www.sciencemag.org/cgi/content/full/342/6161/967/DC1 Supplementary Materials for Commensal Bacteria Control Cancer Response to Therapy by Modulating the Tumor Microenvironment Noriho Iida, Amiran Dzutsev, C. Andrew Stewart, Loretta Smith, Nicolas Bouladoux, Rebecca A. Weingarten, Daniel A. Molina, Rosalba Salcedo, Timothy Back, Sarah Cramer, Ren-Ming Dai, Hiu Kiu, Marco Cardone, Shruti Naik, Anil K. Patri, Ena Wang, Francesco M. Marincola, Karen M. Frank, Yasmine Belkaid, Giorgio Trinchieri,* Romina S. Goldszmid* *Corresponding author. E-mail: [email protected] (G.T.); [email protected] (R.S.G.) Published 22 November 2013, Science 342, 967 (2013) DOI: 10.1126/science.1240527 This PDF file includes: Materials and Methods Figures S1 to S20 References
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Commensal Bacteria Control Cancer Response to Therapy by Modulating the Tumor Microenvironment
Noriho Iida, Amiran Dzutsev, C. Andrew Stewart, Loretta Smith, Nicolas Bouladoux, Rebecca A. Weingarten, Daniel A. Molina, Rosalba Salcedo, Timothy Back,
Sarah Cramer, Ren-Ming Dai, Hiu Kiu, Marco Cardone, Shruti Naik, Anil K. Patri, Ena Wang, Francesco M. Marincola, Karen M. Frank, Yasmine Belkaid,
volume. Elemental Analysis was completed using ICP-MS. Calibration samples were prepared
from a serial dilution of a 1 ug/g and 10 ng/g Ag (NIST SRM 3140) solution. Briefly, 10, 25, 50,
75, 100, 150, 200 pg/g standards were prepared by diluting 0.100, 0.250, 0.500, 0.750, 1.00,
1.50, and 2 mL of a 1 ng/g Pt solution and was transferred to a 18 mL LDPE sample vial. The
standards were diluted to a total of 10 mL using 1.5% HNO3: 4% HCl solution, respectively.
The weight of the empty vial, the vial containing sample, and the vial containing the final
dilution was recorded. The exact concentration in each standard was determined by difference.
The two masses analyzed were Platinum (Pt, analyte) and Indium (In, internal standard).
Samples were run in triplicate, with each one or the triplicate consisting of 10 measurement
repetitions. A calibration curve was constructed from the external calibration samples. The
Platinum signal was normalized for instrumental changes by dividing the Platinum counts
(analyte) by the Indium counts (internal standard). A calibration curve was run at the beginning
and end of the sampling sequence, and the two curves were averaged together to create the curve
used to quantify the samples. The limit of detection (LOD) was determined using the method
outlined by IUPAC utilizing a LOD = 3σ 2,3. More specifically, IUPAC lists the definition of the
limit of detection as:
In the equation, cL is the lowest detectable concentration, sB is the standard deviation of the
quantitative blanks, m is the slope of the calibration curve, and k is the numerical value chosen
for the corresponding confidence interval. For determining the limit of detection for each
sample, the value of k =3 was used. The LOD for the sample run was 0.48 pg/g. The limit of
quantitation (LOQ) was also determined using the formula above with k =10. The LOQ for the
ICP-MS run was 1.6 pg/g. Concentration of platinum was expressed as ng of platinum per gram
Iida et al. 13
DNA. No platinum was detected in the untreated control tumors.
Bioluminescence assay. Twenty-four hours after oxaliplatin injection, EL4-bearing mice were
injected with luminescent probe L-012 (Wako, Richmond, VA) and imaged with IVIS Spectrum
(Xenogen, Alameda, CA) as previously described (36). L-012 shows luminescence after
chemical reaction with ROS. To calculate bioluminescence, flux (photon counts/second)
obtained 16 min after L-012 injection was divided by tumor area.
Statistical analyses. Differences in Kaplan-Meier survival curves were assessed by the log-rank
test. Tumor volumes were analyzed with a repeated measures linear mixed model and all other
outcomes with one-way ANOVA. All p values were two-tailed and were corrected for multiple
comparisons with the Bonferroni or Holm-Sidak methods. The analysis was performed with
Prism version 5.0 (GraphPad software, La Jolla, CA) or SAS version 9.2 (SAS Institute, Inc.,
Cary, NC). Statistical analysis of microarray, nanostring and microbiome datasets was performed
using Partek 6.6 (Partek Inc., St. Louis, MO, USA) and Prism version 5.0 software using one or
two-way ANOVA Method of Moments. The p values were corrected for multiple comparisons
using q-value method.
