Immune checkpoint inhibitors (ICIs) such as anti-PD-1 and anti-CTLA-4 antibodies demonstrate clinical benefit in patients with various solid malignancies, including renal cell carcinoma (RCC), non-small cell lung cancer (NSCLC) and melanoma [1–4]. The efficacy of ICIs has been most strongly established in irresectable cutaneous melanoma, with 1-year overall survival (OS) increasing from ~40% before the era of ICI therapy, to up to 72% now . However, up to 40% of patients with cutaneous melanoma do not respond to ICI therapy and approximately 25% of patients suffer from serious adverse events. A better understanding of the underlying mechanisms that determine the effectiveness of ICIs will help in selecting the right treatment for the right patient and to optimize therapy outcome. Additionally, there is a high grade of so called ‘financial toxicity’ for ICI therapy . Better selection of patients upfront might helpt to decrease overall costs.
Pre-clinical studies indicated a causal relationship between gut microbiome composition and response to ICI [7–9]. A xenograft model has shown that mice receiving a faecal microbiome transplantation (FMT) from patients responding to ICI therapy showed a lower rate of tumour growth after implantation of melanoma cells compared to mice that received a FMT from non-responding patients . In humans, clinical studies have shown different species, such as Bifidobacterium adolescentis, Holdemania filiformis, Faecalibacterium spp. and taxa belonging to the Ruminococcaceae family, to be more abundant in patients with metastatic melanoma responding to ICIs compared to non-responding patients [9–11]. In patients with NSCLC or RCC, higher abundance of species including Veillonella parvula and Akkermansia muciniphila in the gut was observed in responders to anti-PD-1 compared to non-responders and patients with prolonged survival [12,13]. Taxa more abundant in non-responding patients include taxa belonging to Actinomycetales and Lactobacillaceae .
A complementary method of interrogating the gut microbiome is by evaluation of the functional pathways predicted from microbial genomes . Abundance of functional pathways was shown to be relatively stable among healthy individuals, despite inter-individual variation of the gut taxonomical composition . Therefore, interrogation of these microbial pathways gives additional insight in the functionality of the gut microbiome. Although most studies report taxonomical changes alone, one study has reported that patients with metastatic melanoma that respond to ICI therapy have higher abundance of anabolic microbial pathways compared to non-responders .
Due to the lack of a gold-standard, different sample collection methods have been applied. Furthermore, factors that were shown to significantly affect the gut microbiome composition, such as sex, age, BMI, antibiotic (AB) use and proton pump inhibitor (PPI) use are generally not considered [16,17]. In addition, the use of different sequencing technologies impacts the resolution in which a microbial community can be studied . These factors, in addition to differences in statistical methods and small samples sizes hamper direct comparison of study results.
Here, we used a strictly protocolized collection procedure to obtain high-quality fresh frozen stool samples from patients with metastatic melanoma before start of ICI therapy. We collected data on patient factors that influence response to ICIs as well as factors that are known to influence gut microbiome composition. We performed metagenomic shotgun sequencing (MSS) to obtain a high-resolution profile of the gut microbiome. We then assessed the difference in microbial diversity, prevalence and abundance of taxa and of microbial pathways between responders and non-responders to ICI therapy. Finally, we performed an exploratory analysis using a zero-inflated, multivariate model to identify taxa potentially associated with response or non-response, and assessed the association of carriership of significantly associated taxa with progression-free survival (PFS) and OS.
Patients and methods
Patients over the age of 18 with a pathologically confirmed diagnosis of irresectable stage IIIc or stage IV cutaneous melanoma, eligible for systemic ICI therapy (anti-PD-1, anti-CTLA-4 or combination) were approached for study participation. All patients signed informed consent. This study was approved by the UMCG Medical Ethics Committee (registration number 2012/085) and reported to ClinicalTrials.gov (Identifier: NCT02600143).
