CANCER IMMUNOTHERAPY

The commensal microbiome isassociated with anti–PD-1 efficacy inmetastatic melanoma patientsVyara Matson,1* Jessica Fessler,1* Riyue Bao,2,3* Tara Chongsuwat,4 Yuanyuan Zha,4

Maria-Luisa Alegre,4 Jason J. Luke,4 Thomas F. Gajewski1,4†

Anti–PD-1–based immunotherapy has had a major impact on cancer treatment but hasonly benefited a subset of patients. Among the variables that could contribute tointerpatient heterogeneity is differential composition of the patients’ microbiome, whichhas been shown to affect antitumor immunity and immunotherapy efficacy in preclinicalmouse models. We analyzed baseline stool samples from metastatic melanoma patientsbefore immunotherapy treatment, through an integration of 16S ribosomal RNA genesequencing, metagenomic shotgun sequencing, and quantitative polymerase chainreaction for selected bacteria. A significant association was observed between commensalmicrobial composition and clinical response. Bacterial species more abundant inresponders included Bifidobacterium longum, Collinsella aerofaciens, and Enterococcusfaecium. Reconstitution of germ-free mice with fecal material from responding patientscould lead to improved tumor control, augmented T cell responses, and greater efficacy ofanti–PD-L1 therapy. Our results suggest that the commensal microbiome may have amechanistic impact on antitumor immunity in human cancer patients.

The therapeutic efficacy of immunothera-pies targeting the PD-1/PD-L1 interaction isfavored in patients who show evidence of aT cell–inflamed tumormicroenvironment atbaseline (1, 2). Therefore, host and tumor

factors that regulate themagnitude of endogenousimmune priming and T cell infiltration into thetumor microenvironment are being sought as anopportunity to further expand therapeutic efficacy(3). Preclinical studies have indicated that thecomposition of the commensal microbiome couldexert a major influence; mice with favorable mi-crobiota showed far greater therapeutic activityof anti–PD-L1 treatment than did mice with anunfavorable microbiome, and this benefit couldbe transferred by cohousing or fecal transplant(4). These observations prompted an analogousanalysis of the humanmicrobiomewith respect totherapeutic efficacy of anti–PD-1 in cancer patients.To evaluatewhether commensal bacterial com-

positionmight be associated with clinical efficacyof PD-1 blockade immunotherapy, stool sampleswere collected from 42 patients before treatmentas part of a multidimensional biomarker analysisinmetastatic melanoma. Themajority of patientsreceived an anti–PD-1 regimen; four patients re-ceivedanti–CTLA-4 treatment, but thedownstreamdata conclusions did not change with the removalof these subjects, so they were retained in theanalysis. Clinical response rate was determined

in a blinded manner from biomarker results byusing Response Evaluation Criteria In SolidTumors (RECIST) version 1.1. There were 16responders (from here on, referred to as R) and26 nonresponders (NR), yielding a response rateof 38%, which is in line with published clinicaldata of anti–PD-1 therapy in metastatic mela-noma patients (5, 6). No major differences inpatient characteristics were observed in R versusNR, except a borderline difference in prior (butnot current) smoking history (table S1).To determine whether the composition of the

commensal microbiota is associated with clinicalresponse, we integrated three methods for DNAsequence–based bacterial identification (fig. S1A).First, using 16S ribosomal RNA (rRNA) geneamplicon sequencing, we identified operationaltaxonomic units (OTUs) with taxonomic assign-ment present at different abundance in R versusNR (table S2). We used a Basic Local AlignmentSearch Tool (BLAST) search of the 16S sequencesagainst the National Center for BiotechnologyInformation (NCBI) database to reveal potentialspecies-level identities (table S3). Further level ofconfidence in species identification was gainedby matching the OTUs from the 16S data set tospecies-level identities revealed through metage-nomic shotgun sequencing (table S4). We usedspecies-specific quantitative polymerase chainreaction (PCR) for those candidate species havingpreviously validated primers (table S5). Comparedwith the 16S analysis, themetagenomic sequencingyielded a smaller number of species differentiallyrepresented in R versus NR, which overlappedwith the 16S results (table S6). Treating theseassays as a screen formaximizing the number ofcandidate species, we used the 16S sequenc-ing method as a starting point in our analysis.

