Cancer Biol Med 2021. doi: 10.20892/j.issn.2095-3941.2020.0450

ORIGINAL ARTICLE

The commensal consortium of the gut microbiome is associated with favorable responses to anti-programmed death protein 1 (PD-1) therapy in thoracic neoplasms

Huihui Yin1*, Lu Yang2*, Gongxin Peng3, Ke Yang4, Yuling Mi5, Xingsheng Hu2, Xuezhi Hao2, Yuchen Jiao1, Xiaobing Wang1, Yan Wang2 1State Key Laboratory of Molecular Oncology; 2Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; 3Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing 100021, China; 4Department of Medical Oncology, Cancer Hospital of Huanxing Chaoyang District Beijing, Beijing 100122, China; 5Department of Medical Oncology, Chaoyang Sanhuan Cancer Hospital, Beijing 100021, China

ABSTRACT Objective: Immune checkpoint inhibitors have revolutionized cancer therapy for multiple types of solid tumors, but as expected, a large percentage of patients do not show durable responses. Biomarkers that can predict clinical responses to immunotherapies at diagnosis are therefore urgently needed. Herein, we determined the associations between baseline gut commensal microbes and the clinical treatment efficiencies of patients with thoracic neoplasms during anti-programmed death protein 1 (PD-1) therapy. Methods: Forty-two patients with advanced thoracic carcinoma who received anti-PD-1 treatment were enrolled in the study. Baseline and time-serial stool samples were analyzed using 16S ribosomal RNA gene sequencing. Tumor responses, patient progression-free survival, and overall survival were used to measure clinical outcomes. Results: The diversities of the baseline gut microbiota were similar between responders (n = 23) and nonresponders (n = 19). The relative abundances of the Akkermansiaceae, Enterococcaceae, Enterobacteriaceae, Carnobacteriaceae and Clostridiales Family XI bacterial families were significantly higher in the responder group. These 5 bacterial families acted as a commensal consortium and better stratified patients according to clinical responses (P = 0.014). Patients with a higher abundance of commensal microbes had prolonged PFS (P = 0.00016). Using multivariable analysis, the abundance of the commensal consortium was identified as an independent predictor of anti-PD-1 immunotherapy in thoracic neoplasms (hazard ratio: 0.17; 95% confidence interval: 0.05–0.55; P = 0.003). Conclusions: Baseline gut microbiota may have a critical impact on anti-PD-1 treatment in thoracic neoplasms. The abundance of gut commensal microbes at diagnosis might be useful for the early prediction of anti-PD-1 immunotherapy responses. KEYWORDS Gut microbiota; commensal microbes; anti-PD-1 immunotherapy; thoracic neoplasms

Introduction additional hallmark of cancer1. Key immune evasive path- ways, including the CD28/cytotoxic T-lymphocyte antigen 4 axis and the programmed death-ligand 1 (PD-L1)/PD-1 Increasing evidence has suggested that avoiding immune axis, which are known as immune checkpoint inhibitors destruction during the pathogenesis of cancer is an (ICIs), are therefore promising therapeutic targets for drug development2-5. ICIs currently approved by the U.S. Food *These authors contributed equally to this work. and Drug Administration for non-small cell lung cancer Correspondence to: Yan Wang and Xiaobing Wang E-mail: [email protected] and [email protected] (NSCLC) include atezolizumab, nivolumab, and pembroli- 4,6,7 ORCID ID: https://orcid.org/0000-0002-1743-6383 and zumab . Furthermore, the success of immunotherapy https://orcid.org/0000-0002-0907-364X for NSCLC patients has led to similar benefits for patients Received: August 03, 2020; accepted December 09, 2020. with other rare thoracic malignancies, such as thymic epi- Available at www.cancerbiomed.org ©2021 Cancer Biology & Medicine. Creative Commons thelial tumors, mesothelioma, and small cell lung cancer Attribution-NonCommercial 4.0 International License (SCLC)8-10. 2 Yin et al. Commensal microbes associated with immunotherapy

Due to the complexity of the immune system, immunothera- and therapeutic responses in NSCLC, renal cell carcinoma, peutic biomarkers are fundamentally different from targeted and melanoma17,27,28. The aim of the present study was there- therapy biomarkers. PD-L1 expression on cancer cells has fore to provide a clear understanding of the predictive poten- always been a research focus11. In the KEYNOTE-024 trial tial of the gut microbiome prior to ICI therapy, by quantitat- involving patients with treatment-naïve advanced NSCLC with ing the relative percentages of putatively identified “beneficial” high PD-L1 expression on the surface of tumor cells and wild- bacteria. type EGFR and ALK, pembrolizumab significantly improved progression-free survival (PFS), overall survival (OS), and the Materials and methods objective response rate (ORR)12. Unlike first-line treatment, PD-L1 status alone is not sufficient to ensure a response to Patients ICI treatment using second-line therapy. The CheckMate 057 study showed that prolonged OS with nivolumab treatment This retrospective study, from January 2018 to July 2019, was correlated with higher levels of tumor PD-L1 expression, included patients with advanced thoracic carcinoma at the but treatment efficacy was also reported in patients with less National Cancer Center/National Clinical Research Center than 1% PD-L1 expressions13. In addition to tumor PD-L1 for Cancer/Cancer Hospital, Chinese Academy of Medical expression, tumor mutational burden, tumor lymphocyte Sciences and Peking Union Medical College. The enrolled infiltrate, peripheral blood biomarkers, and the gut microbi- patients were diagnosed with stage IV thoracic carcinomas and ota are emerging biomarkers for checkpoint inhibitor-based initially received immune monotherapy. The exclusion crite- immunotherapy14-18. ria were patients receiving antibiotics (ATBs) at the initiation The gut microbiota, which is composed of 1013–1014 of immunotherapy. The flow chart for this study is shown in microorganisms, can be considered an endogenous factor Figure 1. This study was approved by the ethics committee of that continuously­ influences daily life19. Animal models for the National Cancer Center/National Clinical Research Center microbiota studies have shown that it has an important effect for Cancer/Cancer Hospital, Chinese Academy of Medical on host physiology, including on the regulation and remod- Sciences and Peking Union Medical College. eling of immune responses20-22. Multiple studies have shown Baseline characteristics were collected from medical records that gut microbes profoundly influence cancer immunother- (Table 1). In general, treatment efficacy evaluation was per- apy23-26. Fecal DNA sequencing prior to ICI treatment identi- formed by imaging examinations every 2 or 3 cycles. Patients fied a relationship between the gut microbiome compositions receiving single-agent nivolumab were assessed every 3 cycles,