Iida et al. 14
Fig. S1. Effect of ABX treatment on tumor gene expression. Gene Ontology (GO) analysis of differentially expressed genes (q<0.1, >1.5 fold difference) in (A) MC38 (8 mice per group) and (B) EL4 tumors (5 mice per group) from H2O- or ABX-treated mice prior to any therapeutic treatment. GO analysis is visualized as a network using Cytoscape. GO terms are displayed as nodes; size is inversely proportional to log2 of the p-value. Clusters of the GO terms were determined using ClusterOne plugin of the Cytoscape. Most common terms in the identified clusters were used to label GO terms. Analysis of genes downregulated (top, blue color) or upregulated after ABX treatment (bottom, red color) is shown.
Iida et al. 15
Fig. S2 Characterization of hematopoietic cell populations in tumor bearing animals. Flow cytometric analysis of EL4 (A) or MC38 (B) tumor-infiltrating leukocyte populations (CD45+ cells) as well as cells of spleens from MC38 bearing mice (C). Data are shown, as absolute cell number of CD45+ leukocytes per mg of tumor weight or as percentages of the Ly6C+MHCII+, Ly6ChiMHCII-, F4/80hi, Ly6Ghi, CD4+ and CD8+ T-cells among total CD45+ cells. Polarized CD4 T cell subsets in the MC38 tumors were determined after ex vivo stimulation with phorbol 12-myristate 13-acetate (PMA) and ionomycin. IFN-γ+CD4+ Th1 cells, IL-4+CD4+ Th2 cells, IL-17A+CD4+ Th17 cells and FoxP3+CD25+CD4+ T-reg cells are shown as a fraction of CD4+ T-cells and IFN-γ+CD8+ Tc1 cells are shown as a fraction of CD8+ T-cells. Data are shown as individual mice and means ±SEM. *P<0.05 and **P<0.01.
Iida et al. 16
Fig. S3. ABX treatment impairs the anti-IL-10R/CpG-ODN therapy efficacy in B16 tumor-
implanted mice and is dependent on TNF. (A) H2O- or ABX-drinking B16 tumor bearing mice were treated with anti-IL-10R/CpG-ODN or left untreated (control). (B) MC38-bearing WT or Tnf-/- mice were treated with anti-IL-10R/CpG-ODN or left untreated (control). Data are shown as means ± SEM. One experiment representative of two performed is shown. * **P<0.01 and ***P<0.001.
Iida et al. 17
Fig. S4. ABX-treatment blocks intracellular cytokine production and upregulation of
costimulatory molecules in tumor infiltrating cells following anti-IL-10R/CpG-ODN
therapy. (A) Examples of the flow cytometry gating strategies utilized (MC38 tumor infiltrating cells). Representative plots shown are gated on live CD45+ cells. (B) TNF-producing MC38 tumor infiltrating cells in H2O or ABX-drinking mice: representative flow cytometry histograms (dark lines represent staining isotype control; light lines control isotype IgG/control ODN treated mice; shadowed histograms anti-IL-10R/CpG-ODN-treated mice, green H20 and orange ABX-treated mice); (C) Percent TNF-producing cells (top panel) and mean fluorescence intensity (MFI) of intracellular TNF (bottom panel) in the indicated MC38-infiltrating cell subsets isolated from H2O- or ABX-drinking mice treated with anti-IL-10R/CpG-ODN or control isotype IgG/control ODN and harvested 3 h after ODN injection. (D) Representative plots of CD86 expression (MFI shown in upper right corner) on CD11c+MHCII+ cells in MC38 tumors harvested 12 h post anti-IL-10R/CpG-ODN or control isotype IgG/control ODN injection. Blue: isotype control; red, anti-CD86 antibody. (E) Representative plots (left, dark lines represent staining isotype control; light lines isotype IgG/control ODN treated mice; shadowed histograms anti-IL-10R/CpG-ODN-treated mice, green H20- and orange ABX-treated mice) and percentages of IL-12p40-producing cells (right) are shown. Data in panel C and E are shown as individual mice and means ±SEM. *P<0.05, **P<0.01 and ***P<0.001.