Clinical and laboratory evaluation
Baseline characteristics such as sex, age, BMI, tumour M-stage and previous anti-cancer therapies were collected. Routine blood biochemistry, including serum lactate dehydrogenase (LDH)-levels, was performed at baseline. Evaluation of clinical symptoms and routine blood biochemistry was performed at the outpatient clinic every 2 or 3 weeks during treatment with the ICI. A list of current medical prescriptions was recorded before the start of therapy and updated during subsequent visits.
Baseline radiological evaluation, consisting of a computed tomography (CT) scan of the thorax, abdomen and pelvis and MRI of the brain, was performed before start of therapy. Follow-up radiological evaluation was performed every 10–14 weeks as long as the patient received systemic therapy. Additional CT- or MRI scans were performed in case of suspicion of progression. If first radiological evaluation after start of therapy was inconclusive, a confirmatory scan was performed 4 weeks later.
Response was assessed according to response evaluation criteria in solid tumours (RECIST) v1.1. Patients with a confirmed response, defined as a complete response, partial response (PR) or stable disease (SD) according to RECIST 12 weeks after start of therapy that was ongoing at the next radiological evaluation, were labelled ‘responder’. In order to include late responders in our analysis, patients with progressive disease (PD) on the first radiological evaluation but a response at the second radiological evaluation compared to baseline were also labelled ‘responder’. Patients with PD on the first radiological evaluation that was confirmed on the next follow-up scan, or patients with PD on the first radiological evaluation that were unable to complete a confirmation scan due to clinical progression were labelled ‘non-responder’.
Stool sample collection and DNA extraction
Patients received oral and written instructions regarding the stool collection procedure. Patients were instructed to collect 1–2 ml of faeces using a collection kit that could be used at home and to store the sample in the home freezer directly after collection. Patients transported samples to the hospital in a frozen, insulated cooling bag to prevent thawing. After arrival in the hospital the samples were directly stored at −80°C until DNA extraction. Microbial DNA was isolated using the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) as previously described .
Sequencing and microbiome profiling
MSS was performed for all samples using the Illumina HISeq platform at the Broad Institute, Cambridge, MA. Raw data was processed consistently with data analysis of 1000IBD and Lifelines-DEEP cohorts as previously described [16,20,21]. In brief, KneadData tools (v0.5.1) were used to process paired-end metagenomic reads in fastq format by trimming reads to PHRED quality 30, and to remove Illumina adapters . Reads that aligned to human genome were removed from metagenomes (GRCh37/hg19) using KneadData integrated Bowtie2 (v184.108.40.206), and quality of processed samples was examined using FastQC toolkit (v0.11.7) to confirm that sequencing and quality control were successful (all samples were found to confer to ‘good quality’ FastQC profile) [23,24]. Taxonomical profiling of metagenomes was performed using MetaPhlAn2 tool (v2.7.2), and relative abundances of genes encoding microbial biochemical pathways were calculated using HUMAnN2 pipeline (v0.11.1) integrated with DIAMOND alignment tool (v0.8.22), uniref90 protein database (v0.1.1) and ChocoPhlAn pangenome database (v0.1.1) [25–28]. Analyses were performed using locally installed tools and databases on CentOS (release 6.9).
Assessment of overall gut microbiome composition and differences in prevalence between responders and non-responders
To assess the alpha-diversity, the Shannon-index was calculated for each faecal sample. The difference in Shannon index between responders and non-responders was assessed using the Kruskal–Wallis rank-sum test. To evaluate the difference in prevalence of taxa between responders and non-responders, abundance data was converted to either one (i.e. abundance > 0, taxa present) or 0 (i.e. abundance = 0, taxa not present). Next, we performed a Fisher’s exact test and logistic regression analysis to assess the difference in prevalence between responders and non-responders for each taxa individually. Correction for multiple testing was performed using the Holm method. Statistical cut-off for nominal and corrected P values was set at P < 0.05.
