2018-肠道微生物组影响基于PD-1的免疫疗法对上皮肿瘤的功效

CANCER IMMUNOTHERAPY Gut microbiome influences efficacy of PD-1based immunotherapy against epithelial tumors Bertrand Routy,1,2,3Emmanuelle Le Chatelier,4Lisa Derosa,1,2,3 Connie P. M. Duong,1,2,5Maryam Tidjani Alou,1,2,3Romain Daillre,1,2,3 Aurlie Fluckiger,1,2,5Meriem Messaoudene,1,2Conrad Rauber,1,2,3Maria P. Roberti,1,2,5 Marine Fidelle,1,3,5Caroline Flament,1,2,5Vichnou Poirier-Colame,1,2,5Paule Opolon,6 Christophe Klein,7Kristina Iribarren,8,9,10,11,12Laura Mondragn,8,9,10,11,12 Nicolas Jacquelot,1,2,3Bo Qu,1,2,3Gladys Ferrere,1,2,3Cline Clmenson,1,13 Laura Mezquita,1,14Jordi Remon Masip,1,14Charles Naltet,15Solenn Brosseau,15 Coureche Kaderbhai,16Corentin Richard,16Hira Rizvi,17Florence Levenez,4 Nathalie Galleron,4Benoit Quinquis,4Nicolas Pons,4Bernhard Ryffel,18 Vronique Minard-Colin,1,19Patrick Gonin,1,20Jean-Charles Soria,1,14Eric Deutsch,1,13 Yohann Loriot,1,3,14Franois Ghiringhelli,16Grard Zalcman,15 Franois Goldwasser,9,21,22Bernard Escudier,1,14,23Matthew D. Hellmann,24,25 Alexander Eggermont,1,2,14Didier Raoult,26Laurence Albiges,1,3,14 Guido Kroemer,8,9,10,11,12,27,28* Laurence Zitvogel1,2,3,5* Immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis induce sustained clinical responses in a sizable minority of cancer patients.We found that primary resistance to ICIs can be attributed to abnormal gut microbiome composition. Antibiotics inhibited the clinical benefit of ICIs in patients with advanced cancer. Fecal microbiota transplantation (FMT) from cancer patients who responded to ICIs into germ-free or antibiotic-treated mice ameliorated the antitumor effects of PD-1 blockade, whereas FMT from nonresponding patients failed to do so. Metagenomics of patient stool samples at diagnosis revealed correlations between clinical responses to ICIs and the relative abundance of Akkermansia muciniphila. Oral supplementation with A. muciniphila after FMTwith nonresponder feces restored the efficacy of PD-1 blockade in an interleukin-12 dependent manner by increasing the recruitment of CCR9+CXCR3+CD4+T lymphocytes into mouse tumor beds. C ancer immunotherapy has become highly successfulagainstanarrayofdistincthem- atologicalandsolidmetastaticmalignancies (16).Administrationofimmunecheckpoint inhibitors (ICIs) unleashes T lymphocyte mediatedimmuneresponsesbysuppressingthe interactionofTcellinhibitoryreceptorswiththeir cognateligandsontumororstromalcells(7).The mostwidelyusedICIsaremonoclonalantibodies (mAbs)targetingprogrammedcelldeathprotein 1(PD-1)anditsligandPD-L1(7).PD-1blockadeis highly efficacious against advanced melanoma, nonsmall cell lung cancer (NSCLC), and renal cellcarcinoma(RCC).Primaryresistance,observed in60 to70%ofcases(3,5,8),hasbeenattributed to low mutational burden, poor intrinsic anti- genicityoftumorcells(9,10),absenceofpriming by potentially immunogenic pretreatment with chemo-orradiotherapy(11),defectiveantigenpre- sentation during the priming phase (12), local immunosuppressionbyextracellularmetabolites (13),andfunctionalexhaustionoftumor-infiltrating lymphocytes (1315). Recent work in mice has highlighted the key role of the gut microbiota in mediating tumor responses to chemotherapeutic agents and im- munotherapies targeting PD-L1 or cytotoxic T lymphocyteassociated protein 4 (CTLA-4) (1621). Therefore, we explored the possibility thatdysbiosis associatedwith malignant disease orconcomitant antibiotic(ATB)usecouldinflu- enceprimaryresistancetoPD-1blockadeintumor- bearing mice and cancer patients. Initially,wecomparedthetherapeuticefficacy