Methods for enhancing immune checkpoint blockade therapy by modulating microbiome

Abstract

To provide compositions for treatment of cancer by modulating a microbiome to enhance the efficacy of immune checkpoint blockade.SOLUTION: A microbiome can be modulated by the administration of butyrate and / or butyrate-producing bacteria. In one embodiment, the present disclosure provides a composition comprising at least one isolated or purified population of bacteria belonging to one or more of Ruminococcaceae, Clostridiaceae, Lachnospiraceae, Micrococcaceae, and / or Veilonellaceae. Also provided herein are methods of determining a response to an immune checkpoint inhibitor by identifying whether a subject has a favorable microbial profile.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001]

[0002] The present invention relates generally to the fields of microbiology, immunology, and medicine. More particularly, the present invention relates to the use of the microbiome to improve the efficacy of immune checkpoint blockade therapy. [Background technology]

[0002]

[0003] Within the past decade, significant advances have been made in the treatment of melanoma through the use of targeted therapies and immunotherapy. In particular, the use of immune checkpoint inhibitors has shown remarkable promise, leading to FDA approval of several agents (e.g., the anti-CTLA-4 antibody ipilimumab and the anti-PD-1 antibodies nivolumab and pembrolizumab) that block immunoregulatory molecules on the surface of T lymphocytes. Importantly, treatment with immune checkpoint blockade can result in long-lasting complete responses, although overall response rates remain low (i.e., 15% with CTLA-4 blockade and 30-40% with PD-1 blockade).

[0003]

[0004] However, immune checkpoint inhibitors can be associated with substantial toxicity, and only a subset of patients may benefit. While efforts are underway to better understand the variability in response to immune checkpoint blockade, what contributes to this enhanced response in these patients remains unclear, and there is an urgent need to identify viable strategies to improve response to treatment in all patients.

[0004]

[0005] The role of the host microbiome in response to cancer therapy is increasingly recognized, and studies suggest that bacteria present in tumors and the gut can influence treatment response. The role of the gut microbiome in shaping immune responses in health and disease is also increasingly recognized. However, significant gaps in translational knowledge exist, and there is an unmet need for therapeutic strategies to enhance responses to immune checkpoint blockade in melanoma and other cancers. Summary of the Invention

[0005]

[0006] In one embodiment, the present disclosure provides a composition comprising at least one isolated or purified population of bacteria belonging to one or more of the families Ruminococcaceae, Clostridiaceae, Lachnospiraceae, Micrococcaceae, and / or Veillonellaceae. In other embodiments, the composition comprises at least two isolated or purified populations of bacteria belonging to one or more of the families Ruminococcaceae, Clostridiaceae, Lachnospiraceae, Micrococcaceae, and / or Veillonellaceae. In certain embodiments, the composition is a live bacterial product, a live biotherapeutic product, or a probiotic composition. In yet other embodiments, the at least one isolated or purified population of bacteria or the at least two isolated or purified populations of bacteria are provided as bacterial spores. In another embodiment, the at least one bacterial population belongs to the order Clostridiales, family XII and / or family XIII of the order Clostridiales. In some aspects, the composition comprises at least two isolated or purified bacterial populations belonging to the family Ruminococcaceae and / or the family Clostridiaceae. In other embodiments, the composition comprises at least one bacterial population belonging to the family Ruminococcaceae and at least one bacterial population belonging to the family Clostridiaceae. In some aspects, the two bacterial populations belonging to the family Ruminococcaceae are further defined as bacterial populations belonging to the genus Ruminococcus. In certain aspects, the at least two isolated or purified bacterial populations belonging to the family Ruminococcaceae are further defined as bacterial populations belonging to the genus Faecalibacterium. In certain embodiments, the population of bacteria belonging to the genus Faecalibacterium is further defined as a population of bacteria belonging to the species Faecalibacterium prausnitzii.In certain embodiments, the population of bacteria belonging to the genus Ruminococcus is further defined as a population of bacteria belonging to the species Ruminococcus bromii. In some embodiments, the at least two isolated or purified populations of bacteria belonging to the family Micrococcaceae are further defined as a population of bacteria belonging to the genus Rothia. In further embodiments, the composition further comprises a population of bacteria belonging to the species Porphyromonas pasteri, Clostridium hungatei, Phascolarctobacterium faecium, the genus Peptoniphilus, and / or the class Mollicutes. In certain embodiments, the composition does not include a population of bacteria belonging to the order Bacteroidales.

[0006]

[0007] Certain embodiments of the present disclosure provide methods of preventing cancer in a subject, the method comprising administering to the subject a composition of embodiments. For example, in some aspects, a method for preventing cancer in a subject at risk of developing cancer (e.g., melanoma) or treating cancer in a subject with a tumor comprises administering to the subject a composition comprising at least one isolated or purified population of bacteria belonging to one or more of the class Clostridia, class Mollicutes, order Clostridiales, family Ruminococcaceae, and / or genus Faecalibacterium, wherein administering the composition increases CD8 expression in the tumor. +Methods are provided that result in an expansion of T lymphocytes. In certain embodiments, the T lymphocytes are cytotoxic T lymphocytes. In yet other embodiments, a method is provided for treating cancer in a subject, comprising administering a composition comprising a population of at least one isolated or purified bacterium belonging to one or more of the class Clostridia, class Mollicutes, order Clostridiales, family Ruminococcaceae, and / or genus Faecalibacterium, wherein administration of the composition results in an expansion of effector CD4 T cells in the systemic circulation or peripheral blood of the subject. + , CD8 +In some embodiments, the method is a method of treating cancer in a subject, comprising administering a composition comprising a population of at least one isolated or purified bacterium belonging to one or more of the class Clostridia, class Mollicutes, order Clostridiales, family Ruminococcaceae, and / or genera Faecalibacterium and / or Ruminococcus, wherein administration of the composition results in a decrease in B cells, regulatory T cells, and / or myeloid-derived suppressor cells in the systemic circulation or peripheral blood of the subject. In another aspect, a method is a method of treating cancer in a subject having a tumor, comprising administering a composition comprising at least one isolated or purified population of bacteria belonging to one or more of the class Clostridia, the class Mollicutes, the order Clostridiales, the family Ruminococcaceae, and / or the genus Faecalibacterium, wherein administering the composition to the subject results in increased expression of CD3, CD8, PD1, FoxP3, Granzyme B, and / or PD-L1 in the tumor immune infiltrate. In yet another aspect, a method is a method of treating cancer in a subject having a tumor, comprising administering a composition comprising at least one isolated or purified population of bacteria belonging to one or more of the class Clostridia, the class Mollicutes, the order Clostridiales, the family Ruminococcaceae, and / or the genus Faecalibacterium, wherein administering the composition to the subject results in decreased expression of RORyT in the tumor immune infiltrate. Also disclosed is a method of treating a tumor in a subject diagnosed with or suspected of having cancer, comprising administering a composition comprising a population of at least one isolated or purified bacterium belonging to one or more of the classes Clostridia, Mollicutes, Clostridiales, Ruminococcaceae, and / or Faecalibacterium, wherein administering the composition to the subject increases or decreases the expression of CD45 in the tumor. + , CD3 + / CD20 + / CD56 + , CD68 + , and / or HLA-DR +Also described are methods for increasing the level of natural effector cells. In some aspects, the compositions of the embodiments are administered in an amount sufficient to increase the level of natural effector cells in a subject. In other aspects, administering the composition to a subject results in an increase in the level of natural effector cells in the subject. For example, administering the composition increases the level of CD45 + CD11b + Ly6G + In some aspects, the compositions of the embodiments are administered in an amount sufficient to reduce the level of suppressor myeloid cells in the subject. In further aspects, administration of the composition to a subject results in a reduction in the level of suppressor myeloid cells in the subject. For example, administration of the composition may increase the number of natural effector cells, such as CD45 + CD11b + CD11c + In certain embodiments, the composition comprises the bacterium Faecalibacterium prausnitzii.

[0007]

[0008] Another embodiment provides a method of treating cancer in a subject, comprising administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject, wherein the subject has been determined to have a favorable microbial profile in the gut microbiome. In some aspects, the favorable microbial profile is further defined as having one or more of the bacterial populations of the probiotic composition or live bacterial product composition of the embodiments. In a further embodiment, a method is provided for predicting a response (e.g., predicting survival) to an immune checkpoint inhibitor in a patient with cancer, comprising detecting a microbial profile in a sample obtained from the patient, wherein the response is favorable if the microbial profile includes one or more of the bacterial populations of the probiotic composition or live bacterial product composition of the embodiments. In certain embodiments, if the patient is predicted to have a favorable response to the immune checkpoint inhibitor, the patient is administered an immune checkpoint inhibitor. In certain embodiments, the favorable microbial profile is a favorable gut microbial profile.

[0008]

[0009] In some embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, belong to one or more of the species, subspecies, or bacterial strains selected from the group consisting of the species in Table 1 having an enrichment index (ei) of greater than 0.5, 0.6, 0.7, 0.8, or 0.9. In certain embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, are selected from the group consisting of the species in Table 1 having an "ei" equal to 1.

[0009]

[0010] In certain embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, are identified by an NCBI Taxonomy ID. ID):717959, 587, 758823, 649756, 44749, 671218, 1264, 1122135, 853, 484018, 46503, 54565, 290052, 216931, 575978, 433321, 1796646 , 213810, 228924, 290054, 1509, 1462919, 29375, 337097, 1298596, 487174, 642492, 1735, 1297424, 742766, 46680, 132925, 411467, 1318 465, 1852367, 1841857, 169679, 1175296, 259063, 172901, 39488, 57172, 28118, 166486, 28133, 1529, 694434, 1007096, 84030, 56774, 102148, 626947, 216933, 1348613, 1472417, 100176, 824, 1471761, 1297617, 288966, 1317125, 28197, 358743, 264639, 1265, 1335, 66219, 69473, 115117, 341220, 1732, 873513, 396504, 1796619, 45851, 2741, 105841, 86332, 1349822, 84037, 180311, 54291, 1217282, 762984, 1185412, 154046, 663278, 1543, 398512, 69825, 1841867, 1535, 1510, 84026, 1502, 1619234, 39497, 1544, 29343, 649762, 332095, 536633 , 1033731, 574930, 742818, 177412, 1121308, 419208, 1673717, 55779, 28117, 626937, 180332, 1776382, 40519, 34062, 40518, 74426, 1216062, 293826, 850, 645466, 474960, 36835, 115544, 1515, 88431, 216932, 1417852, 39492, 1583, 420247, 118967, 169435, 37658, 138595,31971、100886、1197717、234908、537007、319644、168384、915173、95159、1816678、626940、501571、1796620、888727、1147123、376806、1274356、1267、39495、404403、1348、253314、258515、33033、1118061、357276、214851、320502、217731、246787、29371、649764、901、29374、33043、39778、682400、871665、160404、745368、408、1584、333367、47246、1096246、53342、438033、351091、1796622、1776384、817、48256、720554、500632、36849、301302、879970、655811、264463、1532、285、995、242750、29539、1432052、622312、1796636、1337051、328814、28446、1492、820、39496、52786、1549、1796618、582、46507、109327、1531、1382、33039、311460、230143、216935、539、35519、1681、328813、214853、89014、1121115、1585974、29466、1363、292800、270498、214856、142877、133926、209880、179628、1121102、105612、1796615、39777、29353、1579、163665、53443、261299、1302、1150298、938289、358742、471875、938278、1796613、1118057、1077144、1737、218205、1121298、684066、433659、52699、204516、706562、253257、328812、1280、147802、58134、1335613、891、585394、1582、235931、308994、1589、1682、1736、28129、178001、551788、2051、856、118562、101070、515619、40215、187979、82979、29363, 1776391, 1285191, 84112, 157688, 38304, 36850, 341694, 287, 75612, 818, 371674, 338188, 88164, 588581, 676965, 546271, 1236512, 178338, 862517, 157687, 158, 51048, 1583331, 529, 888745, 394340, 40545, 855, 553973, 938293, 93063, 708634, 17999 5, 1351, 476652, 1464038, 555088, 237576, 879566, 1852371, 742727, 1377, 35830, 997353, 218538, 83771, 1605, 28111, 131109, 46609, 690567, 46206, 155615, 51616, 40542, 203, 294, 1034346, 156456, 80866, 554406, 796942, 1002367, 29347, 796944, 61592, 4 87175, 1050201, 762948, 137732, 1211819, 1019, 272548, 1717, 384636, 216940, 2087, 45634, 466107, 1689, 47678, 575, 979627, 840, 1660, 1236517, 617123, 546, 28135, 82171, 483, 501496, 99656, 1379, 84032, 39483, 1107316, 584, 28124, 1033744, 657309, 53 Belongs to a species, subspecies, or bacterial strain identified by an NCBI Taxonomy ID selected from the group consisting of: 6441, 76123, 1118060, 89152, 76122, 303, 1541, 507751, 515620, 38302, 53419, 726, 40324, 1796610, 988946, 1852370, 1017, 1168289, 76936, 94869, 1161098, 215580, 1125779, 327575, 549, 1450648, and 478. In particular embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, are closely related to a species, subspecies, or bacterial strain identified by the NCBI taxonomy IDs listed above. For example, in some embodiments,At least one isolated or purified population of bacteria, or at least two isolated or purified populations of bacteria, belong to a species, subspecies, or strain that comprises a 16S ribosomal RNA (rRNA) nucleotide sequence that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to the 16S rRNA nucleotide sequence of one of the bacteria listed above (i.e., Set 1 bacteria from Table 1) or listed in Table 1, and has an ei greater than 0.5 or equal to 1.