After removing OTUs present in less than 10%of the samples, the 16S sequencing revealed62OTUs of different abundance in R versus NR[P < 0.05, unadjusted, permutation test withQuantitative Insights Into Microbial Ecology(QIIME)] (table S2). Hierarchical clustering ofsamples based on relative abundance of theseOTUs revealed that most patients were accuratelygrouped according to clinical response (fig. S2).Clustering of patients within each clinical groupis depicted in Fig. 1A. Thirty-nine OTUs weremore abundant in R, and 23 were more abun-dant in NR. One BifidobacteriaceaeOTU was sig-nificantly more abundant in R, and a secondBifidobacteriaceae OTU (559527) had borderlinesignificance (P = 0.058, unadjusted) and was in-cluded in the analyses (total = 63 OTUs). Thisobservation recapitulates our previous findingsthat associated Bifidobacteriaceae family mem-berswith improved immune-mediated tumor con-trol and efficacy of anti–PD-L1 therapy inmice (4).A principal component analysis (PCA) of the 63OTUs revealed separation of R fromNR (Fig. 1B).A BLAST search of the 63 OTUs against the

NCBI database of bacterial sequences returnedfor most OTUs multiple species with ≥98% iden-tity (table S3). To gain more accurate species-level characterization, the same samples weresubjected to metagenomic shotgun sequencing(available for 39 of the 42 samples). Illuminapaired-end readswere assigned tomicrobial cladesand analyzed for closest matches to the 63 OTUsidentified with 16S sequencing. Potential speciesmatches were identified for 43 of the original 63OTUs (table S4). Species-specific quantitative PCRassays were performed as an additional approachto assess the identity of species, for which suffi-ciently validated quantitative PCR primers wereavailable (table S5). Thus, integration of the threemethods led to the selection of 10 species differ-entially enriched in R versus NR. Eight of theseweremore abundant in R—Enterococcus faecium,Collinsellaaerofaciens,Bifidobacteriumadolescentis,Klebsiella pneumoniae, Veillonella parvula,Parabacteroides merdae, Lactobacillus sp., andBifidobacterium longum—whereas twoweremoreabundant in NR: Ruminococcus obeum andRoseburia intestinalis. As an example, the inte-grative analysis for B. longum (OTU 559627) isdepicted in Fig. 2, A to C. Similar correlation analy-ses for the remaining nine species are depicted infigs. S3 and S4. Quantitative PCR results for these10 specieswere integrated into a summation quan-titative PCR score for each patient, which wassignificantly higher in responders (P = 0.004)(Fig. 2D).This list of species is likely an underestimate of

the total number of entities showing differentialabundance in R versus NR because of the strin-gency of this composite analysis. For example,Akkermansia muciniphila OTU (185186), in linewith the study of anti–PD-1 efficacy in epithelialcancers by Routy et al. (7), was detected bymeansof 16S sequencing in four patients, and all wereresponders, but statistical analysis of the entirecohort is limited by the number of samples abovethe detection threshold. As an alternative way to

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1Department of Pathology, University of Chicago, Chicago, IL60637, USA. 2Center for Research Informatics, University ofChicago, IL 60637, USA. 3Department of Pediatrics,University of Chicago, IL 60637, USA. 4Department ofMedicine, University of Chicago, Chicago, IL 60637, USA.*These authors contributed equally to this work.†Corresponding author. Email: tgajewsk@medicine.bsd.uchicago.edu

