Abstract
Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8+ cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of TSTR cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.
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Data availability
A detailed description of data availability including data sources and accession numbers of the scRNA-seq datasets included in the original data collection is provided in Supplementary Tables 1 and 2. In this study, we utilized ten newly generated scRNA-seq datasets (labeled as ‘in-house’ in Supplementary Tables 1, column I). Specifically, the AML dataset22 can be downloaded from the European Genome–Phenome Archive (EGA) database under accession number EGAD00001007672. The lung cancer (LC_1) dataset31 can be downloaded from EGA under accession number EGAS00001005021. The lymphoma dataset (LN_2)23 can be downloaded from EGA under accession number EGAS00001006052. The scRNA-seq data generated on PBMC samples from healthy donors (PBMC_3) can be downloaded from EGA under accession number EGAD00001006994. The GBM datasets can be downloaded from the Gene Expression Omnibus (GEO) database under accession number GSE222522. The breast cancer (BRCA_2) dataset, the lung cancer (LC_5) dataset, the ovarian cancer dataset and the STAD dataset can be downloaded from GEO under accession number GSE222859. The scRNA-seq data generated on reactive lymph nodes from healthy donors (LN_1)23 can be downloaded from GEO under accession number GSE203610. For the six scRNA-seq datasets generated from patients who received ICB therapy, their data accession numbers, references and detailed clinical information are provided in Supplementary Table 15. Specifically, the dataset generated by Yost et al.18 can be downloaded from GEO under accession number GSE123814. The dataset generated by Liu et al.44 can be downloaded from GEO under accession number GSE179994. The dataset generated by Caushi et al.63 can be downloaded from GEO under accession number GSE173351. The dataset generated by Zhang et al.64 can be downloaded from GEO under accession number GSE169246. The dataset generated by Bi et al.62 can be downloaded from the single-cell portal of Broad Institute under accession number SCP1288. The dataset generated by Bassez et al.48 can be downloaded from EGA under accession number EGAS00001004809. For the four scRNA-seq datasets used as a demonstration of TCellMap, their data accession numbers and references are provided in Supplementary Table 16. The CosMx SMI datasets generated on NSCLC60 and HCC samples can be downloaded from https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/. For Visium spatial transcriptomics datasets used in this study, the expression count matrices for the BRCA study79 can be downloaded from https://github.com/almaan/her2st. The melanoma Visium data80 can be downloaded from https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. The CSCC Visium data81 can be obtained from GEO under accession number GSE144240. The ccRCC Visium data82 can be obtained from GEO under accession number GSE175540. The LUAD Visium data68 can be obtained from EGA under accession number EGAS00001005021. Further information and requests should be directed to and will be fulfilled by the corresponding author L.W. (LWang22@mdanderson.org). All requests for data and materials will be promptly reviewed by The University of Texas MDACC to verify whether the request is subject to any intellectual property or confidentiality obligations. Any data and materials that can be shared will be released via a Material Transfer Agreement.
Code availability
The R script TCellMap is available at GitHub (https://github.com/Coolgenome/TCM). An open-source implementation of the TESLA algorithm in Python can be downloaded from https://github.com/jianhuupenn/TESLA. The custom script used to overlay the spatial locations of the hypoxia signal and TSTR cells on the same histology image is available at GitHub (https://github.com/Coolgenome/TCM/blob/main/res_largerT.py#L230). Additionally, we have built a user-friendly and interactive online data portal, the SCRP (https://singlecell.mdanderson.org/), for visualizing scRNA-seq data. All scRNA-seq data used to build T cell reference maps in this study can be visualized via the SCRP and queried at https://singlecell.mdanderson.org/TCM/.
