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Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols

Nature Cancer (2024)Cite this article

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Abstract

Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.

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Fig. 1: High-resolution scRNA-seq atlas of mouse tumor-specific CD8+ T cells in cancer.
Fig. 2: Dense time course experiments depict TOT1 dynamics.
Fig. 3: Perturbing the dynamics of TOT1 cells with anti-PD-1 treatment.
Fig. 4: Manipulation of the anti-PD-1 effect on TOT1 dynamics with cDC1 depletion.
Fig. 5: Anti-PD-1 and anti-4-1BB combination treatment synergistically activates CD8+ T cells and elicits a robust tumor-killing response.
Fig. 6: Human breast cancer CD8+ T cell programs and response to anti-PD-1 therapy.

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Data availability

scRNA-seq data that support the findings of this work have been deposited in the Gene Expression Omnibus under accession code GSE249630. Previously published human breast cancer scRNA-seq data that were reanalyzed here are available under accession code EGAS00001004809 (ref. 27). Human pan-cancer processed gene expression data are deposited in Gene Expression Omnibus (accession no. GSE156728)23. All other materials and data supporting the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The algorithms developed and used in this work are available in the GitHub repository at https://github.com/tanaylab/. All scripts and auxiliary scripts used to analyze data and generate figures have been deposited in the GitHub repository at https://github.com/tanaylab/BarboyBercovich.

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Acknowledgements

.We thank N. David for the artwork and the Flow Cytometry unit of the Weizmann Institute for their expertise and assistance. The research of I.A. and A.T. is supported by the Seed Networks for the Human Cell Atlas of the Chan Zuckerberg Initiative and by Merck. Work in I.A. and A.T. groups was supported by the MBZUAI/WIS joint program on artificial intelligence. I.A. is an Eden and Steven Romick Professorial Chair, supported by the HHMI International Scholar Award, funded by the European Union (no. 101055341-TROJAN-Cell), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 259373024 – TRR 167, and the Israel Science Foundation grant no. 1944/22, US DOD - CDMRP - Breast Cancer Research Program - Breakthrough Award (BC201275), the Helen and Martin Kimmel awards for innovative investigation, Israel Science Foundation Precision Medicine Program (IPMP) 607/20, co-funded by the European Union (ERC, MiTE, 101123436), Dwek Institute for Cancer Therapy Research, Moross Integrated Cancer Center, EKARD Institute for Cancer Diagnosis Research, Morris Kahn Institute for Human Immunology, Swiss Society Institute for Cancer Prevention Research, Elsie and Marvin Dekelboum Family Foundation, Lotte and John Hecht Memorial Foundation and the Schwartz Reisman Collaborative Science Program. This study was supported by the Center for Immunotherapy at the Weizmann Institute of Science. A.T.’s laboratory is supported by the European Research Council, the I-CORE for chromatin and RNA regulation and a grant from the Israel Science Foundation. A.T. is a Kimmel investigator.

Author information

Author notes

  1. These authors contributed equally: Oren Barboy, Akhiad Bercovich.

  2. These authors jointly supervised this work: Amos Tanay, Ido Amit.

Authors and Affiliations

  1. Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel

    Oren Barboy, Adam Yalin, Yuval Shapir Itai, Kathleen Abadie, Mor Zada, Eyal David, Shir Shlomi-Loubaton, Yonatan Katzenelenbogen, Diego Adhemar Jaitin, Chamutal Gur, Ido Yofe, Tali Feferman, Rony Dahan & Ido Amit

  2. Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Akhiad Bercovich, Aviezer Lifshitz & Amos Tanay

  3. Department of Synthetic Immunology, Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Hanjie Li

  4. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK

    Yaniv Eyal-Lubling

  5. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Kathleen Abadie

  6. The Hebrew University, Jerusalem, Israel

    Chamutal Gur

  7. Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Merav Cohen

  8. Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, USA

    Evan W. Newell

Authors
  1. Oren Barboy
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Contributions

O.B., A.B., H.L., A.T. and I.A. conceived the project and designed the experiments. O.B., H.L., A.Y., K.A. and Y.S.I. performed the experiments. A.B., O.B., H.L., Y.E.-L., A.T. and I.A. analyzed the data. A.B. and A.T. developed the computational algorithms and analysis pipeline. A.B., O.B., A.T. and I.A. wrote the manuscript. A.T. and I.A. supervised the project. All authors reviewed the results and approved the final version of the manuscript.