43 patients with thoracic neoplasms Collecting feces

Excluding patient with antibiotics (n = 1) Anti-PD-1 therapy Treatment efficacy evaluated by imaging examinations

2 stool samples at the sencond cycle excluded due to antibiotics 16S ribosomal RNA gene sequencing

Baseline stool samples collected Serial stool samples collected (23 responders and 19 nonresponders) (n = 70)

Combination of clinical information and sequencing data

Figure 1 Flow chart of this study. Cancer Biol Med Vol xx, No x Month 2021 3

Table 1 Baseline characteristics of patients with thoracic neoplasms Characteristics Total (n = 42) NR (n = 19) R (n = 23) P Age 0.769 <65 30 (71.4%) 14 (73.7%) 16 (69.6%) ≥65 12 (28.6%) 5 (26.3%) 7 (30.4%) Gender 1 Female 10 (23.8%) 5 (26.3%) 5 (21.7%) Male 32 (76.2%) 14 (73.7%) 18 (78.3%) ECOG PS 1 0 1 (2.4%) 0 (0.0%) 1 (4.3%) 1 17 (40.5%) 8 (42.1%) 9 (39.1%) 2 24 (57.1%) 11 (57.9%) 13 (56.5%) Smoking status 0.453 Nonsmoker 13 (31.0%) 7 (36.8%) 6 (26.1%) Smoker 29 (69.0%) 12 (63.2%) 17 (73.9%) Histology 0.087 Lung adenocarcinoma 15 (35.7%) 9 (47.4%) 6 (26.1%) Lung squamous carcinoma 23 (54.8%) 7 (36.8%) 16 (69.6%) Other† 4 (9.5%) 3 (15.8%) 1 (4.3%) Mutation status 0.122 EGFR 5 (11.9%) 4 (21.1%) 1 (4.3%) ALK 1 (2.4%) 1 (5.3%) 0 (0.0%) KRAS 1 (2.4%) 0 (0.0%) 1 (4.3%) WT/unknown 35 (83.3%) 14 (73.7%) 21 (91.3%) Metastasis sites 0.125 <2 14 (33.3%) 4 (21.1%) 10 (43.5%) ≥2 28 (66.7%) 15 (78.9%) 13 (56.5%) Number of prior systemic regimens 0.002* <3 30 (71.4%) 9 (47.4%) 21 (91.3%) ≥3 12 (28.6%) 10 (52.6%) 2 (8.7%) Previous systemic therapy 0.808 Platinum-based therapy 36 (85.7%) 16 (84.2%) 20 (87.0%) Other systemic therapy 5 (11.9%) 2 (10.5%) 3 (13.0%) Unknown 1 (2.4%) 1 (5.3%) 0 (0.0%) Prior radiotherapy 0.525 No 15 (35.7%) 8 (42.1%) 7 (30.4%) Yes 27 (64.3%) 11 (57.9%) 16 (69.6%) Usage of ATB 1 No 40 (95.2%) 18 (94.7%) 22 (95.7%) Yes 2 (4.8%) 1 (5.3%) 1 (4.3%)

*P < 0.05 was considered significant. †Other included 1 SCLC, 1 NSCLC, 1 thymic squamous carcinoma, and 1 large cell neuroendocrine carcinoma. ATB: antibiotics. 4 Yin et al. Commensal microbes associated with immunotherapy and patients receiving other anti-PD-1 inhibitors were assessed Sequence analysis every 2 cycles. Therapeutic response was evaluated as complete response (CR), partial response (PR), stable disease (SD), and Sequencing data analysis was performed using QIIME version progressive disease (PD) according to the Response Evaluation 1 and was grouped into operational taxonomic units (OTUs) Criteria in Solid Tumors, version 1.1. Responders (R, n = 23) against the SILVA (version 132) database at 97% similarity29,30. were defined as those with CR, PR, or SD. Nonresponders Rarefaction curve analysis, alpha diversity, and beta diversity (NR, n = 19) were defined as those with PD. The ORR was of the gut microbiota were assessed by Microbiome Analyst defined as the percentage of patients experiencing an objec- packages31. The Chao1, Shannon, and Simpson indices were tive response (CR or PR) as the best response to anti-PD-1 calculated to measure the richness and evenness of OTUs ­therapy, while disease control rate (DCR) was categorized as within a sample31. Beta diversity analysis between samples was the percentage of CR, PR, or SD. PFS was defined as the period assessed using the Bray-Curtis distance and visualized by prin- from the initiation of anti-PD-1 antibody treatment to the cipal coordinate analysis (PCoA) plots31. The cumulative-sum date of disease progression. OS was defined as the time from scaling method was used to determine the microbiome com- anti-PD-1 therapy initiation to death. The last follow-up was position from phylum to genus levels32. Heat map visualiza- April 27, 2020. tion of gut microbiota was performed at the family level, and was clustered using hierarchical clustering with Euclidean Sample collection and DNA extraction distance.

