Neuropilin-1 is a T cell memory checkpoint limiting long-term antitumor immunity


Robust CD8+ T cell memory is essential for long-term protective immunity but is often compromised in cancer, where T cell exhaustion leads to loss of memory precursors. Immunotherapy via checkpoint blockade may not effectively reverse this defect, potentially underlying disease relapse. Here we report that mice with a CD8+ T cell–restricted neuropilin-1 (NRP1) deletion exhibited substantially enhanced protection from tumor rechallenge and sensitivity to anti-PD1 immunotherapy, despite unchanged primary tumor growth. Mechanistically, NRP1 cell-intrinsically limited the self-renewal of the CD44+PD1+TCF1+TIM3 progenitor exhausted T cells, which was associated with their reduced ability to induce c-Jun/AP-1 expression on T cell receptor restimulation, a mechanism that may contribute to terminal T cell exhaustion at the cost of memory differentiation in wild-type tumor-bearing hosts. These data indicate that blockade of NRP1, a unique ‘immune memory checkpoint’, may promote the development of long-lived tumor-specific Tmem that are essential for durable antitumor immunity.

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Fig. 1: NRP1 limits CD8+ T cell–mediated antitumor immunity to tumor rechallenge and anti-PD1 immunotherapy.
Fig. 2: NRP1 promotes terminal exhaustion in tumor-infiltrating CD8+ T cells.
Fig. 3: NRP1 cell-intrinsically limits the in vivo persistence of antigen-specific CD8+ T cells.
Fig. 4: Impact of NRP1 on the transcriptome of antigen-specific CD8+ T cells in vivo.
Fig. 5: NRP1 inhibits c-Jun/AP-1 activation in chronically stimulated CD8+ T cells.
Fig. 6: Elevated NRP1+ TEM in patients with cancer is associated with poor survival and decreased response to ICB therapy.

Data availability

Bulk RNA-seq datasets of Nrp1+/+ and Nrp1−/− pMel-T cells have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession code GSE151494. Source data are provided with this paper.

Code availability

Computational and mathematical codes used in the RNA-seq data analyses have been described in the article. Additional information is available from the corresponding author on reasonable and appropriate request. Source data are provided with this paper.


  1. 1.

    Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Khalil, D. N., Smith, E. L., Brentjens, R. J. & Wolchok, J. D. The future of cancer treatment: immunomodulation, CARs and combination immunotherapy. Nat. Rev. Clin. Oncol. 13, 273–290 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Reading, J. L. et al. The function and dysfunction of memory CD8+ T cells in tumor immunity. Immunol. Rev. 283, 194–212 (2018).

    CAS  PubMed  Google Scholar 

  4. 4.

    Fares, C. M., Van Allen, E. M., Drake, C. G., Allison, J. P. & Hu-Lieskovan, S. Mechanisms of resistance to immune checkpoint blockade: why does checkpoint inhibitor immunotherapy not work for all patients? Am. Soc. Clin. Oncol. Educ. Book 39, 147–164 (2019).

    PubMed  Google Scholar 

  5. 5.

    Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Kawasaki, T. et al. A requirement for neuropilin-1 in embryonic vessel formation. Development 126, 4895–4902 (1999).

    CAS  PubMed  Google Scholar 

  9. 9.

    Kitsukawa, T. et al. Neuropilin–semaphorin III/D-mediated chemorepulsive signals play a crucial role in peripheral nerve projection in mice. Neuron 19, 995–1005 (1997).

    CAS  PubMed  Google Scholar 

  10. 10.

    Gu, C. et al. Neuropilin-1 conveys semaphorin and VEGF signaling during neural and cardiovascular development. Dev. Cell 5, 45–57 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Roy, S. et al. Multifaceted role of neuropilins in the immune system: potential targets for immunotherapy. Front. Immunol. 8, 1228 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bruder, D. et al. Neuropilin-1: a surface marker of regulatory T cells. Eur. J. Immunol. 34, 623–630 (2004).

    CAS  PubMed  Google Scholar 

  13. 13.

    Delgoffe, G. M. et al. Stability and function of regulatory T cells is maintained by a neuropilin-1–semaphorin-4a axis. Nature 501, 252–256 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Overacre-Delgoffe, A. E. et al. Interferon-γ drives Treg fragility to promote anti-tumor immunity. Cell 169, 1130–1141.e11 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Battaglia, A. et al. Metastatic tumour cells favour the generation of a tolerogenic milieu in tumour draining lymph node in patients with early cervical cancer. Cancer Immunol. Immunother. 58, 1363–1373 (2009).