Corresponding authors

Correspondence to Amos Tanay or Ido Amit.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 CD8+ T cell profiling across different treatments, perturbations, and tissues. Related to Fig. 1.

(a) Density of cells from different tissue compartment, cell origin, treatment protocols, and key genes expression plotted on top of the mouse manifold 2D projection. (b) Heatmap of LFP values for marker genes across metacells for the mouse atlas, with color showing rough clustering and cell state annotation. (c) Heatmap of the percentage of cells per mouse across all metacells. (d) Metacells normalized fraction of expression (EGC), showing the smooth transition of states from Tcf7 expression exclusively to shared Tcf7/Pdcd1 and exclusive Pdcd1 expression. (e) Inferred gene programs are not shown in Fig. 1d.

Extended Data Fig. 2 Temporal dynamics of CD8+ T cell in cancer. Related to Fig. 2.

(a) FACS plots of TIM3+ versus PD1+ expression in Tot1, Tbystander, and Tendo at day 5 after adoptive transfer (day 15 post tumor inoculation) in the B16-OVA TME. Error bars represent the standard error measurement SEM. Statistics are based on one-way ANOVA with Tukey’s post hoc test. ***P < 0.0006. (b) Means across mice of the percentage of Tot1 (colored by trajectory) or Tbystander (green) cells in bins of the inferred trajectories, at days 3 and 6 post adoptive transfer. (c) Means per mouse of the fraction of Tot1 cells active for the different functional programs (Y-axis), with mice orders by the defined stages of the dynamics. Each black dot is a mouse, and curves represent the smoothed trends across stages. (d) Means across Tot1 cells per mouse of genes’ normalized expression (Y-axis), with mice orders by the defined stages of the dynamics. Each black dot is a mouse, and curves represent the smoothed gene expression across stages.

Source data

Extended Data Fig. 3 Temporal dynamics of CD8+ T cells with anti-PD1. Related to Fig. 3.

(a) Tumor progression over time of B16-OVA bearing mice with (black, N = 11) or without aPD1 treatment (red, N = 9). Statistics are based on two-way ANOVA with Tukey’s post hoc test. Error bars are in SEM. (b) Tumor sizes on days 26 to 28 (after tumor injection) of B16-OVA bearing mice inoculated with Tot1 cells and treated (or not) with aPD1. Data is representative of 3 independent experiments. Tot1, n = 22; TOT1 + aPD1, n = 21. Statistics are based on an unpaired, two-tailed Student’s t-test. Error bars are in SEM. ****P < 0.0001. (c) Left: Tot1 percentage from total CD8+ T cells over time in B16-OVA TME treated (or not) with aPD1. Right: TIM3 + PD1+ percentage from Tot1 overtime in B16-OVA TME, treated (or not) with aPD1. Data were pooled from an individual experiment. n = 4 per group. Statistics are based on two-way ANOVA with Tukey’s post hoc test. Error bars are in SEM. **P = 0.0011; ***P < 0.001; ****P < 0.0001. (d) Tot1 proliferation in the tumor on day 5 after adoptive transfer. Data were pooled from an individual experiment. n = 4 per group. Statistics are based on an unpaired, two-tailed Student’s t-test. Error bars are in SEM. **P = 0.0028. (e) Draining lymph node Tot1 mean proliferating fraction (left) and fraction of cells in bins (right), at days 3 (top) and 6 (bottom) post transfer, with control curves in grey and aPD1 curves in blue. (f) Tumor Tbystander mean proliferating fraction (left) and fraction of cells in bins (right), at days 3 (top) and 6 (bottom) post transfer, with control curves in grey and aPD1 curves in green. (g) Spleen (left) and tumor (right) Tot1 mean proliferating fraction of cells in bins at days 3 (top) and 6 (bottom) post transfer, with control curves in grey and aPD1 curves in color. (h) Scatter plots of log2 down-sampled UMIs pooled across different metacell states, with each dot representing a gene, and red dot with names have > 1 log fold change difference in expression between control (X-axis) and aPD1 (Y-axis) Tot1 cells.

Source data

Extended Data Fig. 4 Profiling neoantigen-specific CD8+ T-cells using LLC tumor model. Related to Fig. 3g.

