**Fig. 8 | Spatial patterning of core purine regulators in adult glioblastoma and pediatric high-grade glioma.** A Given the apparent spatial coordination of CD73, adenosine, and functional states such as hypoxia, we sought to better understand the precise spatial landscape of purinergic signaling in glioblastoma. Segmentation and Voronoi visualization of each cell type revealed an apparent non-random distribution of cells closely intermingled tumor and myeloid cells. B, C 18-plex cyclic multiplexed immunofluorescence (CyCIF) of an adult glioblastoma specimen with high-resolution deconvolution imaging and 3D reconstruction using a Deltavision Elite microscope confirmed close spatial co-localization of SOX2-positive tumor-associated CD73 (arrows) and PU.1/CD163-positive myeloid cell-associated CD39 proteins (arrowheads, representative region shown, CyCIF was performed once); scale bars 5 µm. D Latent Dirichlet Association (LDA) probabilistic modeling was used to globally analyze CyCIF data from the larger dataset of 172 IDH-WT glioblastoma to reduce cell populations into neighborhoods ("topics") defined by single-cell marker-expression patterns. This confirmed the presence of prominent neighborhoods with a spatial association between tumor (SOX2), myeloid (CD163/PU.1), and core purine regulators (CD39, CD73) (Topic 11), as well as other neighborhoods corresponding to various tissue niches. E, F Glioblastomas are heterogeneous collections of intermixed cellular neighborhoods, including large niches with prominent tumor-CD73:myeloid-CD39 interaction (E, arrows). G To assess the

strength of this spatial correlation, we next performed spatial statistics analysis, showing that tumor CD73 is strongly associated with myeloid CD39 (*p* = 4.78e-19, Pearson's correlation test, unpaired, two-sided) and PD-L1 protein (*p* = 1.84e-64), but less so with endothelial or lymphoid cells and myeloid cells themselves.

**H, I** Tumor CD73 was also correlated (*p* = 5.56e-56, Pearson's correlation test, unpaired, two-sided) with regions of elevated nuclear HIF1A expression, and myeloid cells (PU.1-positive) with coordinate CD163 (*p* = 3.72e-19, Pearson's correlation test, unpaired, two-sided) and CD11b (*p* = 2.55e-50) expression. **J** Mantel-Cox survival analysis showed that cases with higher levels of spatial interaction between tumor CD73 and myeloid CD39 showed particularly poor progression-free survival (PFS). **K** 24-plex CyCIF analysis of a second cohort of 95 pediatric CNS tumors, including 28 pediatric HGG and 11 H3K27M-mutant diffuse midline gliomas, showing similar co-localization of tumor CD73 and myeloid CD39 (representative staining from a high-grade glioma specimen shown, CyCIF was performed once for each specimen); scale bars, 50 µm. **L** Spatial statistics again showed a strongly significant spatial correlation between tumor CD73 and myeloid CD39 (*p* = 1.56e-8, Pearson's correlation test, unpaired, two-side), as well as myeloid CD163 (*p* = 9.76e-8) and CD11b (*p* = 9.80e-5) in pediatric high-grade gliomas and diffuse midline gliomas.

Further analysis in Fig. 1H-J and Fig. S1f, g were performed in R (v4.2.0) using the Seurat (v4.1.1) package⁷⁸ and the figures were plotted using the ggplot2 package (v3.3.6). Pathway enrichment analyses were performed using GSEA⁴⁸ and GO Enrichment Analysis⁷⁹⁻⁸².

Single-cell analyses of an additional cohort of 7 newly diagnosed adult glioblastomas, and 4 recurrent adult glioblastomas in Fig. 2D-M and Fig. S3a-m were performed using the data from a previously published study (Pombo Antunes et al. 2021)⁵². UMAP and dataplots were generated using the Brain Immune Atlas interface (https://www.brainimmuneatlas.org/umap-humanGbm-diag-full.php). The processed log TPM gene-cell count matrix and cell annotation matrices were downloaded for analysis from the Brain Immune Atlas (https://www.brainimmuneatlas.org/download.php).

Single-cell analyses of microglia from a cohort of 15 brain specimens from individuals undergoing surgery for epilepsy, glioma, or metastasis presented in Figs. S3n-p and S12b were performed using data from a previously published study (Sankowski et al. 2019). T-stochastic neighborhood embedding plots (t-SNE) were generated using the associated Single cell data viewer (https://single-cell.shinyapps.io/human-microglia/).

Single-cell analysis of a cohort of 13 developing human brains presented in Fig. S2 were performed using data from a previously published study (Nowakowski et al. Science 2017). T-stochastic neighborhood embedding plots (t-SNE) were generated and analyzed in the UCSC Cell Browser (https://cells.ucsc.edu/?ds=cortex-dev).

## Animal management

For mouse brain mass spectrometry experiments, brains were received after removal from the Mayo Clinic. Mice were maintained with a 12 h light, 12 h dark schedule with no entrance of researchers during the dark cycle. Temperature was 68-72 °F with 30-70% humidity. Chow was available ad libitum (fat content 21.6%, PicoLab mouse Diet, Cat # 5058). There were 5 mice per ventilated cage on corn cob bedding. Water was accessible at all times. Cervical dislocation without anesthesia was used to sac the mice. The research protocol was approved by the Mayo Clinic Institutional Review Board (IRB) and Brigham and Women's Hospital Institutional Review Board (IRB) and adhered to all ethical guidelines.

## Immune-related signatures assessment in TCGA GBM RNA-seq data

TCGA GBM RNA-seq data were processed using Kallisto⁸³ and the Gencode GTF⁸⁴ by the Piccolo team⁸⁵. This counts matrix, along with the metadata table were downloaded from https://osf.io/gqrz9/files. Immune cell population signatures from Pombo Antunes et al.⁵¹, were

assessed by computing the log2 mean of transcriptomic markers expression for each population (Supplementary Data 1). Inflammation-related signatures were taken from Gromeier et al.⁸⁶ (Supplementary Data 2). Heatmaps were drawn using the ComplexHeatmap (v3.15) R package⁸⁷, scaled per gene list for visualization. Violin plots of computed expression means were done using the geom_violin function from the ggplot2 (v3.3.6) R package⁸⁸. Statistics (Wilcoxon test, unpaired, two-sided) between *NTSE*, *ENTPDI* single or double high and low sample groups were computed using the geom_signif function from the ggsignif R package. Tumor purity of TCGA GBM bulk RNA-seq data estimated with the ABSOLUTE R package⁸⁹ or the combination of several tools (CPE: consensus measurement of purity estimations) were extracted from Aran et al.⁹⁰. 168 samples had an acceptable estimation of tumor purity in Aran et al. Of the 168 samples, 155 were primary tumors, and 13 recurrent tumors. Clinical data regarding recurrence status was extracted from TCGA.

## MALDI tissue preparation

Frozen resection tissue from 9 glioblastomas were selected for MALDI MSI. Immunohistochemistry showed that 4 cases had 'high' CD73 by IHC (IHC score = 2 or 3), while 5 cases had 'low' CD73 by IHC (IHC score = 0 or 1). Tissue was sectioned to 10 µm, thaw mounted onto indium-tin-oxide (ITO) slides, and serial sections were obtained for H&E staining. A high-resolution image of the whole H&E tissues was obtained through image stitching (Zeiss Observer Z1, Oberkochen, Germany) using a plan-apochromat lens (20×) using an AxioCam MR3 camera. Matrix preparation of 1,5-diaminonaphthalene hydrochloride was prepared to a concentration of 4.3 mg/mL in 4.5/5/0.5 HPLC grade water/ethanol/1M HCl (v/v/v). The matrix was sprayed using a TM-sprayer (HTX imaging, Carrboro, NC) with parameters of a flow rate (0.09 mL/min), spray nozzle velocity (1200 mm/min), spray nozzle temperature (75 °C), nitrogen gas pressure (10 psi), track spacing (2 mm) and a four-pass spray.