Assessment of relative microbial abundance between responders and non-responders
First, we performed an univariate linear regression analysis to assess the difference in relative abundance of taxa between responders and non-responders. Next, to correct for important confounding factors, we performed a multivariate linear regression analysis with covariates age, sex, BMI, M-stage (AJCC version 8), LDH-level (>250 U/L vs. <250 U/L), previous anti-melanoma therapy, systemic therapy (anti-PD1 or anti-PD-1/anti-CTLA-4 combination), AB use (yes/no), PPI use (yes/no) and colitis during ICI therapy (yes/no, any grade). In order to account for the high number of zero-valued observations in the dataset, we utilized a zero-inflated model. All linear models were created utilizing the ‘Multivariate Association with Linear Models’ (MaAsLin package for R) . False-discovery rate (FDR) corrected P values were calculated using the Benjamini–Hochberg method. Statistical cut-off was set at FDR <0.05.
Association with overall and progression-free survival
To evaluate the possibility of an association between taxa carriership and OS or PFS, we performed an univariate Cox-Regression analysis for all taxa differentially abundant between responders and non-responders (FDR < 0.05). Next, we performed a multivariate Cox regression analysis for those taxa significantly associated in the univariate analysis, including relevant variables (those associated with OS or PFS according to univariate Cox regression analysis) as covariates. Statistical cut-off for P values was set at 0.05. For taxa associated with OS or PFS, Kaplan–Meier curves were plotted. All statistical analyses were performed with R studio (v.1.0.143).
Baseline patient characteristics and response assessment
Pre-treatment stool samples were obtained from 25 patients (Table 1). We observed no significant differences in baseline characteristics between responders vs. non-responders. Twenty-three patients were treated with anti-PD-1 monotherapy and two with anti-PD-1/anti-CTLA-4 combination therapy.
Radiological evaluation consisting of a CT-scan of the thorax, abdomen and pelvis and MRI of the brain was performed before start of therapy and every 10–14 weeks thereafter for as long as therapy continued. Ten out of 25 patients showed a confirmed response and were labelled ‘responder’ (Table S1, Supplemental digital content 1, http://links.lww.com/MR/A205). In addition, two patients with PD on the first scan showed PR on their confirmatory scan and were also labelled ‘responder’. Three patients showed PD on the first scan that was confirmed on a follow-up scan. These patients were labelled ‘non-responder’. Seven patients had PD on the first scan and did not complete a follow-up scan due to disease progression. These patients were also labelled ‘non-responder’. One patient showed rapid clinical and biochemical progression without evidence for treatment-related toxicity. This patient was unable to complete the first scan and was labelled ‘non-responder’. Finally, two patients showed SD on the first scan. One patient showed PD on the confirmatory scan. The other patient was unable to complete the confirmatory scan due to clinical and biochemical deterioration without evidence of toxicity, followed by the patient’s death 11 weeks later. Both patients were labelled ‘non-responder’. In total, 12 out of 25 patients were identified as ‘responder’ and 13 patients as ‘non-responder’.
Microbial abundance and microbial pathway abundance in pre-treatment samples
A total of 790 taxa were identified. Taxa present in less than 25% of samples were excluded. Additionally, to include taxa only present in either responders or non-responders, taxa present in at least 50% of responders or non-responders were included, resulting in 192 bacterial taxa included in this analysis (Fig. 1a).
For the microbial pathways, we applied the same selection criteria described above, resulting in 260 microbial pathways available for testing.
Overall gut microbiome composition
At the species level, there was an even distribution of most taxa (Fig. 1c). We observed no taxa exclusively abundant in responders or non-responders at the species level. No significant difference in alpha-diversity (Shannon diversity index) was observed between responders and non-responders (median responders: 2.8, non-responders: 2.7, Kruskal–Wallis P = 0.46; Fig. 2).