[0010]

[0011] In yet other embodiments, the at least one isolated or purified bacterial population, or the at least two isolated or purified bacterial populations, are selected from the group consisting of Bacteroides coagulans, Clostridium aldenense, Clostridium aldrichii, Clostridium alkalicellulosi, Clostridium amygdalinum, Clostridium asparagiforme, Clostridium icellulosi, Clostridium citroniae, Clostridium clariflavum DSM 19732, Clostridium clostridioforme, Clostridium clostridioforme, Clostridium colinum, Clostridium fimetarium, Clostridium hiranonis, Clostridium fungatei, Clostridium hylemonae DSM 15053, Clostridium indolis, Clostridium lactatifermentans, Clostridium leptum, Clostridium methylpentosum, Clostridium oroticum, Clostridium papyrosolvens DSM 2782, Clostridium populeti, Clostridium propionicumpropionicum, Clostridium saccharolyticum, Clostridium scindens, Clostridium sporosphaeroides, Clostridium stercorarium, Clostridium straminisolvens, Clostridium sufflavum, Clostridium termitidis, Clostridium thermosuccinogenes, Clostridium viride, Clostridium xylanolyticum, Desulfotomaculum guttoideum, Eubacterium rectale ATCC 33656, Eubacterium dolichum, Eubacterium eligens ATCC 27750, Eubacterium hallii, Eubacterium infirmum, Eubacterium siraeum, Eubacterium tenue, Ruminococcus torques, Acetanaerobacterium elongatum, Acetatifactor muris muris), Acetivibrio cellulolyticus, Acetivibrio ethanol guignenseethanolgignens, Acholeplasma brassicae 0502, Acholeplasma parvum, Acholeplasma vituli, Acinetobacter junii, Actinobacillus porcinus, Actinomyces bowdenii, Actinomyces dentalis, Actinomyces odontolyticus, Acutalibacter muris, Aerococcus viridans, Aeromicrobium fastidiosum, Alistipes finegoldii finegoldii, Alistipes obesi, Alistipes onderdonkii, Alistipes putredinis, Alistipes shahii, Alistipes shahii WAL 8301, Alistipes timonensis JC136, Alkalibacter saccharofermentans, Alkaliphilus mentalliredigens QYMF, Allisonella histaminiformans, Allobaculum stercoricanis DSM 13633, Alloprevotella rava, Alloprevotella tannerae, Anaerobic bacteriumchartisolvens, Anaerobiospirillum thomasii, Anaerobium acetethylicum, Anaerococcus octavius ​​NCTC 9810, Anaerococcus provenciensis, Anaerococcus vaginalis ATCC 51170, Anaerocolumna jejuensis, Anaerofilum agile, Anaerofustis stercorihominis, Anaeroglobus geminatus geminatus, Anaeromassilibacillus senegalensis, Anaeroplasma abactoclasticum, Anaerorhabdus furcosa, Anaerosporobacter mobilis, Anaerostipes butyraticus, Anaerostipes caccae, Anaerostipes hadrus, Anaerotruncus colihominis, Anaerovorax odorimutans, Anoxybacillus lupiensis rupiensis, Aquabacterium limnoticum, Arcobacter butzleri, Arthrospira platensis, Asaccharobacter celatus, Atopobium parvumparvulum, Bacteroides caccae, Bacteroides caecimuris, Bacteroides cellulosilyticus, Bacteroides clarus YIT 12056, Bacteroides dorei, Bacteroides eggerthii, Bacteroides finegoldii, Bacteroides fragilis, Bacteroides gallinarum, Bacteroides massiliensis, Bacteroides oleiciplenus YIT 12058, Bacteroides plebeius DSM 17135, Bacteroides rodentium JCM 16496, Bacteroides thetaiotaomicron, Bacteroides uniformis, Bacteroides xylanisolvens XB1A, Bacteroides xylanolyticus, Barnesiella intestinihominis, Beduini massiliensis, Bifidobacterium bifidum bifidum, Bifidobacterium dentium, Bifidobacterium longum subsp. infantis, Blautia cesimuliscaecimuris, Blautia coccoides, Blautia faecis, Blautia glucerasea, Blautia hansenii DSM 20583, Blautia hydrogenotrophica, Blautia luti , Blautia luti DSM 14534, Blautia wexlerae DSM 19850, Budvicia aquatica, Butyricicoccus pullicaecorum, Butyricimonas paravirosa, Butyrivibrio crossotus, Caldicoprobacter oshimai, Caloramator coolhaasii, Caloramator proteoclasticus, Caloramator quimbayensis, Campylobacter gracilis gracilis, Campylobacter rectus, Campylobacter ureolyticus DSM 20703, Capnocytophaga gingivalis, Capnocytophaga leadbetteri, Capnocytophaga sputigena, Casaltella massiliensis, Catabacter hongkongensis, Catenibacterium mitsuokai, Christensenella minuta, Christensenella timonensis timonensis, Chryseobacterium taklimakanense, Citrobacter freundii, Cloacibacillusporcorum, Clostridioides difficile ATCC 9689 = DSM 1296, Clostridium amylolyticum, Clostridium bowmanii, Clostridium butyricum, Clostridium cadaveris, Clostridium colicanis, Clostridium gasigenes, Clostridium lentocellum DSM 5427, Clostridium oceanicum, Clostridium oryzae, Clostridium paraptrificum paraputrificum, Clostridium pascui, Clostridium perfringens, Clostridium quinii, Clostridium saccharobuthylicum, Clostridium sporogenes, Clostridium ventriculi, Collinsella aerofaciens, Comamonas testosteroni, Coprobacter fastidiosus NSB1, Coprococcus eutactus, Corynebacterium diphtheriae diphtheriae, Corynebacterium durum, Corynebacterium mycetoidesmycetoides, Corynebacterium pyruviciproducens ATCC BAA-1742, Corynebacterium tuberculostearicum, Culturomica massiliensis, Cuneatibacter caecimuris, Defluviitalea saccharophila, Delftia acidovorans, Desulfitobacterium chlororespirans, Desulfitobacterium metalliredusens metallireducens, Desulfosporosinus acididurans, Desulfotomaculum halophilum, Desulfotomaculum intricatum, Desulfotomaculum tongense, Desulfovibrio desulfuricans subsp. desulfuricans, Desulfovibrio idahonensis, Desulfovibrio litoralis, Desulfovibrio piger, Desulfovibrio simplex simplex, Desulfovibrio zosterae, Desulfuromonas acetoxidans, Dethiobacter alkaliphilus AHT1, Dethiosulfatibacter aminovorans, Dialister invisus, Dialister propionicifaciens, Dielma fastidiosa, Dietzia alimentaria 72, Dorea longicatena, Dysgonomonas gadei ATCC BAA-286, Dysgonomonas mossii, Eggerthella lenta, Eikenella corrodens, Eisenbergiella tyi tayi, Emergencia timonensis, Enorma massiliensis phI, Enterococcus faecalis, Enterorhabdus muris, Ethanoligenens harbinense YUAN-3, Eubacterium coprostanoligenes, Eubacterium limosum, Eubacterium oxidoreducens, Eubacterium sulci ATCC 35585, Eubacterium uniforme uniforme), Eubacterium ventriosum, Eubacterium xylanophilum, Extibacter muris, Ezakiella peruensisperuensis, Faecalibacterium prausnitzii, Faecalicoccus acidiformans, Faecalitalea cylindroides, Filifactor villosus, Flavonifractor plautii, Flintibacter butyricus, Frisingicoccus caecimuris, Fucophilus fucoidanolyticus, Fusicatenibacter saccharivorans, Fusobacterium mortiferum mortiferum, Fusobacterium nucleatum subsp. vincentii, Fusobacterium simiae, Fusobacterium varium, Garciella nitratireducens, Gemella haemolysans, Gemmiger formicilis, Gordonibacter urolithinfaciens, Gracilibacter thermotolerans JW / YJL-S1, Granulicatella elegans elegans, Guggenheimella bovis, Haemophilus haemolyticus, Helicobacter typhlonius, Hesperia stercoli,stercorisuis, Holdemanella biformis, Holdemania massiliensis AP2, Howardella ureilytica, Hungatella effluvii, Hungatella hathewayi, Hydrogenoanaerobacter saccharovorans ium saccharovorans, Ihubacter massiliensis, Intestinibacter bartlettii, Intestinimonas butyriciproducens, Irregularibacter muris, Kiloniella laminariae DSM 19542, Kroppenstedtia guangzhouensis, Lachnoanaerobaculum orale, Lachnoanaerobaculum umeaense, Lachnoclostridium phytofermentans phytofermentans, Lactobacillus acidophilus, Lactobacillus algidus, Lactobacillus animalis, Lactobacillus casei, Lactobacillus delbrueckii, Lactobacillus fornicalis, Lactobacillus iners, Lactobacillus pentosus, Lactobacillus rogosae, Lactococcus garvieae, Lactonifactor longoviformis longoviformis, Leptotrichia buccalis, Leptotrichia hofstadii, Leptotrichia hongkongensishongkongensis, Leptotrichia wadei, Leuconostoc inhae, Levyella massiliensis, Loriellopsis cavernicola, Lutispora thermophila, Marinilabilia salmonicolor JCM 21150, Marvinbryantia formatexigens, Mesoplasma photuris, Methanobrevibacter smithii ATCC 35061, Methanomassiliicoccus ruminiensis luminyensis B10, Methylobacterium extorquens, Mitsuokella jalaludinii, Mobilitalea sibirica, Mobiluncus curtisii, Mogibacterium pumilum, Mogibacterium timidum, Moorella glycerini, Moorella humiferrea, Moraxella nonliquefaciens, Moraxella osloensis, Morganella morganii morganii, Moriella indoligenes, Muribaculum intestinale, Murimonas intestini, Natranaerovirga pectinivorapectinivora, Neglecta timonensis, Neisseria cinerea, Neisseria oralis, Nocardioides mesophilus, Novibacillus thermophilus, Ochrobactrum anthropi, Odoribacter splanchnicus, Olsenella profusa, Olsenella uli, Oribacterium asaccharolyticum ACB7, Oribacterium sinus sinus, Oscillibacter ruminantium GH1, Oscillibacter valericigenes, Oxobacter pfennigii, Pantoea agglomerans, Papillibacter cinnamivorans, Parabacteroides faecis, Parabacteroides goldsteinii, Parabacteroides gordonii, Parabacteroides merdae, Parasporobacterium paucivorans paucivorans, Parasutterella excrementihominis, Parasutterella secunda, Parvimonas micra, Peptococcus nigerniger, Peptoniphilus duerdenii ATCC BAA-1640, Peptoniphilus grossensis ph5, Peptoniphilus koenoeneniae, Peptoniphilus senegalensis JC140, Peptostreptococcus stomatis, Phascolarctobacterium succinatutens, Phocea massiliensis, Pontibacter indicus, Porphyromonas venonis bennonis, Porphyromonas endodontalis, Porphyromonas pasteuri, Prevotella bergensis, Prevotella buccae ATCC 33574, Prevotella denticola, Prevotella enoeca, Prevotella fusca JCM 17724, Prevotella loescheii, Prevotella nigrescens, Prevotella oris, Prevotella pallens ATCC 700821, Prevotella stercorea DSM 18206, Prevotellamassilia timonensis, Propionispira arcuata, Proteus mirabilis, Providencia letegerirettgeri, Pseudobacteroides cellulosolvens ATCC 35603 = DSM 2933, Pseudobutyrivibrio ruminis, Pseudoflavonifractor capillosus ATCC 29799, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas mandelii, Pseudomonas nitroreducens, Pseudomonas putida, Raoultella ornithinolytica ornithinolytica, Raoultella planticola, Raoultibacter massiliensis, Robinsoniella peoriensis, Romboutsia timonensis, Roseburia faecis, Roseburia hominis A2-183, Roseburia intestinalis, Roseburia inulinivorans DSM 16841, Rothia dentocariosa ATCC 17931, Ruminiclostridium thermocellum thermocellum, Ruminococcus albus, Ruminococcus bromii, Ruminococcus callidus, Ruminococcus champanellensis 18P13 =JCM17042, Ruminococcus faecis JCM 15917, Ruminococcus flavefaciens, Ruminococcus gauvreauii, Ruminococcus lactaris ATCC 29176, Rummeliibacillus pycnus, Saccharofermentans acetigenes, Scardovia wiggsiae, Schlegelella thermodepolymerans, Sedimentibacter hongkongensis, Selenomonas sputigena ATCC 35185, Slackia exigua ATCC 700122, Slackia piriformis YIT 12062, Solitalea canadensis, Solobacterium moorei moorei, Sphingomonas aquatilis, Spiroplasma alleghenense, Spiroplasma chinense, Spiroplasma chrysopicola, Spiroplasma culicicola, Spiroplasma lampyridicola, Sporobacter termitidis, Staphylococcus aureus, Stenotrophomonas maltophilia, Stomatobaculum longum, Streptococcus agalactiae ATCC 13813, Streptococcus cristatus, Streptococcus equinus, Streptococcus gordoniigordonii, Streptococcus lactarius, Streptococcus parauberis, Subdoligranulum variabile, Succinivibrio dextrinosolvens, Sutterella stercoricanis, Sutterella wadsworthensis, Syntrophococcus sucromutans, Syntrophomonas zehnderi OL-4, Terrisporobacter mayombei, Thermoleophilum albumin album, Treponema denticola, Treponema socranskii, Tyzzerella nexilis DSM 1787, Vallitalea guaymasensis, Vallitalea pronyensis, Vampirovibrio chlorellavorus, Veillonella atypica, Veillonella denticariosi, Veillonella dispar, Veillonella parvula, Victivallis badensis vadensis, Vulcanibacillus modesticaldus, and Weissella confusa.

[0011]

[0012] In certain embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified populations, belong to a bacterial species selected from the species in Table 2, designated with a response status of Responder (R). In yet further embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, belong to a species, subspecies, or strain that comprises a 16S ribosomal RNA (rRNA) nucleotide sequence that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to a 16S rRNA nucleotide sequence of a bacterium selected from the species in Table 2, designated with a response status of Responder (R). In certain embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, comprise a 16S ribosomal RNA (rRNA) nucleotide sequence that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to a 16S rRNA nucleotide sequence of a bacterium selected from the group consisting of the species in Table 2, designated with a response status of Responder (R), having an uncorrected p-value of less than 0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01.