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Acknowledgements
This study was supported in part by the National Institutes of Health/NCI grants R01CA266280 (to L.W.), U01CA264583 (to H.K. and L.W.), the start-up research fund and the University Cancer Foundation via the Institutional Research Grant Program at the University of Texas MDACC and The Break Through Cancer Foundation (to L.W.), the Cancer Prevention and Research Institute of Texas awards RP200385 (to L.W. and M.R.G.), RP220101 (to H.K. and L.W.) and RP150079 (to H.K.), research funding from Johnson and Johnson Lung Cancer Initiative (to H.K.), the DOD team grants CA160445 and CA200990 (to J.A.A.), a Leukemia and Lymphoma Society Scholar Award (to M.R.G.) and the generous philanthropic contributions to MDACC Moon Shots Program. In addition, A.M. was supported by the Sheikh Khlaifa bin Zayed Foundation and the MDACC Moon Shots Program in Pancreas Cancer. L.W., M.R.G. and H.K. are Andrew Sabin Family Foundation Fellows at MDACC. M.G. is a Cancer Prevention and Research Institute of Texas Scholar in Cancer Research. C.S. is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (CC2041), the UK Medical Research Council (CC2041) and the Wellcome Trust (CC2041). K.L. was supported by the UK Medical Research Council (MR/V033077/1), the Rosetrees Trust and Cotswold Trust (A2437), Melanoma Research Alliance and Cancer Research UK (C69256/A30194). A.S. is supported by an NCI T32CA217789 MDACC postdoctoral fellowship. This study was also supported by MDACC Support Grant (CA016672) and a grant from the Emerson Collective to A.A.J. We thank G. Benavides, P. Wang, R. Jiang, A. Liu and K.-A. Vu from the University of Texas MDACC for their excellent technical support in developing and testing the SCRP web portal.
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Authors and Affiliations
Contributions
L.W. conceived this study. L.W., K.L., H.K., M.R.G. and C.Y. jointly supervised the study. Y.C., J.J.Q., X.L., Q.D., A.S., P.G., A.A.J., X.D.C., S.S., M.P.P., C.H., Y.Y.E., J.V.H., J.A.A., C.S., H.A.A., M.G., K.B., M.R.G., H.K. and K.L. contributed to sample and information collection and data generation. Y.L. reviewed the CT images. L.W. supervised the bioinformatics data processing and analysis. Y.C. performed bioinformatics data analysis. E.D., G.H., G.P., K.T., M.D., R.W., D.H., F.P., X.Y., Y.L., S.Z. and J.H. assisted with data processing and analysis. A.L., D.I. and R.L. performed RNAscope assay. L.W., Y.C., Y.L., K.L., C.Y., M.C., H.K., M.L., A.R., A.F. and A.M. contributed to data interpretation. L.W., Y.C. and K.L. wrote and revised the manuscript and all co-authors reviewed the manuscript.
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Competing interests
A.A.J. has served as a consultant for Guidepoint, Gerson Lehrman Group, Nuprobe, AvengeBio, Agenus, AstraZeneca, Iovance, Bristol-Myers Squibb, Eisai, GSK/Tesaro, Macrogenics, Instill Bio, Immune-Onc Therapeutics, Obsidian, Alkermes and Roche/Genentech and he receives research support to his institution from AstraZeneca, Bristol-Myers Squibb, Merck, Eli Lilly, Pfizer, Aravive and Iovance. He is a shareholder in AvengeBio. A.M. receives royalties from Cosmos Wisdom Biotechnology and Thrive Earlier Detection, an Exact Sciences Company. A.M. is also a consultant for Freenome and Tezcat Biotechnology. A.R. has received honoraria and serves on the scientific advisory board of Adaptive Biotechnologies. H.K. reports funding from Johnson and Johnson. M.R.G. receives research funding from Allogene, Kite/Gilead, Sanofi and Abbvie, has received honoraria from Monte Rosa Therapeutics, Daiichi Sankyo and Tessa Therapeutics and has stock ownership interest in KDAc