Fresh feces were collected at the pretreatment visit and contin- Statistical analysis uously collected before each cycle of infusion (Supplementary Figure S1). A collection kit including protectant medium was Statistical analyses were conducted with SPSS statistical soft- given to patients. Samples were collected by patients and ware for Windows, version 22.0 (IBM, Armonk, NY, USA) frozen at -80 °C. Total fecal DNA extraction was conducted and Prism 6.0 (GraphPad, San Diego, CA, USA). The Chao1, according to the noncommercial protocol Q recommended Shannon, and Simpson indices were used to compare the by the International Human Microbiome Consortium differences between samples, and a nonparametric Mann- (http://www.human-microbiome.org/). The DNA concen- Whitney test or Kruskal-Wallis test was used to compare dif- tration was determined using a Qubit™ dsDNA HS Assay Kit ferences in the relative abundance of taxa. Receiver operating (Thermo Fisher Scientific, Waltham, MA, USA). DNA ­integrity characteristic (ROC) curves were used to calculate the cut-off was evaluated by 1% agarose gel electrophoresis. Samples with value of 5 bacterial families and the commensal consortium. sterile water served as negative controls. The Fisher test was performed to determine the correlation between the gut microbiota and response rate. Survival curves PCR amplification and Illumina sequencing were estimated using the Kaplan-Meier method (log-rank test). Univariate and multivariate analyses were conducted PCR amplification of the V4 variable region was performed using the Cox regression model. All reported tests were two- using 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and tailed and considered significant at P < 0.05. 806R (5′-GGACTACNVGGGTWTCTAAT-3′) primers. PCR products were purified using AMPure XP beads (Beckman Results Coulter, Brea, CA, USA). Purified amplified fragments were then amplified by 8 cycles of PCR using a KAPA HiFi HotStart Patient characteristics Ready Mix (2×) (Roche, Basel, Switzerland) and Illumina adaptor-specific primers (Illumina, San Diego, CA, USA). A total of 42 patients were enrolled in this study (Table 1). Indexed libraries were then purified with AMPure XP beads Thirty (71.4%) of the 42 patients were younger than (Beckmann Coulter) and sequenced on an Illumina HiSeq 65-years-old at initial diagnosis, 32 (76.2%) of the 42 platform (Illumina). Details on the 16S ribosomal RNA gene patients were male, and 29 patients (69.0%) were smokers. amplicon preparation protocol with Illumina were previously The performance status ranged from 0 to 2, with 57.1% of described (https://www.illumina.com/). patients having a performance status equal to 2 prior to ICI Cancer Biol Med Vol xx, No x Month 2021 5 initiation. Of these patients, 23 (54.8%) had lung squamous index, or Simpson index at the OTU level between the R and carcinomas, 15 had lung adenocarcinomas, 1 had a SCLC, NR groups at baseline (P = 0.545, P = 0.719, and P = 0.628, 1 had a NSCLC, 1 had a thymic squamous carcinoma, and respectively; Figure 2A, 2B, and 2C). We further analyzed the 1 had a large cell neuroendocrine carcinoma. Five patients beta diversity between the two groups, which reflects the sim- had EGFR mutations, 1 had a KRAS mutation, and 1 had an ilarity or difference between sample groups33. The beta diver- ALK fusion. Twenty-eight (66.7%) patients had more than 2 sity results were visualized by PCoA. PCoA based on the OTU metastatic sites at the initiation of immunotherapy. Thirty profile showed no separation between the two groups (Figure (71.4%) patients received systemic therapies of less than 2D). the third-line setting prior to immunotherapy. Thirty-six (85.7%) patients received platinum-based therapies before Differences in the baseline microbiome the initiation of ICIs, 27 (64.3%) patients received radiother- compositions between the two groups apy before ICIs, and 2 (4.8%) patients received ATBs during ICI treatment. A significant difference was found between the Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria R and NR cohorts in terms of the number of prior systemic were the main microbiomes at the phylum level in the cur- regimens (P = 0.002). No ­significant difference was found in rent study (Figure 3A). However, they were not signifi- other patient characteristics between the R and NR cohorts. cantly different between the R and NR groups (Figure 3B). We then compared the different compositions at the family­ Gut microbiome diversity at baseline between level and identified 5 bacterial families that were more responders and nonresponders to anti-PD-1 abundant in the R group than in the NR group (Figure 4A, therapy 4B, and Supplementary Table S1). Akkermansiaceae and Enterococcaceae were enriched in the R group (P = 0.041 Rarefaction curve analysis was performed and tended to reach and P = 0.023, respectively). Akkermansia and Enterococcus a plateau, indicating that the sequencing depth was sufficient are commensal genera in patients with NSCLC who benefit to estimate the total microbial diversity (Supplementary from anti-PD-1 therapy17. The bacterial genus Granulicatella, Figure S2A and S2B). The alpha diversity captures the rich- which represents the majority of the Carnobacteriaceae ness and evenness of the OTU distribution in the commu- family, was significantly associated with superior clini- nity33. The Chao1 index, Shannon index, and Simpson index cal responses (P = 0.039, Supplementary Figure S3 and were then selected for the analysis of alpha diversity. There Table S2). Some studies also showed that the bacterial gen- was no significant difference in the Chao1 index, Shannon era, Granulicatella and Peptoniphilus, as well as the bacterial