[0012]

[0013] In certain embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, comprise a 16S ribosomal RNA (rRNA) nucleotide sequence that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to a 16S rRNA nucleotide sequence of a bacterium selected from the group consisting of the species in Table 1, designated with a response status of Responder (R), having an uncorrected p-value of less than 0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01. In certain embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, is a species, subspecies, or strain of bacteria that comprises a 16S rRNA gene sequence that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to a sequence of SEQ ID NOs: 1-876.

[0013]

[0014] In some embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified populations, belong to a bacterial species selected from the species in Table 2, designated with a response status of Responder (R). In particular embodiments, at least one isolated or purified bacterial population, or at least two isolated or purified bacterial populations, belong to a bacterial species selected from the group consisting of the species in Table 2, designated with a response status of Responder (R), with an uncorrected p-value of less than 0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01. In yet another aspect, at least one isolated or purified population of bacteria, or at least two isolated or purified populations of bacteria, are selected from the group consisting of SEQ ID NOs: 877-926, SEQ ID NOs: 927-976, SEQ ID NOs: 977-1026, SEQ ID NOs: 1027-1076, SEQ ID NOs: 1077-1126, SEQ ID NOs: 1127-1176, SEQ ID NOs: 1177-1226, SEQ ID NOs: 1227-1276, SEQ ID NOs: SEQ ID NOs: 1277 to 1326, SEQ ID NOs: 1327 to 1376, SEQ ID NOs: 1377 to 1426, SEQ ID NOs: 1427 to 1476, SEQ ID NOs: 1477 to 1526, SEQ ID NOs: 1527 to 1576, SEQ ID NOs: 1577 to 1626, SEQ ID NOs: 1627 to 1676, SEQ ID NOs: 1677 to 1726, SEQ ID NOs: 1727 to 1776, SEQ ID NOs: 1777 to 1826, SEQ ID NOs: 1827 to 1876, SEQ ID NOs: 18 77 to 1926, SEQ ID NOs: 1927 to 1976, SEQ ID NOs: 1977 to 2026, SEQ ID NOs: 2027 to 2076, SEQ ID NOs: 2077 to 2126, SEQ ID NOs: 2127 to 2176, SEQ ID NOs: 2177 to 2226, SEQ ID NOs: 2227 to 2276, SEQ ID NOs: 2277 to 2326, SEQ ID NOs: 2327 to 2376, SEQ ID NOs: 2377 to 2426, SEQ ID NOs: 2427 to 2476, SEQ ID NOs: 2477 to 2526 526, SEQ ID NOs:2527-2576, SEQ ID NOs:2577-2626, and SEQ ID NOs:2627-2676 (see Table 2A).

[0014]

Table 1

[0015] In certain embodiments, at least one isolated or purified bacterial population, or two bacterial populations, have a sequence identity of at least 29% to SEQ ID NOs: 877-926, at least 16.5% to SEQ ID NOs: 927-976, at least 48.5% to SEQ ID NOs: 977-1026, at least 28% to SEQ ID NOs: 1027-1076, at least 93.5% to SEQ ID NOs: 1077-1126, at least 99.5% to SEQ ID NOs: 1127-1176, at least 109.5% to SEQ ID NOs: 1177-1226, at least 119.5% to SEQ ID NOs: 1197-1228, at least 120.5% to SEQ ID NOs: 1207-1228, at least 121.5% to SEQ ID NOs: 1217-1228, at least 122.5% to SEQ ID NOs: 1217-1228, at least 123.5% to SEQ ID NOs: 1217-1228, at least 124.5% to SEQ ID NOs: 1217-1228, at least 125.5% to SEQ ID NOs: 1217-1228, at least 126.5% to SEQ ID NOs: 1217-1228, at least 127.5% to SEQ ID NOs: 1217-1228, at least 128.5% to SEQ ID NOs: 1217-1228, at least 129.5% to SEQ ID NOs: 1217-1228, at least 129.5% to SEQ ID NOs: 26, at least 99% identity to SEQ ID NOs: 1227 to 1276, 100% identity to SEQ ID NOs: 1277 to 1326, at least 21.5% identity to SEQ ID NOs: 1327 to 1376, 100% identity to SEQ ID NOs: 1377 to 1426, at least 97% identity to SEQ ID NOs: 1427 to 1476, at least 55.5% identity to SEQ ID NOs: 1477 to 1526, 100% identity to SEQ ID NOs: 1527 to 1576, at least 34% identity to SEQ ID NOs: 1577 to 1626 identity to SEQ ID NOs: 1627 to 1676, at least 14% identity to SEQ ID NOs: 1677 to 1726, 100% identity to SEQ ID NOs: 1727 to 1776, at least 93% identity to SEQ ID NOs: 1777 to 1826, at least 45% identity to SEQ ID NOs: 1827 to 1876, at least 99% identity to SEQ ID NOs: 1877 to 1926, at least 74% identity to SEQ ID NOs: 1927 to 1976, 100% identity to SEQ ID NOs: 2027 to 2076 100% identity to SEQ ID NOs:2077-2126, at least 20% identity to SEQ ID NOs:2127-2176, at least 84% identity to SEQ ID NOs:2177-2226, at least 35.5% identity to SEQ ID NOs:2177-2226, at least 32.5% identity to SEQ ID NOs:2227-2276, at least 70% identity to SEQ ID NOs:2277-2326, 100% identity to SEQ ID NOs:2327-2376, at least 70.5% identity to SEQ ID NOs:2377-2426, at least 99% identity to SEQ ID NOs:2427-2476.The species, subspecies, or strain is selected from the group consisting of a species, subspecies, or strain comprising a nucleotide sequence with at least 5% identity to SEQ ID NOs: 2477-2526, at least 68.5% identity to SEQ ID NOs: 2477-2526, 100% identity to SEQ ID NOs: 2527-2576, at least 97.5% identity to SEQ ID NOs: 2577-2626, or 100% identity to SEQ ID NOs: 2627-2676.

[0016] In certain embodiments, at least one isolated or purified population of bacteria, or two populations of bacteria, have a sequence identity of at least 29% to the gene of Faecalibacterium sp. CAG:74 corresponding to SEQ ID NOs:877-926, at least 16.5% to the gene of Clostridiales NK3B98 corresponding to SEQ ID NOs:927-976, at least 48.5% to the gene of Subdoligranulum sp. 4_3_54A2FAA corresponding to SEQ ID NOs:977-1026, at least 28% to the gene of Faecalibacterium sp. CAG:74 corresponding to SEQ ID NOs:1027-1076, at least 93.5% to the gene of Oscillibacter sp. CAG:155 corresponding to SEQ ID NOs:1077-1126, at least 10% to the gene of Oscillibacterium sp. CAG:155 corresponding to SEQ ID NOs:112 at least 99.5% identity to the gene of Clostridium sp. CAG:7 corresponding to SEQ ID NOs: 1177-1226, at least 99.5% identity to the gene of Eubacterium sp. CAG:86 corresponding to SEQ ID NOs: 1177-1226, at least 99% identity to the gene of Firmicutes CAG:176 corresponding to SEQ ID NOs: 1227-1276, 100% identity to the gene of Akkermansia sp. CAG:344 corresponding to SEQ ID NOs: 1277-1326, at least 21.5% identity to the gene of Faecalibacterium sp. CAG:74 corresponding to SEQ ID NOs: 1327-1376, 100% identity to the gene of Clostridium sp. JCC corresponding to SEQ ID NOs: 1427-1476; at least 97% identity to the gene of Faecalibacterium prausnitzii SL3 / 3 corresponding to SEQ ID NOs: 1477-1526.5% identity, 100% identity to the gene of Clostridium sp. CAG:242 corresponding to SEQ ID NOs: 1527-1576, at least 34% identity to the gene of Clostridium sp. CAG:226 corresponding to SEQ ID NOs: 1577-1626, at least 14% identity to the gene of Ruminococcus sp. CAG:382 corresponding to SEQ ID NOs: 1627-1676, 100% identity to the gene of Bifidobacterium bifidum S17 corresponding to SEQ ID NOs: 1677-1726 at least 93% identity to the gene of Roseburia sp. CAG:309 corresponding to SEQ ID NOs:1727-1776; 100% identity to the gene of Alistipes zimonensis JC136 corresponding to SEQ ID NOs:1777-1826; at least 45% identity to the gene of Firmicutes CAG:103 corresponding to SEQ ID NOs:1827-1876; at least 99% identity to the gene of Alistipes senegalensis JC50 corresponding to SEQ ID NOs:1877-1926; at least 74% identity to the gene of Firmicutes CAG:176 corresponding to SEQ ID NOs:1927-1976; 100% identity to the gene of Subdoligranurum sp. CAG:314 corresponding to SEQ ID NOs:2027-2076; at least 20% identity to the gene of Clostridium sp. CAG:226 corresponding to SEQ ID NOs:2077-2126; at least 84% identity to the gene of Firmicutes fungus CAG:124 corresponding to SEQ ID NOs:2127-2176; at least 35.5% identity to the gene of Intestinimonas butyriciproduscens corresponding to SEQ ID NOs:2177-2226; at least 32% identity to the gene of Clostridium sp. CAG:226 corresponding to SEQ ID NOs:2227-2276.5% identity, at least 70% identity to the gene of Firmicutes CAG:124 corresponding to SEQ ID NOs: 2277-2326, 100% identity to the gene of Faecalibacterium prausnitzii L2-6 corresponding to SEQ ID NOs: 2327-2376, at least 70.5% identity to the gene of Ruminococcaceae D16 corresponding to SEQ ID NOs: 2377-2426, Clostridium spiroforme DSM corresponding to SEQ ID NOs: 2427-2476 1552, at least 68.5% identity to the gene of Intestinimonas butyriciproduscens corresponding to SEQ ID NOs:2477-2526, 100% identity to the gene of Phascolarctobacterium sp. CAG:207 corresponding to SEQ ID NOs:2527-2576, at least 97.5% identity to the gene of Faecalibacterium prausnitzii L2-6 corresponding to SEQ ID NOs:2577-2626, or 100% identity to the gene of Streptococcus parasanguinis ATCC 15912 corresponding to SEQ ID NOs:2627-2676.

[0017] In some embodiments, the bacteria are lyophilized or freeze-dried. In certain embodiments, the composition is formulated for oral delivery. For example, a composition formulated for oral delivery is a tablet or capsule. In certain embodiments, the tablet or capsule comprises an acid-resistant enteric coating. In certain embodiments, a composition comprising at least one isolated or purified bacterial population or at least two isolated or purified bacterial populations is formulated for rectal administration via colonoscopy, nasogastric sigmoidoscopy, or enema. In some embodiments, the composition is lyophilized or frozen. In certain embodiments, the composition can be reformulated for final delivery including liquids, suspensions, gels, gel tabs, semisolids, tablets, sachets, lozenges, capsules, or as an enteral formulation. In some embodiments, the composition is formulated for multi-dose administration. In some embodiments, at least one isolated or purified bacterial population or at least two isolated or purified bacterial populations comprise an antibiotic resistance gene. In some embodiments, at least one isolated or purified bacterial population or at least two isolated or purified bacterial populations are populations of short-chain fatty acid-producing bacteria. In certain embodiments, the population of short-chain fatty acid-producing bacteria is a population of butyrate-producing bacteria. In certain embodiments, at least one immune checkpoint inhibitor is administered intravenously and the population of butyrate-producing bacteria is administered orally.

[0018]

[0018] Embodiments of the present disclosure provide methods of treating cancer in a subject, the method comprising administering a therapeutically effective amount of a short-chain fatty acid, such as butyric acid, and / or a short-chain fatty acid-producing bacterial population, such as a butyric acid-producing bacterial population, to the subject, wherein the subject has been administered an immune checkpoint inhibitor. In some aspects, the method further comprises administering at least one immune checkpoint inhibitor. In certain aspects, more than one checkpoint inhibitor is administered. In some aspects, the method further comprises administering a prebiotic or probiotic.

[0019] In some aspects, the population of short-chain fatty acid-producing bacteria includes bacteria comprising an antibiotic resistance gene. In some aspects, the population of butyric acid-producing bacteria includes bacteria comprising an antibiotic resistance gene. In some embodiments, the method further includes administering such a population of short-chain fatty acid-producing antibiotic-resistant bacteria, e.g., such a population of butyric acid-producing antibiotic-resistant bacteria, to a subject with cancer. In some embodiments, the method further includes administering to the subject an antibiotic to which the population of short-chain fatty acid-producing antibiotic-resistant bacteria, such as the population of butyric acid-producing antibiotic-resistant bacteria, is resistant, where the antibiotic resistance gene confers resistance to the antibiotic.

[0020] In some embodiments, the population of butyric acid-producing bacteria includes one or more bacterial species from the order Clostridiales. In certain embodiments, the one or more bacterial species are from the family Ruminococcaceae, Christensenellaceae, Clostridiaceae, or Coriobacteriaceae. In certain embodiments, the one or more bacterial species are selected from the group consisting of Faecalibacterium prausnitzii, Ruminococcus albus, Ruminococcus bromii, Ruminococcus callidus, Ruminococcus flavefaciens, Ruminococcus champanerensis, Ruminococcus faecius, Ruminococcus gobroii, Ruminococcus gnavus, Ruminococcus hansenii, Ruminococcus hydrogenotrophicus, Ruminococcus lactalis, Ruminococcus luti, Ruminococcus obeum, Ruminococcus palustris, and the like. In one particular aspect, the one or more bacterial species is selected from the group consisting of: Bacillus subtilis, Bacillus niger, Bacillus subtilis ... In certain embodiments, the population of butyrate-producing bacteria does not include bacterial species of the family Prevotellaceae or the order Bacteroidetales.

