The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients

Abstract

Anti-PD-1-based immunotherapy has had a major impact on cancer treatment but has only benefited a subset of patients. Among the variables that could contribute to interpatient heterogeneity is differential composition of the patients' microbiome, which has been shown to affect antitumor immunity and immunotherapy efficacy in preclinical mouse models. We analyzed baseline stool samples from metastatic melanoma patients before immunotherapy treatment, through an integration of 16S ribosomal RNA gene sequencing, metagenomic shotgun sequencing, and quantitative polymerase chain reaction for selected bacteria. A significant association was observed between commensal microbial composition and clinical response. Bacterial species more abundant in responders included Bifidobacterium longum, Collinsella aerofaciens, and Enterococcus faecium. Reconstitution of germ-free mice with fecal material from responding patients could lead to improved tumor control, augmented T cell responses, and greater efficacy of anti-PD-L1 therapy. Our results suggest that the commensal microbiome may have a mechanistic impact on antitumor immunity in human cancer patients.

Fig. 1.. Distinct commensal microbial communities in anti–PD-1 responding patients and nonresponding patients as assessed with 16S rRNA gene amplicon sequencing.

(A) Relative abundance of differentially abundant taxa in responders versus non-responders; 62 OTUs were identified as different with P < 0.05 (unadjusted, permutation test). An additional OTU 559527 (arrow) identified as Bifidobacteriaceae approached significance (P = 0.058). Hierarchical clustering of the samples was performed within each clinical group. Individual samples are organized in columns, labeled with patient identification number. Asterisks indicate samples used in further in vivo experiments. The ID of de novo assembled OTUs (new clean-up reference OTUs picked with QIIME) were abbreviated to show only the individual identifier digits, and the full OTU IDs are provided in table S4. (B) PCA of relative abundance of the 63 OTUs shown in Fig. 1A.

Fig. 2.. Identification of commensal bacterial species associated with patient clinical response to anti–PD-1 therapy.

(A) Spearman’s correlation coefficients between the relative abundances of Bifidobacteriaceae OTU 559527 from the 16S data set and species-level identities suggested by shotgun sequencing. The species profiled with shotgun sequencing were compared with the taxonomy of OTUs generated from 16S sequencing at the family level. (B) Spearman’s correlation between abundance of OTU 559527 from the 16S data set and B. longum identified by means of metagenomics shotgun sequencing analysis (left) and quantitative PCR (right). Shaded band indicates 95% confidence interval (CI) of the values fitted by linear regression. (C) Relative abundance in responders (R) versus nonresponders (NR) of OTU 559527 (16S sequencing; left), B. longum (shotgun sequencing; middle), and B. longum (quantitative PCR; right). LD, limit of detection. (D) Quantitative PCR score representing aggregate data for the relative abundances of 10 species correlated to OTUs with differential abundance in R versus NR. Wilcoxon-Mann-Whitney test (nonparametric) was used to compare quantitative PCR score between R and NR groups. (E) Ratio of beneficial to nonbeneficial OTU numbers for each patient versus the patient’s RECIST aggregate tumor measurement change. Dashed lines label RECIST% = −30 and ratio = 1.5. Only the 43 16S OTUs confirmed with shotgun metagenomic sequencing were included. P < 0.05 was considered statistically significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Fig. 3.. Human commensal communities modulate antitumor immunity in a mouse melanoma model.

GF mice were gavaged with fecal material from three responder (P28, P34, and P09) and three nonresponder (P06, P21, and P11) patient donors. (A) B16. SIY melanoma was injected subcutaneously 2 weeks after gavage; tumor growth data are from one (groups C, D, E, and F) or two experiments (groups A and B) with 7 to 11 mice per group per experiment. Error bars represent mean + SEM. (B) Relative abundance of 207 OTUs from patient donors that colonized in mice and were differentially abundant between slow-and fast-tumor-growth groups. Columns depict individual mice arranged in groups A through F. Groups A1, B1, A2, and B2 are from two independent duplicate experiments. Rows indicate individual OTUs with exact reference ID match between human and mouse 16S rRNA data sets. (C) In groups A and B, ex vivo activation of splenocytes by SIY peptide was measured with IFN-γ ELISPOT 3 weeks after tumor injection. (D and E) Tumor-infiltrating SIY-specific CD8⁺ T cells (D) and FoxP3⁺ regulatory T cells (E) were enumerated with flow cytometry. (F) Efficacy of anti–PD-L1 therapy was determined in groups A and B. Data are from one experiment with 7 or 8 mice per group. (G) Relative abundance in mouse groups A and B of key species validated for quantitative PCR scoring. Six out of the 10 species are shown that gave positive PCR signals. The remaining four species were absent from these particular recipient groups. Tumor growth curves were analyzed with two-way analysis of variance by Tukey’s multiple comparisons post-test; flow cytometry and quantitative PCR data were analyzed by Wilcoxon-Mann-Whitney test (nonparametric). P < 0.05 was considered statistically significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.