Abstract
Despite breakthroughs in cancer immunotherapy, most tumor-reactive T cells cannot persist in solid tumors due to an immunosuppressive environment. We developed Tres (tumor-resilient T cell, https://resilience.ccr.cancer.gov/), a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as transforming growth factor-β1, tumor necrosis factor-related apoptosis-inducing ligand and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in melanoma, lung cancer, triple-negative breast cancer and B cell malignancies using bulk T cell transcriptomic data from pre-treatment tumors from patients who received immune-checkpoint inhibitors (n = 38), infusion products for chimeric antigen receptor T cell therapies (n = 34) and pre-manufacture samples for chimeric antigen receptor T cell or tumor-infiltrating lymphocyte therapies (n = 84). Further, Tres identified FIBP, whose functions are largely unknown, as the top negative marker of tumor-resilient T cells across many solid tumor types. FIBP knockouts in murine and human donor CD8+ T cells significantly enhanced T cell-mediated cancer killing in in vitro co-cultures. Further, Fibp knockout in murine T cells potentiated the in vivo efficacy of adoptive cell transfer in the B16 tumor model. Fibp knockout T cells exhibit reduced cholesterol metabolism, which inhibits effector T cell function. These results demonstrate the utility of Tres in identifying biomarkers of T cell effectiveness and potential therapeutic targets for immunotherapies in solid tumors.
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Data availability
The RNA-seq data generated by this study have been deposited to the NCBI Gene Expression Omnibus with accession ID GSE186428. All flow analysis raw data are available in FCS format at the Flowrepository76 with accession IDs as listed in Supplementary Table 10 and source data tables for each figure. All gene scores computed by the Tres model are available at https://resilience.ccr.cancer.gov.
For public datasets, single-cell RNA-seq data collected from public studies for training Tres signatures are listed in Supplementary Table 1. T cell transcriptomic data for validating the Tres model are available for download as processed data at https://github.com/data2intelligence/Tres_main. Bulk-tumor transcriptomic datasets of ICI pretreatment tumors are listed in Supplementary Table 4. Public ChIP-seq profiles of SREBF2 were originally available from GSE31477 and binding peak calls reprocessed by the Cistrome pipeline are available at http://cistrome.org. The CCLE cancer cell line expression profiles are available from https://depmap.org. Genome-wide CRISPR screen data on human donor T cells are downloaded from supplementary materials of the original studies12,13 and GSE174255. The MILE leukemia dataset is available from GSE13159.
For public databases, TIDE is available at http://tide.dfci.harvard.edu. Cistrome is available at http://cistrome.org. MSigDB, which provides KEGG gene sets (v.7.4) of cell cycle and DNA replication, is available at https://www.gsea-msigdb.org.
Source data for all main and extended data figures are provided. Raw western blots are shown in Supplementary Figs. 1, 4 and 5. Raw Incucyte images are shown in Supplementary Fig. 2. Source data are provided with this paper.
Code availability
The interactive Tres server is available at https://resilience.ccr.cancer.gov. The source code and test data for the Tres model and T cell efficacy prediction are available at https://github.com/data2intelligence/Tres. The programs for reproducing major results, including computing Tres signatures and predicting immunotherapy response, are available at https://github.com/data2intelligence/Tres_main. Source data are provided with this paper.
Change history
11 August 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41591-022-01997-8
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Acknowledgements
We thank S. Rosenberg, F. Lowery, K. Sri and M. Carrington for their helpful discussions. This work utilized the computational resources of the NIH HPC Biowulf cluster. This work is supported by the intramural budget allocation from the NCI, NIH, awarded to P.J. Y.Z. is partially supported by a Bau Tsu Zung Bau Kwan Yeu Hing Research and Clinical Fellowship from the University of Hong Kong. T.V. is supported by the NCI iCURE and T2I Postdoc fellowship.
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Authors and Affiliations
Contributions
P.J. and Y.Z. designed the study and wrote the manuscript. P.J. performed all computational analysis and created the Tres package and web server. Y.Z. and T.V. performed wet-laboratory experiments. R.K. provided human donor T cells. C.S. and C.D. provided Pmel-1 TCR transgenic mice. R.P. deployed the Tres server under the government domain. K.A. and X.G. provided the laboratory space. L.G., J.H., F.L., D.P., T.N., Z.C., C.W. and G.M. participated in discussions.