## MALDI FT-ICR mass spectrometry imaging

The mass spectrometry imaging experiments were conducted using a 9.4 Tesla Solarix XR FT-ICR MS (Bruker Daltonics, Billerica, MA) in negative ion mode. Using a tune mix solution (Agilent Technologies, Santa Clara, CA) the method was calibrated through the electrospray source. The instrumental parameters included a defined pixel step size of 100 µm that covered the m/z range of 58-1200. Each pixel consisted of 200 shots at a laser power of 12% (arbitrary scale) and frequency of 1000 Hz. To bias the acquisition to improve the capture of the purine metabolites mass range, continuous accumulation of selected ions (CASI) mode was used, in which the Q1 was set to m/z
<figure><img src="image_47.png" /><figcaption>**Fig. 9 | Spatial topography of purine signaling in glioblastoma and pediatric high-grade glioma.** Collectively, data suggests a model in which *EGFR* activation (adult IDH-WT glioblastoma), *PDGFRA* activation (pediatric high-grade glioma) and/or hypoxia promote CD73 expression by tumor cells in an inflammatory</figcaption></figure>

microenvironment. Spatial co-localization of tumor cell-specific CD73 with microglial CD39 augments adenosine production in the tumor microenvironment, promoting immune tolerance and tumorigenesis through action on multiple cell populations. Created with BioRender.com.

350 with an isolation window of 400. A standard of adenosine was spotted on the same slide containing the tissues and imaged at the same time of the MSI acquisition. Further validation on metabolite annotations was performed by tandem MSMS from tissues using a timsTOF FleX (Bruker Daltonics, Billerica, MA) and compared to MS/MS measurements performed from standards (Supplementary Data 11). Ion images and mass spectra were viewed and processed using SCiLS Lab software (version 2019c premium, Bruker Daltonics, Billerica, MA), in which the dataset was normalized to the total ion current (TIC). Metabolites were putatively annotated using an accurate mass of Δppm <2.5 and MSMS measurements, and cross-matched using the Metlin metabolite database (https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage)⁹¹.

## t-CyCIF protocol

The detailed protocol for tissue-based cyclic immunofluorescence (t-CyCIF)⁹ is available at protocols.io (dx.doi.org/10.17504/protocols.io.bjiukkew). The BOND RX Automated IHC/ISH Stainer was used to bake FFPE slides at 60 °C for 30 min, dewax using Bond Dewax solution at 72 °C, and perform antigen retrieval using EpiTOPE Retrieval 1 (LeicaTM) solution at 100 °C for 20 min. Slides underwent multiple cycles of antibody incubation, imaging, and fluorophore inactivation. All antibodies were incubated overnight at 4 °C in the dark. Slides were stained with Hoechst 33342 for 10 min at room temperature in the dark following antibody incubation in every cycle. Coverslips were wet-mounted using 200 µL of 10% Glycerol in PBS prior to imaging.

For conventional CyCIF, images were taken using a 20× objective (0.75 NA) on a CyteFinderII slide scanning fluorescence microscope (RareCyte Inc. Seattle WA) using CyteFinder software (v3.11.024) or on an IN Cell 6000 Analyzer using Acquisition Software v6.1 and v7.3.

For 3D reconstruction, images were taken using a 60× objective (1.42 NA) on a Deltavision Elite deconvolution microscope using SoftWoRx software (v.7.0.0).

Fluorophores were inactivated by incubating slides in a 4.5% H₂O₂, 24 mM NaOH in PBS solution and placing under an LED light source for 1 h.

For CyCIF experiments, the EPR6115 anti-CD73 clone was used following cross-validation of multiple clones.

## Antibodies

The antibodies used in this study are provided in the Antibody List (see Supplementary Table 1).

## t-CyCIF image processing and data quantification

Image analysis was performed with the Docker-based NextFlow pipeline MCMICRO⁶⁶ and with customized scripts in Python (v3.6), ImageJ (v1.53r), and MATLAB (v2017, 2018a, 2019b). All code is available on GitHub (https://github.com/labsyspharm/cd73_coy_spatialcorrelation) and Zenodo (https://zenodo.org/record/6628875#.YqJVfqjMJD8). DOI: 10.5281/zenodo.6628875. Briefly, after raw images were acquired, the stitching and registration of different tiles and cycles were done with MCMICRO⁶⁶ using the ASHLAR (v1.10.2)⁹² module. The assembled OME.TIFF files from each slide were then passed through
<figure><img src="image_64.png" /><figcaption>**Fig. 9 | Spatial topography of purine signaling in glioblastoma and pediatric high-grade glioma.** Collectively, data suggests a model in which *EGFR* activation (adult IDH-WT glioblastoma), *PDGFRA* activation (pediatric high-grade glioma) and/ or hypoxia promote CD73 expression by tumor cells in an inflammatory</figcaption></figure>

microenvironment. Spatial co-localization of tumor cell-specific CD73 with microglial CD39 augments adenosine production in the tumor microenvironment, promoting immune tolerance and tumorigenesis through action on multiple cell populations. Created with BioRender.com.

350 with an isolation window of 400. A standard of adenosine was spotted on the same slide containing the tissues and imaged at the same time of the MSI acquisition. Further validation on metabolite annotations was performed by tandem MSMS from tissues using a timsTOF Flex (Bruker Daltonics, Billerica, MA) and compared to MS/MS measurements performed from standards (Supplementary Data 11). Ion images and mass spectra were viewed and processed using SciLS Lab software (version 2019c premium, Bruker Daltonics, Billerica, MA), in which the dataset was normalized to the total ion current (TIC). Metabolites were putatively annotated using an accurate mass of Δppm <2.5 and MSMS measurements, and cross-matched using the Metlin metabolite database (https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage)⁹¹.

## t-CyCIF protocol

The detailed protocol for tissue-based cyclic immunofluorescence (t-CyCIF)⁹ is available at protocols.io (dx.doi.org/10.17504/protocols.io.bjiukkew). The BOND RX Automated IHC/ISH Stainer was used to bake FFPE slides at 60 °C for 30 min, dewax using Bond Dewax solution at 72 °C, and perform antigen retrieval using EpiTOPE Retrieval 1 (LeicaTM) solution at 100 °C for 20 min. Slides underwent multiple cycles of antibody incubation, imaging, and fluorophore inactivation. All antibodies were incubated overnight at 4 °C in the dark. Slides were stained with Hoechst 33342 for 10 min at room temperature in the dark following antibody incubation in every cycle. Coverslips were wet-mounted using 200 µL of 10% Glycerol in PBS prior to imaging.

For conventional CyCIF, images were taken using a 20× objective (0.75 NA) on a CyteFinderII slide scanning fluorescence microscope (RareCyte Inc. Seattle WA) using CyteFinder software (v3.11.024) or on an IN Cell 6000 Analyzer using Acquisition Software v6.1 and v7.3.

For 3D reconstruction, images were taken using a 60× objective (1.42 NA) on a Deltavision Elite deconvolution microscope using SoftWoRx software (v.7.0.0).

Fluorophores were inactivated by incubating slides in a 4.5% H₂O₂, 24 mM NaOH in PBS solution and placing under an LED light source for 1 h.

For CyCIF experiments, the EPR6115 anti-CD73 clone was used following cross-validation of multiple clones.

## Antibodies

The antibodies used in this study are provided in the Antibody List (see Supplementary Table 1).

## t-CyCIF image processing and data quantification

Image analysis was performed with the Docker-based NextFlow pipeline MCMICRO⁶⁶ and with customized scripts in Python (v3.6), ImageJ (v1.53r), and MATLAB (v2017, 2018a, 2019b). All code is available on GitHub (https://github.com/labsyspharm/cd73_coy_spatialcorrelation) and Zenodo (https://zenodo.org/record/6628875#.YqJVfqjMJD8). DOI: 10.5281/zenodo.6628875. Briefly, after raw images were acquired, the stitching and registration of different tiles and cycles were done with MCMICRO⁶⁶ using the ASHLAR (v1.10.2)⁹² module. The assembled OME.TIFF files from each slide were then passed through
quantification modules. For background subtraction, a rolling ball algorithm with 50-pixel radius was applied using ImageJ/Fiji. For segmentation and quantification, UNMICST2⁹³ was used supplemented by customized ImageJ scripts⁹ to generate single-cell data. More details and source code can be found at www.cycif.org.