[0021] In certain embodiments, administering butyric acid comprises administering a prodrug or salt of butyric acid, hi certain embodiments, administering butyric acid comprises administering sodium butyrate, arginine butyrate, ethyl butyryl lactate, tributyrin, 4-phenylbutyric acid, pivaloyloxymethyl butyric acid (AN-9), or butylidene dibutyric acid (AN-10).

[0022] In some embodiments, the butyrate or butyrate-producing bacterial population is administered orally, by colonoscopy, sigmoidoscopy, rectally via an enema, or by direct injection. In certain embodiments, at least one immune checkpoint inhibitor is administered intravenously, and the butyrate and / or butyrate-producing bacterial population is administered orally.

[0023] In some embodiments, the at least one checkpoint inhibitor is selected from an inhibitor of CTLA-4, PD-1, PD-L1, PD-L2, LAG-3, BTLA, B7H3, B7H4, TIM3, KIR, or A2aR. In certain embodiments, the at least one immune checkpoint inhibitor is a human programmed cell death 1 (PD-1) axis-binding antagonist. In some embodiments, the PD-1 axis-binding antagonist is selected from the group consisting of a PD-1 binding antagonist, a PDL1 binding antagonist, and a PDL2 binding antagonist. In certain embodiments, the PD-1 axis-binding antagonist is a PD-1 binding antagonist. In some embodiments, the PD-1 binding antagonist inhibits binding of PD-1 to PDL1 and / or PDL2. In certain embodiments, the PD-1 binding antagonist is a monoclonal antibody or an antigen-binding fragment thereof. In detailed embodiments, the PD-1 binding antagonist is nivolumab, pembrolizumab, pidilizumab, KEYTRUDA®, AMP-514, REGN2810, CT-011, BMS 936559, MPDL328OA, or AMP-224. In some embodiments, the at least one immune checkpoint inhibitor is an anti-CTLA-4 antibody. In certain embodiments, the anti-CTLA-4 antibody is tremelimumab, YERVOY®, or ipilimumab. In certain embodiments, the at least one immune checkpoint inhibitor is an anti-killer cell immunoglobulin-like receptor (KIR) antibody. In some embodiments, the anti-KIR antibody is lirilumab.

[0024] In certain embodiments, the cancer is a skin cancer such as basal cell skin cancer, squamous cell skin cancer, or melanoma. In other embodiments, the skin cancer is a skin cancer selected from the group consisting of dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, and angiosarcoma. In certain embodiments, the melanoma is metastatic melanoma. In other embodiments, the melanoma is lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

[0025] In certain embodiments, the method further comprises administering at least one additional anti-cancer treatment. In some embodiments, the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy. In some embodiments, the biological therapy is a monoclonal antibody, siRNA, miRNA, antisense oligonucleotide, ribozyme, or gene therapy.

[0026] In some embodiments, the at least one immune checkpoint inhibitor and / or at least one additional anti-cancer treatment are administered intratumorally, intraarterially, intravenously, intravascularly, intrathoracically, intraperitoneally, intrathecally, intramuscularly, endoscopically, intralesionally, percutaneously, subcutaneously, topically, stereotactically, orally, or by direct injection or perfusion. In certain embodiments, the at least one immune checkpoint inhibitor is administered intravenously, and the butyrate and / or butyrate-producing bacterial population is administered orally.

[0027]

[0027] Another embodiment provides a method for treating cancer in a subject, comprising administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject, wherein the subject has been determined to have a favorable microbial profile in the gut microbiome. In some embodiments, a suitable microbial profile is further defined as having (a) high alpha diversity of the gut microbiome; (b) high abundance of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, in the gut microbiome; (c) one or more (e.g., two, three, four, five, six, seven, eight, nine, ten, or more) bacteria selected from the group consisting of the species in Table 1, with an enrichment index (ei) in the gut microbiome greater than 0.5, 0.6, 0.7, 0.8, or 0.9, or equal to 1; (d) one or more (e.g., two, three, four, five, six, seven, eight, nine, ten, or more bacterial species) of the bacterial species in Table 2, labeled with a responder (R) response status, in the gut microbiome; and / or (e) beta diversity (e.g., by weighted UniFrac distance) clustering around the R centroid.

[0028]

[0028] In some embodiments, a suitable microbial profile is further defined as the presence or high abundance of bacteria from the phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, genus Ruminococcus, genus Faecalibacterium, genus Hydrogenoanaerobacterium, phylum Actinobacteria, class Coriobacteriia, order Coriobacteriales, family Coriobacteriaceae, domain Archaea, phylum Cyanobacteria, phylum Euryarchaeota, or family Christensenellaceae. In certain embodiments, a suitable microbial profile is further defined as the absence or low abundance of bacteria from the Escherichia coli species, Anaerotruncus colihominis species, Dialister genus, Veillonellaceae family, Bacteroidetes phylum, Bacteroidales class, Bacteroidia order, or Prevotellaceae family. In certain embodiments, a suitable microbial profile is defined as the presence or high abundance of bacteria from the Clostridiales order and the absence or low abundance of bacteria from the Bacteroidales order. In some embodiments, a suitable microbial profile is further defined as a high abundance of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria. In certain embodiments, butyrate-producing bacteria include one or more species from the genus Ruminococcus or Faecalibacterium.

[0029] In some embodiments, the subject is determined to have a favorable microbial profile or a favorable gut microbiome by analyzing the microbiome in a patient sample. In certain embodiments, the patient sample is a fecal sample or an oral sample. In some embodiments, analyzing includes performing 16S ribosomal sequencing and / or metagenomic whole genome sequencing.

[0030] In a further embodiment, a method for predicting response to an immune checkpoint inhibitor (e.g., patient survival) in a patient with cancer, comprising detecting a microbial profile in a sample obtained from the patient, wherein the microbial profile is characterized by: (a) high alpha diversity; (b) high abundance of short-chain fatty acid producers, such as butyrate producers; (c) one or more species (e.g., α- and β-actin-producing bacteria) selected from the group consisting of: (a) high alpha diversity; (b) high abundance of short-chain fatty acid producers, such as butyrate producers; (c) an enrichment index (ei) greater than or equal to 0.5, 0.6, 0.7, 0.8, or 0.9, or one or more species (e.g., α- and β-actin-producing bacteria) in Table 1 (e.g., α- and β-actin-producing bacteria) in the sample obtained from the patient. (d) one or more of the bacterial species in Table 2 (e.g., two, three, four, five, six, seven, eight, nine, ten, or more bacterial species) displayed with a responder (R) response status; (e) low abundance of the Bacteroidales order; and / or (f) significantly distinct clusters by beta-diversity-weighted UniFrac distance, whereby the patient is predicted to have a favorable response to the immune checkpoint inhibitor. In certain embodiments, if the patient is predicted to have a favorable response to the immune checkpoint inhibitor, the patient is administered an immune checkpoint inhibitor. In certain embodiments, the patient is administered a second immune checkpoint inhibitor. In certain embodiments, the favorable microbial profile is a favorable gut microbial profile.

[0031]

[0031] In certain embodiments, the cancer is a skin cancer such as basal cell skin cancer, squamous cell skin cancer, or melanoma. In other embodiments, the skin cancer is a skin cancer selected from the group consisting of dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, and angiosarcoma. In other embodiments, the melanoma is lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma. In certain embodiments, the immune checkpoint inhibitor is an anti-PD1 monoclonal antibody or an anti-CTLA4 monoclonal antibody.

[0032] In some embodiments, the population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, includes one or more bacterial species from the order Clostridiales. In certain embodiments, the one or more species are from the family Ruminococcaceae, Christensenellaceae, Clostridiaceae, or Coriobacteriaceae. In certain embodiments, the one or more species are selected from the group consisting of Faecalibacterium prausnitzii, Ruminococcus albus, Ruminococcus bromii, Ruminococcus callidus, Ruminococcus flavefaciens, Ruminococcus champanerensis, Ruminococcus faecalis, Ruminococcus gobroii, Ruminococcus gnavus, Ruminococcus hansenii, Ruminococcus hydrogenotrophicus, Ruminococcus lactalis, Ruminococcus In certain embodiments, the one or more species is selected from the group consisting of: Rutti, Ruminococcus obeum, Ruminococcus palustris, Ruminococcus pasteurii, Ruminococcus productus, Ruminococcus sinkii, Ruminococcus turkes, Subdoligranulum variabile, Butyrivibrio fibrisolvens, Roseburia intestinalis, Anerostipes cassae, Blautia obeum, Eubacterium nodatum, and Eubacterium oxidoreducens. In certain embodiments, the one or more species is Faecalibacterium prausnitzii.

[0033] In further aspects, the methods further comprise administering an immune checkpoint inhibitor to the subject predicted to have a favorable response to the immune checkpoint inhibitor. In some aspects, the immune checkpoint inhibitor is an anti-PD1 monoclonal antibody or an anti-CTLA4 monoclonal antibody.

[0034]

[0034] In some aspects, the method further comprises administering at least one additional anti-cancer treatment. In certain aspects, the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy. In certain aspects, the at least one additional anti-cancer treatment is a short-chain fatty acid such as butyric acid, and / or a short-chain fatty acid-producing bacterial population, such as a butyric acid-producing bacterial population. In detailed aspects, the at least one anti-cancer treatment is a composition of the embodiments. In some aspects, the method further comprises administering a prebiotic or probiotic.

[0035]

[0035] In another embodiment, a method for predicting a response to an immune checkpoint inhibitor in a patient with cancer is provided, comprising detecting a microbial profile in a sample obtained from the patient, wherein the patient is predicted to not respond favorably to the immune checkpoint inhibitor if the microbial profile includes: (a) a low abundance of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria; (b) one or more of the bacterial species in Table 2, which are labeled with a response status of non-responder (NR); (c) low alpha diversity; and / or (d) a high amount of Bacteroidales. In a further aspect, the method further comprises administering to the patient a probiotic composition or live bacterial product of an embodiment if the patient is predicted to not respond favorably to the immune checkpoint inhibitor. In yet a further aspect, the patient predicted to not respond favorably to the immune checkpoint inhibitor is administered an immune checkpoint inhibitor after administration of the probiotic composition or live bacterial product of an embodiment.

[0036]

[0036] In a further aspect, the method further comprises administering an additional anti-cancer treatment, which is at least one non-immune checkpoint inhibitor, to the subject who is predicted not to respond favorably to the immune checkpoint inhibitor.

[0037] In further aspects, the method includes administering at least one anti-cancer treatment to the subject. In some aspects, the at least one anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, immune checkpoint inhibitor, second immune checkpoint inhibitor, or biological therapy. In particular aspects, the at least one additional anti-cancer treatment is a short-chain fatty acid such as butyrate, and / or a short-chain fatty acid-producing bacterial population, such as a butyrate-producing bacterial population. In some aspects, the anti-cancer treatment is a prebiotic or probiotic. In detailed aspects, the probiotic is a probiotic composition of the embodiments.

[0038] In certain embodiments, the cancer is a skin cancer such as basal cell skin cancer, squamous cell skin cancer, or melanoma. In other embodiments, the skin cancer is a skin cancer selected from the group consisting of dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, and angiosarcoma. In other embodiments, the melanoma is lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

[0039] In some aspects, a patient is predicted to not respond favorably to an immune checkpoint inhibitor if the microbial profile includes one or more of the bacterial species in Table 2 labeled with a response status of non-responder (NR) or a high amount of Bacteroidales. In some aspects, a patient is predicted to not respond favorably to an immune checkpoint inhibitor if the microbial profile includes one, two, three, four, five, six, seven, eight, nine, ten, or more of the bacterial species in Table 2 labeled with a response status of non-responder (NR). In a further aspect, a method includes administering to a patient a composition comprising a probiotic composition or live bacterial product of an embodiment.

[0040]

[0040] Other objects, features, and advantages of the present invention will become apparent from the following detailed description. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the present invention, are intended for illustrative purposes only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

[0041] The following drawings form part of the present specification and are incorporated to further support certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein. [Brief explanation of the drawings]

[0042] [Figure 1A]

