Report

Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients

See allHide authors and affiliations

Science  05 Jan 2018:
Vol. 359, Issue 6371, pp. 97-103
DOI: 10.1126/science.aan4236
  • Fig. 1 Higher gut microbiome diversity is associated with improved response to anti–PD-1 immunotherapy in patients with metastatic melanoma.

    (A) Schema of sample collection and analyses. (B) Stacked bar plot of phylogenetic composition of common bacterial taxa (>0.1% abundance) at the order level in oral (n = 109, top) and fecal (n = 53, bottom) samples by 16S rRNA sequencing. (C) Inverse Simpson diversity scores of the gut microbiome in R (n = 30) and NR (n = 13) to anti–PD-1 immunotherapy by Mann-Whitney U rank sum (MW) test. Error bars represent the distribution of diversity scores. (D) Phylogenetic composition of fecal samples (n = 39) at the family level (>0.1% abundance) at baseline. High [blue, >11.63 (inverse Simpson score), n = 13], intermediate (gold, 7.46 to 11.63, n = 13), and low (red, <7.46, n = 13) diversity groups were determined using tertiles of inverse Simpson scores. (E) Kaplan-Meier (KM) plot of PFS by fecal diversity: high (median PFS undefined), intermediate (median PFS = 232 days), and low (median PFS = 188 days). High versus intermediate diversity (HR 3.60, 95% CI 1.02 to 12.74) and high versus low (HR 3.57, 95% CI 1.02 to 12.52) by univariate Cox model. (F) Principal coordinate analysis of fecal samples (n = 43) by response using weighted UniFrac distances. *P < 0.05; **P < 0.01.

  • Fig. 2 Compositional differences in the gut microbiome are associated with responses to anti–PD-1 immunotherapy.

    (A) Heatmap of OTU abundances in R (n = 30) and NR (n = 13). Columns denote patients grouped by response and sorted by diversity within R and NR groups; rows denote bacterial OTUs grouped into three sets according to their enrichment and/or depletion in R versus NR and then sorted by mean abundance within each set. Set 1 (enriched in R), Set 2 (unenriched), and Set 3 (enriched in NR). (B) Phylogenetic composition of OTUs within each set described in (A) at the order level. (C) Taxonomic cladogram from LEfSe showing differences in fecal taxa. Dot size is proportional to the abundance of the taxon. Letters correspond to the following taxa: (a) Gardnerella vaginalis, (b) Gardnerella, (c) Rothia, (d) Micrococcaceae, (e) Collinsella stercoris, (f) Bacteroides mediterraneensis, (g) Porphyromonas pasteri, (h) Prevotella histicola, (i) Faecalibacterium prausnitzii, (j) Faecalibacterium, (k) Clostridium hungatei, (l) Ruminococcus bromii, (m) Ruminococcaceae, (n) Phascolarctobacterium faecium, (o) Phascolarctobacterium, (p) Veilonellaceae, (q) Peptoniphilus, and (r) Desulfovibrio alaskensis. (D) Linear discriminant analysis (LDA) scores computed for differentially abundant taxa in the fecal microbiomes of R (blue) and NR (red). Length indicates effect size associated with a taxon. P = 0.05 for the Kruskal-Wallis H statistic; LDA score > 3. (E) Differentially abundant gut bacteria in R (blue) versus NR (red) by MW test [false-discovery rate (FDR)–adjusted] within all taxonomic levels. (F) Pairwise comparisons by MW test of abundances of metagenomic species identified by metagenomic WGS sequencing in fecal samples (n = 25) for R (n = 14, blue) and NR (n = 11, red). *P < 0.05; **P < 0.01. Colors reflect gene abundances visualized as “barcodes” with the following order of intensity: white (0) < light blue < blue < green < yellow < orange < red for increasing abundance, where each color change corresponds to a fourfold abundance change. In these barcodes, metagenomic species appear as vertical lines (coabundant genes in a sample) colored according to the gene abundance.

  • Fig. 3 Abundance of crOTUs within the gut microbiome is predictive of response to anti–PD-1 immunotherapy.

    (A) Top: Unsupervised hierarchical clustering by complete linkage of Euclidean distances by crOTU abundances in 43 fecal samples. Bottom: Stacked bar plot of relative abundances at the order level by crOTU community type. (B) Association of crOTU community types with response to anti–PD-1 by Fisher’s exact test: crOTU community type 1 (black, n = 11; R = 11, NR = 0) and crOTU community type 2 (orange, n = 32; R = 19, NR = 13). R, blue bars; NR, red bars. (C) Comparison KM plot PFS curves by log-rank test in patients with high abundance (dark blue, n = 19, median PFS = undefined) or low abundance (light blue, n = 20, median PFS = 242 days) of Faecalibacterium (top PFS curve) or with high abundance (dark red, n = 20, median PFS = 188 days) or low abundance (light red, n = 19, median PFS = 393 days) of Bacteroidales (bottom PFS curve). (D) Unsupervised hierarchical clustering of pathway class enrichment calculated as the number of MetaCyc pathways predicted in the metagenomes of fecal samples from 25 patients (R = 14, NR = 11). Columns represent patient samples (R, blue; NR, red), and rows represent enrichment of predicted MetaCyc pathways (blue, low enrichment; black, medium enrichment; yellow, high enrichment). Black text, biosynthetic pathways; blue text, degradative pathways. *P < 0.05.