A P = 0.545 B P = 0.719 C P = 0.628 D

5.0 0.50 4,000 0.95 4.5 0.25

3,000 4.0 0.90 NR 0.00 R

3.5 Axis.2 [9.4%] 2,000 0.85 –0.25 Alpha-diversity index: chao1 Alpha-diversity index: shannon 3.0 Alpha-diversity index: simpso n –0.50 1,000 0.80 –0.6 –0.3 0.0 0.3 0.6 R R Axis.1 [13.2%] NR NR NR R

Figure 2 Gut microbiota diversity between the responder (R, n = 23) and nonresponder (NR, n = 19) groups at baseline. (A, B, C) Chao1 index, Shannon index, and Simpson index between the R and NR groups at the operational taxonomic unit level. Statistical analysis was per- formed using the Mann-Whitney test. P > 0.05 for all tests. (D) Principal coordinate analysis was based on the Bray-Curtis distance. P > 0.05 was determined by permutational multivariate analysis of variance. 6 Yin et al. Commensal microbes associated with immunotherapy

A Firmicutes Prevalence 0.0 Bacteroidetes 0.1 0.2 Proteobacteria 0.3 0.4 0.5 Phylum Actinobacteria 0.6 0.7 0.8 Verrucomicrobia 0.9 1.0 Not_assigned

0.010 0.016 0.027 0.044 0.073 0.120 0.197 0.325 0.534 0.877 Detection threshold [relative abundance (%)]

B Actinobacteria Bacteroidetes Firmicutes Proteobacteria 0.3 P = 0.853 0.8 P = 0.158 0.8 P = 0.448 0.25 P = 0.136 0.20 0.6 0.6 0.2 0.15 0.4 0.4 0.10 0.1 0.2 0.2 0.05 Relative abundance Relative abundance Relative abundance Relative abundance 0.0 0.0 0.0 0.00 R R R R NR NR NR NR Baseline Baseline Baseline Baseline

Figure 3 Gut microbial percentages at the phylum level in anti-PD-1 responding patients and nonresponding patients. (A) The core phyla microbiome in the current study. Bacterial phyla among samples prevalence above 20% and a relative abundance of greater than 0.01% are presented. (B) Comparison of the 4 main bacterial phyla between responders (R, n = 23) and nonresponders (NR, n = 19). Statistical analyses were performed using the Mann-Whitney test. P > 0.05 for all tests. family, Enterobacteriaceae, are found in the normal gut micro- a higher DCR (P = 0.000; Figure 5C and Supplementary biota and are commensal microbes34-36. Table S3). The median follow-up was 13.44 months [95% confidence interval (CI): 11.62–15.25]. The median PFS Association between baseline commensal for all patients in this study was 2.82 months (95% CI: microbes and PFS 0.41–5.24) and the median OS was 16.00 months (95% CI: 11.84–20.16). Baseline fecal commensal microbes were Five bacterial families enriched in the R group were regarded significantly associated with the patient PFS (P = 0.00016; as a commensal consortium, and the relative abundance of Figure 5D); however, it did not influence the OS (P = 0.84; the commensal consortium stratified patients more accu- Supplementary Figure S5). Patients with a higher commen- rately according to clinical responses (P = 0.014; Figure 5A). sal bacterial abundance had a prolonged PFS. More precisely, Receiver operating characteristic curve analysis was per- the Akkermansiaceae, Enterococcaceae, Enterobacteriaceae, formed, and the cut-off abundances of the 5 bacterial families, Carnobacteriaceae, and Clostridiales Family XI were all as well as the commensal consortium, were calculated. The over-represented at diagnosis in patients with longer PFS cut-off value of the commensal consortium was 0.003, which (Supplementary Figure S6A–6E). The predictive capability stratified patients with high levels of commensal microbes of baseline commensal microbes was evaluated using Cox versus low levels of commensal microbes (Figure 5B). Next, proportional hazards regression. Using univariate analysis, we examined the impact of baseline commensal microbes on the mutation status of patients (P = 0.011), the number of the response rate. None of the patients achieved a complete prior systemic regimens (P = 0.000), and baseline fecal com- response (CR) in the current study. A high level of commen- mensal microbes (P = 0.000) were all significantly associated sal microbes compared with the low level group was not with PFS. Moreover, using multivariate analysis, the base- different in terms of ORR, but significantly associated with line fecal commensal consortium and the number of prior Cancer Biol Med Vol xx, No x Month 2021 7

A Samlpetype 6 Samlpetype Eggerthellaceae NR Coriobacteriales_incertae_sedis 4 R Bifidobacteriaceae Streptococcaceae 2 Akkermansiaceae Synergistaceae 0 Fusobacteriaceae Lactobacillaceae −2 Acidaminococcaceae Desulfovibrionaceae −4 Barnesiellaceae Ambiguous_taxa −6 Not_assigned Moraxellaceae Clostridiales_vadinBB60_group Amily_XIII Defluviitaleaceae Clostridiales family_XI Christensenellaceae Mitochondria Eubacteriaceae TM7_phylum_sp__oral_clone_FR058 Porphyromonadaceae Rikenellaceae Bacteroidaceae Tannerellaceae Carnobacteriaceae Marinifilaceae Pseudomonadaceae Ruminococcaceae Peptostreptococcaceae Prevotellaceae Neisseriaceae Lachnospiraceae Clostridiaceae_1 Enterococcaceae Enterobacteriaceae Leuconostocaceae Erysipelotrichaceae Campylobacteraceae Burkholderiaceae Pasteurellaceae Veillonellaceae Aerococcaceae Actinomycetaceae Coriobacteriaceae Propionibacteriaceae Corynebacteriaceae Micrococcaceae Atopobiaceae Saccharimonadaceae WY04SD2V4 WY09SD1V4 WY109SD1V4 WY125SD1V4 WY131SD1V4 WY147SD1V4 WY150SD1V4 WY157SD1V4 WY22SD1V4 WY29SD1V4 WY35SD1V4 WY45SD1V4 WY49SD1V4 WY59SD1V4 WY64SD1V4 WY75SD1V4 WY78SD1V4 WY85SD1V4 WY86SD1V4 WY07SD1V4 WY08SD1V4 WY103SD1V4 WY10SD1V4 WY17SD1V4 WY20SD1V4 WY39SD1V4 WY41SD1V4 WY46SD1V4 WY47SD1V4 WY53SD1V4 WY56SD1V4 WY62SD1V4 WY70SD1V4 WY77SD1V4 WY92SD1V4 WY93SD1V4 WY127SD1V4 WY141SD1V4 WY148SD1V4 WY104SD1V4 WY139SD1V4 WY105SD1V4