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represent the aggregate data toward developmentof a candidate predictive biomarker, the totalnumbers of potentially “beneficial” and “non-beneficial” OTUs were scored for each patient(fig. S5), and a ratiowas calculated.When plottedagainst the absolute change in tumor size asassessed with RECIST, a clean correlation wasobserved so that patients with a ratio over 1.5 allshowed clinical response (Fig. 2E). These resultssuggest that the commensal microbiota compo-sition might be useful as a biomarker to predictresponse to checkpoint blockade therapy, whichmotivated comparison with other candidate pre-dictive biomarkers. Archived pretreatment tumorspecimens that passed quality control were avail-able for 15 patients (5 R, 10NR).Microbial compo-sition remained significantly different in R versusNR for this subset (P < 0.01) (fig. S6, A and B).Exome sequencing followed by enumeration ofnonsynonymous somatic mutations showed atrend of higher frequency in R, as did levels ofPD-L1 and PD-1 mRNA (fig. S6, C to E) andenumeration of baseline CD8+ T cells by meansof immunohistochemistry (fig. S6F). Althoughthese trends not reaching statistical significancelevel of 0.05 were likely limited by sample size,themicrobiota parameters stillmarkedly separatedresponders and nonresponders.The strong correlation between commensal

bacteria and clinical response to immunotherapysuggested a potential causal effect, in light of datademonstrating an immune-potentiating impact ofthe microbiome inmouse tumormodels (4, 8–10).To investigate the capability of human commensalmicrobes to potentiate antitumor T cell responses,we used germ-free (GF)mice as recipients.We hadpreviously reported that spontaneous immune-mediated tumor control in Taconic mice couldbe improved by means of fecal microbiota trans-fer from mice obtained from a different vendor,the Jackson Laboratories (4). In setting up thecurrent model, we found that B16.SIY melanomatumor growth in GF mice was similar to that inspecific pathogen–free (SPF) mice (both fromTaconic), and colonization of GF mice with fecesfromTaconic SPFmice did not affect this baselinegrowth rate (fig. S8). These results suggest a re-duced spontaneous immune-mediated tumor con-trol inherent to GF mice, which makes themsuitable recipients for human-derived microbiota,with an opportunity to detect improved anti-tumor immunity depending on microbial com-position. Fecal material was transferred fromthree R and three NR into cohorts of GF mice(Fig. 1A and figs. S2, S5, and S7), followed byimplantation of B16.SIY melanoma cells 2 weekslater. The human microbiota–colonized mousegroups segregated into two phenotypes with re-spect to tumor growth rate: (i) a faster growinggroup and (ii) a slower growing group (Fig. 3A).Two of three mouse cohorts reconstituted withR fecal material had slower baseline tumorgrowth, and two of the three cohorts reconstitutedfrom NR showed faster baseline tumor growth.Thus, the ability of the human microbiota tosupport improved tumor control in mice usually,but not always, paralleled the clinical response to

anti–PD-1 seen in the donor patient. Achievingslower tumor growth with fecal transplant aloneis similar to previous mouse studies, in whichtransfer of feces from Jackson into Taconic micewas sufficient for a partial therapeutic effect owingto a more favorable microbiome (4).Composition of bacterial taxa that successfully

reconstituted mice and fidelity to the originalhuman donor were assessed with 16S rRNA geneamplicon sequencing. Groups C and D, whichdid not show the same pattern of tumor controlas the therapeutic outcome in the original hu-man donors, showed a large degree of differenceof microbiota composition from the original hu-man donors (fig. S9). In agreement, a binary Bray-Curtis dissimilarity index for each donor/recipient

pair was highest, at 0.7, for cohorts C andD versus0.5 to 0.6 for the rest of the groups. We concludethat whereas reconstitution of GF mice with hu-man fecal material often recapitulates the micro-bial composition and the phenotype of the humandonor, in some cases there is a high degree of driftso that some bacteria expand and others contractto a degree that is sufficient to change phenotype.Nonetheless, for the reconstituted GF mice thatdo recapitulate the clinical outcomeof the originaldonor, this model system may be useful for theultimate isolation of specific bacteria that regulateantitumor immunity in vivo.We focused on mouse groups A and B for

further mechanistic studies. There was a highlevel of consistency between repeated experiments,

Matson et al., Science 359, 104–108 (2018) 5 January 2018 2 of 5

Fig. 1. Distinct commensal microbial communitiesin anti–PD-1 responding patients and nonrespond-ing patients as assessed with 16S rRNA geneamplicon sequencing. (A) Relative abundance ofdifferentially abundant taxa in responders versus non-responders; 62 OTUs were identified as different withP < 0.05 (unadjusted, permutation test). An additionalOTU 559527 (arrow) identified as Bifidobacteriaceaeapproached significance (P = 0.058). Hierarchicalclustering of the samples was performed within eachclinical group. Individual samples are organized incolumns, labeled with patient identification number.Asterisks indicate samples used in further in vivoexperiments. The ID of de novo assembled OTUs (newclean-up reference OTUs picked with QIIME) wereabbreviated 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.

