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Programmable bacteria induce durable tumor regression and systemic antitumor immunity

Nature Medicine volume 25, pages 1057–1063 (2019)Cite this article

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Abstract

Synthetic biology is driving a new era of medicine through the genetic programming of living cells1,2. This transformative approach allows for the creation of engineered systems that intelligently sense and respond to diverse environments, ultimately adding specificity and efficacy that extends beyond the capabilities of molecular-based therapeutics3,4,5,6. One particular area of focus has been the engineering of bacteria as therapeutic delivery systems to selectively release therapeutic payloads in vivo7,8,9,10,11. Here we engineered a non-pathogenic Escherichia coli strain to specifically lyse within the tumor microenvironment and release an encoded nanobody antagonist of CD47 (CD47nb)12, an anti-phagocytic receptor that is commonly overexpressed in several human cancer types13,14. We show that delivery of CD47nb by tumor-colonizing bacteria increases activation of tumor-infiltrating T cells, stimulates rapid tumor regression, prevents metastasis and leads to long-term survival in a syngeneic tumor model in mice. Moreover, we report that local injection of CD47nb-expressing bacteria stimulates systemic tumor-antigen-specific immune responses that reduce the growth of untreated tumors, providing proof-of-concept for an abscopal effect induced by an engineered bacterial immunotherapy. Thus, engineered bacteria may be used for safe and local delivery of immunotherapeutic payloads leading to systemic antitumor immunity.

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Fig. 1: Quorum-induced release of functional anti-CD47 blocking nanobody by engineered immunotherapeutic bacteria encoding a SLC.
Fig. 2: Intratumoral production of CD47 nanobody by eSLC elicits antitumor responses in multiple syngeneic mouse tumor models.
Fig. 3: Immunotherapeutic eSLC–CD47nb bacteria induce robust adaptive antitumor immune responses.
Fig. 4: Systemic adaptive immunity following bacterial therapy limits growth of untreated tumors.

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Acknowledgements

This work was supported by the NIH Pathway to Independence Award (R00CA197649–02) (to T.D.), DoD Idea Development Award (LC160314) (to T.D.), DoD Era of Hope Scholar Award (BC160541) (to T.D.), NIH R01GM069811 (to T.D.), NIH K22AI127847 (to N.A.), Searle Scholars Program SSP-2017-2179 (to N.A.), Bonnie J. Addario Lung Cancer Foundation Young Investigators Team Award (to N.A. and T.D.) and the Roy and Diana Vagelos Precision Medicine Pilot Grant (to N.A. and T.D.). Research reported in this publication was performed in the Columbia University Department of Microbiology and Immunology Flow Cytometry Core facility. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank K. T. Fortson and O. Velasquez for technical assistance with flow cytometry experiments and in vivo tumor experiments, respectively; M. O. Din for input pertaining to SLC characterization experiments; and V. Yeong and members of the Obermeyer group for assistance with affinity chromatography and protein purification. We thank R. L. Vincent, T. M. Savage, K. A. Kaiser and L. F. Loffredo for critical review of the manuscript.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Sreyan Chowdhury, Samuel Castro, Courtney Coker, Taylor E. Hinchliffe & Tal Danino

  2. Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA

    Sreyan Chowdhury & Nicholas Arpaia

  3. Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    Nicholas Arpaia & Tal Danino

  4. Data Science Institute, Columbia University, New York, NY, USA

    Tal Danino

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  1. Sreyan Chowdhury
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Contributions

S. Chowdhury, N.A. and T.D. conceived and designed the study. S. Chowdhury, S. Castro, C.C. and T.E.H. performed in vivo experiments. S. Chowdhury performed in vitro characterization of eSLC–CD47nb and conducted immunophenotyping experiments. S. Chowdhury, N.A. and T.D. analyzed data and wrote the manuscript with input from all other authors.

Corresponding authors

Correspondence to Nicholas Arpaia or Tal Danino.

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Competing interests

S. Chowdhury, N.A. and T.D. have filed a provisional patent application with the US Patent and Trademark Office (US Patent Application No. 62/747,826) related to this work. T.D. and N.A. have a financial interest in GenCirq, Inc.

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Peer review information: Saheli Sadanand and Joao Monteiro were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Extended data

Extended Data Fig. 1 Map of plasmids used in this study.

a, pSC01, single plasmid with synchronized lysis circuit. b, pSC02, stabilized plasmid that drives constitutive expression of HA-tagged anti-CD47 nanobody. c, pSC03, empty vector control.