## Single-cell data QC for t-CyCIF

Single-cell data was passed through several QC steps during the generation of the cell feature table. Initial QC was done simultaneously with segmentation and quantification, so that cells lost from the specimen in the later cycles would not be included in the output. Next, single-cell data was filtered based on mean Hoechst staining intensity across cycles; cells with coefficient of variation (CV) greater than three standard deviations from the mean were discarded as were any objected identified by segmentation as “cells” but having no DNA intensity. These steps are designed to eliminate cells in which the nuclei are not included as a result of sectioning. Highly autofluorescent (AF) cells (measured in cycle 1 or 2) were also removed from the analysis, using a customized MATLAB script that applied a Gaussian Mixture Model (GMM) to identify high-AF population. More details and scripts can be found online (https://github.com/sorgerlab/cycif).

## Blinding

When visual tissue analyses were performed (e.g., histologic tissue QC, IF/IHC evaluation and QC, CD73 IHC scoring by a pathologist), relevant investigators (S.C., S.S.) were blind to the precise diagnosis (e.g., IDH-mutant vs. IDH-wildtype, H3K27M-mutant diffuse midline glioma vs. pediatric HGG, NOS) and clinical (e.g., patient demographics, PFS, OS), molecular (e.g., IDH1/2-status, EGFR-status, MGMT-status, MMR-status, etc.), and other relevant data to avoid bias in assessment of tissue adequacy and scoring.

Some investigators (S.W., J.R.L.) performing the initial single-cell, spatial statistics, and outcome analyses using CyCIF imaging data were not involved in the initial tumor cohort selection or visual tissue analysis, and were generally blind to source tissue of cells except where necessary during the analysis of single-cell imaging data to further avoid potential bias in the processing of single-cell data and application of analytic methods to this data.

## Data exclusion and quality control

For tissue microarray data (CyCIF, immunohistochemistry), tissue quality control (QC) was performed by a pathologist, and a small minority of cases in which the tissue core was insufficient for analysis (folded, minimal tissue, absent tissue) were excluded, blinded to experimental parameters such as marker expression, diagnosis, and clinical data. Whenever data was filtered to select a sub-population for analysis or a figure (e.g., primary IDH-wildtype glioblastoma) the procedure is detailed in the text, figure legend, or methods section, and additional sub-cohort data is described (see Supplementary Data). For single-cell immunofluorescence data, QC was performed as described in the Methods section subtitled “Single-cell data QC for t-CyCIF.”

## Spatial analysis

Spatial correlation functions were evaluated as the Pearson correlation between pairs of cells as a function of their relative distance. Specifically, the Pearson correlation between two variables U and V was evaluated between a cell of type A and its k'th nearest neighbor of type B, and the average distance to k'th nearest neighbors was evaluated in micrometers. In each correlation function A and B were some combination of the cell-type classes assigned to each cell (e.g., Tumor and Myeloid, Tumor and All, Tumor and non-Tumor). U and V were either marker log-intensity values or a binary variable indicating whether a cell was or was not of a given type (e.g., CD73 and CD39 values, HIF1a

values, and Endothelial binary). In Supplementary Fig. S12a, A = Tumor, B = Myeloid, U = CD73, V = CD39.

Spatial correlation functions were evaluated for each core up to k = 200, and then fit to a single exponential with two parameters:

$$C(d) = c^* \exp(b^*d)$$

for the correlation C(d) as a function of distance d. The fitted parameter c, correlation strength, was used as a summary of the spatial correlation in each core (Fig. 8G–I, L; Fig. S12a, d). If the 95% confidence interval of c contained 0, c was considered statistically insignificant and set to 0 for downstream analysis. ‘High interaction’ in Fig. 8J was defined as the condition that all TMA cores from a given patient having significant values for c.

## Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

Data (publicly available and newly generated) used in the preparation of this manuscript are detailed in the Source Data files provided with the manuscript (see Table of Contents in Supplementary Data 12). The processed cyclic immunofluorescence (CyCIF) single-cell data feature tables and mass spectrometry data for the adult glioma, pediatric glioma, and mouse brain datasets are deposited and available at the Synapse repository: https://www.synapse.org/#!Synapse:syn30803357⁶³.

The processed cyclic immunofluorescence (CyCIF) single-cell data feature tables and mass spectrometry data for the adult glioma, pediatric glioma, and mouse brain datasets are deposited and available at the Synapse repository: https://www.synapse.org/#!Synapse:syn30803357⁶³.

The publicly available adult glioblastoma single-cell RNA sequencing data (Neftel et al.) are available in the Broad Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma#study-summary)⁴¹.

The publicly available adult glioma single-cell immune RNA-sequencing data (Pombo-Antunes et al.) are available in the Brain Immune Atlas (https://www.brainimmuneatlas.org)⁵¹.

The publicly available developing brain single-cell RNA-sequencing data (Nowakowski et al.) are available in the UCSC Cell Browser (https://cells.ucsc.edu/?ds=cortex-dev)⁴⁷.

The publicly available human microglia single-cell RNA-sequencing data (Sankowski et al.) are available in the human microglia browser (https://single-cell.shinyapps.io/human-microglia)⁵⁰.

The publicly available processed TCGA data are available at https://www.cbioportal.org/⁵³.

The publicly available processed CPTAC/CBTTC pediatric glioma data are available at: http://pbt.cptac-data-view.org/⁶².

The publicly available TCGA GBM bulk full-length RNA-seq processed by the Piccolo’s team are available at: https://osf.io/gqrz9/files⁸⁵.

Mass spectrometry metabolite identities were cross-matched using the Metlin metabolite database (https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage)⁹¹.

Mass spectrometry data is available at the NIH Common Fund’s National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org where it has been assigned Project ID (PRO01406). The data can be accessed directly via its Project DOI: (https://doi.org/10.21228/M80421)⁶⁴.

Remaining data are available within the Article, Supplementary Information, Source Data, or Supplementary Data.

There are no restrictions on data access. Raw imaging data from cyclic immunofluorescence experiments are available from the
quantification modules. For background subtraction, a rolling ball algorithm with 50-pixel radius was applied using ImageJ/Fiji. For segmentation and quantification, UNMICST2⁹³ was used supplemented by customized ImageJ scripts⁹ to generate single-cell data. More details and source code can be found at www.cycif.org.

## Single-cell data QC for t-CyCIF

Single-cell data was passed through several QC steps during the generation of the cell feature table. Initial QC was done simultaneously with segmentation and quantification, so that cells lost from the specimen in the later cycles would not be included in the output. Next, single-cell data was filtered based on mean Hoechst staining intensity across cycles; cells with coefficient of variation (CV) greater than three standard deviations from the mean were discarded as were any objected identified by segmentation as “cells” but having no DNA intensity. These steps are designed to eliminate cells in which the nuclei are not included as a result of sectioning. Highly autofluorescent (AF) cells (measured in cycle 1 or 2) were also removed from the analysis, using a customized MATLAB script that applied a Gaussian Mixture Model (GMM) to identify high-AF population. More details and scripts can be found online (https://github.com/sorgerlab/cycif).

## Blinding

When visual tissue analyses were performed (e.g., histologic tissue QC, IF/IHC evaluation and QC, CD73 IHC scoring by a pathologist), relevant investigators (S.C., S.S.) were blind to the precise diagnosis (e.g., IDH-mutant vs. IDH-wildtype, H3K27M-mutant diffuse midline glioma vs. pediatric HGG, NOS) and clinical (e.g., patient demographics, PFS, OS), molecular (e.g., IDH1/2-status, EGFR-status, MGMT-status, MMR-status, etc.), and other relevant data to avoid bias in assessment of tissue adequacy and scoring.

Some investigators (S.W., J.R.L.) performing the initial single-cell, spatial statistics, and outcome analyses using CyCIF imaging data were not involved in the initial tumor cohort selection or visual tissue analysis, and were generally blind to source tissue of cells except where necessary during the analysis of single-cell imaging data to further avoid potential bias in the processing of single-cell data and application of analytic methods to this data.

## Data exclusion and quality control

For tissue microarray data (CyCIF, immunohistochemistry), tissue quality control (QC) was performed by a pathologist, and a small minority of cases in which the tissue core was insufficient for analysis (folded, minimal tissue, absent tissue) were excluded, blinded to experimental parameters such as marker expression, diagnosis, and clinical data. Whenever data was filtered to select a sub-population for analysis or a figure (e.g., primary IDH-wildtype glioblastoma) the procedure is detailed in the text, figure legend, or methods section, and additional sub-cohort data is described (see Supplementary Data). For single-cell immunofluorescence data, QC was performed as described in the Methods section subtitled “Single-cell data QC for t-CyCIF.”