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both with respect to tumor growth rate andmicrobial colonization (Fig. 3B, group A1 versusA2 and B1 versus B2 comparisons). To determinewhether the difference in tumor control could beattributed to host immunity, interferon-g (IFN-g)enzyme-linked immunosorbent spot (ELISPOT)of ex vivo SIY-stimulated splenocytes was per-formed and indicated an increased frequency ofactivated T cells fromRmicrobiota-reconstitutedmice 3 weeks after inoculation with B16.SIY mel-anoma cells (Fig. 3C). Analysis of the tumormicro-

environment also showed a significantly greaternumber of SIY-specific CD8+ T cells, but not ofFoxP3+CD4+ regulatory T cells, in these mice (Fig.3, D and E), which is consistent with increasedpriming of tumor antigen–specific CD8+ T cells.Anti–PD-L1 was markedly efficacious in mice col-onized with R microbiota yet completely ineffec-tive in NR-derived mice (Fig. 3F), demonstratinga profound impact of the commensal microbiotaon immunotherapy efficacy in vivo. Interroga-tion of fecal DNA from these mice by means of

quantitative PCR recapitulated the results fromour analysis of patients. Of the PCR reactionsvalidated in patients, six were observed in re-constituted mice, with the same pattern of en-richment as was seen in patients (Fig. 2G).Together, our data suggest that the composi-

tion of the commensal microbiota in patients isassociated with therapeutic efficacy of anti–PD-1 monoclonal antibody (mAb). AlthoughB. longum was one commensal identified in thecurrent study that had also been found inmouse

Matson et al., Science 359, 104–108 (2018) 5 January 2018 3 of 5

Fig. 2. Identification of commensal bacte-rial species associated with patient clinicalresponse to anti–PD-1 therapy. (A) Spearman’scorrelation coefficients between the relativeabundances of Bifidobacteriaceae OTU 559527from the 16S data set and species-levelidentities suggested by shotgun sequencing.The species profiled with shotgun sequencingwere compared with the taxonomy of OTUsgenerated from 16S sequencing at the familylevel. (B) Spearman’s correlation betweenabundance of OTU 559527 from the 16S dataset and B. longum identified by means ofmetagenomics shotgun sequencing analysis(left) and quantitative PCR (right). Shadedband 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 ofdetection. (D) Quantitative PCR scorerepresenting aggregate data for the relativeabundances of 10 species correlated toOTUs with differential abundance inR versus NR. Wilcoxon-Mann-Whitney test(nonparametric) was used to comparequantitative PCR score between R andNR groups. (E) Ratio of beneficial tononbeneficial OTU numbers for each patientversus the patient’s RECIST aggregatetumor measurement change. Dashed lineslabel RECIST% = –30 and ratio = 1.5. Onlythe 43 16S OTUs confirmed with shotgunmetagenomic sequencing were included.P < 0.05 was considered statisticallysignificant; *P < 0.05, **P < 0.01,***P < 0.001, ****P < 0.0001.

