicating normalized expression of selected markers on CD45+ meta-clusters identified by PhenoGraph. B, CD4+ T cell cluster P33 and CD8+ T cell cluster P24 frequencies at pre-treatment (T0) and after two cycles (T2) or four cycles (T4) of combination ICT in responders (n=7) and non-responders (n=7). P values were calculated using Mann-Whitney U tests (two-sided). Q values were calculated with the output p values. C, Heat map displaying correlation matrix of clusters from PBMC samples and TIL. The Pearson correlation coefficient between each RCC PBMC cluster (above the threshold described in the Methods) and each TIL clusters was computed using z-scored values (for RCC PBMC and TIL clusters, respectively, to account for their separate normalization) across all 29 channels shared between each experiment.
T cell phenotypes in single cell suspensions of tumors from immune-checkpoint naïve patients were analyzed by mass cytometry and identified using the PhenoGraph algorithm on viable CD45+CD3+ cells. A, Scatter plot indicating the absolute number of CD45+CD3+ live singlets in tumor single cell suspensions from immune checkpoint therapy naïve patients (n=37). Dashed line depicting the 600-cell threshold for sample inclusion (see Methods). B, Box-plots indicating frequencies of selected T cell meta-clusters across tumor types (number of patients: NSCLC n=10, RCC n=11, CRC n=9). Samples are identical to samples used in Figs 2, 3, 5. C, Histograms depicting expression of immune markers on the respective CD4 and CD8 T cell meta-clusters indicated on the left. Related to Fig. 1f.
A, Histograms depicting the expression of immune markers on the respective meta- clusters indicated on the left and in Fig. 2a. B, Contour plots indicating the gating strategy used to manually define myeloid cells phenotypically similar to L8 metacluster identified by PhenoGraph. All cells were gated on CD45+ live cells according to the gating strategy outlined in Figure S1. C, Box plots indicating manually gated L8 subset frequencies as percentage of CD45+ live cells. (Number of patients, GBM=7, NSCLC=11, RCC=11, pCRC=7, mCRC=4, PCa=5). For pairwise comparisons, p values were computed by Mann-Whitney tests. Q values were calculated with the output p values using the Benjamini-Hochberg method. D, Histogram overlay of CD73 expression of CD68+ cells in normal donor PBMCs (blue) and GBM-TILs (red) by CyTOF. E, Representative IHC images of GBM patient samples F, Box-plot indicating density of CD3+, CD8+ and CD68+ cells/mm2 in IHC sections of GBM patients samples (n=7) G, Representative images of multicolor IF in GBM tumor samples (n=6). H, Box-plot indicating percentage of CD68+ cells and CD68+CD73+ cells in total nucleated cells (n=6).
Extended Data Fig. 6 Similarities of tumor infiltrating leukocyte phenotypes between first and second cohort of untreated GBM patients.
Leukocyte phenotypes in single cell suspensions of tumors from immune-checkpoint naïve patients were analyzed by mass cytometry and identified using the PhenoGraph algorithm on viable CD45+ cells. A, Grouped box-plots indicating frequencies of CD45+ cells as indicated in single cell suspension of tumors from untreated cohort 1 patients (n=7) and untreated cohort 2 patients (n=9). Boxes indicate interquartile range with central bar indicating median and whiskers indicating the range. Individual patients are represented with dots. B, Heatmap indicating normalized expression of selected markers on CD45+ meta-clusters identified by PhenoGraph from cohort 2 patients.
Extended Data Fig. 7 Distribution of tumor infiltrating leukocyte phenotypes in pembrolizumab treated and untreated GBM patients.
Leukocyte phenotypes in single cell suspensions of tumors from immune-checkpoint naïve patients (untreated) and Pembrolizumab treated patients (pembro) were analyzed by masscytometry and identified using the PhenoGraph algorithm on viable CD45+ cells. A, Scatter plot indicating the absolute number of CD45+ live singlets in single cell suspension of tumors from untreated patients (n=8) and pembro treated patients (n=5). B, Grouped box-plots indicating CD45+ immune meta-cluster frequencies identified by PhenoGraph in untreated (n=7) and pembrolizumab treated tumors (n=5).
Extended Data Fig. 8 Distribution of tumor infiltrating leukocyte phenotypes from orthotopically injected GL-261 gliomas in untreated wild type and CD73-/- mice.
CD73-/- and WT mice were inoculated with GL-261 gliomas intracranially. A, Left: Box plots indicating tumor sizes as determined by MRI in WT (blue) and CD73-/- mice (red). Data is representative of two independent experiments, n=5 mice per group. P values were calculated using Mann-Whitney U tests. Boxes indicate interquartile range with central bar indicating median and whiskers indicating the range. Right: Indicated are representative MRI images on day 14 of tumor cell inoculation. Arrows indicate the tumor bulks. B, Kaplan-Meier plot showing overall survival of untreated wild-type or CD73-/- (n=10) orthotopically injected with GL-261 gliomas, P values were calculated using a logrank test (two sided). Data shown are representative of two experiments. C, Representative heatmap indicating intra-tumoral CD11b+ immune populations in both WT and CD73-/- mice bearing GBM tumors by FlowSOM analysis. D, The clusters on the right indicate clusters that show significant changes. P-values were calculated using Mann-Whitney U tests (two sided) and corrected for multiple comparisons using the Benjamini-Hochberg method. Data is representative of two independent experiments, n=5 mice per group. E, Bar graphs depicting CD45+ immune cluster frequencies identified by heatmap (n=5 mice per group). Boxes indicate interquartile range with central bar indicating median and whiskers indicating the range. Individual mice are represented with dots.
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Goswami, S., Walle, T., Cornish, A.E. et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat Med 26, 39–46 (2020) doi:10.1038/s41591-019-0694-x