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Competing interests
NCI, NIH has submitted a provisional patent application no. 63/246,743 for using FIBP KO to enhance cellular immunotherapy efficacies, with P.J. and Y.Z. as inventors. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Quantifying immunosuppression of T cells in single-cell data.
a, Correlations between immunosuppressive signaling and T cell proliferation or cytotoxicity, shown with the violin plot smoothed by a kernel density estimator (n = 168 per group). Each dot represents a tumor. The p-value was from the two-sided Wilcoxon rank-sum test. b, Association between TGF-β1 signaling and SMAD transcription factor (TF) activities, an example from a melanoma tumor18. Each dot presents a CD8 + T cell. The Y-axis presents the TGF-β1 activities. The X-axis presents the SMAD TF activities. The p-value was from the two-sided Wilcoxon rank-sum test, comparing TGF-β1 activities between T cells with positive and negative SMAD activities. c, Receiver operating characteristic (ROC) curve for the quality of signaling activities. At different thresholds, the ROC curve presents false-positive rates against true-positive rates of predicting SMAD or REL activities through TGF-β1 or TRAIL signaling activities, respectively. The diagonal line represents random expectation. d, Quality of signaling activities across single-cell cohorts. Each dot presents a single-cell cohort. The area under the ROC curve (AUC) measures the quality of TGF-β1 or TRAIL signaling, shown with violin plots smoothed by a density kernel estimator. P-values were from the two-sided Wilcoxon signed-rank test, comparing group values with 0.5 (random expectation). e, Correlation between PGE2 signaling and cAMP pathway activities, an example of a melanoma tumor18. Each dot represents a CD8 + T cell. The X-axis shows the PGE2 signaling activity. The Y-axis shows the cAMP geneset value. f, Correlations between PGE2 signaling and cAMP activities. Each dot represents a tumor. The correlations were shown through box plots. The thick line represents the median value. The bottom and top of the boxes are the 25th and 75th percentiles (interquartile range). Whiskers encompass 1.5 times the interquartile range. The p-value was from the two-sided Wilcoxon signed-rank test, comparing group medians and zero. g, Gene expression of immunosuppressive signals. Each dot represents a patient. Y-axis presents the average gene expression across all cells for each lineage (X-axis). All lineages were ranked by the median expression and only the top ten profiles were shown in box plots as panel f.
Extended Data Fig. 2 Modeling transcriptomic signatures of tumor-resilient T cells.
a, Variable interaction test. The correlation between immunosuppression (through TRAIL or PGE2 signaling) and CD8 + T cell proliferation is plotted as Fig. 1c. Individual cells were split into high or low groups according to the expression of IL7R or FIBP (Cutoff = 2). P-values and t-values (Tres gene scores) were from the two-sided interaction test using continuous values without any cutoffs. b, Distribution of interaction test p-values, an example from a melanoma tumor #9818. Two-sided student t-test p-values were computed for every gene and shown by a histogram. c, Tres gene scores of T cell function markers. Each dot represents a tumor. Tres scores computed for each signal are shown through violin plots smoothed by a kernel density estimator (n = 139, 137, 132 for TGF-β1, TRAIL, PGE2 groups). P-values were from the two-sided Wilcoxon signed-rank test comparing group values and zero. d, Tres gene scores of immunosuppressive signal receptors, shown as panel c. Scores for FIBP and IL7R are attached as references. e, Similarity of Tres scores from different single-cell cohorts. The hierarchical clustering is based on Pearson correlations among median Tres scores among all patients and immunosuppressive signals in each cohort. f, Receiver operating characteristic (ROC) curve for the quality of Tres score. The ROC curve presents false-positive rates against true-positive rates of predicting T-cell persistence markers through Tres gene scores at different thresholds, using the example data in Fig. 1e. The diagonal line represents random expectation. The area under the ROC curve (AUC) is a quantitative measure of Tres score quality. g, Quality of Tres scores. Each dot represents a tumor with sample counts labeled beneath each group. The AUC in each cohort was shown through violin plots, smoothed by a kernel density estimator. P-values (labeled above each group) were computed through the two-sided Wilcoxon signed-rank test, comparing the group values and the random expectation 0.5. h, ROC curves for Tres scores computed using in vitro cell line data from the CCLE project77. Results from the two most frequent lineages (with more than 100 cell lines) are shown as panel f.