Therapeutics. X.L. reports consulting or advisory role for EMD Serono (Merck KGaA), AstraZeneca, Spectrum Pharmaceutics, Novartis, Eli Lilly, Boehringer Ingelheim and Bristol-Myers Squibb, Dachii, Hengrui Therapeutics and she receives research funding from Lilly (Inst), Boehringer Ingelheim (Inst) and Regeneron (Inst). Y.Y.E reports research funding from Spectrum Pharmaceuticals, AstraZeneca, Takeda, Xcovery, Lilly, Elevation Oncology, Turning Point Therapeutics and he serves as a consultant for Lilly, AstraZeneca, Turning Point Therapeutics. J.V.H. reports consulting or advisory role for AstraZeneca, Bristol-Myers Squibb, Spectrum Pharmaceuticals, Guardant Health, Hengrui Pharmaceutical, GlaxoSmithKline, EMD Serono, Lilly, Takeda, Sanofi/Aventis, Genentech/Roche, Boehringer Ingelheim, Catalyst Biotech, Foundation Medicine, Novartis, Mirati Therapeutics, BrightPath Biotheraputics, Janssen, Nexus Health Systems, Pneuma Respiratory, Kairos Ventures, Roche and Leads Biolabs and he receives research funding from AstraZeneca (Inst), Spectrum Pharmaceuticals and GlaxoSmithKline. J.V.H. has a licensing agreement with Spectrum regarding intellectual property for treatment of EGFR and HER2 exon 20 mutations and has stock ownership interest in Cardinal Spine and Bio-Tree. K.L. has a patent on indel burden and CPI response pending and he received honoraria from Roche Tissue Diagnostics and research funding from Cancer Research UK TDL/Ono/LifeArcAlliance and he reports a consulting role with Monopteros Therapeutics. H.K.K. reports funding from Johnson and Johnson. C.S. acknowledges grants from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Pfizer, Roche-Ventana, Invitae, Ono Pharmaceutical and Personalis. He is Chief Investigator for the AZ MeRmaiD 1 and 2 clinical trials and is the Steering Committee Chair. He is also Co-Chief Investigator of the NHS Galleri trial funded by GRAIL and a paid member of GRAIL’s Scientific Advisory Board (SAB). He receives consultant fees from Achilles Therapeutics (also an SAB member), Bicycle Therapeutics (also an SAB member), Genentech, Medicxi, China Innovation Center of Roche formerly Roche Innovation Center, Shanghai, Metabomed and the Sarah Cannon Research Institute. C.S. has received honoraria from Amgen, AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Illumina, MSD, Novartis, Pfizer and Roche-Ventana. C.S. has previously held stock options in Apogen Biotechnologies and GRAIL and currently has stock options in Epic Bioscience, Bicycle Therapeutics and has stock options and is a co-founder of Achilles Therapeutics. C.S. declares a patent application (PCT/US2017/028013) for methods for lung cancer; targeting neoantigens (PCT/EP2016/059401); identifying patient response to ICB (PCT/EP2016/071471), determining HLA LOH (PCT/GB2018/052004); predicting survival rates of patients with cancer (PCT/GB2020/050221), identifying patients who respond to cancer treatment (PCT/GB2018/051912); and methods for lung cancer detection (US20190106751A1). C.S. is an inventor on a European patent application (PCT/GB2017/053289) relating to assay technology to detect tumor recurrence. This patent has been licensed to a commercial entity and under their terms of employment, C.S. is due a revenue share of any revenue generated from such license(s). The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Major T cell types.
a) Global UMAP of all T cells and major T cell types. The subpopulations of CD4, CD8, proliferative, and unconventional T cells were further separated and defined by subsequent clustering analysis. b) Bubble plot showing the average expression levels and cellular fractions of representative marker genes across six major T cell types.