## Spatial analysis

Spatial correlation functions were evaluated as the Pearson correlation between pairs of cells as a function of their relative distance. Specifically, the Pearson correlation between two variables U and V was evaluated between a cell of type A and its k'th nearest neighbor of type B, and the average distance to k'th nearest neighbors was evaluated in micrometers. In each correlation function A and B were some combination of the cell-type classes assigned to each cell (e.g., Tumor and Myeloid, Tumor and All, Tumor and non-Tumor). U and V were either marker log-intensity values or a binary variable indicating whether a cell was or was not of a given type (e.g., CD73 and CD39 values, HIF1a

values, and Endothelial binary). In Supplementary Fig. S12a, A = Tumor, B = Myeloid, U = CD73, V = CD39.

Spatial correlation functions were evaluated for each core up to k = 200, and then fit to a single exponential with two parameters:

$$C(d) = c^* \exp(b^*d)$$

for the correlation C(d) as a function of distance d. The fitted parameter c, correlation strength, was used as a summary of the spatial correlation in each core (Fig. 8G–I, L; Fig. S12a, d). If the 95% confidence interval of c contained 0, c was considered statistically insignificant and set to 0 for downstream analysis. ‘High interaction’ in Fig. 8J was defined as the condition that all TMA cores from a given patient having significant values for c.

## Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

## Data availability

Data (publicly available and newly generated) used in the preparation of this manuscript are detailed in the Source Data files provided with the manuscript (see Table of Contents in Supplementary Data 12). The processed cyclic immunofluorescence (CyCIF) single-cell data feature tables and mass spectrometry data for the adult glioma, pediatric glioma, and mouse brain datasets are deposited and available at the Synapse repository: https://www.synapse.org/#!Synapse:syn30803357⁶³.

The processed cyclic immunofluorescence (CyCIF) single-cell data feature tables and mass spectrometry data for the adult glioma, pediatric glioma, and mouse brain datasets are deposited and available at the Synapse repository: https://www.synapse.org/#!Synapse:syn30803357⁶³.

The publicly available adult glioblastoma single-cell RNA sequencing data (Neftel et al.) are available in the Broad Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP393/single-cell-rna-seq-of-adult-and-pediatric-glioblastoma#study-summary)⁴¹.

The publicly available adult glioma single-cell immune RNA-sequencing data (Pombo-Antunes et al.) are available in the Brain Immune Atlas (https://www.brainimmuneatlas.org)⁵¹.

The publicly available developing brain single-cell RNA-sequencing data (Nowakowski et al.) are available in the UCSC Cell Browser (https://cells.ucsc.edu/?ds=cortex-dev)⁴⁷.

The publicly available human microglia single-cell RNA-sequencing data (Sankowski et al.) are available in the human microglia browser (https://single-cell.shinyapps.io/human-microglia)⁵⁰.

The publicly available processed TCGA data are available at https://www.cbioportal.org/⁵³.

The publicly available processed CPTAC/CBTTC pediatric glioma data are available at: http://pbt.cptac-data-view.org/⁶².

The publicly available TCGA GBM bulk full-length RNA-seq processed by the Piccolo’s team are available at: https://osf.io/gqrz9/files⁸⁵.

Mass spectrometry metabolite identities were cross-matched using the Metlin metabolite database (https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage)⁹¹.

Mass spectrometry data is available at the NIH Common Fund’s National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org where it has been assigned Project ID (PRO01406). The data can be accessed directly via its Project DOI: (https://doi.org/10.21228/M80421)⁶⁴.

Remaining data are available within the Article, Supplementary Information, Source Data, or Supplementary Data.

There are no restrictions on data access. Raw imaging data from cyclic immunofluorescence experiments are available from the
corresponding authors on request. These files are large and may require significant computational pre-processing for final data analysis. The processes used by the authors are delineated in the Methods section and/or code repository (https://github.com/labsyspharm/cd73_coy_spatialcorrelation), but interested parties should first consider using processed counts tables available in the Synapse repository (https://www.synapse.org/#!Synapse:syn30803357)⁶³. Source data are provided with this paper.

## Code availability

Custom code used in this manuscript are available at the following repositories: https://github.com/labsyspharm/cd73_coy_spatialcorrelation, https://zenodo.org/record/6628875#.YqJVfqjMJD8. Digital Object Identifier (DOI) 10.5281/zenodo.6628875.

There are no restrictions on access. Further information and requests should be directed to the corresponding authors and will be fulfilled.

## References

1. Sharma, P. et al. The next decade of immune checkpoint therapy. *Cancer Discov.* **11**, 838–857 (2021).

2. Iwai, Y. et al. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. *Proc. Natl Acad. Sci. USA* **99**, 12293–12297 (2002).

3. von Bernstorff, W. et al. Systemic and local immunosuppression in pancreatic cancer patients. *Clin. Cancer Res.* **7**, 925s–932s (2001).

4. Ostuni, R., Kratochvill, F., Murray, P. J. & Natoli, G. Macrophages and cancer: From mechanisms to therapeutic implications. *Trends Immunol.* **36**, 229–239 (2015).

5. Lyssiotis, C. A. & Kimmelman, A. C. Metabolic interactions in the tumor microenvironment. *Trends Cell Biol.* **27**, 863–875 (2017).

6. Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. *Nat. Med.* **20**, 436–442 (2014).

7. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. *Nat. Methods* **11**, 417–422 (2014).

8. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. *Cell* **174**, 968–981.e15 (2018).

9. Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. *Elife* **7**, e31657 (2018).

10. Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. *Nat. Biotechnol.* **37**, 1080–1090 (2019).

11. Tsujikawa, T. et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. *Cell Rep.* **19**, 203–217 (2017).

12. Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. *Cell Syst.* **2**, 225–238 (2016).

13. Gaglia, G. et al. Temporal and spatial topography of cell proliferation in cancer. *Nat. Cell Biol.* **24**, 316–326 (2022).

14. Lin, J.-R. et al. Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer. Preprint at bioRxiv https://doi.org/10.1101/2021.03.31.437984 (2021).

15. Risom, T. et al. Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. *Cell* **185**, 299–310.e18 (2022).

16. Rozenblatt-Rosen, O. et al. The human tumor atlas network: Charting tumor transitions across space and time at single-cell resolution. *Cell* https://doi.org/10.1016/j.cell.2020.03.053 (2020).

17. Wen, P. Y. et al. Glioblastoma in adults: A Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. *Neuro Oncol.* **22**, 1073–1113 (2020).

18. Mackay, A. et al. Integrated molecular meta-analysis of 1000 pediatric high-grade and diffuse intrinsic pontine glioma. *Cancer Cell* **32**, 520–537.e5 (2017).

19. Touat, M., Idbah, A., Sanson, M. & Ligon, K. L. Glioblastoma targeted therapy: Updated approaches from recent biological insights. *Ann. Oncol.* **28**, 1457–1472 (2017).

20. Nayak, L. et al. Randomized phase II and biomarker study of pembrolizumab plus bevacizumab versus pembrolizumab alone for patients with recurrent glioblastoma. *Clin. Cancer Res.* **27**, 1048–1057 (2021).

21. Reardon, D. A. et al. Effect of nivolumab vs bevacizumab in patients with recurrent glioblastoma: The CheckMate 143 phase 3 randomized clinical trial. *JAMA Oncol.* **6**, 1003–1010 (2020).

22. Touat, M. et al. Mechanisms and therapeutic implications of hypermutation in gliomas. *Nature* **580**, 517–523 (2020).

23. Zhao, J. et al. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. *Nat. Med.* **25**, 462–469 (2019).

24. Cloughesy, T. F. et al. Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. *Nat. Med.* **25**, 477–486 (2019).

25. Schalper, K. A. et al. Neoadjuvant nivolumab modifies the tumor immune microenvironment in resectable glioblastoma. *Nat. Med.* **25**, 470–476 (2019).

26. Moesta, A. K., Li, X.-Y. & Smyth, M. J. Targeting CD39 in cancer. *Nat. Rev. Immunol.* **20**, 739–755 (2020).

27. Verkratsky, A. & Burnstock, G. Biology of purinergic signalling: Its ancient evolutionary roots, its omnipresence, and its multiple functional significance. *Bioessays* **36**, 697–705 (2014).