[0023] Figure 1 shows that increased gut microbiome diversity is associated with an enhanced response to PD-1 blockade in patients with metastatic melanoma. (A) Schematic of sample collection and analysis. [Figure 1B](B) Stacked bar graphs of the phylogenetic composition of common bacterial taxa (>0.1% abundance) at the order level in oral (n=109, top) and fecal (n=53, bottom) samples by 16S rRNA sequencing. [Figure 1C] (C) Bipartite network diagram for matched oral and fecal samples from 48 anti-PD-1 treated patients. Edges connect species-level OTUs to the sample nodes in which they are found. [Figure 1D] (D) Inverse Simpson diversity scores for the gut microbiome in R (n=30) and NR (n=13) patients responding to anti-PD-1 therapy by Mann-Whitney (MW) test. [Figure 1E] (E) Phylogenetic composition of 39 fecal samples at the family level (>0.1% abundance) at baseline. Tertiles of inverse Simpson scores were used to define high (>11.63, n=13), intermediate (7.46-11.63, n=13), and low (<7.46, n=13) diversity groups. [Figure 1F](F) Kaplan-Meier (KM) plot of progression-free survival (PFS) by fecal diversity: high (median PFS unspecified), moderate (median PFS = 232 days), and low (median PFS = 188 days). Univariate Cox model for high versus moderate diversity (HR = 3.60, 95% CI = 1.02-12.74) and high versus low diversity (low HR = 3.57, 95% CI = 1.02-12.52). *p<0.05, **p<0.01. [Figure 1G] (G) Principal coordinate analysis of fecal samples (n=43) by response using weighted UniFrac distances. [Figure 2A] Figure 1 shows that compositional differences in the gut microbiome are associated with response to PD-1 blockade. (A) Heatmap of OTU abundance in R (n=30) and NR (n=13). Columns depict patients, and rows depict bacterial species grouped into three sets according to enrichment in NR versus R. [Figure 2B] (B) Phylogenetic composition of OTUs within each set at the order level. [Figure 2C] (C) LEfSe taxonomic cladogram showing differences in fecal taxa. Dot size is proportional to taxon abundance. [Figure 2D](D) LDA scores calculated for the indicated differentially abundant taxa in the fecal microbiomes of R and NR. The length indicates the effect size associated with the taxon. p=0.05 for the Kruskal-Wallis test and LDA score>3. [Figure 2E] (E) Compositional differences in the gut microbiome are associated with response to PD-1 blockade. (F) Differential abundance of gut bacteria in R versus NR by MW test (FDR corrected) within all taxonomic levels. [Figure 2F] (F) Paired comparison of the abundance of bacterial species identified by metagenomic WGS in 25 fecal samples: R (n=14), NR (n=11). *p<0.05, **p<0.01. [Figure 3A] Figure 1 shows that crOTU abundance within the gut microbiome predicts response to PD-1 blockade. (A) Unsupervised hierarchical clustering by complete linkage of crOTU abundance in 43 fecal samples. [Figure 3B] (B) The abundance of crOTUs in the gut microbiome predicts response to PD-1 blockade. (C) Fisher's exact test shows the association of crOTU clusters with response to anti-PD-1. Cluster 1 of crOTUs (n=14: R=14, NR=0); Cluster 2 (n=29: R=16, NR=13). [Figure 3C] (C) KM plots for PFS by crOTU cluster: crOTU cluster 1 (median PFS unspecified), crOTU cluster 2 (median PFS = 242 days). [Figure 3D](D) Fecal taxa differentially abundant in cluster 1 of crOTUs versus cluster 2 of crOTUs by MW test (FDR corrected) within all taxonomic levels. [Figure 3E] (E) PFS in patients with high (n=19, median PFS unspecified) or low (n=20, median PFS=242 days) abundance of F. prausnitzii (top) or high (n=20, median PFS=188 days) or low (n=19, median PFS=393 days) abundance of Bacteroidales (bottom). [Figure 3F] (F) Unsupervised hierarchical clustering of pathway class abundances inferred from MetaCyc pathways predicted in 28 fecal samples from 25 patients (R=14, NR=11). Regular text: biosynthetic pathways; bold text: degradative pathways. *p<0.05. [Figure 4A] Figure 1 shows that a favorable gut microbiome is associated with systemic anti-tumor immunity. (A) IHC quantification of CD8+ infiltrates before treatment in counts per mm2 by one-sided MW test in R (n=15) and NR (n=6). *p=0.04. [Figure 4B] (B) Paired Spearman rank correlation heatmap of significantly different taxa in baseline fecal samples (n=15) and CD3, CD8, PD-1, FoxP3, GzmB, and RORγT densities in counts per mm2, and PD-L1 by H-score in matched tumors. [Figure 4C] (C) Univariate linear regression of CD8+ counts per mm in tumors versus the abundance of Faecalibacterium (open circles and dashed line; r2=0.42, p=0.0067) and Bacteroidales (filled circles and solid line; r2=0.056, p=0.38) in the gut. [Figure 4D] A favorable gut microbiome is associated with systemic anti-tumor immunity. (D) Paired Spearman rank correlation heatmap between significantly different fecal taxa and baseline peripheral blood frequencies of CD4+ effector T cells, CD8+ T cells, myeloid dendritic cells, monocytes, B cells, Tregs, and MDSCs by flow cytometry. [Figure 4E] (E) Multiplex IHC showing representative images. [Figure 4F] (F) Frequencies of immune cells, lymphoid cells, myeloid cells, and MHC II in patients with high Faecalibacterium or Bacteroidales in the gut. [Figure 4G] (G) A favorable gut microbiome is associated with systemic anti-tumor immunity. (H) Proposed mechanism of action of favorable and unfavorable gut microbiome on tumor immunity. [Figure 5A] (A) Mutations per megabase in tumors from patients with matched fecal microbiome samples (n=7 R vs. 3 NR) and driving mutation landscapes in tumors from patients with matched fecal microbiome samples (n=3 NR vs. 7 R). [Figure 5B](B) No differences are observed in the mutational landscapes of R and NR in response to PD-1 blockade. (C) Total nonsynonymous mutation burden in available tumors (n=8 R vs. n=4 NR, p=0.683) by two-sided Mann-Whitney (MW) test. [Figure 6] Figure 1 shows differences in community structure between oral and fecal microbiomes. A bipartite network diagram of operational taxonomic units (OTUs) derived from bacterial 16S rRNA from 109 oral and 53 fecal samples. Edges connect species-level OTUs (diamonds) to sample nodes from the oral cavity (open circles) and feces (filled circles) where the OTUs are found. [Figure 7A] Figure 1 shows that fecal microbiome diversity increases with R versus anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=30, open circles) and NR (n=13, filled circles) using (A) Shannon's index, (B) Simpson's index, and (C) Chao's index by two-sided MW test. *p<0.05, **p<0.01. [Figure 7B] Figure 1 shows that fecal microbiome diversity increases with R versus anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=30, open circles) and NR (n=13, filled circles) using (A) Shannon's index, (B) Simpson's index, and (C) Chao's index by two-sided MW test. *p<0.05, **p<0.01. [Figure 7C] Figure 1 shows that fecal microbiome diversity increases with R versus anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=30, open circles) and NR (n=13, filled circles) using (A) Shannon's index, (B) Simpson's index, and (C) Chao's index by two-sided MW test. *p<0.05, **p<0.01. [Figure 8A]Figure 1 shows that no difference in oral microbiome diversity was observed between R and NR in response to anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=54, open circles) and NR (n=32, filled circles) using a two-tailed MW test: (A) inverse Simpson index (p=0.107), (B) Shannon index (p=0.139), (C) Simpson index (p=0.136), and (D) Chao 1 index (p=0.826). [Figure 8B] Figure 1 shows that no difference in oral microbiome diversity was observed between R and NR in response to anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=54, open circles) and NR (n=32, filled circles) using a two-tailed MW test: (A) inverse Simpson index (p=0.107), (B) Shannon index (p=0.139), (C) Simpson index (p=0.136), and (D) Chao 1 index (p=0.826). [Figure 8C] Figure 1 shows that no difference in oral microbiome diversity was observed between R and NR in response to anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=54, open circles) and NR (n=32, filled circles) using a two-tailed MW test: (A) inverse Simpson index (p=0.107), (B) Shannon index (p=0.139), (C) Simpson index (p=0.136), and (D) Chao 1 index (p=0.826). [Figure 8D] Figure 1 shows that no difference in oral microbiome diversity was observed between R and NR in response to anti-PD-1 therapy. Comparison of alpha diversity scores in R (n=54, open circles) and NR (n=32, filled circles) using a two-tailed MW test: (A) inverse Simpson index (p=0.107), (B) Shannon index (p=0.139), (C) Simpson index (p=0.136), and (D) Chao 1 index (p=0.826). [Figure 9A]Figure 1 shows that high fecal microbiome diversity is associated with prolonged PFS. Gut microbiota at baseline and subsequent treatment course for each subject (n=39). (A) Horizontal bars represent the alpha diversity score, measured by the inverse Simpson's index, for each patient. (B) Time series plot showing the number of days elapsed since receiving treatment. At last follow-up, x=progression, o=no progression. [Figure 9B] Figure 1 shows that high fecal microbiome diversity is associated with prolonged PFS. Gut microbiota at baseline and subsequent treatment course for each subject (n=39). (A) Horizontal bars represent the alpha diversity score, measured by the inverse Simpson's index, for each patient. (B) Time series plot showing the number of days elapsed since receiving treatment. At last follow-up, x=progression, o=no progression. [Figure 10A] Oral microbiome diversity is not associated with PFS. Oral microbiota by patient (n=86) and subsequent treatment course. (A) Stacked bars represent the phylogenetic composition of each sample at the baseline family level. All patients were categorized into high (>6.17), moderate (3.26-6.17), and low (<3.26) diversity groups based on inverse Simpson score tertiles, as indicated. (B) Kaplan-Meier plots of progression-free survival by the indicated oral diversity tertiles: high (n=29, median PFS=279 days), moderate (n=28, median PFS not specified), and low (n=29, median PFS=348 days). Log-rank p=0.34 for high versus moderate, and p=0.54 for high versus low. (C) Horizontal bars represent alpha diversity scores as measured by the inverse Simpson index. (D) Time series plot showing days elapsed since treatment was administered. x = progression, o = no progression at the last follow-up. [Figure 10B]Oral microbiome diversity is not associated with PFS. Oral microbiota by patient (n=86) and subsequent treatment course. (A) Stacked bars represent the phylogenetic composition of each sample at the baseline family level. All patients were categorized into high (>6.17), moderate (3.26-6.17), and low (<3.26) diversity groups based on inverse Simpson score tertiles, as indicated. (B) Kaplan-Meier plots of progression-free survival by the indicated oral diversity tertiles: high (n=29, median PFS=279 days), moderate (n=28, median PFS not specified), and low (n=29, median PFS=348 days). Log-rank p=0.34 for high versus moderate, and p=0.54 for high versus low. (C) Horizontal bars represent alpha diversity scores as measured by the inverse Simpson index. (D) Time series plot showing days elapsed since treatment was administered. x = progression, o = no progression at the last follow-up. [Figure 10C] Oral microbiome diversity is not associated with PFS. Oral microbiota by patient (n=86) and subsequent treatment course. (A) Stacked bars represent the phylogenetic composition of each sample at the baseline family level. All patients were categorized into high (>6.17), moderate (3.26-6.17), and low (<3.26) diversity groups based on inverse Simpson score tertiles, as indicated. (B) Kaplan-Meier plots of progression-free survival by the indicated oral diversity tertiles: high (n=29, median PFS=279 days), moderate (n=28, median PFS not specified), and low (n=29, median PFS=348 days). Log-rank p=0.34 for high versus moderate, and p=0.54 for high versus low. (C) Horizontal bars represent alpha diversity scores as measured by the inverse Simpson index. (D) Time series plot showing days elapsed since treatment was administered. x = progression, o = no progression at the last follow-up. [Figure 10D]Oral microbiome diversity is not associated with PFS. Oral microbiota by patient (n=86) and subsequent treatment course. (A) Stacked bars represent the phylogenetic composition of each sample at the baseline family level. All patients were categorized into high (>6.17), moderate (3.26-6.17), and low (<3.26) diversity groups based on inverse Simpson score tertiles, as indicated. (B) Kaplan-Meier plots of progression-free survival by the indicated oral diversity tertiles: high (n=29, median PFS=279 days), moderate (n=28, median PFS not specified), and low (n=29, median PFS=348 days). Log-rank p=0.34 for high versus moderate, and p=0.54 for high versus low. (C) Horizontal bars represent alpha diversity scores as measured by the inverse Simpson index. (D) Time series plot showing days elapsed since treatment was administered. x = progression, o = no progression at the last follow-up. [Figure 11A] Figure 1 shows enrichment index (ei) scores and relative abundance thresholds for OTUs in 86 oral microbiome samples and 43 fecal microbiome samples. Distribution of enrichment scores for bacterial OTUs at the species level for (A) fecal microbiome samples and (B) oral microbiome samples by set. The boundaries of each set are indicated. Distribution of relative abundance by log10 of species in (C) fecal microbiome samples and (D) oral microbiome samples. The range of each abundance category is indicated. [Figure 11B]Figure 1 shows enrichment index (ei) scores and relative abundance thresholds for OTUs in 86 oral microbiome samples and 43 fecal microbiome samples. Distribution of enrichment scores for bacterial OTUs at the species level for (A) fecal microbiome samples and (B) oral microbiome samples by set. The boundaries of each set are indicated. Distribution of relative abundance by log10 of species in (C) fecal microbiome samples and (D) oral microbiome samples. The range of each abundance category is indicated. [Figure 11C] Figure 1 shows enrichment index (ei) scores and relative abundance thresholds for OTUs in 86 oral microbiome samples and 43 fecal microbiome samples. Distribution of enrichment scores for bacterial OTUs at the species level for (A) fecal microbiome samples and (B) oral microbiome samples by set. The boundaries of each set are indicated. Distribution of relative abundance by log10 of species in (C) fecal microbiome samples and (D) oral microbiome samples. The range of each abundance category is indicated. [Figure 11D] Figure 1 shows enrichment index (ei) scores and relative abundance thresholds for OTUs in 86 oral microbiome samples and 43 fecal microbiome samples. Distribution of enrichment scores for bacterial OTUs at the species level for (A) fecal microbiome samples and (B) oral microbiome samples by set. The boundaries of each set are indicated. Distribution of relative abundance by log10 of species in (C) fecal microbiome samples and (D) oral microbiome samples. The range of each abundance category is indicated. [Figure 12A]Figure 1 shows that there are no significant differences in oral microbiome OTUs between R and NR patients responding to anti-PD-1 therapy by enrichment index (ei) scores. (A) Heatmap of species abundance in R (n=52) and NR (n=34) for each set of bacterial OTUs based on ei scores as indicated. Each column represents a patient, and each row represents a bacterial OTU. High, moderate, and low levels are indicated. (B) Phylogenetic composition of bacterial OTUs within each set at the order level. [Figure 12B] Figure 1 shows that there are no significant differences in oral microbiome OTUs between R and NR patients responding to anti-PD-1 therapy by enrichment index (ei) scores. (A) Heatmap of species abundance in R (n=52) and NR (n=34) for each set of bacterial OTUs based on ei scores as indicated. Each column represents a patient, and each row represents a bacterial OTU. High, moderate, and low levels are indicated. (B) Phylogenetic composition of bacterial OTUs within each set at the order level. [Figure 13A] High-dimensional class comparison using LEfSe reveals increased abundance of Bacteroidales in the oral microbiome of NR in response to anti-PD-1 therapy. (A) Taxonomic cladograms from LEfSe showing differences in oral taxa. Taxa enriched in R and NR, respectively, are displayed with dot size proportional to the taxon abundance. [Figure 