Extended Data Fig. 2 Characterization of CD8 T cell clusters.
a) Bubble plot showing the average expression levels and cellular fractions of selected marker genes in 14 defined CD8 T cell clusters. The complete list of the top 50 most significant differentially expressed genes (DEGs) is provided in Supplementary Table 3. b) Monocle 3 trajectory analysis of CD8 T cell differentiation demonstrating multiple possible routes. c) The UMAP and density plots before and after downsampling analysis. UMAP (top) and density plots (bottom) of CD8 T cells demonstrating T cell distribution across four main tissue groups. High relative cell density is shown as bright magma. For CD8 T cells, the downsampled cell number is 11,592 cells for each tissue group. d) Box plot showing cell fractions of CD8 T cell subsets across four tissue groups. Each dot represents a sample. H, normal tissues from healthy donors; U, tumor-adjacent uninvolved tissues; P, primary tumor tissues; M, metastatic tumor tissues. The one-sided Games-Howell test was applied to calculate the p values between those four tissue groups (n = 20, 51, 156, 39), followed by FDR (false discovery rate) correction. FDR-adjusted p value: *≤0.05; **≤0.01; ***≤0.001; ****≤0.0001. Boxes, median ± the interquartile range; whiskers, 1.5× interquartile range.
Extended Data Fig. 3 Characterization of CD4 T cell clusters.
a) Bubble plot showing marker gene expression across 12 defined CD4 T cell clusters. The complete list of the top 50 most significant DEGs is provided in the Supplementary Table 5. b) The UMAP and density plots before and after downsampling analysis. UMAP (left) and density plots (right) of CD4 T cells demonstrating T cell distribution across four main tissue groups. High relative cell density is shown as bright magma. For CD4 T cells, the downsampled cell number is 10,703 cells for each tissue group. c) Box plot showing cell fractions of 12 CD4 T cell subsets across four tissue groups. Each dot represents a sample. H, normal tissues from healthy donors; U, tumor-adjacent uninvolved tissues; P, primary tumor tissues; and M, metastatic tumor tissues. The one-sided Games-Howell test was applied to calculate the p values between those four tissue groups (n = 20, 53, 158, 39), followed by FDR (false discovery rate) correction. FDR-adjusted p value: *≤0.05; **≤0.01; ***≤0.001; ****≤0.0001. Boxes, median ± the interquartile range; whiskers, 1.5× interquartile range. d) Monocle 3 trajectory analysis of CD4 T cells. Cells are color coded for their corresponding pseudotime. e) Ridge plots show the distribution of inferred pseudotime across CD4 T cell clusters.
Extended Data Fig. 4 Characterization of unconventional T cells and proliferative T cells.
a) UMAP view of 5 innate T cell clusters. b) Bubble plot showing marker gene expression across 5 innate T cell clusters. The complete list of top 50 most significant DEGs is provided in the Supplementary Table 9. c) UMAP view of 8 proliferative T cell clusters. d) Bubble plot showing marker gene expression across 8 proliferative T cell clusters. The complete list of top 50 most significant DEGs is provided in the Supplementary Table 10. e) Sankey diagram showing the mapping of four proliferative CD8 subsets to the rest of CD8 T cell clusters after regressing out cell proliferative markers. f) Sankey diagram showing the mapping of two proliferative CD4 subsets (P_c6_Treg and P_c1) to the rest of CD4 T cell clusters after regressing out cell proliferative markers.
Extended Data Fig. 5 Correlations with tumor mutational burden (TMB) and patient survival in TCGA cohorts.
a) Correlation between the abundance of 9 T cell states (estimated via T cell deconvolution analysis using unique gene signatures in Supplementary Table 12) and TMB across 52 cancer types and their genotypic/molecular subtypes (labeled on the left with numbers indicating sample size). A total of 11,051 TCGA tumors with bulk RNA-seq data available were included and samples with low abundance of T cells (the bottom 25% of the ranked data) as estimated using MCP-counter were excluded (Supplementary Table 13). TMB and leukocyte fractions were from TCGA pan-cancer study by Thorsson et al.77. The annotation of cancer types and their genotypic/molecular subtypes was adopted from our recent study by Han et al. (Nature Communication, 12, 5606, 2021). The size of the rectangle is proportional to statistical significance (p-value, two-sided spearman correlation test, FDR-adjusted) and the color intensity is proportional to Spearman correlation coefficient (rho). Boxes, median ± interquartile range; whiskers, 1.5× interquartile range. b) Correlation with patient overall survival (OS). The size of the rectangle is proportional to statistical significance (FDR-adjusted p-value) and the color intensity is proportional to log scaled hazard ratio (HR).