ofPD-1mAbaloneorcombinedwithCTLA-4mAb in mice with established MCA-205 sarcoma and RET melanoma. Mice were reared in specific pathogenfree(SPF)conditionsandtreated for 14 days with broad-spectrum combination ATB (ampicillin + colistin + streptomycin) or left un- treated. ATB treatment significantly compro- misedtheantitumoreffectsandsurvivalofmice treated with PD-1 mAb alone or in combination with CTLA-4 mAb (Fig. 1, A and B). We next addressed the impact of ATB on pa- tientswithadvancedNSCLC(n = 140),RCC (n = 67), or urothelial carcinoma (n = 42) who re- ceivedPD-1/PD-L1mAbafteroneorseveralprior therapies.Out ofall 249 patients, 69 (28%)were prescribed ATB (b-lactam+/inhibitors, fluoro- quinolones, or macrolides) within 2 months be- fore,or1 month after,thefirst administrationof PD-1/PD-L1 mAb. Patients generally took ATB orally for common indications (dental, urinary, andpulmonaryinfections).Therewerenomajor statistical differences in baseline clinical charac- teristics between ATB-treated and untreated pa- tients(tables S1 toS6).Progression-free survival (PFS)andoverallsurvival(OS)weresignificantly shorterintheATB-treatedgroupwhenallpatients were combined (Fig. 1C). Similarly, PFS and/or OS were shorter in ATB-treated groups when in- dividual tumor types were considered (Fig. 1, D and E, and fig. S1, A to C). In univariate and mul- tivariate Cox regression analyses, ATB repre- sentedapredictorofresistancetoPD-1blockade, independent from classical prognostic markers in NSCLC and RCC (tables S7 to S9). A vali- dationcohort of239 advanced NSCLC patients confirmedthenegativeimpactofATBuptakeon OS during PD-1/PD-L1 inhibition (fig. S1D). In contrast, protonpumpinhibitors, amedication that can also alter the microbiota composition, failed to affect PFS or OS in these patients (fig. S2) (22). On the basis of previous observations that ATB can transiently change the composi- tion of the gut microbiome (23), we hypothe- sized that dysbiosis might affectthe therapeutic efficacy of ICIs. RESEARCH Routy et al., Science 359, 9197 (2018)5 January 20181 of 7 1Gustave Roussy Cancer Campus (GRCC), Villejuif, France.2Institut National de la Sant et de la Recherche Medicale (INSERM) U1015 and Equipe LabelliseLigue Nationale contre le Cancer, Villejuif, France. 3Univ. Paris-Sud, Universit Paris-Saclay, Gustave Roussy, Villejuif, France.4MGP MetaGnoPolis, INRA, Universit Paris-Saclay, Jouy-en-Josas, France.5Center of Clinical Investigations in Biotherapies of Cancer (CICBT) 1428, Villejuif, France. 6Gustave Roussy, Laboratoire de Pathologie Exprimentale, 94800 Villejuif, France.7Centre de Recherche des Cordeliers, INSERM, Universit Paris Descartes, Sorbonne Paris Cit, UMRS 1138, Universit Pierre et Marie Curie Universit Paris 06, Sorbonne Universits, Paris, France. 8Metabolomics and Cell Biology Platforms, GRCC, Villejuif, France. 9Paris Descartes University, Sorbonne Paris Cit, Paris, France.10Equipe 11 LabelliseLigue Nationale contre le Cancer, Centre de Recherche des Cordeliers, Paris, France. 11INSERM U1138, Paris, France.12Universit Pierre et Marie Curie, Paris, France.13Department of Radiation Oncology, INSERM U1030, and Molecular Radiotherapy, Gustave Roussy, Universit Paris-Saclay, F-94805 Villejuif, France. 14Department of Medical Oncology, Gustave Roussy, Villejuif, France.15Thoracic Oncology DepartmentCIC1425/CLIP2 Paris-Nord, Hospital Bichat-Claude Bernard, AP-HP, Universit Paris-Diderot, Paris, France. 