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models to be associated with improved immune-mediated tumor control (4), it seems likely thatmultiple specific bacteria may contribute to im-proved antitumor immunity in patients. In addi-tion to the panel of bacteria overrepresented inresponders, several OTUs were overrepresentedin nonresponders, and prior work in mice hasindicated that some commensals have the poten-tial tobe immune-inhibitory—for example, throughthe induction of FoxP3+ regulatory T cells (11, 12).In addition, our current cohort suggested that aratio of “beneficial”OTUs to “nonbeneficial”OTUs

was the strongest predictor of clinical response.This may indicate that a higher frequency ofbeneficial bacteria, together with a lower frequen-cy of bacteria with negative impact, may combinefor the most favorable clinical outcome.Several of the bacterial species that were iden-

tified in the current study to be differentially abun-dant in responding versus nonrespondingpatientshave been examined previously for mechanisticimpact on host immune responses in GF micein vivo (13). Monocolonization with several spe-cies found to be at increased frequency in our

responders—includingE. faecium,C. aerofaciens,B. adolescentis, and P. merdae—were reported toresult in a decreased frequency of peripherally de-rived colonic regulatory T cells as compared withthat of other bacterial species. An increased fre-quency of the Batf3-lineage dendritic cells (DCs)and greater T helper cell 1 (TH1) responses werealso found with bacteria currently identified tobe more abundant in responders (13). Decreasedregulatory T cells, increased Batf3 DCs, and aug-mented TH1responses would all be expected to im-prove immune-mediated tumor control. Although

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Fig. 3. Human commensal communitiesmodulate antitumor immunity in a mousemelanoma model. GF mice were gavagedwith fecal material from three responder(P28, P34, and P09) and three nonresponder(P06, P21, and P11) patient donors. (A) B16.SIY melanoma was injected subcutaneously2 weeks after gavage; tumor growth data arefrom one (groups C, D, E, and F) or twoexperiments (groups A and B) with 7 to11 mice per group per experiment. Error barsrepresent mean + SEM. (B) Relative abun-dance of 207 OTUs from patient donors thatcolonized in mice and were differentiallyabundant between slow- and fast-tumor-growth groups. Columns depict individualmice arranged in groups A throughF. Groups A1, B1, A2, and B2 are from twoindependent duplicate experiments. Rowsindicate individual OTUs with exact referenceID match between human and mouse 16SrRNA data sets. (C) In groups A and B,ex vivo activation of splenocytes by SIYpeptide was measured with IFN-gELISPOT 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 wasdetermined in groups A and B. Data are fromone experiment with 7 or 8 mice per group.(G) Relative abundance in mouse groups Aand B of key species validated for quantitativePCR scoring. Six out of the 10 species areshown that gave positive PCR signals. Theremaining four species were absent fromthese particular recipient groups. Tumorgrowth curves were analyzed with two-wayanalysis of variance by Tukey’s multiple com-parisons post-test; flow cytometryand quantitative PCR data were analyzedby Wilcoxon-Mann-Whitney test(nonparametric). P < 0.05 was consideredstatistically significant; *P < 0.05, **P < 0.01,***P < 0.001, ****P < 0.0001.

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care should be taken extrapolating these resultsin the setting of a complex microbiota, the datasuggest that patient responder–associated bac-teria may have a distinct effect on innate andadaptive immunity both locally and systemically.Two additional studies have identified an asso-

ciation of the commensal microbiome with anti–PD-1 mAb efficacy in different solid cancers (7, 14).Our approaches differ in the method of segre-gation of patients between R and NR groups andin some methods of analysis. Therefore, directcomparison of the differentially enriched com-mensal microbiota in R and NR patients acrosspublications should be addressed with caution.Nonetheless, there is agreement with respect to amechanistic impact of the microbiome on effica-cy of anti–PD-1 immunotherapy because fecaltransfer from R versus NR patients to GF micewas also able to recapitulate the patient phenotype.In addition to the microbiome, it is apparent

that additional tumor and host factors can affectthe efficacy of antitumor immunity and cancerimmunotherapy. Tumor-intrinsic activation of theWnt/b-catenin pathway (15) and deletion ormuta-tion of Pten (16) have been shown to lead todeficient T cell infiltration into the tumor micro-environment and resistance to checkpoint block-ade immunotherapy. Germline polymorphisms inimmune regulatory genes also have the potentialto influence the magnitude of spontaneous anti-tumor T cell responses (17). Our results described

here open the avenue for integrating commensalmicrobial composition, alongwith tumorgenomicsand germline genetics, into a multiparametermodel with which to maximize the ability ofpredictingwhich patients are likely to respond toimmunotherapies such as anti–PD-1.