Extended Data Fig. 6 Correlation with patient survival in the CPI1000+ cohorts.
Association with ICT response in three large cohorts of cancer patients from the CPI1000+ cohort with both RNA sequencing and clinical response data available are shown. Samples predominantly represented baseline pretreatment specimens, treated with single-agent immune checkpoint inhibitor (CPI) and without prior CPI treatment. Patients of the bladder cancer cohort (Bladder_U_MAR) and renal cancer cohort (Renal_U_MCD) received single-agent anti-PD-L1 therapy and patients of the combined melanoma cohort received either single-agent anti-CTLA-4 or anti-PD-1 therapy. More details on the clinical data (for example, drug treatment and biopsy timepoint, radiological response) of these patients can be found in the Supplementary Table 1 of the original study by Litchfield et al.56. Immune deconvolution was performed on normalized gene expression data from the original study using the 9 gene signatures included in the Supplementary Table 12. For each cohort, we assessed the radiological response rates in patient groups with all the different possible combinations of T cell state gene signature expression. Patient groups showing the highest ICT radiological response rates (among top 6) or the lowest response rates (among bottom 6) are shown. Sig_1, T cell state 1; Sig_2, T cell state 2; Res%, response rate. Hi, high expression group; lo, low expression group. Hi and lo groups were split based on the group median value of gene signature expression. Recurrently presented gene signatures are highlighted in color.
Extended Data Fig. 7 Detection of in situ HSPA1A and HSPA1B expression in tumor-infiltrating T cells in NSCLC and HCC samples by CosMx.
Two consecutive tissue sections from a NSCLC sample (sections ‘Lung 5_Rep1’ and ‘Lung 5-Rep3’) and one tissue section from a hepatocellular carcinoma (HCC) sample (section ‘CancerousLiver’) were profiled. (Column 1) Cells in physical locations (x, y coordinates). Color denotes cell type. Spatial mapping of CD3D (Column 2), HSPA1A (Column 4, Row 1/3/5), and HSPA1B (Column 3, Row 1/3) expression in T cells (note that, the HCC data does not include HSPA1B). (Column 2, Row 2/4/6) A zoom-in view of a representative area of their corresponding images in Column 1 showing lymphocyte aggregates enriched with T cells. (Column 3, Row 2/4/6) a zoom-in view of their corresponding images in Column 2 showing subcellular localization of CD3D, HSPA1B, and/or HSPA1A transcripts. (Column 4, Row 2/4/6) a further zoom-in view of their corresponding images in Column 3 showing co-localization of CD3D, HSPA1B, and/or HSPA1A transcripts in the same cells. Cell segmentation was done by the original study60. The outlines of cell nuclei were determined based on DAPI staining and the cell boundaries were determined based on morphology markers for membrane (for example, CD298) combined with a machine learning approach60.
Extended Data Fig. 8 Co-mapping of TSTR cells and hypoxia-related gene expression using spatial transcriptomics.
(Top row) Mapping of TSTR cells (in red) on the histology image based on corresponding spatial transcriptomics data generated from the same tissue section. (Rest of the rows) spatial co-mapping of TSTR cells (in red) and hypoxia-related gene expression (in blue, the darker the color, the higher the level of gene expression) in the same regions as shown in the top row. BRCA, breast cancer; CSCC, cutaneous squamous cell carcinoma; GAC, gastric adenocarcinoma; ccRCC, clear cell renal cell carcinoma; LUAD, lung adenocarcinoma.