28. Cekic, C. & Linden, J. Purinergic regulation of the immune system. *Nat. Rev. Immunol.* **16**, 177–192 (2016).

29. Abbracchio, M. P., Burnstock, G., Verkratsky, A. & Zimmermann, H. Purinergic signalling in the nervous system: An overview. *Trends Neurosci.* **32**, 19–29 (2009).

30. Keough, M. B. & Monje, M. Neural signaling in cancer. *Annu. Rev. Neurosci.* https://doi.org/10.1146/annurev-neuro-111020-092702 (2022).

31. Bavaresco, L. et al. The role of ecto-5'-nucleotidase/CD73 in glioma cell line proliferation. *Mol. Cell. Biochem.* **319**, 61–68 (2008).

32. Sek, K. et al. Targeting adenosine receptor signaling in cancer immunotherapy. *Int. J. Mol. Sci.* **19**, 3837 (2018).

33. Stagg, J. et al. CD73-deficient mice have increased antitumor immunity and are resistant to experimental metastasis. *Cancer Res.* **71**, 2892–2900 (2011).

34. Goswami, S. et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. *Nat. Med.* **26**, 39–46 (2020).

35. Jiang, T. et al. Comprehensive evaluation of NT5E/CD73 expression and its prognostic significance in distinct types of cancers. *BMC Cancer* **18**, 267 (2018).

36. Quezada, C. et al. 5'-ectonucleotidase mediates multiple-drug resistance in glioblastoma multiforme cells. *J. Cell. Physiol.* **228**, 602–608 (2013).

37. Torres, A. et al. Extracellular adenosine promotes cell migration/ invasion of glioblastoma stem-like cells through A3 adenosine receptor activation under hypoxia. *Cancer Lett.* **446**, 112–122 (2019).

38. Wink, M. R. et al. Altered extracellular ATP, ADP and AMP catabolism in glioma cell lines. *Cancer Lett.* **198**, 211–218 (2003).

39. Cappellari, A. R., Vasques, G. J., Bavaresco, L., Braganhol, E. & Battastini, A. M. O. Involvement of ecto-5'-nucleotidase/CD73 in U138MG glioma cell adhesion. *Mol. Cell. Biochem.* **359**, 315–322 (2012).

40. Wang, J. & Matosevic, S. NT5E/CD73 as correlative factor of patient survival and natural killer cell infiltration in glioblastoma. *J. Clin. Med.* **8**, 1526 (2019).
corresponding authors on request. These files are large and may
require significant computational pre-processing for final data analy-
sis. The processes used by the authors are delineated in the Methods
section and/or code repository (https://github.com/labsyspharm/
cd73_coy_spatialcorrelation), but interested parties should first con-
sider using processed counts tables available in the Synapse repository
(https://www.synapse.org/#!Synapse:syn30803357)⁶³. Source data are
provided with this paper.

Code availability

Custom code used in this manuscript are available at the
following repositories: https://github.com/labsyspharm/cd73-
coy_spatialcorrelation, https://zenodo.org/record/6628875#.
YqJVfqjMJD8. Digital Object Identifier (DOI) 10.5281/
zenodo.6628875.

There are no restrictions on access. Further information and requests
should be directed to the corresponding authors and will be fulfilled.

References

1. Sharma, P. et al. The next decade of immune checkpoint therapy. *Cancer Discov.* **11**, 838–857 (2021).

2. Iwai, Y. et al. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. *Proc. Natl Acad. Sci. USA* **99**, 12293–12297 (2002).

3. von Bernstorff, W. et al. Systemic and local immunosuppression in pancreatic cancer patients. *Clin. Cancer Res.* **7**, 925s–932s (2001).

4. Ostuni, R., Kratochvill, F., Murray, P. J. & Natoli, G. Macrophages and cancer: From mechanisms to therapeutic implications. *Trends Immunol.* **36**, 229–239 (2015).

5. Lyssiotis, C. A. & Kimmelman, A. C. Metabolic interactions in the tumor microenvironment. *Trends Cell Biol.* **27**, 863–875 (2017).

6. Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. *Nat. Med.* **20**, 436–442 (2014).

7. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. *Nat. Methods* **11**, 417–422 (2014).

8. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. *Cell* **174**, 968–981.e15 (2018).

9. Lin, J.-R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. *Elife* **7**, e31657 (2018).

10. Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. *Nat. Biotechnol.* **37**, 1080–1090 (2019).

11. Tsujikawa, T. et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. *Cell Rep.* **19**, 203–217 (2017).

12. Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. *Cell Syst.* **2**, 225–238 (2016).

13. Gaglia, G. et al. Temporal and spatial topography of cell prolifera-
tion in cancer. *Nat. Cell Biol.* **24**, 316–326 (2022).

14. Lin, J.-R. et al. Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer. Preprint at bioRxiv https://doi.org/10.1101/2021.03.31.437984 (2021).

15. Risom, T. et al. Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. *Cell* **185**, 299–310.e18 (2022).

16. Rozenblatt-Rosen, O. et al. The human tumor atlas network: Charting tumor transitions across space and time at single-cell resolution. *Cell* https://doi.org/10.1016/j.cell.2020.03.053 (2020).

17. Wen, P. Y. et al. Glioblastoma in adults: A Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. *Neuro Oncol.* **22**, 1073–1113 (2020).

18. Mackay, A. et al. Integrated molecular meta-analysis of 1000 pediatric high-grade and diffuse intrinsic pontine glioma. *Cancer Cell* **32**, 520–537.e5 (2017).

19. Touat, M., Idbah, A., Sanson, M. & Ligon, K. L. Glioblastoma targeted therapy: Updated approaches from recent biological insights. *Ann. Oncol.* **28**, 1457–1472 (2017).

20. Nayak, L. et al. Randomized phase II and biomarker study of pembrolizumab plus bevacizumab versus pembrolizumab alone for patients with recurrent glioblastoma. *Clin. Cancer Res.* **27**, 1048–1057 (2021).

21. Reardon, D. A. et al. Effect of nivolumab vs bevacizumab in patients with recurrent glioblastoma: The CheckMate 143 phase 3 randomized clinical trial. *JAMA Oncol.* **6**, 1003–1010 (2020).

22. Touat, M. et al. Mechanisms and therapeutic implications of hypermutation in gliomas. *Nature* **580**, 517–523 (2020).

23. Zhao, J. et al. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. *Nat. Med.* **25**, 462–469 (2019).

24. Cloughesy, T. F. et al. Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. *Nat. Med.* **25**, 477–486 (2019).

25. Schalper, K. A. et al. Neoadjuvant nivolumab modifies the tumor immune microenvironment in resectable glioblastoma. *Nat. Med.* **25**, 470–476 (2019).

26. Moesta, A. K., Li, X.-Y. & Smyth, M. J. Targeting CD39 in cancer. *Nat. Rev. Immunol.* **20**, 739–755 (2020).

27. Verkratsky, A. & Burnstock, G. Biology of purinergic signalling: Its ancient evolutionary roots, its omnipresence, and its multiple functional significance. *Bioessays* **36**, 697–705 (2014).

28. Cekic, C. & Linden, J. Purinergic regulation of the immune system. *Nat. Rev. Immunol.* **16**, 177–192 (2016).

29. Abbracchio, M. P., Burnstock, G., Verkratsky, A. & Zimmermann, H. Purinergic signalling in the nervous system: An overview. *Trends Neurosci.* **32**, 19–29 (2009).

30. Keough, M. B. & Monje, M. Neural signaling in cancer. *Annu. Rev. Neurosci.* https://doi.org/10.1146/annurev-neuro-111020-092702 (2022).

31. Bavaresco, L. et al. The role of ecto-5'-nucleotidase/CD73 in glioma cell line proliferation. *Mol. Cell. Biochem.* **319**, 61–68 (2008).

32. Sek, K. et al. Targeting adenosine receptor signaling in cancer immunotherapy. *Int. J. Mol. Sci.* **19**, 3837 (2018).

33. Stagg, J. et al. CD73-deficient mice have increased antitumor immunity and are resistant to experimental metastasis. *Cancer Res.* **71**, 2892–2900 (2011).