REFERENCES AND NOTES

1. P. C. Tumeh et al., Nature 515, 568–571 (2014).2. M. Ayers et al., J. Clin. Invest. 127, 2930–2940 (2017).3. L. Corrales, V. Matson, B. Flood, S. Spranger, T. F. Gajewski,

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12204–12209 (2010).13. N. Geva-Zatorsky et al., Cell 168, 928–943.e11 (2017).14. V. Gopalakrishnan et al., Science eaan4236 (2017).15. S. Spranger, R. Bao, T. F. Gajewski, Nature 523, 231–235

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ACKNOWLEDGMENTS

We thank E. Chang, A. Sivan, C. Nagler, L. Huang, P. Bleda-Ferre,N. Hubert, Z. Early, and B. Theriault for helpful discussions andM. Jarsulic, B. Choy, N. Martinec, S. Owens, S. Greenwald,H. Morrison, and J. Polinski for technical assistance. This work was

supported by NIH grant R35 CA210098, a Team Science Awardfrom the Melanoma Research Alliance, the American CancerSociety–Jules L. Plangere Jr. Family Foundation Professorship inCancer Immunotherapy, funds from the University of ChicagoMedicine Comprehensive Cancer Center, the University of ChicagoCancer Biology Training program (T32 CA009594), and TheCenter for Research Informatics of The University of ChicagoBiological Science Division. The bioinformatics analysis wasperformed on Gardner High-Performance Computing clusters atCenter for Research Informatics, Biological Sciences Division. Datareported in this study are tabulated in the main text andsupplementary materials. The 16S and shotgun sequencing wereperformed at the Argonne National Laboratory and the Universityof Chicago-affiliated Marine Biological Laboratory, respectively;data files were deposited into The NCBI Sequence Read Archive(SRA) and are available under the accession no. SRP116709.Custom code and additional processed data used in this studyare publicly available on GitHub at https://github.com/cribioinfo/sci2017_analysis. T.F.G. is an advisory board member forRoche-Genentech, Merck, Abbvie, Bayer, Aduro, and Fog Pharma.T.F.G. receives research support from Roche-Genentech, BMS, Merck,Incyte, Seattle Genetics, and Ono. T.F.G. is a shareholder/cofounderof Jounce Therapeutics. The University of Chicago holds a licensingarrangement with Evelo. T.F.G is an inventor on U.S. patentUS20160354416 A1 submitted by the University of Chicago thatcovers the use the microbiota to improve cancer immunotherapy.J.J.L. is a consultant to and receives research funding fromBristol-Myers Squibb and Merck.

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/359/6371/104/suppl/DC1Materials and MethodsFigs. S1 to S9Tables S1 to S6References (18–53)

9 July 2017; accepted 13 November 201710.1126/science.aao3290

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patientsPD-1 efficacy in metastatic melanoma−The commensal microbiome is associated with anti

F. GajewskiVyara Matson, Jessica Fessler, Riyue Bao, Tara Chongsuwat, Yuanyuan Zha, Maria-Luisa Alegre, Jason J. Luke and Thomas

DOI: 10.1126/science.aao3290 (6371), 104-108.359Science 

, this issue p. 91, p. 104, p. 97; see also p. 32Scienceflora could help patients combat cancer.imbalance in gut flora composition, which correlated with impaired immune cell activity. Thus, maintaining healthy gutblockade and found a greater abundance of ''good'' bacteria in the guts of responding patients. Nonresponders had an

studied melanoma patients receiving PD-1et al. and Gopalakrishnan et al.response to immunotherapy. Matson . Oral supplementation of the bacteria to antibiotic-treated mice restored theAkkermansia muciniphilaof the bacterium

They profiled samples from patients with lung and kidney cancers and found that nonresponding patients had low levels show that antibiotic consumption is associated with poor response to immunotherapeutic PD-1 blockade.et al.Routy

Resident gut bacteria can affect patient responses to cancer immunotherapy (see the Perspective by Jobin).Good bacteria help fight cancer

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