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Flt3 ligand augments immune responses to anti-DEC-205-NY-ESO-1 vaccine through expansion of dendritic cell subsets

Nature Cancer volume 1, pages 1204–1217 (2020)Cite this article

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Abstract

Generating responses to tumor antigens poses a challenge for immunotherapy. This phase II trial (NCT02129075) tested fms-like tyrosine kinase 3 (Flt3) ligand pre-treatment enhancement of responses to dendritic cell (DC)-targeting vaccines. We evaluated a regimen of Flt3L (CDX-301) to increase DCs and other antigen-presenting cells, poly-ICLC (TLR3 agonist that activates DCs) and a vaccine comprising anti-DEC-205-NY-ESO-1, a fusion antibody targeting CD205, linked to NY-ESO-1. High-risk melanoma patients were randomized to vaccine, with and without CDX-301. The end point was immune response to NY-ESO-1. Flt3L increased peripheral monocytes and conventional DCs (cDCs), including cross-presenting cDC1 and cDC2 and plasmacytoid DCs. Significant increases in humoral and T-cell responses and activation of DCs, natural killer cells and T cells were elicited. Transcriptional analyses revealed gene signatures associated with CDX-301 induction of an early, durable immune response. This study reveals in vivo effects of Flt3L on innate immune cells in the setting of vaccination, leading to an immunogenic vaccine regimen.

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Fig. 1: Study overview/schema and antigen-specific (anti-NY-ESO-1) immune responses.
Fig. 2: Effect of Flt3L on PBMC subset frequencies and absolute cell numbers and increases in DC subsets.
Fig. 3: Flt3L treatment is associated with increased numbers of activated cells.
Fig. 4: Gene expression profiling, gene level and cell-type level analyses.
Fig. 5: Gene expression profiling, module repertoire analyses.

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Data availability

Source data are available for this study. Data supporting the findings of this study are available from the corresponding authors upon request. Data are immediately available via the CITN Labkey data-sharing platform by request for login credentials to cimldata@fredhutch.org for database access at https://ciml.labkey.com/home/project-begin.view. Source data are provided with this paper.

Code availability

Codes, raw data and analyses for gene expression analyses have been deposited at: https://github.com/patrickjdanaher/CITN07-nCounter-data-code-results.

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Acknowledgements

We are deeply indebted to the patients and their families and to staff at all clinical sites. This work was supported by the following grants from the National Cancer Institute: U01CA154967 and UM1CA154967 (to M.A.C. and S.P.F.); Cancer Center Support Grant P30 CA015704 (to M.A.C.); R01CA201189 (to N.B.) and R01CA180913 (to N.B.).

Author information

Authors and Affiliations

  1. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Nina Bhardwaj, Philip A. Friedlander, Ana B. Blazquez & Sreekumar Balan

  2. NYU Perlmutter Cancer Center, New York, NY, USA

    Anna C. Pavlick

  3. Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

    Marc S. Ernstoff

  4. Cleveland Clinic, Cleveland, OH, USA

    Brian R. Gastman

  5. Duke University Medical Center, Durham, NC, USA

    Brent A. Hanks

  6. Providence Portland Medical Center, Portland, OR, USA

    Brendan D. Curti

  7. University of Wisconsin, Madison, WI, USA

    Mark R. Albertini

  8. University of Chicago Comprehensive Cancer Center, Chicago, IL, USA

    Jason J. Luke

  9. Sidra Medicine, Doha, Qatar

    Davide Bedognetti, Darawan Rinchai & Ena Wang

  10. NanoString Technologies, Seattle, WA, USA

    Joseph M. Beechem, Patrick Danaher & Sarah Warren

  11. Celldex Therapeutics, Hampton, NJ, USA

    Andrea S. Crocker, Thomas Hawthorne, Tibor Keler, Laura A. Vitale & Michael J. Yellin

  12. Cancer Immunotherapy Trials Network, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Leonard D’Amico, Bruce W. Hess, Lisa Lundgren, Nirasha Ramchurren, Martin A. Cheever & Steven P. Fling

  13. Genocea Biosciences, Cambridge, MA, USA

    Thomas A. Davis

  14. University of Washington, Seattle, WA, USA

    Chihiro Morishima & Mary L. Disis

  15. Oncovir, Inc., Washington, DC, USA

    Andres M. Salazar

  16. Axio Research, Seattle, WA, USA

    Bob A. Salim

  17. National Institutes of Health, Bethesda, MD, USA

    Elad Sharon

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  1. Nina Bhardwaj
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Contributions

The author contributions were as follows: protocol/trial design (N.B., M.J.Y., T.A.D., L.L., T.K., A.M.S., E.S., C.M., M.L.D. and M.A.C.); patient accrual (N.B., P.A.F., A.C.P., M.S.E., B.R.G., B.A.H., B.D.C., M.R.A. and J.J.L.); data generation/analysis (N.B., D.B., J.M.B., A.B.B., S.B., A.S.C., L.D., P.D., T.H., B.W.H., T.K., N.R., D.R., L.L., B.A.S., L.A.V., E.W., C.M., S.W., M.L.D., M.A.C. and S.P.F.); and manuscript writing (N.B., D.B., P.D., L.D., N.R., M.S.E., B.R.G., M.A.C. and S.P.F.).

Corresponding authors

Correspondence to Nina Bhardwaj or Steven P. Fling.