34. Goswami, S. et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. *Nat. Med.* **26**, 39–46 (2020).

35. Jiang, T. et al. Comprehensive evaluation of NT5E/CD73 expression and its prognostic significance in distinct types of cancers. *BMC Cancer* **18**, 267 (2018).

36. Quezada, C. et al. 5'-ectonucleotidase mediates multiple-drug resistance in glioblastoma multiforme cells. *J. Cell. Physiol.* **228**, 602–608 (2013).

37. Torres, Á. et al. Extracellular adenosine promotes cell migration/ invasion of glioblastoma stem-like cells through A3 adenosine receptor activation under hypoxia. *Cancer Lett.* **446**, 112–122 (2019).

38. Wink, M. R. et al. Altered extracellular ATP, ADP and AMP catabolism in glioma cell lines. *Cancer Lett.* **198**, 211–218 (2003).

39. Cappellari, A. R., Vasques, G. J., Bavaresco, L., Braganhol, E. & Battastini, A. M. O. Involvement of ecto-5'-nucleotidase/CD73 in U138MG glioma cell adhesion. *Mol. Cell. Biochem.* **359**, 315–322 (2012).

40. Wang, J. & Matosevic, S. NT5E/CD73 as correlative factor of patient survival and natural killer cell infiltration in glioblastoma. *J. Clin. Med.* **8**, 1526 (2019).
41. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. *Cell* **178**, 835–849.e21 (2019).

42. Francis, J. M. et al. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. *Cancer Discov.* **4**, 956–971 (2014).

43. Yan, A. et al. CD73 promotes glioblastoma pathogenesis and enhances its chemoresistance via A2B adenosine receptor signaling. *J. Neurosci.* **39**, 4387–4402 (2019).

44. Zhou, Y. et al. The adenosine A1 receptor antagonist DPCPX inhibits tumor progression via the ERK/JNK pathway in renal cell carcinoma. *Cell Physiol. Biochem.* **43**, 733–742 (2017).

45. Bennett, M. L. et al. New tools for studying microglia in the mouse and human CNS. *Proc. Natl Acad. Sci. USA* **113**, E1738–E1746 (2016).

46. Speir, M. L. et al. UCSC cell browser: Visualize your single-cell data. *Bioinformatics.* **37**, 4578–4580 (2021).

47. Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. *Science* **358**, 1318–1323 (2017).

48. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. *Proc. Natl Acad. Sci. USA* **102**, 15545–15550 (2005).

49. Synnestvedt, K. et al. Ecto-5'-nucleotidase (CD73) regulation by hypoxia-inducible factor-1 mediates permeability changes in intestinal epithelia. *J. Clin. Invest.* **110**, 993–1002 (2002).

50. Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. *Nat. Neurosci.* **22**, 2098–2110 (2019).

51. Pombo Antunes, A. R. et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. *Nat. Neurosci.* **24**, 595–610 (2021).

52. Antonioli, L., Pacher, P., Vizi, E. S. & Haskó, G. CD39 and CD73 in immunity and inflammation. *Trends Mol. Med.* **19**, 355–367 (2013).

53. Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. *Cell* **173**, 400–416.e11 (2018).

54. Bendell, J. C. et al. Safety and efficacy of the anti-CD73 monoclonal antibody (mAb) oleclumab ± durvalumab in patients (pts) with advanced colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), or EGFR-mutant non-small cell lung cancer (EGFRm NSCLC). *J. Clin. Oncol.* **39**, 9047–9047 (2021).

55. Kim, D.-W. et al. Abstract CT163: CD73 inhibitor oleclumab plus osimertinib for advanced EGFRm NSCLC: First report of a Phase 1b/2 study. *Cancer Res.* **81**, CT163 (2021).

56. Coy, S. et al. Multiplexed immunofluorescence reveals potential PD-1/PD-L1 pathway vulnerabilities in craniopharyngioma. *Neuro Oncol.* **20**, 1101–1112 (2018).

57. Donson, A. M. et al. Molecular analyses reveal inflammatory mediators in the solid component and Cyst fluid of human adamantomatous craniopharyngioma. *J. Neuropathol. Exp. Neurol.* **76**, 779–788 (2017).

58. Sholl, L. M. et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. *JCI Insight* **1**, e87062 (2016).

59. Tu, E. et al. Anti-PD-L1 and anti-CD73 combination therapy promotes T cell response to EGFR-mutated NSCLC. *JCI Insight* **7**, e142843 (2022).

60. Passarelli, A., Aieta, M., Sgambato, A. & Gridelli, C. Targeting immunometabolism mediated by CD73 pathway in EGFR-mutated non-small cell lung cancer: A new hope for overcoming immune resistance. *Front. Immunol.* **11**, 1479 (2020).

61. Xu, S. et al. Synergy between the ectoenzymes CD39 and CD73 contributes to adenosinergic immunosuppression in human malignant gliomas. *Neuro-Oncol.* **15**, 1160–1172 (2013).

62. CPTAC Data Viewer: PBT. http://pbt.cptac-data-view.org/ (2020).

63. Source_Data - syn30803357 - Files. https://www.synapse.org/#! Synapse:syn30803357 (2022).

64. Metabolomics Workbench: NIH Data Repository https://doi.org/10.21228/M80421 (2022).

65. Du, Z. et al. Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. *Nat. Protoc.* **14**, 2900–2930 (2019).

66. Schapiro, D. et al. MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging. *Nat. Methods* **19**, 311–315 (2022).

67. Puchalski, R. B. et al. An anatomic transcriptional atlas of human glioblastoma. *Science* **360**, 660–663 (2018).

68. Nirmal, A. et al. The spatial landscape of progression and immunoeediting in primary melanoma at single cell resolution. *Cancer Discov.* **12**, 1518–1541 (2022).

69. McCaffrey, E. F. et al. The immunoregulatory landscape of human tuberculosis granulomas. *Nat. Immunol.* **23**, 318–329 (2022).

70. Gutmann, D. H. & Kettenmann, H. Microglia/brain macrophages as central drivers of brain tumor pathobiology. *Neuron* **104**, 442–449 (2019).

71. Alessi, J. V. M. et al. Clinicopathologic and genomic correlates of tumor-infiltrating immune cells and immunotherapy efficacy in NSCLC. *J. Clin. Oncol.* **39**, 9121–9121 (2021).

72. Lin, J.-R. et al. Abstract 482: Rapid highly multiplexed immunoprofiling of human fixed tissues by Orion imaging. *Cancer Res.* **81**, 482 (2021).

73. Lopez, B. G. C. et al. Multimodal platform for assessing drug distribution and response in clinical trials. *Neuro Oncol.* **24**, 64–77 (2022).

74. Basu, S. S. et al. Rapid MALDI mass spectrometry imaging for surgical pathology. *NPJ Precis. Oncol.* **3**, 17 (2019).

75. Balkwill, F. Cancer and the chemokine network. *Nat. Rev. Cancer* **4**, 540–550 (2004).

76. Takenaka, M. C. et al. Control of tumor-associated macrophages and T cells in glioblastoma via AHR and CD39. *Nat. Neurosci.* **22**, 729–740 (2019).

77. Schapiro, D. et al. MITI minimum information guidelines for highly multiplexed tissue images. *Nat. Methods* **19**, 262–267 (2022).

78. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. *Nat. Biotechnol.* **36**, 411–420 (2018).

79. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. *Nat. Genet.* **25**, 25–29 (2000).

80. Mi, H. et al. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). *Nat. Protoc.* **14**, 703–721 (2019).

81. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. *Nucleic Acids Res.* **47**, D419–D426 (2019).

82. The Gene Ontology Consortium. The Gene Ontology resource: Enriching a GOld mine. *Nucleic Acids Res.* **49**, D325–D334 (2021).

83. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. *Nat. Biotechnol.* **34**, 525–527 (2016).

84. Harrow, J. et al. GENCODE: The reference human genome annotation for The ENCODE Project. *Genome Res.* **22**, 1760–1774 (2012).

85. Tatlow, P. J. & Piccolo, S. R. A cloud-based workflow to quantify transcript-expression levels in public cancer compendia. *Sci. Rep.* **6**, 39259 (2016).

86. Gromeier, M. et al. Very low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy. *Nat. Commun.* **12**, 352 (2021).

87. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. *Bioinformatics* **32**, 2847–2849 (2016).
41. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. *Cell* **178**, 835–849.e21 (2019).

42. Francis, J. M. et al. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. *Cancer Discov.* **4**, 956–971 (2014).

43. Yan, A. et al. CD73 promotes glioblastoma pathogenesis and enhances its chemoresistance via A2B adenosine receptor signaling. *J. Neurosci.* **39**, 4387–4402 (2019).

44. Zhou, Y. et al. The adenosine A1 receptor antagonist DPCPX inhibits tumor progression via the ERK/JNK pathway in renal cell carcinoma. *Cell Physiol. Biochem.* **43**, 733–742 (2017).

45. Bennett, M. L. et al. New tools for studying microglia in the mouse and human CNS. *Proc. Natl Acad. Sci. USA* **113**, E1738–E1746 (2016).

46. Speir, M. L. et al. UCSC cell browser: Visualize your single-cell data. *Bioinformatics.* **37**, 4578–4580 (2021).

47. Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. *Science* **358**, 1318–1323 (2017).

48. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. *Proc. Natl Acad. Sci. USA* **102**, 15545–15550 (2005).

49. Synnestvedt, K. et al. Ecto-5'-nucleotidase (CD73) regulation by hypoxia-inducible factor-1 mediates permeability changes in intestinal epithelia. *J. Clin. Invest.* **110**, 993–1002 (2002).

50. Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. *Nat. Neurosci.* **22**, 2098–2110 (2019).

51. Pombo Antunes, A. R. et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. *Nat. Neurosci.* **24**, 595–610 (2021).

52. Antonioli, L., Pacher, P., Vizi, E. S. & Haskó, G. CD39 and CD73 in immunity and inflammation. *Trends Mol. Med.* **19**, 355–367 (2013).

53. Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. *Cell* **173**, 400–416.e11 (2018).

54. Bendell, J. C. et al. Safety and efficacy of the anti-CD73 monoclonal antibody (mAb) oleclumab ± durvalumab in patients (pts) with advanced colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), or EGFR-mutant non-small cell lung cancer (EGFRm NSCLC). *J. Clin. Oncol.* **39**, 9047–9047 (2021).

55. Kim, D.-W. et al. Abstract CT163: CD73 inhibitor oleclumab plus osimertinib for advanced EGFRm NSCLC: First report of a Phase 1b/2 study. *Cancer Res.* **81**, CT163 (2021).

56. Coy, S. et al. Multiplexed immunofluorescence reveals potential PD-1/PD-L1 pathway vulnerabilities in craniopharyngioma. *Neuro Oncol.* **20**, 1101–1112 (2018).

57. Donson, A. M. et al. Molecular analyses reveal inflammatory mediators in the solid component and Cyst fluid of human adamantomatous craniopharyngioma. *J. Neuropathol. Exp. Neurol.* **76**, 779–788 (2017).

58. Sholl, L. M. et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. *JCI Insight* **1**, e87062 (2016).

59. Tu, E. et al. Anti-PD-L1 and anti-CD73 combination therapy promotes T cell response to EGFR-mutated NSCLC. *JCI Insight* **7**, e142843 (2022).

60. Passarelli, A., Aieta, M., Sgambato, A. & Gridelli, C. Targeting immunometabolism mediated by CD73 pathway in EGFR-mutated non-small cell lung cancer: A new hope for overcoming immune resistance. *Front. Immunol.* **11**, 1479 (2020).

61. Xu, S. et al. Synergy between the ectoenzymes CD39 and CD73 contributes to adenosinergic immunosuppression in human malignant gliomas. *Neuro-Oncol.* **15**, 1160–1172 (2013).

62. CPTAC Data Viewer: PBT. http://pbt.cptac-data-view.org/ (2020).

63. Source_Data - syn30803357 - Files. https://www.synapse.org/#! Synapse:syn30803357 (2022).

64. Metabolomics Workbench: NIH Data Repository https://doi.org/10.21228/M80421 (2022).

65. Du, Z. et al. Qualifying antibodies for image-based immune profiling and multiplexed tissue imaging. *Nat. Protoc.* **14**, 2900–2930 (2019).

66. Schapiro, D. et al. MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging. *Nat. Methods* **19**, 311–315 (2022).

67. Puchalski, R. B. et al. An anatomic transcriptional atlas of human glioblastoma. *Science* **360**, 660–663 (2018).

68. Nirmal, A. et al. The spatial landscape of progression and immunoeediting in primary melanoma at single cell resolution. *Cancer Discov.* **12**, 1518–1541 (2022).

69. McCaffrey, E. F. et al. The immunoregulatory landscape of human tuberculosis granulomas. *Nat. Immunol.* **23**, 318–329 (2022).

70. Gutmann, D. H. & Kettenmann, H. Microglia/brain macrophages as central drivers of brain tumor pathobiology. *Neuron* **104**, 442–449 (2019).

71. Alessi, J. V. M. et al. Clinicopathologic and genomic correlates of tumor-infiltrating immune cells and immunotherapy efficacy in NSCLC. *J. Clin. Oncol.* **39**, 9121–9121 (2021).

72. Lin, J.-R. et al. Abstract 482: Rapid highly multiplexed immunoprofiling of human fixed tissues by Orion imaging. *Cancer Res.* **81**, 482 (2021).

73. Lopez, B. G. C. et al. Multimodal platform for assessing drug distribution and response in clinical trials. *Neuro Oncol.* **24**, 64–77 (2022).

74. Basu, S. S. et al. Rapid MALDI mass spectrometry imaging for surgical pathology. *NPJ Precis. Oncol.* **3**, 17 (2019).

75. Balkwill, F. Cancer and the chemokine network. *Nat. Rev. Cancer* **4**, 540–550 (2004).

76. Takenaka, M. C. et al. Control of tumor-associated macrophages and T cells in glioblastoma via AHR and CD39. *Nat. Neurosci.* **22**, 729–740 (2019).

77. Schapiro, D. et al. MITI minimum information guidelines for highly multiplexed tissue images. *Nat. Methods* **19**, 262–267 (2022).

78. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. *Nat. Biotechnol.* **36**, 411–420 (2018).

79. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. *Nat. Genet.* **25**, 25–29 (2000).

80. Mi, H. et al. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). *Nat. Protoc.* **14**, 703–721 (2019).

81. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. *Nucleic Acids Res.* **47**, D419–D426 (2019).

82. The Gene Ontology Consortium. The Gene Ontology resource: Enriching a GOld mine. *Nucleic Acids Res.* **49**, D325–D334 (2021).

83. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. *Nat. Biotechnol.* **34**, 525–527 (2016).

84. Harrow, J. et al. GENCODE: The reference human genome annotation for The ENCODE Project. *Genome Res.* **22**, 1760–1774 (2012).

85. Tatlow, P. J. & Piccolo, S. R. A cloud-based workflow to quantify transcript-expression levels in public cancer compendia. *Sci. Rep.* **6**, 39259 (2016).

86. Gromeier, M. et al. Very low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy. *Nat. Commun.* **12**, 352 (2021).

87. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. *Bioinformatics* **32**, 2847–2849 (2016).
88. Wickham, H. ggplot2. *WIREs Comput. Stat.* **3**, 180–185 (2011).

89. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. *Nat. Biotechnol.* **30**, 413–421 (2012).

90. Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. *Nat. Commun.* **6**, 8971 (2015).

91. METLIN Gen2. https://metlin.scripps.edu/landing_page.php?
pgcontent=mainPage.

92. Muhlich, J., Chen, Y.-A., Russell, D. & Sorger, P. K. Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software. Preprint at bioRxiv https://doi.org/
10.1101/2021.04.20.440625 (2021).