16Department of Medical Oncology, Center GF Leclerc, Dijon, France.17Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 18Molecular Immunology and Embryology, UMR 7355, CNRS, University of Orleans, Orlans, France.19Department of Pediatric Oncology, GRCC, Villejuif, France. 20Preclinical Research Platform, GRCC, Villejuif, France.21Department of Medical Oncology, Cochin Hospital, Assistance PubliqueHpitaux de Paris, Paris, France. 22Immunomodulatory Therapies Multidisciplinary Study Group (CERTIM), Paris, France.23INSERM U981, GRCC, Villejuif, France.24Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 25Department of Medicine, Weill Cornell Medical College, New York, NY, USA.26URMITE, Aix Marseille Universit, UM63, CNRS 7278, IRD 198, INSERM 1095, IHUMditerrane Infection, 13005 Marseille, France. 27Ple de Biologie, Hpital Europen Georges Pompidou, Assistance PubliqueHpitaux de Paris, Paris, France. 28Department of Womens and Childrens Health, Karolinska University Hospital, 17176 Stockholm, Sweden. *Corresponding author. Email: laurence.zitvogelgustaveroussy.fr (L.Z.); kroemerorange.fr (G.K.) on December 28, 2018 /Downloaded from To explore the composition of the gut micro- biota, we used quantitative metagenomics by shotgun sequencing,reaching20millionshort DNA sequence reads per sample, followed by analysis of the results in a reference catalog of 9.9 million genes (24). Total DNA was extracted from 100 patients diagnosed with NSCLC (n = 60)andRCC(n=40)beforestartingtherapyand serially after PD-1 blockade (tables S10 to S13). The higher richness of the samples evaluated at the gene count or metagenomic species (MGS) levelscorrelatedwiththeclinicalresponsedefined bytheabsenceofprogressionofdisease,6months afterinitiationofICIsbasedonRECIST1.1criteria (Fig.2A)(25).StoolrichnessincreasedattheMGS level over the course of therapy, more in RCC patients than in NSCLC patients (fig. S3). For each sample, MGS occurrence was visualized using “barcodes” (i.e., heat maps reflecting the abundance of 50 marker genes for each MGS) (fig. S4). The taxonomical annotation of each MGS was based on gene homology to previously sequenced organisms (using blastN against the nucleotide and whole-genome shotgun data banks). When segregating responders (R) from non- responders (NR) (according to the best clinical responseasassessedbyRECIST1.1),weobserved anoverrepresentationofunclassifiedandclassi- fiedFirmicutes, aswellasdistinctbacterial gen- era (such asAkkermansiaandAlistipes)(Fig.2B and fig. S4). The commensal that was most sig- nificantly associated with favorable clinical out- comeinbothNSCLCandRCCwasA.muciniphila (P = 0.004 considering all patients, P = 0.003 excludingATB-treatedpatients)(Fig.2B;fig.S4, A and B; and tablesS10 toS13). Whenanalyzing PFSaccordingtoRECIST1.1,A.muciniphilawas also enriched in patients with PFS longer than 3monthsrelativetothosewithPFSshorterthan 3 months, both in the whole cohort (P = 0.028, fig. S5A) and when excluding patients on ATB (P= 0.007; Fig.2C and fig. S5B). A.muciniphila was also enriched when analyzing the NSCLC cohortalone(P=0.045withorwithoutATB,fig. S6A;P=0.026excludingATB,fig.S6B)alongwith other commensals such as Ruminococcus spp., Alistipes spp., and Eubacterium spp., with a rel- ative underrepresentation of Bifidobacterium adolescentis, B. longum, and Parabacteroides distasonis. More precisely,the fecal presence of A.muciniphilawasdetectablein69%(11/16)and 58% (23/40) of patients exhibiting a partial re- sponse or stable disease, respectively, whereas it could only be detected in 34% (15/44) of pa- tients who progressed or died (P = 0.007, Fig. 2D). A