(a) Schematics of the experimental design using LLC tumor-bearing mice, without treatment or treated with aPD1. Time points of tumor dissection were on day 9 and day 13 after tumor injection. Cells were taken for FACS analysis and scRNA sequencing. (b) Scatter plots of log2 down-sampled UMIs pooled across neoantigen-specific cells active for the Naïve/Memory-Like (left) or Effector/Dysfunction programs (right), with each dot representing a gene, and red dot with names are > 2 log fold change difference in expression between control (X-axis) and aPD1 (Y-axis) treated mice. (c) Boxplots showing the tumor fraction of Tendo cells active for the functional programs not included in Fig. 3g, with each dot representing an individual mouse, comparing control (yellow, N = 7 for Neoantigen-specific, N = 8 for Neoantigen Non-specific) to aPD1 treated (blue, N = 5 for Neoantigen-specific, N = 7 for Neoantigen Non-specific) mice divided into neoantigen-specific and non-specific cells in each treatment condition. Data in c was analyzed by Wilcoxon rank-sum exact test (two sided). In box plots central mark is median, with 5th and 95th percentiles at the whiskers, 25th and 75th percentiles at the box and minima and maxima marked by dots.

Extended Data Fig. 5 Conservation of CD8+ transcriptional states between B16-OVA and MC-38. Related to Fig. 3.

(a) Log fold change factor heatmaps of the differential expression screen across all tested genes from Fig. 3f. (b-c) Boxplots showing the tumor fraction of Tendo cells active for the different functional programs, with each dot representing an individual mouse, compared between aPD1 treated (blue, Day 3 - N = 3 for B16-OVA and N = 6 for MC-38. Day 6 - N= for B16-OVA and N = 4 for MC-38) and treatment naïve (yellow, Day 3 - N = 4 for B16-OVA and N = 7 for MC-38. Day 6 - N = 6 for B16-OVA and N = 5 for MC-38), for B16-OVA (left) and MC-38 (right) mice, at a timepoint corresponding to day 3 post Tot1 transfer (B) and 6 days post transfer (C). (d) Control MC38 vs B16-OVA Tendo log fold change of differentially abundant neighborhoods from Milo analysis, divided by cell state annotation. Data in b and c were analyzed by Wilcoxon rank-sum exact test (two sided). In box plots central mark is median, with 5th and 95th percentiles at the whiskers, 25th and 75th percentiles at the box and minima and maxima marked by dots.

Extended Data Fig. 6 Modeling T cell dynamics following cDC1 perturbation. Related to Fig. 4.

(a) Boxplot showing the tdLN percentage of Tot1 out of total CD8 cells, with each dot representing an individual mouse, compared between treatment naïve (yellow, N = 11), aPD1 (blue, N = 14), DT (purple, N = 11) and DT+aPD1 (pink, N = 15) treated mice, at day 6 post transfer. *P = 0.02638. (b) Metacells 2D projection of the Tot1 and Tendo transcriptional profiles from the XCR1-DTR experiments, with colors showing annotations transferred from the CD8 atlas (top). Median normalized protein values per metacell are shown as color (blue low, red high) for CD62L, TIM3 and PDCD1 (bottom). (c) Boxplots showing the tumor FACS mean protein values for the Tot1 cells with transcriptional profiles from the XCR1-DTR mice, with each dot representing an individual mouse, compared between treatment naïve (yellow, N = 4), aPD1 (blue, N = 5), DT (purple, N = 3) and DT+aPD1 (pink, N = 5) treated mice, at day 6 post transfer. Statistics by Wilcoxon rank sum test. (d) Scatter plots of log2 down-sampled UMIs pooled across Tot1 cells active for the Naïve/Memory-Like (left) or Effector/Dysfunction programs (right), with each dot representing a gene, and red dot with names are > 2 log fold change difference in expression between aPD1 treated + PBS (X-axis) and aPD1 treated + DT (Y-axis) mice. (e) Boxplots showing the tumor fraction of Tot1 active for functional programs not shown in Fig. 4d, with each dot representing an individual mouse, compared between treatment naïve (yellow, N = 9), aPD1 (blue, N = 7), DT (purple, N = 8) and DT+aPD1 (pink, N = 9) treated mice, at day 6 post transfer. (f) Boxplots showing the fraction of Tot1 active for the functional programs for the in-vitro B16-OVA and Tot1 co-culture experiments, with each dot representing a different plate, compared between treatment naïve (yellow, N = 4) and aPD1 (blue, N = 7) treatment plates (left). Scatter plots show a comparison between the fractions of active cells for a program (X) and CFSE, PD1, CD62L or TIM3 protein levels per plate. Data in a,c,e and f were analyzed by Wilcoxon rank-sum exact test (two sided). In box plots central mark is median, with 5th and 95th percentiles at the whiskers, 25th and 75th percentiles at the box and minima and maxima marked by dots.