Extended Data Fig. 2 E. coli that are capable of synchronized lysis produce functional anti-CD47 nanobodies.

a, Bacterial growth dynamics over time in agar-pad microscopy experiments. b, Immunoblot of bacterial culture supernatants (S) and cell pellets (P) in strains with and without SLC designed to constitutively produce the HA-tagged CD47 nanobody. a.u., arbitrary units. Data are representative of three independent experimental replicates. The immunoblot has been cropped to show relevant bands; a molecular weight marker is indicated on the right (in kDa). The uncropped blot is available as Source Data. c, A20 cells were co-incubated with a fixed concentration of FITC-conjugated isotype control (IgG2a–FITC) antibody along with increasing concentrations of bacterial lysates of bacteria that constitutively expressed CD47nb or empty vector. Data are representative of two independent experimental replicates. d, A20 cells were co-incubated with a fixed concentration of FITC-conjugated anti-CD47 (miap301) antibody along with serial dilutions of recombinant 6×His-tagged rCD47nb. Data are representative of two independent experimental replicates. e, In vitro phagocytosis by BMDMs of DiI-labeled A20 cells pretreated with PBS, miap301 or IgG2a isotype control, or with serial dilutions of eSLC or eSLC–CD47nb lysates in PBS. Data are mean ± s.e.m., n = 4 fields of view from three technical replicates.

Source data

Extended Data Fig. 3 Individual kinetics of intratumoral bacterial immunotherapy.

a, Individual tumor growth trajectories (n = 7 mice per group); related to data shown in Fig. 2a. b, Representative images of subcutaneous A20-tumor-bearing BALB/c mice treated with PBS, eSLC or eSLC–CD47nb. c, Relative body weight of A20-tumor-bearing BALB/c mice over time (n = 7 mice per group; P > 0.05, two-way ANOVA with Tukey’s multiple comparisons test). Data are mean ± s.e.m. d, Individual tumor growth trajectories after treatment with eSLC (intratumoral injection (IT)), miap301 (intraperitoneal injection (IP)), eSLC (intratumoral injection) and miap301 (intraperitoneal injection), eSLC–CD47nb lysate, rCD47nb (intratumoral injection) or eSLC–CD47nb (intratumoral injection); related to data shown in Fig. 2e.

Extended Data Fig. 4 Immunotherapeutic bacteria limit tumor growth in syngeneic mouse models of melanoma and triple-negative breast cancer.

a, Relative body weight of B16-F10-bearing C57BL/6 mice over time. Mice were treated with PBS (n = 4 mice), eSLC (n = 5 mice), miap301 (n = 5 mice) or eSLC–CD47nb (n = 6 mice) (P < 0.05, two-way ANOVA with Tukey’s multiple comparisons test). Data are mean ± s.e.m. b, Individual tumor growth trajectories of subcutaneous 4T1 tumors after intratumoral injection of eSLC (n = 7 tumors) or eSLC–CD47nb (n = 8 tumors); related to data shown in Fig. 2f. c, Individual tumor growth trajectories of subcutaneous B16-F10 melanoma after intraperitoneal injection of miap301 or intratumoral injection of PBS, eSLC or eSLC–CD47nb; related to data shown in Fig. 2h.

Extended Data Fig. 5 Intravenous bacterial immunotherapy limits tumor growth in mice in a subcutaneous A20 lymphoma model.

a, Individual tumor growth trajectories of subcutaneous A20 tumors after intraperitoneal injection of miap301 (n = 10 tumors) or intravenous injection of eSLC (n = 9 tumors) or eSLC–CD47nb (n = 10 tumors); related to data shown in Fig. 2i. b, Biodistribution of eSLC–CD47nb E. coli on day 8 after the final intravenous bacterial treatment. Excised tumors, livers, spleens and kidneys were homogenized, serially diluted and plated onto LB–agar plates. Colonies were counted to determine the CFU g−1 of tissue (n = 3 per group). Data are mean ± s.e.m. c, Relative body weight of A20-tumor-bearing BALB/c mice that received intravenous injections of bacteria (eSLC, n = 5 mice; eSLC–CD47nb, n = 4 mice) or intraperitoneal injections of miap301 (n = 5 mice) (P > 0.05, two-way ANOVA with Tukey’s multiple comparisons test). Data are mean ± s.e.m.

Extended Data Fig. 6 Immunophenotyping of tumor-infiltrating myeloid and lymphoid cell subsets after intratumoral injection of bacteria.