13B] (B) High-dimensional class comparison using LEfSe reveals increased abundance of Bacteroidales in the oral microbiome of NR in response to anti-PD-1 therapy. (C) Histogram of LDA scores calculated for differentially abundant taxa between the oral microbiomes of R and NR, where bar length indicates the effect size associated with the taxon. p=0.05 for the Kruskal-Wallis test and LDA score>3. [Figure 14A]Gut microbiome diversity and composition are stable over time. (A) Alpha diversity of the gut microbiome by inverse Simpson over time in three patients (R) with longitudinal collection. (B) Principal component analysis using unweighted UniFrac distances. (C) Stacked bars showing the composition of the gut microbiome in patients over time at the eye level. [Figure 14B] Gut microbiome diversity and composition are stable over time. (A) Alpha diversity of the gut microbiome by inverse Simpson over time in three patients (R) with longitudinal collection. (B) Principal component analysis using unweighted UniFrac distances. (C) Stacked bars showing the composition of the gut microbiome in patients over time at the eye level. [Figure 14C] Gut microbiome diversity and composition are stable over time. (A) Alpha diversity of the gut microbiome by inverse Simpson over time in three patients (R) with longitudinal collection. (B) Principal component analysis using unweighted UniFrac distances. (C) Stacked bars showing the composition of the gut microbiome in patients over time at the eye level. [Figure 15A] (A) Clustering by relative OTU abundance shows no association with response to anti-PD-1 therapy. (B) Unsupervised hierarchical clustering by complete linkage using Euclidean distance based on OTU abundance in (A) 43 fecal microbiome samples and (B) 86 oral microbiome samples. Each column represents a unique microbiome sample, while each row represents a unique OTU. [Figure 15B](A) Clustering by relative OTU abundance shows no association with response to anti-PD-1 therapy. (B) Unsupervised hierarchical clustering by complete linkage using Euclidean distance based on OTU abundance in (A) 43 fecal microbiome samples and (B) 86 oral microbiome samples. Each column represents a unique microbiome sample, while each row represents a unique OTU. [Figure 16A] Figure 1 shows that clusters based on crOTU abundance in the oral microbiome are not associated with response to PD-1 blockade. (A) Unsupervised hierarchical clustering of 86 oral microbiome samples by complete linkage based on crOTU abundance. (B) Comparison of clusters by response showing crOTU cluster 1 (n=11, R=9, and NR=2) and cluster 2 (n=75, R=45, and NR=30). p=0.20 by two-tailed Fisher's exact test. [Figure 16B] Figure 1 shows that clusters based on crOTU abundance in the oral microbiome are not associated with response to PD-1 blockade. (A) Unsupervised hierarchical clustering of 86 oral microbiome samples by complete linkage based on crOTU abundance. (B) Comparison of clusters by response showing crOTU cluster 1 (n=11, R=9, and NR=2) and cluster 2 (n=75, R=45, and NR=30). p=0.20 by two-tailed Fisher's exact test. [Figure 17] Figure 1 shows metabolic profiles based on KEGG orthologs that differ in the gut microbiome of R versus NR in response to PD-1 blockade. Unsupervised hierarchical clustering of common functional pathways (found in at least 20 samples) in 28 fecal samples from 25 patients (n = 14 R and 11 NR) according to the relative abundance of KEGG orthologs. [Figure 18A]Figure 1 shows that responders to PD-1 blockade exhibit enrichment of tumor immune infiltrate at baseline. Immunohistochemical quantification of (A) CD3, (B) PD-1, (C) FoxP3, (D) GzmB, (E) PD-L1, and (F) RORγT as counts per mm2 or H-score in responders (R) and non-responders (NR) to anti-PD-1, and representative images at 40x magnification. [Figure 18B] Figure 1 shows that responders to PD-1 blockade exhibit enrichment of tumor immune infiltrate at baseline. Immunohistochemical quantification of (A) CD3, (B) PD-1, (C) FoxP3, (D) GzmB, (E) PD-L1, and (F) RORγT as counts per mm2 or H-score in responders (R) and non-responders (NR) to anti-PD-1, and representative images at 40x magnification. [Figure 18C] Figure 1 shows that responders to PD-1 blockade exhibit enrichment of tumor immune infiltrate at baseline. Immunohistochemical quantification of (A) CD3, (B) PD-1, (C) FoxP3, (D) GzmB, (E) PD-L1, and (F) RORγT as counts per mm2 or H-score in responders (R) and non-responders (NR) to anti-PD-1, and representative images at 40x magnification. [Figure 18D] Figure 1 shows that responders to PD-1 blockade exhibit enrichment of tumor immune infiltrate at baseline. Immunohistochemical quantification of (A) CD3, (B) PD-1, (C) FoxP3, (D) GzmB, (E) PD-L1, and (F) RORγT as counts per mm2 or H-score in responders (R) and non-responders (NR) to anti-PD-1, and representative images at 40x magnification. [Figure 18E]Figure 1 shows that responders to PD-1 blockade exhibit enrichment of tumor immune infiltrate at baseline. Immunohistochemical quantification of (A) CD3, (B) PD-1, (C) FoxP3, (D) GzmB, (E) PD-L1, and (F) RORγT as counts per mm2 or H-score in responders (R) and non-responders (NR) to anti-PD-1, and representative images at 40x magnification. [Figure 18F] Figure 1 shows that responders to PD-1 blockade exhibit enrichment of tumor immune infiltrate at baseline. Immunohistochemical quantification of (A) CD3, (B) PD-1, (C) FoxP3, (D) GzmB, (E) PD-L1, and (F) RORγT as counts per mm2 or H-score in responders (R) and non-responders (NR) to anti-PD-1, and representative images at 40x magnification. [Figure 19] Figure 1 shows that patients with high abundance of Faecalibacterium genus exhibit a favorable anti-tumor immune infiltrate prior to anti-PD-1 therapy. Figure 2 shows Spearman's rank correlation heatmap of the abundance of all genera within the family Ruminococcaceae in counts per mm2 by IHC in the fecal microbiome (n=15). Positive correlation, negative correlation, and no correlation are displayed. [Figure 20A]Figure 1 shows that the abundance of Faecalibacterium and Bacteroidales in the fecal microbiome have significantly different associations with the tumor immune infiltrate before PD-1 blockade. Linear regressions between the abundance of Faecalibacterium and Bacteroidales and the density in counts per mm2 or H-score by IHC of (A) CD3, (B) GzmB, (C) PD-1, (D) PD-L1, (E) FoxP3, and (F) RORγT in tumors from patients treated with anti-PD-1 at baseline. Lines show regressions for Faecalibacterium (thin line, values ​​in plain letters) and Bacteroidales (thick line, values ​​in bold) with associated r2 and p-values. [Figure 20B] Figure 1 shows that the abundance of Faecalibacterium and Bacteroidales in the fecal microbiome have significantly different associations with the tumor immune infiltrate before PD-1 blockade. Linear regressions between the abundance of Faecalibacterium and Bacteroidales and the density in counts per mm2 or H-score by IHC of (A) CD3, (B) GzmB, (C) PD-1, (D) PD-L1, (E) FoxP3, and (F) RORγT in tumors from patients treated with anti-PD-1 at baseline. Lines show regressions for Faecalibacterium (thin line, values ​​in plain letters) and Bacteroidales (thick line, values ​​in bold) with associated r2 and p-values. [Figure 20C]Figure 1 shows that the abundance of Faecalibacterium and Bacteroidales in the fecal microbiome have significantly different associations with the tumor immune infiltrate before PD-1 blockade. Linear regressions between the abundance of Faecalibacterium and Bacteroidales and the density in counts per mm2 or H-score by IHC of (A) CD3, (B) GzmB, (C) PD-1, (D) PD-L1, (E) FoxP3, and (F) RORγT in tumors from patients treated with anti-PD-1 at baseline. Lines show regressions for Faecalibacterium (thin line, values ​​in plain letters) and Bacteroidales (thick line, values ​​in bold) with associated r2 and p-values. [Figure 20D] Figure 1 shows that the abundance of Faecalibacterium and Bacteroidales in the fecal microbiome have significantly different associations with the tumor immune infiltrate before PD-1 blockade. Linear regressions between the abundance of Faecalibacterium and Bacteroidales and the density in counts per mm2 or H-score by IHC of (A) CD3, (B) GzmB, (C) PD-1, (D) PD-L1, (E) FoxP3, and (F) RORγT in tumors from patients treated with anti-PD-1 at baseline. Lines show regressions for Faecalibacterium (thin line, values ​​in plain letters) and Bacteroidales (thick line, values ​​in bold) with associated r2 and p-values. [Figure 20E]Figure 1 shows that the abundance of Faecalibacterium and Bacteroidales in the fecal microbiome have significantly different associations with the tumor immune infiltrate before PD-1 blockade. Linear regressions between the abundance of Faecalibacterium and Bacteroidales and the density in counts per mm2 or H-score by IHC of (A) CD3, (B) GzmB, (C) PD-1, (D) PD-L1, (E) FoxP3, and (F) RORγT in tumors from patients treated with anti-PD-1 at baseline. Lines show regressions for Faecalibacterium (thin line, values ​​in plain letters) and Bacteroidales (thick line, values ​​in bold) with associated r2 and p-values. [Figure 20F] Figure 1 shows that the abundance of Faecalibacterium and Bacteroidales in the fecal microbiome have significantly different associations with the tumor immune infiltrate before PD-1 blockade. Linear regressions between the abundance of Faecalibacterium and Bacteroidales and the density in counts per mm2 or H-score by IHC of (A) CD3, (B) GzmB, (C) PD-1, (D) PD-L1, (E) FoxP3, and (F) RORγT in tumors from patients treated with anti-PD-1 at baseline. Lines show regressions for Faecalibacterium (thin line, values ​​in plain letters) and Bacteroidales (thick line, values ​​in bold) with associated r2 and p-values. [Figure 21] Figure 1 shows the gating strategy for flow cytometry analysis of peripheral blood in patients treated with anti-PD-1 therapy. PBMCs at baseline from patients treated with anti-PD-1 were analyzed by gating on CD19+ B cells, CD3+CD8+ T cells, CD3+CD4+ T cells (CD3+CD4+FoxP3+ regulatory T cells and CD3+CD4+FoxP3− effector T cells), monocytes (based on CD14 / HLA-DR), and MDSCs (CD3−CD19−HLADRCD33+CD11b+). [Figure 22A]Figure 1 shows that patients with high Faecalibacterium abundance exhibit a peripheral cytokine profile favorable for response to PD-1 blockade at baseline and an enhanced cytokine response over the course of treatment. (A) Spearman rank correlation heatmap between abundance of Clostridiales, Faecalibacterium, Ruminococcaceae, and Bacteroidales and peripheral concentrations of cytokines in pg / mL by multiplex bead assay. Positive correlation, negative correlation, and no correlation are displayed. Changes in cytokine production in serum of responders (n=2) and non-responders (n=2) to anti-PD-1 therapy for (B) IP-10 (p=0.042 and p=0.344, respectively), (C) MIP-1β (p=0.043 and p=0.898, respectively), and (D) IL-17A (p=0.072 and p=0.862, respectively) by fold change from baseline by paired ratio t-test. [Figure 22B] Figure 1 shows that patients with high Faecalibacterium abundance exhibit a peripheral cytokine profile favorable for response to PD-1 blockade at baseline and an enhanced cytokine response over the course of treatment. (A) Spearman rank correlation heatmap between abundance of Clostridiales, Faecalibacterium, Ruminococcaceae, and Bacteroidales and peripheral concentrations of cytokines in pg / mL by multiplex bead assay. Positive correlation, negative correlation, and no correlation are displayed. Changes in cytokine production in serum of responders (n=2) and non-responders (n=2) to anti-PD-1 therapy for (B) IP-10 (p=0.042 and p=0.344, respectively), (C) MIP-1β (p=0.043 and p=0.898, respectively), and (D) IL-17A (p=0.072 and p=0.862, respectively) by fold change from baseline by paired ratio t-test. [Figure 22C]Figure 1 shows that patients with high Faecalibacterium abundance exhibit a peripheral cytokine profile favorable for response to PD-1 blockade at baseline and an enhanced cytokine response over the course of treatment. (A) Spearman rank correlation heatmap between abundance of Clostridiales, Faecalibacterium, Ruminococcaceae, and Bacteroidales and peripheral concentrations of cytokines in pg / mL by multiplex bead assay. Positive correlation, negative correlation, and no correlation are displayed. Changes in cytokine production in serum of responders (n=2) and non-responders (n=2) to anti-PD-1 therapy for (B) IP-10 (p=0.042 and p=0.344, respectively), (C) MIP-1β (p=0.043 and p=0.898, respectively), and (D) IL-17A (p=0.072 and p=0.862, respectively) by fold change from baseline by paired ratio t-test. [Figure 22D] Figure 1 shows that patients with high Faecalibacterium abundance exhibit a peripheral cytokine profile favorable for response to PD-1 blockade at baseline and an enhanced cytokine response over the course of treatment. (A) Spearman rank correlation heatmap between abundance of Clostridiales, Faecalibacterium, Ruminococcaceae, and Bacteroidales and peripheral concentrations of cytokines in pg / mL by multiplex bead assay. Positive correlation, negative correlation, and no correlation are displayed. Changes in cytokine production in serum of responders (n=2) and non-responders (n=2) to anti-PD-1 therapy for (B) IP-10 (p=0.042 and p=0.344, respectively), (C) MIP-1β (p=0.043 and p=0.898, respectively), and (D) IL-17A (p=0.072 and p=0.862, respectively) by fold change from baseline by paired ratio t-test. [Figure 23]Figure 1 shows the gating strategy for bone marrow multiplex IHC in tumors from patients treated with PD-1 blockade at baseline. Immune cells (CD45+), lymphoid cells (CD45+CD3+CD20+CD56+), myeloid cells (CD45+CD3-CD20-CD56-), mast cells (CD45+CD3-CD20-CD56-HLADR-tryptase+), granulocytes (CD45+CD3-CD20-CD56-HLADRCD66b+), M1 tumor-associated macrophages (CD45+CD3-CD20-CD56-HLADR+CSF1R+C), and tumor-associated macrophages (CD45+CD3-CD20-CD56-HLADR+CSF1R+C). Bone marrow multiplex immunohistochemistry gating strategy showing M2 tumor-associated macrophages (CD45+CD3-CD20-CD56-HLADR+CSF1R+CD163+), M3 tumor-associated macrophages (CD45+CD3-CD20-CD56-HLADR+CSF1R-DCSIGN-), mature dendritic cells (CD45+CD3-CD20-CD56-HLADR+CSF1R-DCSIGN+), and immature dendritic cells (CD45+CD3-CD20-CD56-HLADR+CSF1R-DCSIGN+). [Figure 24A] (A) Multiplex immunohistochemistry showing representative bone marrow immune cell staining at 40x magnification. [Figure 24B] (B) High baseline abundance of Faecalibacterium is associated with increased immune infiltrates before PD-1 blockade. (C) Quantification of CD45, CD3 / CD20 / CD56, CD68, CD66b, tryptase, HLA-DR, CD163, and DC-SIGN as counts per mm. [Figure 24C](C) Quantification of myeloid cells, lymphoid cells, mast cells, granulocytes, M1 and M2 tumor-associated macrophages, immature dendritic cells, and mature dendritic cells as a percentage of total CD45+ immune cells in patients with high Faecalibacterium abundance (n=2) or high Bacteroidales abundance (n=2). [Figure 25A] Figure 1 shows that fecal microbiota transplantation (FMT) with a favorable gut microbiome reduces tumor growth in germ-free (GF) mice. (A) Experimental design of FMT1 experiments in germ-free (GF) mice. Time is expressed in days (D) relative to the day of tumor injection (8 x 10 tumor cells). (B) Differences in tumor size implanted in responder (R)-FMT and non-responder (NR)-FMT mice or control mice. Tumor volumes at 14 days after tumor injection are plotted, with each value representing a single mouse. [Figure 25B] Figure 1 shows that fecal microbiota transplantation (FMT) with a favorable gut microbiome reduces tumor growth in germ-free (GF) mice. (A) Experimental design of FMT1 experiments in germ-free (GF) mice. Time is expressed in days (D) relative to the day of tumor injection (8 x 10 tumor cells). (B) Differences in tumor size implanted in responder (R)-FMT and non-responder (NR)-FMT mice or control mice. Tumor volumes at 14 days after tumor injection are plotted, with each value representing a single mouse. [Figure 26A]Figure 1 shows that suitable FMT promoted innate effectors and reduced myeloid suppressor infiltration into the spleens of GF mice. (A) Flow cytometric quantification showing the frequency of CD45+ immune cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). (B) Flow cytometric quantification showing the frequency of CD45+CD11b+CD11c+ natural effector cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). (C) Flow cytometric quantification showing the frequency of CD45+CD11b+CD11c+ suppressor cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). [Figure 26B] Figure 1 shows that suitable FMT promoted innate effectors and reduced myeloid suppressor infiltration into the spleens of GF mice. (A) Flow cytometric quantification showing the frequency of CD45+ immune cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). (B) Flow cytometric quantification showing the frequency of CD45+CD11b+CD11c+ natural effector cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). (C) Flow cytometric quantification showing the frequency of CD45+CD11b+CD11c+ suppressor cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). [Figure 26C]Figure 1 shows that suitable FMT promoted innate effectors and reduced myeloid suppressor infiltration into the spleens of GF mice. (A) Flow cytometric quantification showing the frequency of CD45+ immune cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). (B) Flow cytometric quantification showing the frequency of CD45+CD11b+CD11c+ natural effector cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). (C) Flow cytometric quantification showing the frequency of CD45+CD11b+CD11c+ suppressor cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). [Figure 27A] Figure 1 shows that suitable FMT increases CD45+ cells and CD8+ in the intestine and tumors of GF mice. Representative immunofluorescence staining of (A) tumors and (B) intestines from control (left), NR-FMT (middle), and R-FMT (right) GF mice after FMT for CD45, CD8, and nuclei (DAPI). (C) Tumors (top), and side-by-side comparisons of R-FMT mice (n=2 over 12 fields, median = 433.5 cells per HPF), NR-FMT mice (n=2 over 12 fields, median = 325 cells per HPF), and control mice (n=2 over 9 fields, median = 412 cells per HPF) (p=0.30 (R-FMT vs. control)). Figure 1 shows quantification of CD8+ cell density in the peritoneal and intestinal tracts (bottom) (n=2 R-FMT over 7 regions, median=67 cells per HPF; n=2 NR-FMT over 5 regions, median=24 cells per HPF; n=2 control over 10 regions, median=47 cells per HPF) (p=0.17 (R-FMT vs control)). [Figure 27B]Figure 1 shows that suitable FMT increases CD45+ cells and CD8+ in the intestine and tumors of GF mice. Representative immunofluorescence staining of (A) tumors and (B) intestines from control (left), NR-FMT (middle), and R-FMT (right) GF mice after FMT for CD45, CD8, and nuclei (DAPI). (C) Tumors (top), and side-by-side comparisons of R-FMT mice (n=2 over 12 fields, median = 433.5 cells per HPF), NR-FMT mice (n=2 over 12 fields, median = 325 cells per HPF), and control mice (n=2 over 9 fields, median = 412 cells per HPF) (p=0.30 (R-FMT vs. control)). Figure 1 shows quantification of CD8+ cell density in the peritoneal and intestinal tracts (bottom) (n=2 R-FMT over 7 regions, median=67 cells per HPF; n=2 NR-FMT over 5 regions, median=24 cells per HPF; n=2 control over 10 regions, median=47 cells per HPF) (p=0.17 (R-FMT vs control)). [Figure 27C]Figure 1 shows that suitable FMT increases CD45+ cells and CD8+ in the intestine and tumors of GF mice. Representative immunofluorescence staining of (A) tumors and (B) intestines from control (left), NR-FMT (middle), and R-FMT (right) GF mice after FMT for CD45, CD8, and nuclei (DAPI). (C) Tumors (top), and side-by-side comparisons of R-FMT mice (n=2 over 12 fields, median = 433.5 cells per HPF), NR-FMT mice (n=2 over 12 fields, median = 325 cells per HPF), and control mice (n=2 over 9 fields, median = 412 cells per HPF) (p=0.30 (R-FMT vs. control)). Figure 1 shows quantification of CD8+ cell density in the peritoneal and intestinal tracts (bottom) (n=2 R-FMT over 7 regions, median=67 cells per HPF; n=2 NR-FMT over 5 regions, median=24 cells per HPF; n=2 control over 10 regions, median=47 cells per HPF) (p=0.17 (R-FMT vs control)). [Figure 28A] Figure 1 shows that FMT of a favorable gut microbiome in GF mice reduces tumor growth and enhances response to anti-PD-L1 therapy. (A) Experimental design of FMT2 experiments in germ-free (GF) mice. Time is expressed in days (D) relative to the day of tumor injection (2.5 x 10-5 tumor cells). [Figure 28B] (B) Figure 1. (A) shows that FMT of a favorable gut microbiome in GF mice reduces tumor growth and enhances response to anti-PD-L1 therapy. (B) Figure 1. (B) Differences in tumor size implanted in R-FMT mice (R, squares) and NR-FMT mice (NR, triangles), or control mice (circles). Tumor volumes are plotted 14 days after tumor implantation; each value represents a single mouse. [Figure 28C](C) Tumor growth curves for each GF mouse from anti-PD-L1-treated (3 x 100 μg i.p. every 3 days) R-FMT mice (squares, n = 2, median tumor volume = 403.7 mm), NR-FMT mice (triangles, n = 2, median tumor volume = 2301 mm), and control mice (circles, n = 2, median tumor volume = 771.35 mm). p = 0.20 (R-FMT vs. NR-FMT), p = 0.33 (NR-FMT vs. control, two-tailed MW test). The dotted black line marks the tumor size cutoff (500 mm) for anti-PD-L1 treatment. [Figure 29A] (A) Flow cytometric quantification of tumor-infiltrating CD45+ immune cells in R-FMT, NR-FMT, and control mice, as indicated. (B) Representative flow cytometric plots of CD45+CD11b+CD11c+ natural effector cells and (D) CD45+CD11b+CD11c+ suppressor myeloid cells in control (left), NR-FMT (middle), and R-FMT (right) mice. (C) Flow cytometric quantification showing the frequency of tumor-infiltrating CD45+CD11b+Ly6G+ natural effector cells and (E) CD45+CD11b+CD11c+ suppressor cells in R-FMT, NR-FMT, and control mice, as indicated. [Figure 29B](A) Flow cytometric quantification of tumor-infiltrating CD45+ immune cells in R-FMT, NR-FMT, and control mice, as indicated. (B) Representative flow cytometric plots of CD45+CD11b+CD11c+ natural effector cells and (D) CD45+CD11b+CD11c+ suppressor myeloid cells in control (left), NR-FMT (middle), and R-FMT (right) mice. (C) Flow cytometric quantification showing the frequency of tumor-infiltrating CD45+CD11b+Ly6G+ natural effector cells and (E) CD45+CD11b+CD11c+ suppressor cells in R-FMT, NR-FMT, and control mice, as indicated. [Figure 29C] (A) Flow cytometric quantification of tumor-infiltrating CD45+ immune cells in R-FMT, NR-FMT, and control mice, as indicated. (B) Representative flow cytometric plots of CD45+CD11b+CD11c+ natural effector cells and (D) CD45+CD11b+CD11c+ suppressor myeloid cells in control (left), NR-FMT (middle), and R-FMT (right) mice. (C) Flow cytometric quantification showing the frequency of tumor-infiltrating CD45+CD11b+Ly6G+ natural effector cells and (E) CD45+CD11b+CD11c+ suppressor cells in R-FMT, NR-FMT, and control mice, as indicated. [Figure 29D](A) Flow cytometric quantification of tumor-infiltrating CD45+ immune cells in R-FMT, NR-FMT, and control mice, as indicated. (B) Representative flow cytometric plots of CD45+CD11b+CD11c+ natural effector cells and (D) CD45+CD11b+CD11c+ suppressor myeloid cells in control (left), NR-FMT (middle), and R-FMT (right) mice. (C) Flow cytometric quantification showing the frequency of tumor-infiltrating CD45+CD11b+Ly6G+ natural effector cells and (E) CD45+CD11b+CD11c+ suppressor cells in R-FMT, NR-FMT, and control mice, as indicated. [Figure 29E] (A) Flow cytometric quantification of tumor-infiltrating CD45+ immune cells in R-FMT, NR-FMT, and control mice, as indicated. (B) Representative flow cytometric plots of CD45+CD11b+CD11c+ natural effector cells and (D) CD45+CD11b+CD11c+ suppressor myeloid cells in control (left), NR-FMT (middle), and R-FMT (right) mice. (C) Flow cytometric quantification showing the frequency of tumor-infiltrating CD45+CD11b+Ly6G+ natural effector cells and (E) CD45+CD11b+CD11c+ suppressor cells in R-FMT, NR-FMT, and control mice, as indicated. [Figure 30A]GF mice undergoing FMT from NR-donors exhibit highly activated Th17 compartments. (A) Representative images of IHC staining for the nuclear receptor retinoic acid-related orphan receptor gamma t (RORγT) in tumors from R-FMT (right), NR-FMT (middle), and control (left) mice. Arrows indicate RORγT-positive cells. [Figure 30B] (B) GF mice undergoing FMT from NR- donors have a highly activated Th17 compartment. (C) IHC quantification showing the number of RORγT+ Th17 cells as counts per mm2 in tumors of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). [Figure 30C] (C) Flow cytometric quantification of the frequency of CD4+FoxP3+ regulatory T cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). [Figure 30D] (D) Flow cytometric quantification of the frequency of CD4+IL17+ Th17 cells in the spleens of R-FMT mice (R), NR-FMT mice (NR), and control mice (C). [Figure 31A]Figure 1 shows upregulation of PD-L1 in the tumor microenvironment of mice undergoing R-FMT versus NR-FMT by mass cytometry (CyTOF). (A) t-SNE plots for total viable cells (left) isolated from tumors derived from control, NR-, and R-engrafted mice by mass cytometry, as indicated. t-SNE plots for total viable cells overlaid with CD45 (center) and PD-L1 (right). Equal numbers of cells are shown from each group. (B) (Top left) t-SNE plots for total CD45+ cells isolated from tumors derived from control, NR-FMT, and R-FMT mice by CyTOF, as indicated. (Top right) Density plots for total CD45+ cells isolated from tumors derived from the indicated experimental groups. (Bottom) t-SNE plots for total CD45+ cells overlaid with expression of the indicated markers. [Figure 31B] Figure 1 shows upregulation of PD-L1 in the tumor microenvironment of mice undergoing R-FMT versus NR-FMT by mass cytometry (CyTOF). (A) t-SNE plots for total viable cells (left) isolated from tumors derived from control, NR-, and R-engrafted mice by mass cytometry, as indicated. t-SNE plots for total viable cells overlaid with CD45 (center) and PD-L1 (right). Equal numbers of cells are shown from each group. (B) (Top left) t-SNE plots for total CD45+ cells isolated from tumors derived from control, NR-FMT, and R-FMT mice by CyTOF, as indicated. (Top right) Density plots for total CD45+ cells isolated from tumors derived from the indicated experimental groups. (Bottom) t-SNE plots for total CD45+ cells overlaid with expression of the indicated markers. [Figure 32A]Figure 1 shows that FMT from a different R donor in GF mice confirms the impact of a favorable gut microbiome on tumor growth. (A) Experimental design of FMT2 experiments in germ-free (GF) mice. Time is expressed in days (D) relative to the day of tumor injection (2.5 x 10 tumor cells). [Figure 32B] (B) FMT from a different R donor in GF mice confirms the impact of a favorable gut microbiome on tumor growth. (C) Differences in tumor size implanted in R-FMT mice (squares) and NR-FMT mice (triangles), or control mice (circles). Tumor volumes are plotted 14 days after tumor implantation; each value represents a single mouse. [Figure 32C] (C) Tumor growth curves for each GF mouse derived from an R-FMT mouse (square, tumor volume = 414.3 mm), an NR-FMT mouse (triangle, tumor volume = 1909.1 mm), and a control (circle, n = 3, median tumor volume = 1049.3 mm). [Figure 33A]Genetically identical Jackson C57 / BL6 and Taconic C57 / BL6 mice exhibit differential tumor growth (faster in Jackson) (A), survival (longer in Taconic) (B-C), and microbiome composition (single-housed Taconic: upper right; single-housed Jackson: lower right) (D) after implantation of murine melanoma tumors (BRAF mutant, PTEN null). Cohousing Taconic and Jackson mice resulted in similar tumor growth (C), and increased microbiome similarity was observed by principal coordinate analysis (D). Differential abundance at the genus level was observed in singly-housed Jackson and Taconic mice, but no differences were observed after cohousing (E). FIG. 10 shows that oral administration of butyric acid significantly retards tumor growth in melanoma-implanted mice (F). [Figure 33B] Genetically identical Jackson C57 / BL6 and Taconic C57 / BL6 mice exhibit differential tumor growth (faster in Jackson) (A), survival (longer in Taconic) (B-C), and microbiome composition (single-housed Taconic: upper right; single-housed Jackson: lower right) (D) after implantation of murine melanoma tumors (BRAF mutant, PTEN null). Cohousing Taconic and Jackson mice resulted in similar tumor growth (C), and increased microbiome similarity was observed by principal coordinate analysis (D). Differential abundance at the genus level was observed in singly-housed Jackson and Taconic mice, but no differences were observed after cohousing (E). FIG. 10 shows that oral administration of butyric acid significantly retards tumor growth in melanoma-implanted mice (F). [Figure 33C]Genetically identical Jackson C57 / BL6 and Taconic C57 / BL6 mice exhibit differential tumor growth (faster in Jackson) (A), survival (longer in Taconic) (B-C), and microbiome composition (single-housed Taconic: upper right; single-housed Jackson: lower right) (D) after implantation of murine melanoma tumors (BRAF mutant, PTEN null). Cohousing Taconic and Jackson mice resulted in similar tumor growth (C), and increased microbiome similarity was observed by principal coordinate analysis (D). Differential abundance at the genus level was observed in singly-housed Jackson and Taconic mice, but no differences were observed after cohousing (E). FIG. 10 shows that oral administration of butyric acid significantly retards tumor growth in melanoma-implanted mice (F). [Figure 33D] Genetically identical Jackson C57 / BL6 and Taconic C57 / BL6 mice exhibit differential tumor growth (faster in Jackson) (A), survival (longer in Taconic) (B-C), and microbiome composition (single-housed Taconic: upper right; single-housed Jackson: lower right) (D) after implantation of murine melanoma tumors (BRAF mutant, PTEN null). Cohousing Taconic and Jackson mice resulted in similar tumor growth (C), and increased microbiome similarity was observed by principal coordinate analysis (D). Differential abundance at the genus level was observed in singly-housed Jackson and Taconic mice, but no differences were observed after cohousing (E). FIG. 10 shows that oral administration of butyric acid significantly retards tumor growth in melanoma-implanted mice (F). [Figure 33E]Genetically identical Jackson C57 / BL6 and Taconic C57 / BL6 mice exhibit differential tumor growth (faster in Jackson) (A), survival (longer in Taconic) (B-C), and microbiome composition (single-housed Taconic: upper right; single-housed Jackson: lower right) (D) after implantation of murine melanoma tumors (BRAF mutant, PTEN null). Cohousing Taconic and Jackson mice resulted in similar tumor growth (C), and increased microbiome similarity was observed by principal coordinate analysis (D). Differential abundance at the genus level was observed in singly-housed Jackson and Taconic mice, but no differences were observed after cohousing (E). FIG. 10 shows that oral administration of butyric acid significantly retards tumor growth in melanoma-implanted mice (F). [Figure 33F] Genetically identical Jackson C57 / BL6 and Taconic C57 / BL6 mice exhibit differential tumor growth (faster in Jackson) (A), survival (longer in Taconic) (B-C), and microbiome composition (single-housed Taconic: upper right; single-housed Jackson: lower right) (D) after implantation of murine melanoma tumors (BRAF mutant, PTEN null). Cohousing Taconic and Jackson mice resulted in similar tumor growth (C), and increased microbiome similarity was observed by principal coordinate analysis (D). Differential abundance at the genus level was observed in singly-housed Jackson and Taconic mice, but no differences were observed after cohousing (E). FIG. 10 shows that oral administration of butyric acid significantly retards tumor growth in melanoma-implanted mice (F). [Figure 34A]Figure 16S analysis of fecal samples from R and NR donors and germ-free recipient mice. Comparison of the relative abundance of (A) Faecalibacterium, (B) Ruminococcaceae, and (C) Bacteroidales at 14 days after tumor injection. Data from two independent experiments are presented. **p<0.01. [Figure 34B] Figure 16S analysis of fecal samples from R and NR donors and germ-free recipient mice. Comparison of the relative abundance of (A) Faecalibacterium, (B) Ruminococcaceae, and (C) Bacteroidales at 14 days after tumor injection. Data from two independent experiments are presented. **p<0.01. [Figure 34C] Figure 16S analysis of fecal samples from R and NR donors and germ-free recipient mice. Comparison of the relative abundance of (A) Faecalibacterium, (B) Ruminococcaceae, and (C) Bacteroidales at 14 days after tumor injection. Data from two independent experiments are presented. **p<0.01. DETAILED DESCRIPTION OF THE INVENTION