Extended Data Fig. 7 Dynamics of aPD1 therapy with 4-1BB agonist. Related to Fig. 5.

(a) Heatmaps showing per mouse (rows) the percentage of Tot1 cells in bins across the trajectories in the tumor with a4-1BB + aPD1 combination treatment at day 6 after adoptive transfer. (b) Means across mice of the percentage of Tot1 cells in bins of the inferred trajectories, comparing combination treated mice (colored) vs. treatment naïve (grey circles) and aPD1 treated (grey triangles) mice, at day 6 post transfer for the combination and time-corresponding stages 3 and 4 from Fig. 2e. (c) Bar graphs of the down-sampled UMI log-fold change of a4-1BB + aPD1 vs. control pooled Tot1 cells, after controlling for distributional shifts across the inferred trajectories. Genes with at least 0.7 log fold change in one or more trajectories at day 6 post transfer are shown. Positive values are higher in combination treated cells.

Extended Data Fig. 8 Conserved CD8+ T transcriptional programs across mouse and human. Related to Fig. 6.

(a) Heatmap of LFP values for marker genes across metacells of the human breast cancer CD8+ data, with color showing clustering and cell state annotation. (b) Heatmap of Pearson correlation coefficients between normalized UMI contribution of the human inferred programs, and the normalized UMI contribution for human metacells using mouse functional programs, calculated by shared genes. (c) Paired boxplots of the fraction of CD8+ cells active for the different inferred programs not shown in Fig. 6c. Compared between pre- (yellow) and post- aPD1 treated (blue) samples. Each dot and line representing a single patient. N = 25. (d) Scatter plots of log2 down-sampled UMIs pooled across different metacell states in the human breast cancer data, with each dot representing a gene, and red dot with names have > 1 log fold change difference in expression between pre (X-axis) and post aPD1 treatment (Y-axis), showing no robustly differentially expressed genes except for PRDM1. (e) Expander (red, N = 9) vs. non-expander (blue, N = 16) comparison as in Fig. 6d, for the Naïve and Dysf programs. Each dot and line representing a single patient. Data in c and e was analyzed by Wilcoxon rank-sum exact test (two sided). In box plots central mark is median, with 5th and 95th percentiles at the whiskers, 25th and 75th percentiles at the box and minima and maxima marked by dots.

Extended Data Fig. 9 Human pan-cancer analysis. Related to Fig. 6.

(a) Boxplots of the fraction of CD8+ cells active for the breast cancer manifold programs across patients’ tumor samples from different cancer types in the pan-cancer atlas. (b) Boxplots of the fraction of CD8+ cells active for the breast cancer manifold programs across patients’ blood (periphery) samples from the pan-cancer atlas. (c) Heatmap showing the Pearson correlation of the fraction of cells active for the breast cancer manifold programs to the original cell state annotation across patients from the pan-cancer atlas. (d) Scatter plot (top) of the programs score (X) to the mutational burden score (Y), with each dot representing a patient colored by cancer type. Statistics are based on Spearman correlation test. The bar plot (bottom) shows the Pearson correlation of individual programs to the mutational burden score across patients.

Extended Data Fig. 10 Milo differentially abundant neighborhoods analysis. Related to methods.

(a) Log fold change of Milo neighborhoods for Tot1 cells at day 6 post transfer, with positive (blue) values higher in aPD1 treated vs control mice. Neighborhoods are separated by cell state by the consensus of cells’ annotations from our atlas. (b) UMAP 2D projection of the differentially abundant neighborhoods. (c) Log fold change (top-left), 2D projection (bottom-left) and differentially expressed genes of Milo neighborhood groups for Tot1 cells at day 6 post transfer, with positive (blue) values higher in aPD1 treated vs control mice.

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Barboy, O., Bercovich, A., Li, H. et al. Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00734-z

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