Extended Data Fig. 3 Geneset enrichment analysis for Tres gene scores.
a, Gene set enrichment analysis (GSEA)25, an example on αβ T cell proliferation. X-axis presents the gene ranked by median Tres scores (bottom Y-axis) across all tumors and immunosuppressive signals. Y-axis on the top presents enrichment scores at each gene rank. The P-value is computed through the two-sided permutation test (n = 1000 randomizations). HLA-E is the first gene annotated in the pathway. b, Top-three enriched biological processes. For each cancer type, enrichment of gene ontology pathways was analyzed for median Tres scores among all patients. Top-three enriched and depleted pathways for Tres scores related to TGF-β1, TRAIL and PGE2 signaling were shown together. c, Tres scores of non-classical MHC class I molecules. Each dot represents a value from a tumor. Tres scores computed for each signal are shown through violin plots smoothed by a kernel density estimator (n = 139, 137, 132 for TGF-β1, TRAIL, PGE2 groups). P-values were from the two-sided Wilcoxon signed-rank test comparing group values and zero.
Extended Data Fig. 4 Control analyses of the median Tres signature in predicting immunotherapy responses.
a, Prediction through correlations between the median Tres signature and T-cell bulk transcriptomic profiles. b, Tres score correlations with profiles from post-treatment tumors. Each point represents an ICI-treated tumor. Y-axis presents the correlation between the Tres signature and T-cell expression profiles. P-values were from the two-sided Wilcoxon rank-sum test comparing group values. The thick line represents the median value. The bottom and top of the boxes are the 25th and 75th percentiles (interquartile range). Whiskers encompass 1.5 times the interquartile range. c, Tres scores indicate T-cell clinical efficacy. The ROC curve presents false-positive rates against true-positive rates of predicting whether T cells are from responders or non-responders. The performance of diverse signatures was compared. d, Comparison among T-cell signatures in predicting clinical response. The negative predictive value was shown for T-cell efficacy signatures in Supplementary Table 3. All box-plots have the same format as panel b (n = 6 independent datasets per box). e, Lack of associations between Tres score correlations and adoptive cell therapy efficacy in tumors with T-cell infiltration lower than average. The survival of patients upon adoptive T cell transfer was shown for tumors with positive or negative Tres score correlations. P-values were from the two-sided Wald test using continuous values. f, The performance of Tres on predicting ICI outcomes using bulk data. Each dot represents a pretreatment tumor transcriptomics cohort listed in Supplementary Table 4. The first group presents the results using all samples. The other two groups present results using tumors with positive or negative CTL levels. The Y-axis presents Cox-PH risk z-scores as the association between overall survival and Tres signature correlations. Box plots have the same format as panel b. P-values were computed through the two-sided Wilcoxon signed-rank test. g, Tres prediction performance on different combinations of treatments and sample sites in a triple-negative breast cancer study40. The area under the ROC curve (AUC) and confidence intervals were shown with 0.5 as the random expectation. h, Tres prediction performance in tumors when immunosuppressive signals are lower than average. The data and box-plots are shown as panel f.
Extended Data Fig. 5 High FIBP levels in T cells are associated with inferior antitumor efficacy.
a, FIBP is the first among all genes based on the average rank across all signatures in Fig. 3a. b, High FIBP levels in bulk tumors indicate T-cell dysfunction, generated from the TIDE server68. For each neuroblastoma tumor78, the cytotoxic T-lymphocyte (CTL) infiltration level was estimated as the average expression level of CD8A, CD8B, GZMA, GZMB, and PRF1. Each Kaplan–Meier plot presents tumors in two groups: ‘CTL Top’ has above-median CTL values among all samples, while ‘CTL Bottom’ has values below the median. Samples were split according to the FIBP expression level through the best separation criterion maximizing the difference of associations between CTL and survival outcome between two groups. P-values are computed through the two-sided interaction t-test between CTL and FIBP using continuous values without any cutoffs. c, Only Fibp knockout (KO) promoted T-cell mediated cancer killing. Each gene was knocked out through a mixture of three independent gRNAs in Pmel-1 mouse T cells. The abundance of B16-mhgp100 co-cultured with gene KO T cells was measured as the intensity of tdTomato. The Y-axis presents the difference between each target gene KO and Rosa26 control KO. Each point presents the median and standard error of the mean as error bars (n = 3 cell-culture replicates). d, CRISPR screen phenotypes of FIBP compared to CBLB. Each dot represents a gRNA. The Y-axis shows the log2 fold-change (logFC) of gRNA abundances between target and control conditions. Each condition presents mean and standard deviations as error bars across all independent gRNAs (n = 4 for the TCR stimulation screen12, n = 6 for the IL-2 production screen13).