First, 5 × 106 A20 cells were subcutaneously implanted into the hind flanks of BALB/c mice. Subsequently, when tumors reached 100–150 mm3 in volume (day 0), mice were treated with PBS, eSLC or eSLC–CD47nb on days 0, 4 and 7. On day 3 or day 8, tumors were homogenized and tumor-infiltrating myeloid and lymphoid cell subsets were isolated for flow cytometry analysis. a, Frequency of isolated MHC class IIhiCD11b+F4/80+ macrophages on day 3 after treatment (n = 5 tumors per group, *P = 0.0271, unpaired two-tailed t-test). b, Mean fluorescence intensity (MFI) of SIRPα staining within the CD11b+F4/80+ subset on day 8 after treatment (n = 5 tumors per group, *P = 0.0209, unpaired two-tailed t-test). c,d, Frequencies of CTLA4+ cells within the FOXP3−CD4+ T cell population (c; n = 5 tumors per group, **P = 0.0018, unpaired two-tailed t-test) and CD8+ T cell population (d; n = 5 tumors per group, **P = 0.0098, unpaired two-tailed t-test). e,f, Frequencies of TNF+ cells within FOXP3−CD4+ T cell (e) and CD8+ T cell (f) populations following ex vivo stimulation (ns, not significant (P > 0.05), n = 3 tumors per group, unpaired two-tailed t-test). g, Frequency of IL-17+ cells within the FOXP3−CD4+ T cell population following ex vivo stimulation (*P = 0.0402, n = 3 tumors per group, unpaired two-tailed t-test). h, Frequency of IFNγ+ cells within the CD8+ T cell population following ex vivo stimulation (n = 3 tumors per group, P > 0.05, unpaired two-tailed t-test). All data are mean ± s.e.m.

Extended Data Fig. 7 Immunotherapeutic bacteria lead to increased IFNγ production by splenic T cells after stimulation with tumor antigens.

Enzyme-linked immunosorbent assay (ELISA) of IFNγ using supernatants from overnight co-incubation of splenocytes isolated from each of the indicated treatment groups with irradiated A20 cells (n = 2 mice per group of three technical replicates).

Extended Data Fig. 8 Intratumoral bacterial immunotherapy leads to distal tumor control.

a, Individual tumor growth trajectories of treated (injected) and untreated A20 tumors after intratumoral injection of PBS, eSLC or eSLC–CD47nb; related to Fig. 4a,b. b, SLC− and SLC+ E. coli NisLux were intratumorally injected into a single flank of A20-tumor-bearing mice (scale represents radiance (p s−1 cm−2 sr−1)). Luminescence was measured over time using in vivo imaging. Representative image of mouse no. 3 from each group over time. c, Luminescence heat maps over time (n = 5 mice per group). Colors represent average radiance (p s−1 cm−2 sr−1).

Extended Data Fig. 9 Immunophenotyping of tumor-infiltrating lymphocytes in untreated tumors after single-flank injection of bacteria.

First, 5 × 106 A20 cells were implanted into the hind flanks of BALB/c mice. Subsequently, when tumors reached ~100 mm3 in volume (day 0), mice were treated with PBS, eSLC or eSLC–CD47nb on day 0, 4 and 7 by injection into a single tumor. Untreated tumors were extracted and analyzed by flow cytometry on day 8. n = 5 mice per group. a, Frequency of Ki-67+ cells within the FOXP3−CD4+ T cell population (P > 0.05, unpaired two-tailed t-test). b,c, Frequency of tumor-infiltrating IFNγ+ cells within the FOXP3−CD4+ T cell population (b; *P = 0.0327, unpaired two-tailed t-test) and CD8+ T cell population (c; *P = 0.0104, unpaired two-tailed t-test) following ex vivo stimulation with PMA and ionomycin in the presence of brefeldin A. d,e, Frequencies of CTLA4+ cells within the FOXP3−CD4+ T cell population (d; P > 0.05, unpaired two-tailed t-test) and CD8+ T cell population (e; *P = 0.0259, unpaired two-tailed t-test). f, Frequency of tumor-infiltrating IFNγ+ cells within the FOXP3−CD4+ T cell population following ex vivo restimulation with A20-Id peptide (DYWGQGTEL) in the presence of brefeldin A (P > 0.05, unpaired two-tailed t-test). All data are mean ± s.e.m.

Extended Data Fig. 10 Distal tumor control requires SLC+ bacteria engineered to produce CD47nb.

a,b, Individual tumor growth trajectories of treated (injected) and untreated A20 tumors after intratumoral injection of eSLC, eCD47nb or eSLC–CD47nb (n = 4 mice per group); related to Fig. 4h,i.

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Unprocessed Western Blot for Extended Data Fig. 2b.

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Chowdhury, S., Castro, S., Coker, C. et al. Programmable bacteria induce durable tumor regression and systemic antitumor immunity. Nat Med 25, 1057–1063 (2019). https://doi.org/10.1038/s41591-019-0498-z

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