validation cohort of53 patients (27NSCLC and 26 RCC) confirmed that A. muciniphila was enriched in patients with the best clinical response and PFS longer than 3 months (fig. S7 and table S14). These findings show that A. muciniphila was overrepresented at diagnosis in the feces of patients who later benefited from PD-1 inhibition. In an attempt to link gut microbial content to systemic immune tone, we analyzed memory T cell responses from peripheral blood, elicited Routy et al., Science 359, 9197 (2018)5 January 20182 of 7 0 20 40 60 80 100 Progression-free survival (%) Months NSCLC+RCC+UC 0 20 40 60 80 100 0612182430364248 Overall survival (%) Months 180135684118141164 694117611000 NSCLC+RCC+UC Overall survival (%) Months NSCLC 0 20 40 60 80 100 0612182430 Progression-free survival (%) Months 47276522 2061000 RCC RET Tumor size (mm2) Tumor size (mm2) 0204060 0 20 40 60 80 100 * n=10 RET Overall survival (%) Overall survival (%) 020406080 0 20 40 60 80 100 n=14-15 * MCA-205MCA-205 Iso Ctrl PD-1 Water ATB Days after tumor inoculationDays after tumor inoculation Days after tumor inoculationDays after tumor inoculation No ATB: 15.3 mo ATB: 8.3 mo Median OS p=0.001 No ATB: 7.4 mo ATB: 4.3 mo Median PFS p=0.012 No ATB: 4.1 mo ATB: 3.5 mo Median PFS p=0.017 No ATB: 20.6 mo ATB: 11.5 mo Median OS p28.1 IFN-28.1 SpeciesRelative Frequency Differencep-value Enterococcus hirae0.06 Enterococcus durans0.14 Enterococcus gallinarum0.07 Bacillus licheniformis0.14 Enterococcus faecalis0.07 Escherichia coli0.07 Lysinibacillus boronitolerans0.07 Streptococcus gallolyticus0.14 Streptococcus infantarius0.07 Actinobaculum schaalii0.14 Bacillus circulans0.14 Bacillus firmus0.14 Enterococcus mundtii0.14 Streptococcus mutans0.14 Kocuria kristinae0.14 Enterococcus casseliflavus0.14 Staphylococcus haemolyticus0.03 Corynebacterium aurimucosum0.01 Signed p-value 0.001 0.005 0.01 0.05 0.2 1 0.001 0.005 0.01 0.05 0.2 CAG01308, 666, unclassified Firmicutes CAG00676, 1643, unclassified Firmicutes CAG00391, 2019, unclassified Clostridiales CAG00530, 1821, Prevotella CAG00892, 1324, Firmicutes CAG00966, 1209, Firmicutes bacterium CAG:552 CAG00328, 2113, Alistipes indistinctus CAG00355, 2067, Bacteroides sp. CAG:661 CAG01223, 819, unclassified Firmicutes CAG00646, 1668, Alistipes CAG00821, 1461, unclassified Clostridiales CAG01227, 815, unclassified CAG00363, 2056, Intestinimonas CAG00347, 2087, Enterococcus faecium CAG00134, 2690, Cloacibacillus porcorum CAG00274, 2209, Erysipelotrichaceae bacterium 5_2_54FAA CAG01090, 1026, unclassified Firmicutes CAG00871, 1362, Lachnospiraceae CAG00469, 1928, Eubacterium sp. CAG:146 CAG01245, 780, Firmicutes CAG00453, 1942, unclassified CAG00301, 3187, Akkermansia muciniphila CAG00141, 2649, Parabacteroides distasonis CAG00116, 2783, Bacteroides nordii CAG00065, 3272, Blautia CAG00835, 1444, Proteobacteria CAG00175, 2509, Bacteroides clarus CAG01214, 832, Blautia CAG00168, 2534, Clostridiales bacterium VE202-14 CAG00211, 2389, Firmicutes bacterium CAG:227 CAG00008, 6646, Clostridium bolteae CAG00048_1, 1403, Clostridiales CAG00137, 2673, Clostridiales CAG01161, 934, unclassified CAG00308, 2153, unclassified CAG00473, 1920, Prevotella sp. CAG:617 CAG00658, 1661, unclassified Firmicutes CAG00960, 1213, Clostridium sp. CAG:921 CAG01004, 1161, Prevotella CAG01112, 1003, unclassified Clostridiales Signed p-value 0.001 0.005 0.01 0.05 0.2 1 0.001 0.005 0.01 0.05 0.2 CAG00250, 2262, Ruminococcus sp. CAG:353 CAG01342, 613, unclassified Ruminococcaceae CAG00994, 1171, unclassified Firmicutes CAG00695, 