B Relative abundance

NR Enterobacteriaceae * R Akkermansiaceae *

Clostridiales family XI *

Carnobacteriaceae *

Enterococcaceae *

0.02 0.04 0.06 0.08 0.0000 0.0002 0.0004 0.0006

Figure 4 Comparison of the gut microbiota at the family level between responders (R, n = 23) and nonresponders (NR, n = 19). (A) Heat map visualization of gut microbiota at the family level prior to anti-PD-1 treatment. Columns represent individual patients clustered using hierarchical clustering with Euclidean distances. (B) Five bacterial families had significantly different abundances at baseline. Statistical analyses were performed using the Mann-Whitney test. *P < 0.05. 8 Yin et al. Commensal microbes associated with immunotherapy

A B ROC curve 1.0 Baseline commensal microbiome

*P = 0.014 0.8 0.4 NR 0.6 0.3 R

0.2 0.4

0.1 True positive rate 0.2 Relative abundance 0.0 AUC = 0.721 –0.1 0.0 R 0.0 0.2 0.4 0.6 0.8 1.0 NR Baseline Flase positive rate

C Disease control rate D 1.00 *P = 0.000 +

100 + 0.75 + log−rank test P = 0.00016, PD +++ 30.3% +++ HR, 0.21 (95% CI, 0.08 to 0.52) 80 SD +++ PR 0.50 ++ 60 CR 100% 40 60.6% 0.25 Patients (% ) P = 0.00016 ++

20 + Low + High 9.1% 0.00 0 Progression−free survival (probability) 02468101214 Progression−free survival (month) (n = 9) (n = 33) Number at risk Low 91000000 Low-level High 33 18 9 42210 High-level

Figure 5 The association between the baseline commensal microbiome and patient clinical responses to anti–PD-1 therapy. (A) Comparison of the baseline commensal microbiome in responders versus nonresponders. Statistical analysis was performed using the Mann-Whitney test. *P < 0.05. (B) Receiver operating characteristic (ROC) curve analysis. The performance of the classification was evaluated using ROC curves. (C) Disease control rate in patients with high versus low levels of the baseline commensal microbiota. P values were calculated using the Fisher test. (D) Kaplan-Meier plot of progression-free survival with log-rank tests in patients with high versus low commensal microbiota levels.

systemic regimens were still significant factors affecting PFS Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria, (P = 0.003 and P = 0.006, respectively; Table 2). remained stable from baseline to different time points (Kruskal- Wallis test; P > 0.05; Figure 6A). As shown in Figure 6B, the The gut microbiota composition is similar bacterial percentages at the family level were not significantly during anti-PD-1 treatment. altered during the treatment (Kruskal-Wallis test; P > 0.05). Moreover, the percentages of 5 bacterial families abundant in To better characterize the abundance of gut commensal the R group at baseline, as well as the commensal microbiome, bacteria, time-serial fecal samples were collected during slightly fluctuated over the course of anti-PD-1 treatment anti-PD-1 therapy. Diversities calculated by both the Shannon (Kruskal-Wallis test; P > 0.05; Figure 6C). Combined analyses and Simpson indices were not altered by anti-PD-1 ther- of the data showed that the gut microbiome composition was apy (P = 0.434 and P = 0.807, respectively; Supplementary not significantly modified by anti-PD-1 therapy in patients Figure S4A and S4B). Four main bacterial phyla, namely, with advanced thoracic carcinoma. Cancer Biol Med Vol xx, No x Month 2021 9

Table 2 Univariate and multivariate analyses for progression-free survival in the cohort Covariates Comparisons Univariate analysis P Multivariate analysis Median PFS in months (95% CI) P HR (95% CI) Age ≥65 vs. <65 5.13 (2.02–8.23) vs. 1.41 (0.03–2.80) 0.345

Gender Male vs. female 4.11 (0.47–7.45) vs. 1.81 (0.20–3.41) 0.626

Histology Squamous vs. adenocarcinoma 5.13 (2.19–8.07) vs. 1.81 (1.01–2.60) 0.119

Others vs. adenocarcinoma 1.28 (0.67–1.89) vs. 1.81 (1.01–2.60) 0.861

Smoking status Smoker vs. non-smoker 2.83 (0.00–6.05) vs. 2.73 (0.75–4.71) 0.994

ECOG PS 0 vs. 2 5.49 (NA) vs. 2.73 (1.10–4.36) 0.547

1 vs. 2 4.11 (0.36–7.85) vs. 2.73 (1.10–4.36) 0.476

Mutation status EGFR/ ALK vs. others 1.35 (0.12–2.57) vs. 5.13 (1.64–8.61) 0.011* 0.701 0.74 (0.16–3.49)