The predominant phyla in both responders and non-responders were Firmicutes (mean relative abundance of 64% in responders and 61% in non-responders), Actinobacteria (mean relative abundance of 18% in responders and 24% in non-responders) and Bacteroidetes (mean relative abundance of 10% in responders and 10% in non-responders) (Fig. 1b). Taxa belonging to Verrucomicrobia were observed in 12 patients, with a mean relative abundance of 0.3% in responders and 0.5% in non-responders.
Differences in taxa prevalence between responders and non-responders
We observed a significant difference (Fisher’s exact P < 0.05) in the prevalence of Bacteroides massiliensis, Haemophilus parainfluenzae (association also observed at the genus, family and order level), an unclassified Peptostreptococcaceae species, L. bacterium 8 1 57FAA, Parabacteroides distasonis and Gordonibacter pamelaeae (association also observed at the genus level; Table 2). Univariate logistic regression analysis for prevalence of taxa showed a significant association (P < 0.05) for H. parainfluenzae (association also observed at the genus, family and order level), Peptostreptococcaceae (unclassified species), L. bacterium 8 1 57FAA, P. distasonis and G. pamelaeae (association also observed at the genus level; Table 3). Again, significance was lost when correcting for multiple testing.
Results for all taxa are listed in Table S2 (Supplemental digital content 2, http://links.lww.com/MR/A206) and Table S3 (Supplemental digital content 3, http://links.lww.com/MR/A207).
Differences in taxa abundance between responders and non-responders
When performing an univariate linear regression analysis, we observed a significant association with response for B. massiliensis and Eubacterium biforme (nominal P < 0.05). However, after correction for multiple testing, this association was lost (FDR > 0.05; Table S4, Supplemental digital content 4, http://links.lww.com/MR/A208). Since microbial profiles usually contain a large amount of zero-valued observations, we then applied a zero-inflated model to the abundance data.
To correct for variables that are likely to influence gut microbiome composition or response to ICIs, we performed a multivariate linear regression analysis with covariates age, sex, drug use (PPIs, ABs), BMI, melanoma M-stage, systemic therapy, pre-treatment serum LDH-level, colitis during treatment and previous anti-melanoma therapy (BRAF- or CTLA-4-inhibition). When corrected for multiple testing, we observed a significant correlation (FDR < 0.05) for the relative abundance of 68 unique taxa with response to ICI therapy (Tables 4 and 5). Of these, 27 taxa showed a positive correlation with response and the remaining 41 taxa showed a negative correlation with response. Mean relative abundance of 40 taxa (27 species, nine genera, three families and one class) was increased in responders, and mean relative abundance of 28 taxa was increased in non-responders (19 species, four genera, four families and one order) (Fig. 3). At the species level, we found the highest mean relative abundance for Ruminococcus gnavus, Escherichia coli, E. biforme, Phascolarctobacterium succinatutens and Streptococcus salivarius in responders and Bifidobacterium longum, Prevotella copri, Coprococcus sp ART55-1, Eggerthella unclassified and Eubacterium ramulus in non-responders (Fig. 3).
Results for all significantly associated taxa are listed in Table S5 (Supplemental digital content 5, http://links.lww.com/MR/A209).
Association of microbial taxa with progression-free survival and overall survival
We observed a longer PFS for carriers of B. massiliensis compared to non-carriers [Univariate Cox-Regression Wald-test P = 0.04, HR: 3.79, 95% confidence interval (CI): 1.06–13.52] (Fig. 4a and Table S6, Supplemental digital content 6, http://links.lww.com/MR/A210). In contrast, carriers of Peptostreptococcaceae (unclassified species) showed a significantly shorter PFS compared to non-carriers [Univariate Cox-Regression Wald-test P = 0.007, hazard ratio (HR): 0.18, 95%CI: 0.05–0.62] (Fig. 4b). All other taxa showed no significant association with PFS. The univariate analysis showed no significant association with PFS for any of the included variables (i.e. sex, age, tumour M-stage, current systemic therapies, previous treatment with BRAF- or CTLA-4-inhibition, corticosteroid use, LDH-level and BMI), therefore no multivariate Cox regression analysis was performed.