Iida et al. 18
Fig. S5. Effect of ABX-treatment on tumor gene expression induced by anti-IL-10R/CpG-
ODN therapy. mRNA expression of selected genes was determined by Nanostring nCounter gene expression assay in MC38 tumors isolated from H2O- or ABX-treated mice subjected to anti-IL-10R/CpG-ODN or not and harvested 3 h after CpG-ODN injection. Data are shown as individual mice and means ±SEM. *P<0.05, **P<0.01 and ***P<0.001.
Iida et al. 19
Fig. S6. TNF production by tumor infiltrating cells is restored by LPS gavage in ABX-
treated mice and TLR2-deficiency does not affect the effectiveness of anti-IL-10R/CpG-
ODN therapy. (A) Intracellular TNF (flow cytometry) in MC38-infiltrating CD45+ cells isolated 3 h after CpG-ODN injection from H2O- or ABX-treated mice subjected to anti-IL-10R/CpG-ODN therapy and orally gavaged or not with LPS (25 mg/kg, 3 times per week, 2 weeks before and 1 week after tumor injection). Data show individual mice and means ±SEM. (B) MC38 tumor growth in H2O or ABX-treated WT mice or Tlr2-/- mice treated or not with anti-IL-10R/CpG-ODN. *P<0.05, **P<0.01 and ***P<0.001.
Iida et al. 20
Fig. S7. Composition of fecal microbiota segregates mice with high and low intratumoral
TNF production. 16S rDNA data obtained from sequencing fecal samples from H2O-drinking mice collected prior to anti-IL-10R/CpG-ODN therapy was analyzed using unweighted Unifrac analysis and visualized using Principal Component Analysis (PCA). A gradient from blue to red represents relative mRNA Tnf levels from low to high respectively, Tnf levels were estimated using RT-PCR. Left panel shows PCA axis 1 vs axis 2 and right panel axis 1 vs axis 3. One representative of 3 experiments is shown.
Iida et al. 21
Fig. S8. Bacterial diversity analysis. Fecal bacterial 16S rDNA was sequenced in water treated only group (H2O), ABX recovery group (1, 2 and 4 weeks post ABX cessation) and in single antibiotics treated groups [ampicillin (A), imipenem (I), neomycin (N), vancomycin (V)]. Chao (A) and Inverse Simpson (B) indexes of bacterial diversity are shown for individual mice. Red lines represent means ±SD.
Log2 reads/10,000 Figure S9
Iida et al. 23
Fig. S9. Taxonomic tree of fecal bacteria identified in all 16S rDNA sequencing experiments
part of this study. Bacteria names in the boxes indicate from left to right: Kingdom, Phylum, Class, Order, Family and Genus. The same taxonomic tree was used to illustrate the microbiota changes in Fig. S10, S12, and S13. Bars on the right side indicate genus abundance (log2 transformed number of reads per 10,000) in H2O-drinking mice.
Iida et al. 24
Fig. S10. Recovery of microbiota post-ABX treatment. Fecal samples were collected prior to 3-weeks ABX treatment and 1, 2 and 4 weeks post cessation of ABX treatment. 16S rDNA of the identified bacteria was compared between the groups at every taxonomical level and displayed as taxonomical tree. Taxonomical tree branches correspond to the one displayed in Fig.S9. Size of the nodes in the tree labeled “H2O vs 1w” is proportional to number of log2 reads of bacterial phylotypes in H2O drinking mice. Red nodes indicate significantly (q<0.1) higher number of reads in H2O drinking mice compared to mice 1w post ABX cessation, and conversely, blue nodes, lower. Size of the nodes in the tree labeled “1w vs H2O” is proportional to number of log2 reads of bacterial phylotypes in feces of mice 1w post ABX cessation. Red nodes indicate significantly (q<0.1) higher number of reads 1w post ABX cessation compared to H2O drinking mice, and conversely, blue nodes indicate lower number of reads. Size of the nodes in the tree labeled “2w to 1w” is proportional to number of log2 reads of bacterial phylotypes in feces of mice 2w post ABX cessation. Red nodes indicate significantly (q<0.1) higher number of reads 2w post ABX cessation compared to 1w post ABX cessation, and conversely, blue nodes indicate lower number of reads. Size of the nodes in the tree labeled “3w to 1w” is proportional to number of log2 reads of bacterial phylotypes in feces of mice 3w post ABX cessation. Red nodes indicate significantly (q<0.1) higher number of reads 3w post ABX cessation compared to 1w post ABX cessation, and conversely, blue nodes, indicate lower number of reads.