Metastasis sites <2 vs. ≥2 5.49 (0.00–11.31) vs. 2.00 (0.71–3.30) 0.199

Number of prior <3 vs. ≥3 5.49 (3.64–7.34) vs. 1.28 (1.25–1.32) 0.000* 0.006* 0.15 (0.04–0.58) systemic regimens

Previous systemic Other systemic therapy vs. 6.83 (NA) vs. 2.83 (0.20–5.45) 0.987 therapy platinum-based therapy

Prior radiotherapy Yes vs. no 5.13 (1.63–8.62) vs. 1.38 (1.26–1.50) 0.276

Usage of ATB Yes vs. no 0.69 (NA) vs. 2.83 (0.40–5.25) 0.671

Commensal microbiome High vs. low 5.13 (1.52–8.73) vs. 1.28 (0.85–1.71) 0.000* 0.003* 0.17 (0.05–0.55)

*P < 0.05 was considered significant.

Discussion families were also associated with favorable responses to anti-PD-1 treatment. Although the immune system of the This study showed that compositional differences in the base- host is activated by infection, Enterobacteriaceae is required line gut microbiome were associated with anti-PD-1 ther- by intestinal epithelial cells (IECs) to clear the patho- apeutic responses in patients with thoracic neoplasms. The gen36. In mouse models of Citrobacter rodentium infection, relative abundances of 5 bacterial families (Akkermansiaceae, inflammation changes the metabolic landscape of IECs and Enterococcaceae, Carnobacteriaceae, Enterobacteriaceae, and leads to the colonization of anaerobes37. The growth of the Clostridiales Family XI) were significantly different between Enterobacteriaceae family switches the metabolism and oxy- the R and NR groups, and they were all over-represented in the gen availability of IECs, which might cooperate with the R group. The high level of the commensal microbiome group innate immune response of the host38. In the present study, also exhibited an increased DCR and longer PFS. In addition, the main genus detected in the Carnobacteriaceae family was the relative abundance of this baseline commensal consortium Granulicatella. Until now, there has been no report of the was an independent risk factor for checkpoint blockade ther- beneficial roles of gut Granulicatella in patients with tho- apy responses. racic neoplasms during anti-PD-1 therapy. A previous study Routy et al.17 reported that Akkermansia and Enterococcus of HIV-infected individuals showed that Granulicatella was a were commensal bacterial genera, and were significantly asso- commensal microbe in the respiratory tract39. Katagiri et al.40 ciated with clinical responses in NSCLC. They also reported showed that the Carnobacteriaceae family and Granulicatella that Akkermansia was enriched in patients with a longer genus, which were over-represented in the gut microbiome PFS17. Our results confirmed these conclusions at the family after rehabilitation for dysphagia, affected the systemic health level. Moreover, our study showed that the Enterobacteriaceae, of stroke survivors. Moreover, in the current study, the rich- Carnobacteriaceae, and Clostridiales Family XI bacterial ness of the Carnobacteriaceae bacterial family was relatively 10 Yin et al. Commensal microbes associated with immunotherapy

A Actinobacteria Bacteroidetes Firmicutes Proteobacteria Verrucomicrobia Others

Baseline C1 C2 C3 C4 C6 C8 B

C8

C6

C4

C3

C2

C1

Baseline

0.00 0.25 0.50 0.75 1.00 Relative abundance

Bacteroidaceae Others Veillonellaceae Not_assigned Family Ruminococcaceae Bifidobacteriaceae Acidaminococcaceae Prevotellaceae Lachnospiraceae Enterobacteriaceae Tannerellaceae C

Akkermansiaceae Carnobacteriaceae Clostridiales family XI 0.4 P = 0.600 0.003 P = 0.200 0.006 P = 0.305 0.3 0.002 0.004 0.2 0.001 0.002 0.1 0.000 0.000 Relative abundance Relative abundance 0.0 Relative abundance

–0.1 –0.001 –0.002 C1 C2 C3 C4 C6 C8 C1 C2 C3 C4 C6 C8 C1 C2 C3 C4 C6 C8

Baseline Baseline Baseline

Enterobacteriaceae Enterococcaceae Commensal microbiome 0.6 P = 0.769 0.020 P = 0.173 0.6 P = 0.878

0.4 0.015 0.4 0.010 0.2 0.2 0.005 0.0 0.0 Relative abundance 0.000 Relative abundance Relative abundance