    PubMed  Google Scholar 

  16. 16.

    Battaglia, A. et al. Neuropilin-1 expression identifies a subset of regulatory T cells in human lymph nodes that is modulated by preoperative chemoradiation therapy in cervical cancer. Immunology 123, 129–138 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Jackson, S. R., Berrien-Elliott, M., Yuan, J., Hsueh, E. C. & Teague, R. M. Neuropilin-1 expression is induced on tolerant self-reactive CD8+ T cells but is dispensable for the tolerant phenotype. PLoS ONE 9, e110707 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Fleissner, D., Hansen, W., Geffers, R., Buer, J. & Westendorf, A. M. Local induction of immunosuppressive CD8+ T cells in the gut-associated lymphoid tissues. PLoS ONE 5, e15373 (2010).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Leclerc, M. et al. Regulation of antitumour CD8 T-cell immunity and checkpoint blockade immunotherapy by Neuropilin-1. Nat. Commun. 10, 3345 (2019).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Kalia, V., Sarkar, S. & Ahmed, R. CD8 T-cell memory differentiation during acute and chronic viral infections. Adv. Exp. Med. Biol. 684, 79–95 (2010).

    CAS  PubMed  Google Scholar 

  21. 21.

    Bursuker, I. & North, R. J. Immunological consequences of tumor excision: from active immunity to immunological memory. Int. J. Cancer 37, 275–281 (1986).

    CAS  PubMed  Google Scholar 

  22. 22.

    Zhang, P., Cote, A. L., de Vries, V. C., Usherwood, E. J. & Turk, M. J. Induction of postsurgical tumor immunity and T-cell memory by a poorly immunogenic tumor. Cancer Res. 67, 6468–6476 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Wu, T. et al. The TCF1-Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell stemness. Sci. Immunol. 1, eaai8593 (2016).

  24. 24.

    Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    CAS  PubMed  Google Scholar 

  25. 25.

    Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 (2019).

    CAS  PubMed  Google Scholar 

  26. 26.

    Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Man, K. et al. Transcription factor IRF4 promotes CD8+ T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129–1141 (2017).

    CAS  PubMed  Google Scholar 

  28. 28.

    Ji, Y. et al. Repression of the DNA-binding inhibitor Id3 by Blimp-1 limits the formation of memory CD8+ T cells. Nat. Immunol. 12, 1230–1237 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Yang, C. Y. et al. The transcriptional regulators Id2 and Id3 control the formation of distinct memory CD8+ T cell subsets. Nat. Immunol. 12, 1221–1229 (2011).

    CAS  PubMed  Google Scholar 

  30. 30.

    Wang, Y. et al. The transcription factor TCF1 preserves the effector function of exhausted CD8 T cells during chronic viral infection. Front Immunol. 10, 169 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Jadhav, R. R. et al. Epigenetic signature of PD-1+TCF1+CD8 T cells that act as resource cells during chronic viral infection and respond to PD-1 blockade. Proc. Natl Acad. Sci. USA 116, 14113–14118 (2019).

    CAS  PubMed  Google Scholar 

  32. 32.

    Gullicksrud, J. A. et al. Differential requirements for Tcf1 long isoforms in CD8+ and CD4+ T cell responses to acute viral infection. J. Immunol. 199, 911–919 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Leong, Y. A. et al. CXCR5+ follicular cytotoxic T cells control viral infection in B cell follicles. Nat. Immunol. 17, 1187–1196 (2016).

    CAS  PubMed  Google Scholar 

  34. 34.

    Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Ito, D. et al. mTOR complex signaling through the SEMA4A-plexin B2 axis is required for optimal activation and differentiation of CD8+ T cells. J. Immunol. 195, 934–943 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Singh, N. J. Self-reactivity as the necessary cost of maintaining a diverse memory T-cell repertoire. Pathog. Dis. 74, ftw092 (2016).

  37. 37.

    Martinez, G. J. et al. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 42, 265–278 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Su, B. et al. JNK is involved in signal integration during costimulation of T lymphocytes. Cell 77, 727–736 (1994).

    PubMed  Google Scholar 

  39. 39.

    Lynn, R. C. et al. c-Jun overexpression in CAR T cells induces exhaustion resistance. Nature 576, 293–300 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Jansen, C. S. et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature 576, 465–470 (2019).

    CAS  PubMed  Google Scholar 

  41. 41.