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Fig. S11. Effect of single antibiotics on fecal microbiota composition and intratumoral Tnf
mRNA expression after anti-IL-10R/CpG-ODN therapy. Fecal microbiota and response to aIL-10R/CpG-ODN treatment was analyzed in MC38-bearing mice drinking water (H2O), water containing the antibiotic cocktail including vancomycin, imipenem and neomycin (VIN), or the single antibiotics vancomycin (V), imipenem (I), neomycin (N), and ampicillin (A) for 3 weeks. (A) The number of eubacteria 16S rRNA gene copy in feces collected before anti-IL-10R/CpG-ODN treatment was determined by real-time PCR. (B) Heatmap of OTUs (97% similarity) of fecal microbiota was normalized to copy number of 16S per gram of feces. OTUs represented by <0.1% of total reads were removed from the analysis. (C) Fecal microbiota composition was analyzed by principal component analysis of unweighted Unifrac distances. (D) Tnf mRNA expression (real-time PCR, values on Y-axis represent 2- Ct) in tumors from the same animals treated with anti-IL-10R/CpG-ODN and harvested 3 h after CpG injection. In (A) and (D) data show individual mice and means ±SEM. *P<0.05, **P<0.01 and ***P<0.001.
Iida et al. 26
Fig. S12. Changes of bacterial composition due to single antibiotic treatments. Fecal samples were collected 3-weeks after the indicated single antibiotic treatment. 16S rDNA of the identified bacteria was compared between the groups at every taxonomical level and displayed as taxonomical tree. Taxonomical tree branches correspond to the one displayed in Fig.S9. Size of the nodes in the trees is proportional to number of log2 reads of bacterial phylotypes in feces of mice treated with the indicated antibiotic. Red nodes indicate significantly (q<0.1) higher number of reads in single antibiotic treated mice compared to normal H2O drinking, and conversely, blue nodes lower reads number.
Iida et al. 27
Fig. S13. Bacterial genera correlating with intratumoral Tnf mRNA expression after anti-
IL-10R/CpG-ODN treatment in different datasets. (A) Visualization of fecal bacterial genera showing positive (red circles) or negative (green circles) correlation (Spearman's rank correlation, q<0.1) with intratumoral Tnf expression levels in water only group (H2O), antibiotic recovery group (ABX recovery) and single antibiotics treated group (Single ABX). Gram negative bacteria are labeled “G-” and gram positive bacteria “G+”. Fecal samples were collected prior to anti-IL-10R/CpG-ODN therapy and Tnf mRNA expression levels 3 h post anti-IL-10R/CpG-ODN treatment were estimated by RT-PCR. (B) The same bacteria are represented as taxonomical tree with distribution of the branches corresponding to the labels in Fig. S9. Size of the circles is proportional to log2 number of reads found for all datasets combined. Red color of the nodes indicates positive and green indicates negative correlations (q<0.1) with intratumoral Tnf expression levels.
Iida et al. 28
Fig. S14. Correlation with TNF production and recovery dynamics of selected bacteria
genera after ABX withdrawal. (A) Correlation between Alistipes, Ruminococcus and Lactobacillus abundance and anti-IL-10R/CpG induced tumor TNF production analyzed in H2O-drinking mice. (B) Abundance of Alistipes, Ruminococcus and Lactobacillus genera in H2O-drinking mice or in ABX-treated mice 1, 2 or 4 weeks after cessation of ABX treatment. Data are shown as individual mice values and mean ± SEM, counts were normalized to 10,000 reads per sample. *P<0.05, **P<0.01 and ***P<0.001.
Iida et al. 29
Fig. S15. Inoculation with individual bacterial species affects TNF production by tumor
associated myeloid cells in response to anti-IL-10R/CpG-ODN. Control H2O-drinking mice or mice one week after cessation of ABX treatment were exposed to anti-IL-10R/CpG-ODN therapy. (A) A group of ABX pre-exposed mice was subjected to oral gavage with A. shahii. (B) A group of control H20-drinking mice was subjected to oral gavage with L. fermentum. Mice in both groups were sacrificed 3 h after CpG-ODN treatment and intracellular TNF measured in the indicated tumor-associated myeloid cell subsets. Data show individual mice and means ±SEM from combined data from 2 experiments (A) or one representative experiment out of 2 performed (B). *P<0.05 and **P<0.01.