–0.2 –0.005 –0.2 e C1 C2 C3 C4 C6 C8 C1 C2 C3 C4 C6 C8 C1 C2 C3 C4 C6 C8

Baseline Baseline Baselin

Figure 6 Gut microbiota profiles during anti-PD-1 treatment. (A) Main bacterial phylum percentages over the course of anti-PD-1 treatment. (B) Main bacterial family percentages over the course of anti-PD-1 treatment. (C) The percentages of commensal bacterial families over the course of anti-PD-1 treatment. Statistical analysis was performed using the Kruskal-Wallis test. P > 0.05 for all tests. Cancer Biol Med Vol xx, No x Month 2021 11 increased in the R group, indicating that Carnobacteriaceae bacterial families that were abundant in the responders might in the gut benefited anti-PD-1 therapeutic responses. The act as a commensal consortium that more accurately stratified Clostridiales family XI family was also significantly enriched patients according to clinical responses. in the R group. Many members of the Clostridiales family XI, There were some limitations in our study. First, the number including Peptoniphilus, have been shown to utilize peptone of patients enrolled in the present study was relatively small, as the major metabolic product41. This genus colonizes the and the findings require validation in another larger, inde- normal gut and upper respiratory tract of humans35. Many pendent cohort. In addition, due to the limited resolution of species of this genus can act as opportunistic pathogens in 16S sequencing, long-read sequencing or shotgun sequencing immunocompromised patients and are associated with pol- are needed to distinguish between related bacterial families. ymicrobial infections42-44. Because this genus contains not Moreover, owing to the issues mentioned above, we failed to only pathogenic species but also probiotic species, future detect bacteria with a negative impact on ICI therapy at base- studies will be needed to verify the relationship between line, which might also be important in clinical responses27. anti-PD-1 therapy and this bacterial genus. Among clinical characteristics, the present study showed Conclusions that the number of prior systemic regimens was associated with responses to anti-PD-1 therapy. Using multivariate anal- Our findings provided an example of gut-resident commen- yses, patients receiving third-line or the above systemic treat- sal microbiota that were associated with a favorable response ments prior to ICIs had a shorter PFS. The result was mainly to anti-PD-1 therapy in patients with thoracic neoplasms. due to worse performance status and a relatively larger tumor Importantly, when a high level of the commensal consortium burden in the heavily treated patients. The number of prior was present in the pretreatment feces, patients had a better regimens was also associated with decreased OS in another prognosis. cohort of patients with NSCLC receiving ICI therapy45. Previous studies have shown that gut microbiota are not mod- Acknowledgements ified by ICI treatment in patients with NSCLC and metastatic melanoma28,46. Our study also found that the gut microbiota This work was supported by a National Natural Science composition, especially commensal bacteria, remained stable Foundation Fund (Grant Nos. 81472559 and 81772490), in serial stool samples. This suggested that a higher level of the the National Key R&D Program of China (Grant Nos. commensal microbiome at baseline provided long-term bene- 2020YFC2002705 and 2018YFC0115204), and the Chinese fits for patients receiving ICI therapy. Academy of Medical Sciences (CAMS) Innovation Fund for A diverse array of commensal bacteria that reside in the Medical Sciences (CIFMS) (Grant Nos. 2016-I2M-1-001, gastrointestinal tract are important entry sites against inva- 2017-I2M-3-004, 2019-I2M-2-003, and 2019-I2M-1-003), sion from pathogens, and the intestinal immune system is and funding from CSCO-Hengrui Research Funding (Grant devoted to protecting against infections and other diseases47,48. No. Y-HR2018-239). Evidence has emerged that there is a strong correlation between commensal bacteria and clinical responses to immunotherapy. Conflict of interest statement Matson et al.27 showed that the ratio of beneficial bacteria to “non-beneficial” bacteria could be a predictor of clinical No potential conflicts of interest are disclosed. responses to checkpoint blockade therapy in patients with metastatic melanoma. In mouse models of melanoma, the oral References administration of commensal Bifidobacterium improved den- dritic cell function and tumor-specific CD8+ T cell responses, 1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next promoting anti-tumor immunity involving checkpoint block- generation. Cell. 2011; 144: 646-74. 2. Velcheti V, Schalper K. Basic Overview of current immunotherapy ade49. Furthermore, 11 low abundance commensal strains of approaches in cancer. Am Soc Clin Oncol Educ Book. 2016; 35: human gut microbiota induced the accumulation of interfer- 298-308. on-γ-producing CD8 T cells and simultaneously enhanced 3. Sharma P, Allison JP. The future of immune checkpoint therapy. ICI therapy50. 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Supplementary materials

PATIENT ID BASELINE C1 C2 C3 C4 C6 C8 WY04 WY07 WY08 WY09 WY10 WY17 WY20 WY22 WY29 WY35 WY39 WY41 WY45 WY46 WY47 WY49 WY53 WY56 WY59 WY62 WY64 WY70 WY75 WY77 WY78 WY85 WY86 WY92 WY93 WY103 WY104 WY105 WY109 WY125 WY127 WY131 WY139 WY141 WY147 WY148 WY150 WY157

Figure S1 A summary of each patient’s sample time points. Cancer Biol Med Vol xx, No x Month 2021 15

A NR B R WY41SD1V420SD1V4 WY62SD2V4 WY131SD2VWY78SD1V44 WY10SD5V439SD1V441SD2V4 WY150SD1V4 WY77SD1V492SD1V439SD5V4 WY39SD2V4 WY131SD1VWY45SD2V44 WY148SD1V4 WY59SD2VWY09SD3V4 WY45SD1VWY29SD1V4 WY1010SD1V4135S9SD1V4 WY85SD1VWY150SD2V44 WY141SD1V4 WY29SD2VWY109SD1V44 WY39SD3V4 WY77SD2V41017SD3V492SD2V45SD2V4 1,000 WY22SD1V4 1,000 WY10SD3V447SD1V4 WY157SD1VWY09SD1VWY09SD2V44 WY07SD4V492SD3V477SD3V4 WY17SD2V493SD1V410SD4V4 WY22SD2VWY86SD1V4 WY39SD4V4 WY109SD2VWY04SD3V44 WY62SD1V410SD2V4 Samlpetype WY1017SD1V44SD1V4 WY04SD2V4 WY46SD5V493SD2V4 WY59SD1VWY75SD1V4 a NR WY47SD3V407SD3V4 WY147SD1V4 a R WY125SD2VWY09SD4V44 WY103SD1V4 WY35SD1VWY49SD1V4 WY07SD2V4 WY64SD1V4 WY46SD1V4103SD2V4 Richnes s Richnes s WY1246SD2V447SD4V47SD1V4 WY56SD3V446SD4V456SD2V4 WY64SD2VWY75SD3VWY125SD1V44 WY46SD3V4 WY07SD1V4 500 500 WY53SD2V456SD1V470SD1V4