Extended Data Fig. 9 Pan-cancer detection of TSTR cells and Co-mapping of TSTR cells and hypoxia-related gene expression using spatial transcriptomics.
Extra representative tissue sections of 4 cancer types are shown. (Top row) H&E stained tissue images. (Second row) Mapping of T cells and (third row) the TSTR cells on the same histology images (GAC, BRCA, ccRCC, CSCC) or a high-magnification image (ccRCC). GAC, gastric adenocarcinoma; BRCA, breast cancer; ccRCC, clear cell renal cell carcinoma; CSCC, cutaneous squamous cell carcinoma. (Rest of the rows) spatial co-mapping of TSTR cells (in red) and hypoxia-related gene expression (in blue, the darker the color, the higher the level of gene expression) in the same regions as shown in the top row.
Extended Data Fig. 10 The workflow of TCellMap.
a) Schematic view of the bioinformatic flow of TCellMap, created with BioRender.com. b) Leave-one-out cross-validation of the performance of TCellMap using scRNA-seq datasets included in this study. Scatter plot showing the accuracy (ACC) of T cell state prediction. A total of 24 scRNA-seq datasets with ≥5,000T cells were selected (x axis), and the prediction accuracy was calculated by comparing T cell states automatically assigned for 32 states of the 5 major cell types using the reference maps with that manually annotated by this study. The size of the bubble corresponds to the number of T cells in each scRNA-seq dataset. c) Visualization of the output of TCellMap. Four scRNA-seq datasets that were not included in original data collection of this study were used as the query datasets. UMAP views of CD8 (top) and CD4 (bottom) T cells mapped in each query dataset. Cell clusters are color coded in the same way as in Fig. 2a (CD8 T cells map) and Fig. 3a (CD4 T cell map). LUAD, lung adenocarcinoma; CRC, colorectal carcinoma; HCC, hepatocellular cell carcinoma; HNSC, head and neck cancer. The gene expression count matrices were downloaded from the Gene Expression Omnibus (GEO) database and the accession codes (GSE#) are labeled for each dataset. Further details of each query dataset are provided in the Supplementary Table 16.
Supplementary information
Supplementary Information
Supplementary Figs. 1–15
Supplementary Tables
This file contains 17 tabs. Tab 1 is the list of contents. Tabs 2–17 are Supplementary Tables 1–16. Supplementary Table 1. Collection of scRNA-seq datasets. Supplementary Table 2. Clinical information for each dataset. Supplementary Table 3. Top 50 DEGs for 14 CD8+ T cell clusters. Supplementary Table 4. Curated gene signatures used to characterize CD8+ T cells. Supplementary Table 5. Top 50 DEGs for 12 CD4+ T cell clusters. Supplementary Table 6. Curated gene signatures used to characterize CD4+ T cells. Supplementary Table 7. DEGs for seven Treg cell subclusters. Supplementary Table 8. DEGs for five TFH cell subclusters. Supplementary Table 9. Top 50 DEGs for five unconventional T cell clusters. Supplementary Table 10. Top 50 DEGs for eight proliferative T cell subclusters. Supplementary Table 11. Cell number and fraction of each T cell subset. Supplementary Table 12. Signature genes of T cell subsets used for deconvolution analysis. Supplementary Table 13. List of TCGA tumors with low (bottom 25%) MCP-counter inferred signature scores for T cells. Supplementary Table 14. Spatial transcriptomics datasets used in Fig. 5c. Supplementary Table 15. Clinical information of six scRNA-seq datasets used for correlation analysis. Supplementary Table 16. Four scRNA-seq query datasets used for demonstration of TCellMap
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Chu, Y., Dai, E., Li, Y. et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat Med 29, 1550–1562 (2023). https://doi.org/10.1038/s41591-023-02371-y
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DOI: https://doi.org/10.1038/s41591-023-02371-y
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