    Wherry, E. J., Barber, D. L., Kaech, S. M., Blattman, J. N. & Ahmed, R. Antigen-independent memory CD8 T cells do not develop during chronic viral infection. Proc. Natl Acad. Sci. USA 101, 16004–16009 (2004).

    CAS  PubMed  Google Scholar 

  42. 42.

    Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Kaech, S. M., Hemby, S., Kersh, E. & Ahmed, R. Molecular and functional profiling of memory CD8 T cell differentiation. Cell 111, 837–851 (2002).

    CAS  PubMed  Google Scholar 

  44. 44.

    Kaech, S. M. & Ahmed, R. Memory CD8+ T cell differentiation: initial antigen encounter triggers a developmental program in naive cells. Nat. Immunol. 2, 415–422 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Maekawa, Y. et al. Notch2 integrates signaling by the transcription factors RBP-J and CREB1 to promote T cell cytotoxicity. Nat. Immunol. 9, 1140–1147 (2008).

    CAS  PubMed  Google Scholar 

  46. 46.

    Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Byrne, K. T. et al. Autoimmune melanocyte destruction is required for robust CD8+ memory T cell responses to mouse melanoma. J. Clin. Invest. 121, 1797–1809 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  PubMed  Google Scholar 

  49. 49.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. (2004).

  52. 52.

    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).

    CAS  Google Scholar 

  53. 53.

    Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    CAS  PubMed  Google Scholar 

  54. 54.

    Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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We thank all the current and former members in the Vignali Laboratory (; @Vignali_Lab) for all their constructive comments and advice during this project. We also thank K. Adams, D. Pardoll, M. Schollenberger and S. Topalian for assistance in procuring biospecimens from patients at Johns Hopkins; L. D’Cruz (Univeristy of Pittsburgh) for providing the Id3-GFP reporter mouse strain and helpful discussion; H. Shen, D. Falkner and A. McIntyre from the Immunology Flow Core for cell sorting; C. Schmidt, E. Brunazzi and the staff of the Division of Laboratory Animals for genotyping and animal husbandry; S. Canna (University of Pittsburgh) for the LCMV C13 strain; A. Lund (New York University) for the attenuated LM-Ova; and W. Horne, J. Kolls and the University of Pittsburgh HSCRF Genomics Research Core for assistance with sequencing. This work was supported by the National Institutes of Health (grant nos. R01 CA203689 and P01 AI108545 to D.A.A.V.; AI105343, AI117950, AI082630, AI112521, AI115712, AI108545, CA210944 to E.J.W.; and T32 CA082084 and F32 CA247004 to A.M.G), NCI Comprehensive Cancer Center Support CORE grants (nos. P30 CA047904 and P50 CA097190 to R.L.F. and D.A.A.V. and P30 CA006973 to E.J.L.) and the Stand Up 2 Cancer (SU2C) research grant to E.J.W. E.J.W. is supported by the Parker Institute for Cancer Immunotherapy that supports the cancer immunology program at UPenn. E.J.L. is supported by the Bloomberg–Kimmel Institute for Cancer Immunotherapy, the Barney Family Foundation, Moving for Melanoma of Delaware and The Laverna Hahn Charitable Trust. This project benefited from a SPECIAL ORDER BD LSR FORTESSA (funded by NIH grant no. S10 OD011925-01) and an IMAGESTREAMx MARK II (funded by grant no. NIH 1S10 OD019942-01), used in the UPSOM Unified Flow Core. This project also used the Hillman Cancer Center Immunologic Monitoring and Cellular Products Laboratory that is supported in part by award no. P30 CA047904.

Author information




D.A.A.V. conceived, directed and obtained funding for the project. C.L. and D.A.A.V. conceptualized, designed and analyzed the experiments and wrote the manuscript. C.L. performed the experiments. S.M. analyzed the RNA-seq data. A.S., D.P.N. and T.C.B. processed and analyzed the healthy donor and HNSCC PBL specimens. A.L.S.-W. and K.M.V. generated the Rosa26LSL.mAmetrine.2A.Nrp1 and the E8ICreErt2GFP mice. E.N.S. helped with in vitro CD8+ T cell culture and stimulation. A.M.G. helped with the LCMV model. R.L.F. provided PBL samples from patients with HNSCC. E.J.L. contributed to the acquisition, analysis, interpretation and revision of clinical data. E.J.W. provided reagents and contributed advice. C.J.W. contributed to experimental design, analysis and developing mouse strains. All authors provided feedback and approved the manuscript.