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Competing interests

A.S.C., T.H., T.K., L.A.V., T.A.D. and M.J.Y. are/were employed by and are stockholders in Celldex Therapeutics; J.M.B., P.D. and S.W. are employed by NanoString Technologies; A.M.S. is employed by Oncovir; N.B. and J.J.L. receive research support from Celldex (and Oncovir N.B.). L.D., L.L., N.R., B.W.H., M.A.C. and S.P.F. are employed by the Fred Hutchinson Cancer Research Center, which received partial funding from Celldex for this trial.

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Extended data

Extended Data Fig. 1 T cell responses to NY-ESO-1 versus durability, NY-ESO-1 status and disease recurrence.

a, T cell responses remain durable in cohort 1 subjects for several months. IFNγ, TNFα, and IL-2 cytokine production by CD8+ T-cells (lower panel) and CD4+ T cells (upper panel) were determined by ICS. (N = 2 cohort 1, black) and N = 2 cohort 2, white). b, Baseline T cell responses vs NY-ESO-1 status. 43/60 subjects had tumor biopsies tested for NY-ESO-1 expression and 8 of these 43 subjects had tumors which expressed NY-ESO-1. The only three subjects which showed high baseline T-cell responses to NY-ESO-1 were from the group of eight subjects which had a positive NY-ESO-1 tumor status. (Ungrouped analyses: NY-ESO-1pos: N = 8 subjects; NY-ESO-1neg: N = 35 subjects; Not Stained: N = 17 subjects). (By Cohort analyses: NY-ESO-1pos: N = 3 Cohort 1, N = 5 Cohort 2; NY-ESO-1neg: N = 17 Cohort 1, N = 18 Cohort 2; Not Stained: N = 10 Cohort 1, N = 17 Cohort 2. Two-sided t-test). c, Overall T cell responses vs disease recurrence. Cohort 1 showed greater elevation in anti-NY-ESO-1 T-cell responses than cohort 2. This higher antigen-specific response, however, did not translate to lower levels of recurrence in cohort 1 (12/30 cohort 1 subjects experienced recurrence, vs 8/30 in cohort 2). Statistics for non-recurrence (NR) vs recurrence (R) within each cohort were calculated by a 2 tailed t-test. For cohort 1, n = 18 NR subjects vs n = 12R subjects. For cohort 2, n = 21 NR subjects vs n = 9R subjects. b,c, Bars indicate mean±SD.

Source data

Extended Data Fig. 2 Whole blood immunophenotyping by flow cytometry.

Results reveal consistent expansion of specific cell populations in cohort 1. Graphs show individual fold changes from baseline in absolute cell numbers (per mL whole blood) for 16 subjects in cohort 1 for the following cell types: monocytes, conventional DC (cDC), NK cells, B cells, plasmacytoid DC (pDC), and CD56 Bright NK cells. See Fig. 2 for mean changes by cohort.

Source data

Extended Data Fig. 3 DC subset gating and fold increase of DC subsets during Flt3L mobilization.

a, Flow cytometry gating strategy; To determine different DC subsets, gating was performed as follows: Lineage- Blue+ HLA-DR+ CD45RA- CD123- CD11c+ (CD11c+), gating on CD11c+ →CD141+ Clec9a+(DC1), gating on CD11c+ →CD141-Clec9a- CD1c+ (DC2), gating on CD11c+ →CD141-Clec9a- CD1c− CD16+(DC4), gating on Lineage- Blue+ HLA-DR+→CD123+CD45RA+ (DC6), gating on Lineage- Blue+ HLA-DR+ CD123+ CD45RA+ CD33+ AXL- (pre-DC). b, Mean fold increases of the indicated DC subsets from each cohort (analyses of n = 7 independent subjects per cohort)(Two-sided t-test).

Source data

Extended Data Fig. 4 Schema for gene expression profiling analyses.

Cartoon of transcriptional analyses overview showing relationship of data in Fig. 4 to the Modular mapping of blood transcriptome changes induced by treatment (Fig. 5, Extended Data Fig. 5 and Extended Data Fig. 6).

Extended Data Fig. 5 Modular transcriptional fingerprints for group comparison (Cohort 1).

Responsive modules at each time point in comparison to baseline are mapped on a grid for visualization purposes. The color of the spots indicates the direction of changes in transcript abundancies: red for up-regulation and blue for down-regulation. The degree of intensity of the spots denotes the level of activation, which is given by the proportion of transcript coherently regulated. A legend is provided with functional interpretations indicated at each position of the grid by a color code (N = 12 cohort and N = 11 cohort 2).

Source data

Extended Data Fig. 6 Mapping perturbations of the modular repertoire across individual samples.

The responsive modules in individual subjects from cohort 1 (N = 12) after treatment as compared to baseline are mapped into grids. The expression profile of each individual subject was calculated as a FC and difference after treatment as compared to the expression of individual samples at baseline. To determine changes in individual subjects, a cut-off is set against which individual genes constitutive of a module are tested (|FC|> 1.5 and |difference|>100).

Source data

Extended Data Fig. 7 Flow cytometry gating strategy for PBMC subsets.

Flow data (corresponding to Fig. 2c,d and Extended Data Fig. 2) and gating scheme used to analyze fresh whole blood stained with the 12-color PBMC Subset immunophenotyping panel as indicated in the Methods section.

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Bhardwaj, N., Friedlander, P.A., Pavlick, A.C. et al. Flt3 ligand augments immune responses to anti-DEC-205-NY-ESO-1 vaccine through expansion of dendritic cell subsets. Nat Cancer 1, 1204–1217 (2020). https://doi.org/10.1038/s43018-020-00143-y

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