  • Fig. 4 A favorable gut microbiome is associated with enhanced systemic and antitumor immunity.

    (A) Quantification by IHC of the CD8+ T cell infiltrate at pretreatment in tumors in R (n = 15, blue) and NR (n = 6, red) by one-sided MW test. Error bars represent the distribution of CD8+ T cell densities. (B) Pairwise Spearman rank correlation heatmap of significantly different taxa in fecal samples (n = 15) at baseline and CD3, CD8, PD-1, FoxP3, Granzyme B, PD-L1, and RORγT density in matched tumors. (C) Univariate linear regression between CD8+ T cell density in counts per mm2 in the tumor versus Faecalibacterium [blue, coefficient of determination (R2) = 0.42, P < 0.01] and Bacteroidales (red, R2 = 0.06, P = 0.38) abundance in the gut. (D) Pairwise Spearman rank correlation heatmap between significantly different fecal taxa and frequency of indicated cell types by flow cytometry in peripheral blood at baseline. mDC, myeloid dendritic cell. (E) Representative multiplex IHC images and (F) frequency of various immune cell types in patients having high Faecalibacterium (n = 2) or Bacteroidales (n = 2) in the gut. In (E), rectangles identify magnified region. MHC II, major histocompatibility complex II. (G) Experimental design of studies in germ-free (GF) mice. Time in days (indicated as D) relative to tumor injection (2.5 × 105 to 8 × 105 tumor cells). PO, per os (orally); BP, BRAFV600E/PTEN–/–; s.c., subcutaneous; IP, intraperitoneal. (H) Difference in size by MW test of tumors at day 14, implanted in R-FMT (blue) and NR-FMT mice (red), expressed as fold change (FC) relative to average tumor volume of control GF mice. Data from two independent FMT experiments (R-FMT, n = 5, median FC = 0.18; NR-FMT, n = 6, median FC = 1.52). (I) Representative tumor growth curves for each GF mouse from anti–PD-L1 treated R-FMT (blue, n = 2, median tumor volume = 403.7 mm3), NR-FMT (red, n = 3, median tumor volume = 2301 mm3), and control (black, n = 2, median tumor volume = 771.35 mm3) mice. Statistics are as follows: P = 0.20 (R-FMT versus NR-FMT) and P = 0.33 (NR-FMT versus control) by MW test. Dotted black line marks tumor-size cutoff for anti–PD-L1 treatment (500mm3). (J) Quantification of CD8+ density in tumor of R-FMT [n = 2, median = 433.5 cells/high-power field (HPF) across 12 regions], NR-FMT (n = 2, median = 325 cells/HPF across 12 regions), and control mice (n = 2, median = 412 cells/HPF across 9 regions). MW test P = 0.30 (R-FMT versus control). (K) Quantification of CD8+ density in gut R-FMT (n = 2, median = 67 cells/HPF across 7 regions), NR-FMT (n = 2, median = 24 cells/HPF across 5 regions), and control (n = 2, median = 47 cells/HPF across 10 regions). MW test P = 0.17 (R-FMT versus control). *P < 0.05; **P < 0.01; ****P < 0.0001.

Supplementary Materials

  • Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients

    V. Gopalakrishnan, C. N. Spencer, L. Nezi, A. Reuben, M. C. Andrews, T. V. Karpinets, P. A. Prieto, D. Vicente, K. Hoffman, S. C. Wei, A. P. Cogdill, L. Zhao, C. W. Hudgens, D. S. Hutchinson, T. Manzo, M. Petaccia de Macedo, T. Cotechini, T. Kumar, W. S. Chen, S. M. Reddy, R. Szczepaniak Sloane, J. Galloway-Pena, H. Jiang, P. L. Chen, E. J. Shpall, K. Rezvani, A. M. Alousi, R. F. Chemaly, S. Shelburne, L. M. Vence, P. C. Okhuysen, V. B. Jensen, A. G. Swennes, F. McAllister, E. Marcelo Riquelme Sanchez, Y. Zhang, E. Le Chatelier, L. Zitvogel, N. Pons, J. L. Austin-Breneman, L. E. Haydu, E. M. Burton, J. M. Gardner, E. Sirmans, J. Hu, A. J. Lazar, T. Tsujikawa, A. Diab, H. Tawbi, I. C. Glitza, W. J. Hwu, S. P. Patel, S. E. Woodman, R. N. Amaria, M. A. Davies, J. E. Gershenwald, P. Hwu, J. E. Lee, J. Zhang, L. M. Coussens, Z. A. Cooper, P. A. Futreal, C. R. Daniel, N. J. Ajami, J. F. Petrosino, M. T. Tetzlaff P. Sharma, J. P. Allison, R. R. Jenq, J. A. Wargo

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

    Download Supplement
    • Materials and Methods
    • Figs. S1 to S28
    • Tables S1 to S9
    • References

Navigate This Article