Iida et al. 30
Fig. S16. Impaired anti-tumor effect of platinum compounds in ABX-treated and in GF
mice. (A) MC38 tumor growth after oxaliplatin treatment of individual animals from experiment shown in Fig. 4A. (B) EL-4 tumor growth in specific pathogen-free (SPF) or germ-free (GF) mice treated with oxaliplatin or PBS (control). (C) H2O- or ABX-treated MC38-bearing mice were intraperitoneally injected with oxaliplatin (10 mg/kg) or PBS (control). Tumor growth (left) and survival (right) are shown. (D) Subcutaneous EL4 tumor-bearing H2O- or ABX-treated mice were treated with cisplatin (5 mg/kg) or PBS (control). Data in A, B and C are shown as mean ± SEM from one experiment with 5 to 10 mice/group representative of 2 or more experiments; tumor growth (left panels) and survival (right panels) are shown. *P<0.05, **P<0.01, ***P<0.001
Iida et al. 31
Fig. S17. ABX limit the changes in tumor gene expression induced by oxaliplatin treatment. Gene Ontology (GO) analysis of differentially expressed genes (q<0.1, >2 fold difference) in EL4 tumors from H2O- vs ABX-drinking mice 18h post Oxaliplatin treatment from the experiment shown in Fig.4C. GO analysis of genes expressed significantly higher (A) in H2O-drinking mice or in ABX-drinking mice (B). GO analysis is visualized as a network using Cytoscape. GO terms are displayed as nodes; size is inversely proportional to log2 of the p-value. Clusters of the GO terms were determined using ClusterOne plugin of the Cytoscape. Most common terms in the identified clusters were used to label GO terms.
Iida et al. 32
Fig. S18. Expression of selected genes in EL4 tumors from H2O- or ABX-exposed mice
treated or not with oxaliplatin. mRNA gene expression (Nanostring nCounter gene expression assay) was determined in EL4 tumors harvested 48 h after oxaliplatin or PBS injection. Y-axis represents normalized counts. Data are shown as individual mice and means ±SEM. *q<0.05, ** q<0.01 and ***q<0.001.
Iida et al. 33
Fig. S19. The antitumor effect of oxaliplatin is reduced by NAC and requires MyD88 but
not TLR4, IL-1R, IL-18R or TNF. (A) EL4 tumor growth in WT and Tnf-/- mice treated with oxaliplatin or PBS (control). (B) EL4-bearing H2O- or N-acetylcysteine (NAC)-drinking mice were treated with oxaliplatin or PBS (control). Tumor growth (left) and survival (right) are shown. (C) Absolute cell number and frequencies (flow cytometry) of CD11b+Ly6GhiLy6Cint and CD11b+Ly6G-Ly6Chi cells in total CD45+ cells in the blood and EL4 tumors harvested from anti-Gr-1 antibody- or isotype control-injected mice. (D) EL4-bearing WT, Myd88-/-, Tlr4-/-, l1r-/- and Il18r-/- mice were treated with oxaliplatin or PBS (control). Tumor growth (left panels) and survival (right panels) are shown. A, B and D tumor growth data are means ±SEM from one representative experiment with 5-10 mice/group out of 2 or more performed. *P<0.05, **P<0.01 and ***P<0.001.
Iida et al. 34
Fig. S20. ABX treatment decreases tumor DNA damage induced by oxaliplatin but not the
formation of platinum adducts. (A) EL4 tumors from H2O- or ABX-treated mice were analyzed for dsDNA damage after oxaliplatin treatment using gamma-H2AX immunohistochemical labeling to indicate foci of damage. Nuclei with <4 foci were considered negative and positive nuclei were grouped as having >4 foci or pan-nuclear labeling (uncountable foci) as shown in the right inset. Data (means ± SEM) are from one experiment representative of two performed. **P<0.01. (B) Platinum (Pt) bound to EL4 tumor DNA in control PBS-injected mice or 8 h post-oxaliplatin injection was measured by IPS-mass spectrometry. Data shown as individual mice and means ± SEM from 2 experiments combined.
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