1618, unclassified Firmicutes CAG00049, 3561, Bacteroides caccae CAG00391, 2019, unclassified Clostridiales CAG00555, 1782, Flavonifractor CAG00317, 2130, Clostridium sp. CAG:230 CAG00629, 1684, Firmicutes bacterium CAG:124 CAG00854, 2572, Ruminococcaceae CAG00913, 1291, unclassified CAG01200, 863, Clostridiales CAG00559, 1776, unclassified Clostridiales CAG00604, 1714, Firmicutes bacterium CAG:110 CAG00676, 1643, unclassified Firmicutes CAG01245, 780, Firmicutes CAG00862, 1390, Firmicutes bacterium CAG:129 CAG00670, 1648, unclassified Firmicutes CAG00510, 1860, Alistipes CAG00871, 1362, Lachnospiraceae CAG01227, 815, unclassified CAG01262, 750, Blautia CAG00945, 2321, Bacteroides xylanisolvens CAG01090, 1026, unclassified Firmicutes CAG00116, 2783, Bacteroides nordii CAG00064, 3310, unclassified CAG00363, 2056, Intestinimonas CAG00301, 3187, Akkermansia muciniphila CAG00646, 1668, Alistipes CAG00453, 1942, unclassified CAG00469, 1928, Eubacterium sp. CAG:146 CAG01308, 666, unclassified Firmicutes CAG00981, 1189, Erysipelotrichaceae CAG01401, 522, Lachnospiraceae CAG00720, 1590, Anaerotruncus colihominis CAG00168, 2534, Clostridiales VE202-14 CAG00048_1, 1403, Clostridiales CAG00211, 2389, Firmicutes bacterium CAG:227 CAG00141, 2649, Parabacteroides distasonis CAG00690, 1629, unclassified Clostridiales Enriched in NR: Objective response (PD or death) Enriched in R: Objective response (PR and SD) Enriched in NR: Objective response (PD or death) Enriched in R: Objective response (PR and SD) 2 x105 4 x105 6 x105 8 x105 10 x105 Gene Count p = 0.002 0 100 200 300 400 MGS count p = 0.003 0.05 0.1 High cytokine longer PFS High cytokine shorter PFS Akkermansia 0369121518 Progression-free survival (%) Months 151175111 16932000 IFN-18.1 IFN-18.1 p=0.032 CD4 + 0 20 40 60 80 100 Enriched in patients PFS 3 mo PFS6 mo PFS6 mo Fig. 2. Metagenomic analysis of fecal samples predicts response at 3 months of PD-1 mAb treatment in cancer patients. (A) Shotgun sequencing of fecal samples at diagnosis with representation of gene and MGS counts for all cancer patients according to clinical outcome (PFS at 6 months). Data are means SEM of counts for patients experiencing PFS shorter or longer than 6 months. Gene or MGS richness did not predict PFS at 3 months. (B and C) Shotgun sequencing of fecal samples at diagnosis with representation of the relative abundance of each MGS in responders (R) (partial response or stable disease) over nonresponders (NR) (progression or death) defined using the best clinical response according to RECIST1.1 criteria (B) or PFS at 3 months (C) and the corresponding P value on the entire cohort of n = 100 (60 NSCLC and 40 RCC) patients (B) and excluding those who took ATB (C) (fig. S5B), n = 78 (42 R, 36 NR); see also fig. S5A for all patients.T0 samples were analyzed; when not available,T1 specimens were used,asthere wasno statisticaldifferencebetweenT0and T1(fig.S3A). (D) Frequency of patients with detectable A. muciniphila in their feces according to PR (partial response), SD (stable disease), or PD (progressive disease) clinical status, as assessed by metagenomics and analyzed by Cochran-Armitage test. (E and F) Circulating memory Tcell immune responses directed against commensals detected during PD-1 blockade and evaluation of the time to progression. (E) Heat map of the P values for each cytokine and each commensal, segregating NSCLC+RCC patients PFS according to the median value of cytokine production of the whole cohort. Significant P values (0.05, Student t test) are indicated with an aster