Extended Data Fig. 6 In vitro phenotypes of T cells after gene knockouts.
a, Efficacy of human donor T cells on killing A375 or Mel624 cells. The T-cell efficacy was measured as the relative red fluorescent protein (RFP) intensity (Y-axis) of cancer cells across time points (X-axis) as Fig. 4d. The culture conditions include three control gRNAs (AAVS1 for human and Rosa26 for mouse), parental T cells, and cancer cells cultured without T cells. For each condition, the mean and standard deviation (error bars) values were shown (n = 3 cell-culture replicates). b, Result from Pmel-1 TCR T cells on killing B16-mhgp100 cells, shown as panel a. c, Representative CFSE signals of T cell proliferation assay in Pmel-1 CD8 + T cells. The proliferative ability of Pmel-1 T cells were compared in Fibp knockout versus control cells (Rosa26 KO) after restimulation with anti-CD3/28 antibodies for 4 days. Representative CFSE signals from TCR stimulated and unstimulated T cells were shown. d, T cell proliferation assay in Pmel-1 CD8 + T cells. The mean and standard deviation values (n = 3 independent gRNAs) of the proliferation index for each gene were calculated using Flowjo software, with gating strategy in panel e. The comparison was through the two-sided Wilcoxon rank-sum test. e, Gating strategy for CFSE staining analysis. The forward-scatter area (FSC-A) and side-scatter area (SSC-A) are used for dead cell and debris removal. The forward-scatter height (FSC-H), width (FSC-W), side scatter height (SSC-H), width (SSC-W) are used to gate the single cells. We include the unstimulated T cells to define the background populations. Cells from all biological replicates in panel d are pooled together for demonstration. f, Early activation measured 12 h after TCR stimulation. CD69 level was determined by flow cytometry in FIBP and control KO cells in mouse and human. The left panel shows a representative histogram of signal intensity. The right panel shows the mean and standard deviations as error bars (n = 3 independent gRNAs per gene) for marker positive fractions. The comparisons between groups were through the two-sided Wilcoxon rank-sum test.
Extended Data Fig. 7 Analysis of T cells in adoptive cell transfer.
a, Tumor size in different groups randomized at day zero. All mice were randomized into different groups to achieve an even initial tumor size (n = 15 for Rosa26, Cblb, and Fibp knockout (KO) groups, n = 5 for parental and No treatment groups). The tumor area distribution was shown through violin plots smoothed with a kernel density estimator. b, Tumor size at different days post-adoptive cell transfer (ACT) for groups in panel a. c, Average tumor size in negative control groups. The tumor area mean was shown when no mice reached endpoints, with standard errors as error bars. d, Kaplan Meier curves of mice survival durations. The fraction of mice that did not reach the endpoint was shown on different days after ACT. The comparison of survival durations between no-treatment and parental groups was through the two-sided log-rank test. e, In-vivo flow analysis of T-cell phenotype markers. Marker positive fractions of T cells with gene KOs were shown with mean values and standard deviations as error bars (tumor counts labeled under each group, growth curves available in Supplementary Fig. 3). Different groups were compared through the one-sided Wilcoxon rank-sum test, and none of them achieved statistical significance. f, Gating strategy of in-vivo flow analysis. The forward scatter height (FSC-H) and side scatter height (SSC-H) are used for dead cell and debris removal. The forward scatter height (FSC-H), width (FSC-W), side scatter height (SSC-H), and width (SSC-W) are used to gate the single cells. DAPI was used to eliminate dead cells, and CD8 + cells were gated. Then marker positive cells (CD69 in this example) were gated on the basis of the non-staining control.