Corresponding author

Correspondence to Dario A. A. Vignali.

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Competing interests

D.A.A.V. declares competing financial interests and has submitted patents covering Nrp1 that are licensed or pending and is entitled to a share in net income generated from licensing of these patent rights for commercial development. D.A.A.V. consults for and/or is on the scientific advisory board of Tizona, Werewolf, F-Star, Astellas/Potenza, BMS, Oncorus, Innovent Bio, Kronos Bio and G1 Therapeutics; has grants from BMS, Astellas/Potenza, Tizona and TTMS; patents licensed and royalties from Astellas/Potenza, Tizona and BMS; and stock from TTMS, Tizona, Potenza, Oncorus and Werewolf. R.L.F. consults for and/or is on the scientific advisory board for Aduro Biotech, Inc., Astra-Zeneca/MedImmune, Bristol-Myers Squibb, EMD Serono, GlaxoSmithKline, Iovance Biotherapeutics, Inc., MacroGenics, Inc., Merck, Nanobiotix, Numab Therapeutics AG, Oncorus, Inc., Ono Pharmaceutical Co. Ltd, PPD (Benitec, Immunicum), Regeneron Pharmaceuticals, Inc., TTMS and Torque Therapeutics Inc. R.L.F. receives research funding from Astra-Zeneca/MedImmune, Bristol-Myers Squibb, Tesaro and TTMS and has stock from TTMS. E.J.W. has consulting agreements with and/or is on the scientific advisory board for Merck, Roche, Pieris, Elstar and Surface Oncology. E.J.W. is a founder of Surface Oncology and Arsenal Biosciences. E.J.W. has a patent licensing agreement on the PD1 pathway with Roche/Genentech. E.J.L. consults for Array BioPharma, Bristol-Myers Squibb, EMD Serono, MacroGenics, Novartis, Merck, Regeneron and Sanofi Genzyme. E.J.L. receives research funding from Bristol-Myers Squibb, Merck and Regeneron.

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Editor recognition statement Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Characterization of NRP1 expression on CD8+ T cells in vivo.

a, Gating strategy and phenotyping markers used for the identification of CD8+ cell subsets under conditions of homeostatic (naive mice), or sites of immunological challenge (LCMV infection or B16 tumor). b, Expression of NRP1 measured by flow cytometry in subsets of CD8ab+ T cells from spleens of mice infected by acute LCMV (Armstrong, D8 p.i, n = 7) or chronic LCMV (Clone 13, D30 p.i., n = 7). Bar graph tabulating the mean percentage of NRP1+ cells of indicated subsets and symbols represented individual mouse, 2 independent cohorts pooled. Error bars, mean±s.e.m; two-tailed unpaired Student’s t test. c, Representative flow cytometry plots depicting the co-expression of NRP1 and PD1 within naive (spleen-derived) and TEFF CD8+ cells (spleen or intratumoral) from unchallenged or B16-tumor bearing mice. d, Histograms depicting the correlation of NRP1 expressed by intratumoral CD8+ T cells with known markers of activation (CD44, CD69, CD25), cell proliferation (BrdU), naive/memory (CD62L, CD127) and senescence (KLRG1) phenotypes. Source data

Extended Data Fig. 2 Genetic models for the “loss- or gain-of function” manipulations of Nrp1 transcription restrictively in CD8+ T cells.

a, b “Loss-of-function” model: Validation for the CD8-restricted Nrp1 deletion in the E8ICreNrp1L/L mice. (a) Representative histograms for NRP1 staining on CD4+ (green) and CD8+ (blue) tumor-infiltrating lymphocytes (TILs) of B16 tumors (D12). (b), Surface NRP1 expression on major immune cell populations derived from naive spleen (upper) or 1° B16 TILs (lower, D15 post implantation) of E8ICre or E8ICreNrp1L/L mice (n = 5 for each group). Loss of NRP1 protein restrictively by CD8+ cells in the E8ICreNrp1L/L mice was highlighted; c, “Gain-of-function” model: (Upper) Schematic structure of the Rosa26LSL.mAmetrine.2A.Nrp1 targeting construct. (Lower) Tamoxifen-induced CD8-specific (marked by GFP reporter) “constitutive” NPR1 expression (surface NRP1 staining) driven by Rosa26 promoter (marked by Ametrine reporter), in CD8+ T cells from peripheral blood (PB) of naïve mice or 1° B16 TILs. Gated on CD8+GFP+ cells. Source data

Extended Data Fig. 3 CD8-expressing NRP1 is dispensable for controlling the growth of primary tumors.