A significantly longer OS was observed for carriers of Streptococcus parasanguinis compared to non-carriers (Wald-test P = 0.017, HR: 5.05, 95%CI: 1.33–19.21). In line with abovementioned association, carriers of Peptostreptococcaceae (unclassified species) showed a significantly shorter OS compared to non-carriers (Wald-test P = 0.046, HR: 0.12, 95%CI: 0.01–0.96). Additionally, a baseline serum LDH level of > 250U/L was associated with OS (Wald-test P = 0.036) in an univariate Cox-regression analysis. None of the other taxa or other variables tested was associated with OS. When performing a multivariate analysis for each significantly associated taxon independently, with covariate baseline serum LDH level, both species remained significantly associated with OS (S. parasanguinis Wald-test P = 0.008, HR: 6.9, 95%CI: 1.63–29.14, Peptostreptococcaceae (unclassified species) Wald-test P = 0.018, HR: 0.11, 95%CI: 0.01–0.93; Fig. 4c and d). Of note, we observed a significant association between OS and abundance of the genus Anaerostipes in the univariate analysis, but this association was lost when correcting for baseline serum LDH levels (Table S6, Supplemental digital content 6, http://links.lww.com/MR/A210).
Interestingly, when performing a separate multivariate Cox regression analysis for each taxon that showed differential abundance in responders or non-responders (FDR < 0.05) with baseline serum LDH level entered as covariate, we observed a difference in OS between carriers and non-carriers of an additional five taxa, including V. parvula (Wald-test P = 0.016, HR: 8.51, 95%-CI: 1.04 – 69.61), Actinomyces odontolyticus (Wald-test P = 0.02, HR: 8.5, 95%CI: 1.18–61.6) (NB. Association also found at the genus and family level) and Lactobacillaceae (Wald-test P = 0.02, HR: 4.12, 95%CI: 1.03 – 16.42). None of these taxa showed associations with OS in the univariate Cox regression analysis (Table S6, Supplemental digital content 6, http://links.lww.com/MR/A210).
Overall composition of microbial pathway abundances
Of all pathways included, 177 were related to biosynthesis, 47 to degradation, one to detoxification and 35 to energy-metabolism (Fig. 5a). Pathways related to biosynthesis were most predominant in all samples, both in responders and non-responders (79%; Fig. 5b). Pathways related to degradation and energy-metabolism made up 13 and 8% of the samples, respectively. Finally, pathways related to detoxification showed limited relative abundance of 0.1%.
Differences in abundance of microbial pathways between responders and non-responders
We observed no significant differences in microbial pathway abundance between responders and non-responders using univariate and multivariate linear models (Table S7, Supplemental digital content 7, http://links.lww.com/MR/A211).
Utilizing a zero-inflated model, as described above, and maintaining an FDR of 0.05, we observed 17 pathways with a higher mean relative abundance in responders compared to non-responders and two pathways with a higher mean relative abundance in non-responders compared to responders (Fig. 5c). The five highest mean relative abundances in responders were observed for aspartate superpathway (PWY0_781), superpathway of thiamine diphosphate biosynthesis I (THISYN_PWY), NAD/NADH phosphorylation and dephosphorylation (PWY_5083), superpathway of glycolysis, pyruvate dehydrogenase, TCA and glyoxylate bypass (GLYCOLYSIS_TCA_GLYOX_BYPASS) and superpathway of thiamine diphosphate biosynthesis II (PWY_6895). For non-responders, only two pathways showed higher mean relative abundance compared to responders, namely peptidoglycan biosynthesis IV (PWY_6471) and methanogenesis from H2 and CO2 (METHANOGENESIS_PWY).
Results for all pathways are listed in Table S8 (Supplemental digital content 8, http://links.lww.com/MR/A212).