93. Yapp, C. et al. UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.04.02.
438285 (2022).

## Acknowledgements

We thank Dana-Farber/Harvard Cancer Center in Boston, MA, for the use of the Specialized Histopathology Core, which provided tissue processing services. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. The schematic in Fig. 9 was created with BioRender.com. This work was supported by NIH grants U54-CA225088 (PKS, SS), R01-CA194005 (S.S.), R01-CA188288 (K.L.L.), R01-CA219943 (K.L.L.), P01-CA163205 (K.L.L.), U54-CA210180 (N.Y.R.A.), P41-EB028741 (N.Y.R.A.), T32-GMO08313 (SW), T32-GMO07748 (S.C.), T32-EB025823 (S.A.S.) and T32-GMO08208 (R.R.), by the Ludwig Center at Harvard (P.K.S., S.S.), INCa, Fondation ARC and Ligue grant INCa-ARC-LIGUE_ID_16280 (M.T.), a Capital Award from the Massachusetts Life Sciences Center (N.Y.R.A.), by the Specialized Histopathology Core Facility of the Dana-Farber/ Harvard Cancer Center (P30-CA06516), and by National Metabolomics Data Repository - Nextgen Metabolomics Workbench (U2C-DK119886). K.L.L. received support from the National Brain Tumor Society (NBTS) and Accelerate Brain Cancer Cure (ABC2).

## Author contributions

S.C., M.T., and S.S. developed the concept for the study. J.R.L., R.R., C.Y., and C.C.R. collected image data and performed analysis in collaboration with S.C. and S.W. S.A.S., M.R., G.B. and N.Y.R.A. designed, performed, and analyzed mass spectrometry experiments in collaboration with S.C., G.J.B., and S.S. S.C. and P.K. collected and analyzed single cell RNA-sequencing data. L.S. performed and analyzed T.C.G.A. in collaboration with M.T., S.C., and S.S. T.D.R., M.S., and K.A.C. collected and analyzed C.B.T.T.C. pediatric tumors and data in collaboration with S.C. and S.S. J.H. collected and analyzed clinical and demographic data in collaboration with S.C. All authors wrote and/or edited the manuscript. S.S., P.K.S., N.Y.R.A., P.Y.W., P.B., and K.L.L. provided supervision and funding.

## Competing interests

M.T. has a consulting or advisory role with Agios Pharmaceutical, Integragen, and Taiho Oncology, and research funding from Sanofi. P.K.S. is a member of the SAB or BOD member of Applied Biomath, RareCyte Inc., and Glencoe Software; P.K.S. is also a member of the NanoString SAB

and a consultant for Montai Health and Merck. In the last five years the Sorger lab has received research funding from Novartis and Merck. KLL is supported by Eli Lilly and BMS, consults for BMS, Integragen, Travera LLC, is on the SAB of Integragen and Rarecyte, and holds equity in Travera LLC. P.B. receives grant funding from Novartis Institute of Bio-medical Research and Deerfield, and consults for QED Therapeutics, for unrelated projects. P.Y.W. has research support from Agios, Astra Zeneca, Medimmune, Celgene, Eli Lilly, Genentech, Roche, Kazia, MediciNova, Merck, Novartis, Nuvation Bio, Chimerix, Vascular Bio-genics, and VBI Vaccines. P.W. is on the advisory boards of Agios, Astra Zeneca, Black Diamond, Boston Pharmaceuticals, Chimerix, CNS Phar-maceuticals, Elevate Bio, Imvax, Karyopharm, Merck, Mundipharma, Novocure, Novartis, Nuvation Bio, Prelude Therapeutics, Vascular Bio-genics, VBI Vaccines, Voyager, and QED. RareCyte manufactured instruments used for tissue imaging and Glencoe developed the OMERO Database used for image informatics. P.K.S. and S.S. declare that none of these relationships have influenced the content of this manuscript. The remaining authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-022-32430-w.

**Correspondence** and requests for materials should be addressed to Mehdi Touat or Sandro Santagata.

**Peer review information** *Nature Communications* thanks Kiavash Movahedi, Atsuo Sasaki, and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

**Reprints and permission information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2022
88. Wickham, H. ggplot2. *WIREs Comput. Stat.* **3**, 180–185 (2011).

89. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. *Nat. Biotechnol.* **30**, 413–421 (2012).

90. Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. *Nat. Commun.* **6**, 8971 (2015).

91. METLIN Gen2. https://metlin.scripps.edu/landing_page.php?
pgcontent=mainPage.

92. Muhlich, J., Chen, Y.-A., Russell, D. & Sorger, P. K. Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software. Preprint at bioRxiv https://doi.org/
10.1101/2021.04.20.440625 (2021).

93. Yapp, C. et al. UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. Preprint at bioRxiv https://doi.org/10.1101/2021.04.02.
438285 (2022).

## Acknowledgements

We thank Dana-Farber/Harvard Cancer Center in Boston, MA, for the use of the Specialized Histopathology Core, which provided tissue processing services. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. The schematic in Fig. 9 was created with BioRender.com. This work was supported by NIH grants U54-CA225088 (PKS, SS), R01-CA194005 (S.S.), R01-CA188288 (K.L.L.), R01-CA219943 (K.L.L.), P01-CA163205 (K.L.L.), U54-CA210180 (N.Y.R.A.), P41-EB028741 (N.Y.R.A.), T32-GMO08313 (SW), T32-GMO07748 (S.C.), T32-EB025823 (S.A.S.) and T32-GMO08208 (R.R.), by the Ludwig Center at Harvard (P.K.S., S.S.), INCa, Fondation ARC and Ligue grant INCa-ARC-LIGUE_ID_16280 (M.T.), a Capital Award from the Massachusetts Life Sciences Center (N.Y.R.A.), by the Specialized Histopathology Core Facility of the Dana-Farber/ Harvard Cancer Center (P30-CA06516), and by National Metabolomics Data Repository - Nextgen Metabolomics Workbench (U2C-DK119886). K.L.L. received support from the National Brain Tumor Society (NBTS) and Accelerate Brain Cancer Cure (ABC2).

## Author contributions

S.C., M.T., and S.S. developed the concept for the study. J.R.L., R.R., C.Y., and C.C.R. collected image data and performed analysis in collaboration with S.C. and S.W. S.A.S., M.R., G.B. and N.Y.R.A. designed, performed, and analyzed mass spectrometry experiments in collaboration with S.C., G.J.B., and S.S. S.C. and P.K. collected and analyzed single cell RNA-sequencing data. L.S. performed and analyzed T.C.G.A. in collaboration with M.T., S.C., and S.S. T.D.R., M.S., and K.A.C. collected and analyzed C.B.T.T.C. pediatric tumors and data in collaboration with S.C. and S.S. J.H. collected and analyzed clinical and demographic data in collaboration with S.C. All authors wrote and/or edited the manuscript. S.S., P.K.S., N.Y.R.A., P.Y.W., P.B., and K.L.L. provided supervision and funding.

## Competing interests

M.T. has a consulting or advisory role with Agios Pharmaceutical, Integragen, and Taiho Oncology, and research funding from Sanofi. P.K.S. is a member of the SAB or BOD member of Applied Biomath, RareCyte Inc., and Glencoe Software; P.K.S. is also a member of the NanoString SAB

and a consultant for Montai Health and Merck. In the last five years the Sorger lab has received research funding from Novartis and Merck. KLL is supported by Eli Lilly and BMS, consults for BMS, Integragen, Travera LLC, is on the SAB of Integragen and Rarecyte, and holds equity in Travera LLC. P.B. receives grant funding from Novartis Institute of Bio-medical Research and Deerfield, and consults for QED Therapeutics, for unrelated projects. P.Y.W. has research support from Agios, Astra Zeneca, Medimmune, Celgene, Eli Lilly, Genentech, Roche, Kazia, MediciNova, Merck, Novartis, Nuvation Bio, Chimerix, Vascular Bio-genics, and VBI Vaccines. P.W. is on the advisory boards of Agios, Astra Zeneca, Black Diamond, Boston Pharmaceuticals, Chimerix, CNS Phar-maceuticals, Elevate Bio, Imvax, Karyopharm, Merck, Mundipharma, Novocure, Novartis, Nuvation Bio, Prelude Therapeutics, Vascular Bio-genics, VBI Vaccines, Voyager, and QED. RareCyte manufactured instruments used for tissue imaging and Glencoe developed the OMERO Database used for image informatics. P.K.S. and S.S. declare that none of these relationships have influenced the content of this manuscript. The remaining authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-022-32430-w.

**Correspondence** and requests for materials should be addressed to Mehdi Touat or Sandro Santagata.

**Peer review information** *Nature Communications* thanks Kiavash Movahedi, Atsuo Sasaki, and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

**Reprints and permission information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2022