a–e, B16 tumors implanted in the E8ICre or E8ICreNrp1L/L mice were harvested on D15, and the composition of major immune cell populations were analyzed by flow cytometry. (a) bh-SNE depiction of intratumoral CD45+ cells (80,233 in total, pooled from E8ICre and E8ICreNrp1L/L mice), projected by cell source. (b) Phenograph depiction of intratumoral CD45+ cells as in (a), and different cell lineages (colored) were identified by the expression of lineage-specific markers. (c) Percentage of major immune cell populations (indicated) within CD45+ TILs. (d) Numeration of CD45+ cells, presented by counts per gram tumor mass; Error bars, mean±s.e.m; Symbol represented individual mouse (n = 5 for each genotype). (e–h) Growth curves for (e) 1° B16 tumor; (f) B16-OVA tumor (Vaccination with attenuated Listeria monocytogenes expressing Ova peptide (LM-Ova) was given on Day 4) (g) 2° B16 tumor in the E8ICre and E8ICreNrp1L/L mice, challenged on Day 30 (left) or 60 (right); (h) 1° and 2° B16 tumors (+30d rechallenge) implanted in the E8ICreErt2gfpRosa26LSL.Nrp1 and E8ICreErt2gfp mice. Data were representative of 2 independent experiments (e, f); or pooled from 4 independent experiments (g, h). Error bars, mean±s.e.m; Statistical significance was determined by two-tailed unpaired Student’s t test (d) or two-way ANOVA with correction for multiple comparisons (e–h). Source data

Extended Data Fig. 4 NRP1 modulates the memory differentiation of tumor-primed effector CD8+ T cells but not effector polyfunctionality.

a–d, CD8+ T cells from primary B16-gp100 tumors and matched spleens were analyzed on D12 (n = 8 per genotype). (a) Numeration of total Db-gp100+ cells, as well as their subsets (TCM, TEFF, MPECs, SLECs) within intratumoral CD8+ T cells; (b) Ratio between intratumoral Db-gp100+CD8+ MPECs and SLECs; (c-d) Representative flow cytometry plots (c, left) and quantification for the frequencies of Db-gp100+ CD8+ T cells (c, right) and (d) Db-gp100+ TCM in CD8+ T cells from spleen; e, The incidence (left) and growth (right) of 2° B16-gp100 tumor (+30d rechallenge). f, Representative flow plots for the expression of IRs on CD8+ TILs of 1° B16 tumors on D12 (left) and SPICE plots (right) visualization for co-expression of multiple IRs. g, Numeration of 5-IR-co-expressing cells (5-IR+) on D12 (n = 5), by frequency within CD8+ TILs (left) and absolute number per gram tumor mass (right). Data were representative from 2 independent cohorts. h, i Expression of cytokines (IFN-γ against GzmB, TNF-α and IL2, respectively) by the CD8+ TILs on D18 of 1° tumor. SPICE plots visualization for the multi-cytokine producing cells. i, Percentage of single, dual, or multi-cytokine producing cells within the CD8+ TILs as shown in h, n = 6 per genotype. Error bars, mean±s.e.m; Statistical significance was determined by two-tailed unpaired Student’s t test (a–d, g,i) or log-rank test (e, left) or two-way ANOVA with correction for multiple comparisons (e, right). Source data

Extended Data Fig. 5 NRP1 cell-intrinsically limits the in vivo persistence of antigen-specific CD8+ T cells.

a, The level of Db-gp100+ transgenic TCR, activation status (CD44 vs. CD62L); and the ratio within donor pool (CD45.2+, recovered from spleen) of Nrp1–/– and Nrp1+/+ pMel-T cells 12 h after adoptive transfer, prior to B16-gp100 tumor inoculation (D0). b, Correlation between ratio of intratumoral Nrp1–/– vs. Nrp1+/+ donor pMel-T cells with tumor volume (mm3). c, Kinetics of tumor density (counts/mm3 tumor size) of intratumoral Nrp1–/– and Nrp1+/+ pMel-T cells during 1° B16-gp100 growth. (n = 5 for each time point) d, e Frequencies of Nrp1–/– and Nrp1+/+ pMel-T cells within the CD45.2+ donor pool in spleen and peripheral blood (PB) during (d) primary phase and (e) recall phase at the indicated time points (n = 5 for each time point). f, Representative flow cytometry plots for NRP1 expression on pMel-T cells of naïve, effector, MPECs, and TCM phenotypes, respectively. g, Frequencies of Nrp1+/+ or Nrp1/– donor-derived cells of CD27+CD62L (MPECs enriched) or CD27+CD62L+ (TCM) phenotype within CD45.2+ compartment over time, recovered from draining lymph nodes (DLN) and spleen from D12 to D42, n = 5 for each time point. h–i, Representative flow cytometry plots depicting the expression of Bcl2 against Ki67 with NRP1+ vs. NRP1 fractions (f) or Bcl2 vs. IRF4 in Nrp1+/+ or Nrp1–/– pMel-T cells (g) recovered from NdLN, on D12 and D21 post 1° B16-gp100 inoculation. Data in c–e and g were pooled from 2 independent time course cohorts. Error bars, mean±s.e.m; Statistical significance was determined by two-tailed paired Student’s t test (d, e) or two-way ANOVA (g), ****p < 0.0001. Source data