WY53SD1V4 WY08SD1V4

0

250,000 500,000 750,000 250,000 500,000 750,000 Sample size Sample size

Figure S2 Rarefaction curves of fecal samples using 16S ribosomal RNA gene sequencing from the nonresponder (A) and responder groups (B). 16 Yin et al. Commensal microbes associated with immunotherapy

A B WY07 WY08 Family levelGenus level WY10 Carnobacteriaceae Granulicatella WY17 WY20 Enterococcaceae Enterococcus WY39 Akkermansiaceae Akkermansia WY41 WY46 Escherichia_shigella WY47 Enterobacteriaceae Plesiomonas WY53 WY56 Klebsiella R WY62 WY70 Peptoniphilus WY77 Finegoldia WY92 Clostridiales family XI WY93 Parvimonas WY103 Ezakiella WY104 WY105 WY127 WY139 WY141 WY148 C Granulicatella WY04 WY09 WY22 *P = 0.039 WY29 0.003 WY35 NR WY45 R

WY49 e 0.002 WY59 WY64 0.001 WY75 NR WY78 WY85 Relative abundanc 0.000 WY86 WY109 WY125 –0.001 WY131 R WY147 NR WY150 Baseline WY157 EzakiellaParvimonasFinegoldiaPeptoniphiluKlebsiellaPlesios Escherichia_shigellamonaAkkermansias EnterococcuGranulicatella 0

s

1

Figure S3 Comparison of 5 bacterial families with different abundances at the genus level. (A) Heat map of the dominant gut microbial genera belonging to the 5 bacterial families. (B) Description of the commensal microbiome summarized at the family and genus levels. (C) Comparison of the Granulicatella bacterial genus between responders (n = 23) and nonresponders (n = 19). Statistical analysis was performed using the Mann-Whitney test. *P < 0.05. Cancer Biol Med Vol xx, No x Month 2021 17

A B 5.0

0.95 4.5 CLASS Baseline C1 C2 4.0 0.90 C3 C4 C6 3.5 C8 0.85 3.0 P = 0.434 P = 0.807 Alpha-diversity index: simpso n Alpha-diversity index: shannon 0.80

C1 C2 C3 C4 C6 C8 C1 C2 C3 C4 C6 C8

Baseline Baseline

Figure S4 The diversity of the gut microbiome during anti-PD-1 treatment. Dynamics of the Shannon index (A) and Simpson index (B) in longitudinal stool samples. Statistical analysis was performed using the Kruskal-Wallis test. P > 0.05 for all tests.

Kaplan−Meier curve for OS between low−level and high−level commensal microbiome

1.00 +

log−rank test P = 0.84, + 0.75 HR, 0.89 (95% CI, 0.29 to 2.73) +++ +++++++ + ++++

0.50

0.25 Overall survival (probability) P = 0.84 + Low + High 0.00

0246810 12 14 Overall survival (month) Number at risk Low 99886542 High 33 31 27 26 25 14 12 8

Figure S5 Kaplan-Meier plot of overall survival with log-rank tests in patients treated with immune checkpoint inhibitors, stratified by high versus low levels of the baseline commensal microbes. 18 Yin et al. Commensal microbes associated with immunotherapy

Kaplan−Meier curve for PFS between low−level Kaplan−Meier curve for PFS between low−level and A B high−level Enterococcaceae and high−level Akkermansiaceae 1.00 1.00 + + ++

0.75 0.75 log−rank test P = 0.0028, ++++ log−rank test P = 0.029, +++ +++ HR, 0.43 (95% CI, 0.19 to 0.95) +++ HR, 0.30 (95% CI, 0.12 to 0.72) ++ + ++ + 0.50 0.50

0.25 0.25 ++ ++ + P = 0.0028 + P = 0.029 + Low + High Low 0.00 0.00 High Progression−free survival (probability) Progression−free survival (probability) 02468101214 0246810 12 14 Progression−free survival (month) Progression−free survival (month) Number at risk Number at risk Low 10 2000000 Low 16 4311111 High 32 17 9 43220 High 26 15 742210

C Kaplan−Meier curve for PFS between low−level and D Kaplan−Meier curve for PFS between low−level and high−level Enterobacteriaceae high−level Carnobacteriaceae 1.00 1.00 + + + + +++ + log−rank test P = 0.048, 0.75 +++ log−rank test P = 0.009, 0.75 ++ HR, 0.36 (95% CI, 0.16 to 0.81) + HR, 0.45 (95% CI, 0.20 to 1.02) +++ ++ + 0.50 + 0.50 +

+++ +

++ 0.25 0.25 + + ++ P = 0.009 + Low P = 0.048 Low + High High 0.00 Progression−free survival (probability) 0.00 Progression−free survival (probability) 02468101214 02468101214 Progression−free survival (month) Progression−free survival (month) Number at risk Number at risk Low 12 3 210000 Low 21 6211111

High 30 16 843321 High 21 13 842210

E Kaplan−Meier curve for PFS between low−level and high−level Clostridiales Family XI 1.00

+ + 0.75 +++ log−rank test P = 0.027, ++ HR, 0.40 (95% CI, 0.17 to 0.93) ++ + 0.50 +++ + ++ 0.25 + Low + High P = 0.027

Progression−free survival (probability) 0.00 0246810 12 14 Progression−free survival (month) Number at risk Low 25 10 311111

High 17 9742210

Figure S6 Kaplan-Meier plot of progression-free survival using log-rank tests in patients with high versus low levels of Akkermansiaceae (A), Enterococcaceae (B), Enterobacteriaceae (C), Carnobacteriaceae (D), and Clostridiales Family XI (E).