Extended Data Fig. 8 Differentially-expressed genes upon FIBP knockout are enriched for cholesterol metabolism functions.
a, Correlation between FIBP and ABCA1 among acute lymphoblastic leukemia samples (n = 58) from the MILE project57. b, Mean and standard error (error bars) of correlations (n = 22 leukemia subtypes per bar) between FIBP and gene members in the cholesterol metabolism pathway. Gene members that positively or negatively regulate cholesterol metabolism were shown in red or blue colors, respectively. The comparison between the positive and negative groups is through the two-sided Wilcoxon rank-sum test. c, Differential expression values for SREBF2 ChIP-Seq targets and non-targets. The Cistrome database contains two public human ChIP-Seq profiles from B lymphocytes and HepG2 cells79. The regulatory score on each target gene is computed by the RABIT framework58. Target genes are those with regulatory scores larger than 0.5. None target genes are those with zero regulatory scores. The gene count in each group is labeled above each boxplot. The thick line represents the median value. The bottom and top of the boxes are the 25th and 75th percentiles (interquartile range). Whiskers encompass 1.5 times the interquartile range. The comparison between groups is through the two-sided Wilcoxon rank-sum test. d, RT–qPCR validation of essential enzymes in cholesterol synthesis in mouse T cells. Normalized expression levels were shown for Rosa26 control and Fibp knockout conditions. The mean values were shown (n = 2 independent gRNAs). e, RT–qPCR levels of cholesterol metabolic regulators in mouse T cells with Fibp and vector control overexpression. The mean and standard deviation (error bars) were shown (n = 3 cell-culture replicates).
Extended Data Fig. 9 FIBP knockout reduces cholesterol levels in T cells.
a, Gating strategy of in-vivo Filipin III flow analysis. The forward scatter Area (FSC-A) and side scatter area (SSC-A) are used for dead cell and debris removal. The forward scatter height (FSC-H), width (FSC-W), side scatter height (SSC-H), and width (SSC-W) are used to gate the single cells. Live CD8 + T subset is gated on non-staining negative control and single staining positive control. The Filipin-III level was measured as the median fluorescence density. Cells from one example tumor are shown for demonstration. b, Cholesterol levels in Fibp versus Rosa26 knockout (KO) T cells via Filipin III staining, with gating strategy in panel c. The left panel shows representative histograms of signal intensity. The right panel presents mean and standard deviation as error bars (n = 3 independent gRNAs). The p-value was from the two-sided Wilcoxon rank-sum test. c, Gating strategy of in vitro Filipin III analysis. The procedure is the same as panel a, except that the T-cell gating step is not involved as samples are in-vitro cultures of CD8 + T cells. d, Cholesterol levels via oxidation-based quantification. Each KO group has replicates from three independent gRNAs. Each overexpression group has replicates from three cell cultures. The data were presented and compared in the same way as panel b. e, LDL uptake after lipoprotein deprivation. CD8 + T cells with Fibp and Rosa26 control KOs were treated with Dil-LDL after lipoprotein deprivation for 16 h. The LDL uptake was determined using flow cytometry. The left panel shows representative histograms. The right panel presents mean and standard deviation as error bars (n = 3 independent gRNAs).
Extended Data Fig. 10 Simvastatin does not inhibit cholesterol levels in activated T cells.
(a, b), Cholesterol levels upon simvastatin treatments. T cells were treated with simvastatin concurrently with anti-CD3/28 activation (a, During) or 72 hours after anti-CD3/28 activation (b, Post). The median Filipin III intensity was measured with flow cytometry at different simvastatin concentrations, with a representative plot on the left and the median intensity on the right. The gating strategy follows the same scheme in Extended Data Fig. 9c. (c, d), Simvastatin inhibits T-cell proliferation. For each condition, the T-cell expansion index was computed through CFSE staining and flow cytometry analysis. The data is shown in the same way as panels a, b.
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Zhang, Y., Vu, T., Palmer, D.C. et al. A T cell resilience model associated with response to immunotherapy in multiple tumor types. Nat Med 28, 1421–1431 (2022). https://doi.org/10.1038/s41591-022-01799-y
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DOI: https://doi.org/10.1038/s41591-022-01799-y
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