Extended Data Fig. 6 Loss of NRP1 upregulated Id3 and CXCR5 expression on pTEX infiltrating primary B16-gp100 tumors.

The E8ICreNrp1L/L and E8ICre strains on pMel-1 background were further crossed with an Id3-GFP reporter mouse strain, resulting in one mutant allele carrying the modified Id3 locus with insertion of sequence encoding GFP into the ATG initiation codon (Id3gfp/+). a. Scheme for pMel-T cell adoptive transfer. Briefly, congenically-mismatched bulk CD8+ T cells were purified from the following 3 groups of naive mice (pMel-1×E8ICrexId3+/+(CD45.2+Thy1.1+), pMel-1×E8ICrexId3gfp/+ (CD45.2+Thy1.1+Thy1.2+) and pMel-1×E8ICreNrp1L/LxId3gfp/+ (CD45.2+Thy1.2+)), co-transferred at 1:1:1 ratio into CD45.1 recipients, followed by B16-gp100 tumor implantation one day later. Tumor-infiltrating CD8+ T cells were analyzed on D12 or D18. b, Frequency of Id3-GFP+ within Nrp1+/+Id3gfp/+ and Nrp1–/–Id3gfp/+ donor cells; c, Representative flow cytometry plots (left) depicting the phenotype of intratumoral Id3-GFP+ vs. Id3-GFP cells. Bar plot (right) tabulating the composition of Id3-GFP+ and Id3-GFP fractions, from Nrp1+/+ or Nrp1–/–-derived donors, respectively (n = 6 per group). d, Expression of CXCR5 and Ki67 on B16-gp100 tumor-infiltrating pMel-T cells, recovered on D21 post implantation. Cells shown in the representative plot were gated on Nrp1+/+ and Nrp1–/– donors by the congenic markers. Bar graphs tabulating the genomic mean fluorescence intensity (gMFI) and the percentage of CXCR5+ cells within Ki67+ and Ki67 fraction within each genotype. Data were pooled from 2 independent experiments, with 9 recipient mice per experiment in a-c, 4 replicates per group in d. Error bars, mean±s.e.m; Statistical significance was determined by two-tailed paired Student’s t test (b, d). Source data

Extended Data Fig. 7 Phenotypical analysis of in vitro chronically stimulated CD8+ T cells.

a–c, Representative flow cytometry plot depicting the expression of (a) NRP1, and Semaphorin-4a (Sema4a), (b) IRs (PD1 and LAG3) and (c) cytokines (IFN𝝲, Granzyme B and TNF𝛂) on Nrp1+/+ or Nrp1–/– CD8+ cells subjected to chronic antigen stimulation in vitro (scheme described in Fig. 5a); d, e, Visualization of nuclear trans-localization of NFAT1 by image flow cytometry in (d) naive CD8+ T cells or (e) in vitro chronically stimulated (2° re-stim) Nrp1+/+ or Nrp1–/– CD8+ cells, in response to PMA plus Ionomycin (P + I) stimulation; The representative images of co-staining between NFAT1 and nuclear probe (DAPI) in individual cells and the histogram depiction of the localization similarity score between NFAT1 and DAPI were shown. f, (Left) Representative flow plot depicting the gating strategy for measuring c-Jun activation within CD44+PD1+ CD8+ cells from B16-gp100 tumors or matched spleens; (Right) Representative histogram illustrating the expression of c-Jun by indicated cell subsets.

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Liu, C., Somasundaram, A., Manne, S. et al. Neuropilin-1 is a T cell memory checkpoint limiting long-term antitumor immunity. Nat Immunol (2020).

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