In this study, we observed an association between the relative abundance of 68 individual taxa in the gut microbiome and response to ICI therapy. In addition, we observed prolonged OS for carriers of S. parasanguinis, prolonged PFS for carriers of B. massiliensis and shorter OS and PFS for carriers of Peptostreptococcaceae (unclassified species) compared to non-carriers. Finally, we show differential abundance of 17 microbial pathways in the gut of responders compared to non-responders.
In accordance with previous studies, we found the relative abundance of V. parvula and Bacteroides thetaiotaomicron to be positively correlated with response to ICI therapy (coefficient > 0; Table 4). Additionally, as observed in previous studies, relative abundance of E. coli and A. odontolyticus was negatively correlated with response to ICI therapy (coefficient < 0; Table 5) [9,11–13]. Furthermore, we observed a positive correlation between high relative abundance of A. muciniphila and response to ICI therapy. This association has previously only been described in patients with NSCLC or RCC. In contrast to previous findings, we observed high abundance of Bacteroides eggerthii to be correlated with response, while in previous studies B. eggerthii has been associated with non-response . Conversely, high relative abundance of S. parasanguinis, H. filiformis and B. longum taxa that have previously been associated with response, showed a negative correlation with response to ICI therapy in the present study [11,13]. In part, this may be due to differences in study population (e.g. differences in cancer type and systemic therapy). Additionally, variations in stool sample collection procedures, definitions of response and statistical analyses are also likely to have an effect.
Finally, correction for confounding factors that influence either gut microbiome composition or response to ICIs is not widely implemented. For instance, when correcting for confounding factors such as use of ABs, BMI and age, we observed a shift in direction of the correlation (i.e. from positive to negative) at the genus level for Phascolarctobacterium (univariate analysis results: coefficient: −0.91; multivariate analysis results: coefficient: 0.31). This underlines the importance of taking confounders into account when interrogating gut microbiome composition.
In addition to the correlation between taxa abundance and response to ICI therapy, we were able to show an association with OS or PFS for three individual taxa. It should be noted that although higher relative abundance of Peptostreptococcaceae (unclassified species) was positively correlated with response to ICI therapy, we observed shorter PFS for carriers vs. non-carriers. Similarly, for S. parasanguinis a negative correlation with response to ICI therapy was observed, yet longer OS for carriers vs. non-carriers was observed. These differences could be attributable to the difference in prevalence (carrier vs non-carrier) and relative abundance of these taxa.
Currently, FMT studies in patients with metastatic melanoma are recruiting (ClinicalTrials.gov Identifiers: NCT03353402 and NCT03341143). However, despite the rapid developments in this field of research, there is still little overlap in taxa that are found to be associated with response or non-response to ICI therapy between studies (Figure S1, Supplemental digital content 9, http://links.lww.com/MR/A213) [9–13]. Therefore, there remains a need for larger studies with standardized collection procedures, well defined response definitions and uniform statistical methodology. Finally, standard prospective collection of data on important confounding factors such as diet, drug use and BMI is of great importance in gut microbiome studies. The collection of data in this study will continue until 300 patients have been included.
In conclusion, we observed 68 unique taxa to be associated with response to ICI therapy in patients with unresectable metastastic melanoma, including the previously described association of V. parvula, B. thetaiotaomicron, A. muciniphila, E. coli and A. odontolyticus. In addition, we observed prolonged OS for carriers of S. parasanguinis, prolonged PFS for carriers of B. massiliensis and shorter OS and PFS for carriers of Peptostreptococcaceae (unclassified species) compared to non-carriers. Finally, we observed an association between abundance of 17 microbial pathways and response to ICI therapy. These results underline the association between gut microbiome composition and response to ICI therapy in a cohort of patients with cutaneous melanoma. There remains a need for large, prospective studies that take into account important confounding factors in order to confirm these and previously published results.
We would like to thank all participating patients for participating in this trial and M. Fankhauser, Chief Scientific Officer at the SEERAVE Foundation for his feedback and input on the manuscript.
This study was supported by a grant from the SEERAVE foundation to G.A.P.H. and R.K.W. No grant numbers apply.
Conflicts of interest
G.A.P.H. received research funding from BMS (payment to the institution) and is an advisory board member for Bristol-Myers Squibb and Merck Sharp & Dohme. M.J. is an advisory board member for Bristol-Myers Squibb and Merck Sharp & Dohme. R.K.W. received consultancy fees from Takeda Pharmaceuticals and unrestricted research grants from Takeda Pharmaceuticals, Johnson and Johnson, Tramedico and Ferring. For the remaining authors, there are no conflicts of interests.
1. Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz HJ, Morse MA, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (checkmate 142): an open-label, multicentre, phase 2 study. Lancet Oncol. 2017; 18:1182–1191
2. Motzer RJ, Rini BI, McDermott DF, Redman BG, Kuzel TM, Harrison MR, et al. Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J Clin Oncol. 2015; 33:1430–1437
3. Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015; 373:1627–1639
4. Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, et al. Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med. 2015; 372:320–330
5. Ugurel S, Röhmel J, Ascierto PA, Flaherty KT, Grob JJ, Hauschild A, et al. Survival of patients with advanced metastatic melanoma: the impact of novel therapies-update 2017. Eur J Cancer. 2017; 83:247–257
6. Zafar SY. Financial toxicity of cancer care: it’s time to intervene. J Natl Cancer Inst. 2015; 108:1–4
7. Vétizou M, Pitt JM, Daillère R, Lepage P, Waldschmitt N, Flament C, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science. 2015; 350:1079–1084
8. Sivan A, Corrales L, Hubert N, Williams JB, Aquino-Michaels K, Earley ZM, et al. Commensal bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science. 2015; 350:1084–1089
9. Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC, Karpinets TV, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018; 359:97–103
10. Chaput N, Lepage P, Coutzac C, Soularue E, Le Roux K, Monot C, et al. Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann Oncol. 2017; 28:1368–1379
11. Frankel AE, Coughlin LA, Kim J, Froehlich TW, Xie Y, Frenkel EP, Koh AY. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia. 2017; 19:848–855
12. Matson V, Fessler J, Bao R, Chongsuwat T, Zha Y, Alegre ML, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 2018; 359:104–108
13. Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018; 359:91–97
14. Shafquat A, Joice R, Simmons SL, Huttenhower C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol. 2014; 22:261–266
15. The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2013; 486:207–214
16. Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M, Vatanen T, et al.; LifeLines cohort study. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science. 2016; 352:565–569
17. Imhann F, Bonder MJ, Vich Vila A, Fu J, Mujagic Z, Vork L, et al. Proton pump inhibitors affect the gut microbiome. Gut. 2016; 65:740–748
18. Manichanh C, Borruel N, Casellas F, Guarner F. The gut microbiota in IBD. Nat Rev Gastroenterol Hepatol. 2012; 9:599–608
19. Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe. 2014; 15:382–392
20. Lappalainen I, Almeida-King J, Kumanduri V, Senf A, Spalding JD, Ur-Rehman S, et al. The European genome-phenome archive of human data consented for biomedical research. Nat Genet. 2015; 47:692–695
21. The integrative HMP (iHMP) Research Network Consortium. The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe. 2014; 16:276–289
22. McIver LJ, Abu-Ali G, Franzosa EA, Schwager R, Morgan XC, Waldron L, et al. Biobakery: a meta’omic analysis environment. Bioinformatics. 2018; 34:1235–1237
23. Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012; 9:357–359
25. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. Metaphlan2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015; 12:902–903
26. Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. Plos Comput Biol. 2012; 8:e1002358
27. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015; 12:59–60
28. Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. Uniref: comprehensive and non-redundant uniprot reference clusters. Bioinformatics. 2007; 23:1282–1288