[0043]

[0076] Although significant advances have been made in cancer treatment through the use of molecular targeting therapy and immunotherapy, responses are variable and not always durable. Treatment with immune checkpoint inhibitors is associated with a response rate of 15-40% in patients with extensive melanoma, and efforts are underway to identify strategies to enhance response to checkpoint inhibitor therapy. Thus, methods to improve treatment response as well as increase the number of responders are urgently needed.

[0044]

[0077] The present disclosure overcomes the problems associated with current technology by providing a method for modulating the microbiome in cancer patients to improve immune responses against cancer and therapeutic responses to immune checkpoint inhibitors. The study in this disclosure used a large cohort of patients with metastatic melanoma undergoing systemic treatment (n=233), and a subset of this cohort was treated with PD-1-based immunotherapy (n=112). Oral and gut microbiome samples were characterized in these patients by 16S rRNA gene sequencing and metagenomic whole-genome shotgun sequencing. These analyses revealed significant differences in gut microbiome diversity and composition in responders to immune checkpoint blockade therapy (e.g., PD-1-based therapy) compared with non-responders. Responders exhibited significantly greater gut microbiome diversity and significantly greater abundance of specific bacteria (e.g., bacteria within the Clostridiales and Ruminococcaceae families) compared with non-responders. In particular, responders were found to have a higher abundance of Faecalibacterium prausnitzii. These bacteria are known to produce short-chain fatty acids, such as butyrate, which may help maintain the integrity of specific cells (i.e., enterocytes) in the intestinal tract and enhance immunity.

[0045]

[0078] Interestingly, non-responders to treatment had lower levels of these bacteria and significantly higher levels of Bacteroidales bacteria, which have been shown in some studies to downregulate systemic immune responses. Metagenomic analysis via whole-genome shotgun sequencing performed on a subset of these patients validated these findings and further supported the differences in metabolic processes in the bacteria of responders compared to non-responders. Furthermore, modulation of the gut microbiome by co-housing Taconic mice with Jackson mice and oral administration of short-chain fatty acids (e.g., butyrate) resulted in delayed tumor growth in mice with unfavorable gut microbiomes (Jackson mice). These results from human and mouse studies potentially have broad implications for enhancing responses to immune checkpoint blockade through modulation of the gut microbiome.

[0046]

[0079] Importantly, this study demonstrates that patients with a "favorable" gut microbiome (high diversity and a high abundance of Clostridiales and / or Ruminococcaceae bacteria) have enhanced systemic and antitumor immune responses mediated by enhanced antigen presentation at the lymph node and tumor level, as well as preserved effector T cell function in the periphery and tumor microenvironment. In contrast, patients with an "unfavorable" gut microbiome (low diversity and a high relative abundance of Bacteroidetes) have impaired systemic and antitumor immune responses mediated by limited intratumoral infiltration by both lymphoid and myeloid elements, diminished antigen presentation, and peripheral skewing of immunoregulatory cellular and humoral elements, including Tregs and MDSCs.

[0047]

[0080] Further studies were also undertaken in mouse melanoma model systems. These studies showed that mice receiving fecal microbiota transplants from responder populations had reduced tumor growth and increased response to anti-PDL1 therapy. Furthermore, mice receiving responder microbial transplants had a higher percentage of innate effector cells (CD45 + CD11b + Ly6G + ), and a low frequency of suppressor myeloid cells (CD11b + CD11c + In addition, the CD45 + Immune cells and CD8 + These findings highlight the potential for parallel modulation of the gut microbiota to significantly enhance the efficacy of checkpoint blockade and warrant rapid assessment in clinical trials.

[0048]

[0081] Based on these findings, methods for the treatment and diagnosis of cancer are provided herein. In one method, short-chain fatty acids such as butyrate and / or a population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, are administered to patients undergoing treatment with immune checkpoint blockade to enhance the therapeutic response. Also provided herein are methods for using the diversity and composition of the gut microbiome as predictive biomarkers to identify patients who will respond favorably to immune checkpoint blockade. I. Definition

[0049]

[0082] As used herein, "essentially free of" with respect to a named component is used to mean that none of the named components are intentionally formulated into the composition and / or are present only as contaminants or in trace amounts. Thus, the total amount of the named component resulting from any unintentional contamination of the composition is well below 0.01%. Most preferred are compositions that have no detectable amounts of the named component by standard analytical methods.

[0050]

[0083] As used herein, "a" or "an" may mean "one or more than one."

[0051]

[0084] Use of the term "or" in the claims, unless expressly indicated to refer to alternatives only or that the alternatives are mutually exclusive, is used to mean "and / or," although the present disclosure supports both the alternative-only definition and the "and / or" definition. As used herein, the term "another" can mean at least a second or more.

[0052]

[0085] Throughout this application, the term "about" is used to indicate that a value includes the inherent variation of error for the device, method being employed to determine the value, or the variation that exists among study subjects.

[0053]

[0086] The phrases "effective amount" or "therapeutically effective amount" or "sufficient amount" refer to the administration of a drug or agent sufficient to bring about a desired result. Desired results can include a decrease in tumor size, a decrease in the rate of cancer cell proliferation, a decrease in metastasis, an increase in CD8 + Increased T lymphocytes or tumor immune infiltrates, CD45 in tumors + , CD3 + / CD20 + / CD56 + , CD68 + and / or HLA-DR+ cells; increased expression of CD3, CD8, PD1, FoxP3, granzyme B, and / or PD-L1 in the tumor immune infiltrate; decreased expression of RORγT in the tumor immune infiltrate; effector CD4 in the systemic circulation or peripheral blood; + , CD8 + It can be an expansion of T, monocytes, and / or myeloid dendritic cells, a reduction of B cells, regulatory T cells, and / or myeloid-derived suppressor cells in the subject's systemic circulation or peripheral blood, or any combination of the above.

[0054]

[0087] The term "tumor cell" or "cancer cell" describes a cell that exhibits inappropriate, unregulated growth. "Human" tumors are composed of cells that contain human chromosomes. Such tumors include tumors in human patients and tumors that arise from the introduction of malignant cell lines containing human chromosomes into non-human host animals.

[0055]

[0088] The term "antibody" as used herein refers to immunoglobulins, derivatives thereof that retain specific binding ability, and proteins having binding domains that are homologous or largely homologous to those of immunoglobulins. These proteins can be derived from natural sources or can be partially or wholly synthetically produced. Antibodies can be monoclonal or polyclonal. Antibodies can be members of any immunoglobulin class, including any of the human classes: IgG, IgM, IgA, IgD, and IgE. Antibodies used with the methods and compositions described herein are generally derivatives of the IgG class. The term antibody also refers to antigen-binding fragments of antibodies. Examples of such antibody fragments include, but are not limited to, Fab fragments, Fab' fragments, F(ab')2 fragments, scFv fragments, Fv fragments, dsFv diabody fragments, and Fd fragments. Antibody fragments can be produced by any means. For example, an antibody fragment can be enzymatically or chemically produced by fragmentation of an intact antibody, can be recombinantly produced from a gene encoding a partial antibody sequence, or can be wholly or partially synthetically produced. An antibody fragment can optionally be a single-chain antibody fragment. Alternatively, the fragment can comprise multiple chains linked together, for example, by disulfide bonds. The fragment can also optionally be a multimolecular complex. A functional antibody fragment retains the ability to bind to its cognate antigen with an affinity comparable to that of the intact antibody.

[0056]

[0089] As used herein, the term "monoclonal antibody" refers to an antibody obtained from a population of substantially homogeneous antibodies, e.g., a population comprising individual antibodies that are identical except for possible mutations, e.g., naturally occurring mutations that may be present in minor amounts. Thus, the modifier "monoclonal" indicates the character of the antibody as not being a mixture of individual antibodies. In certain embodiments, such monoclonal antibodies typically include antibodies comprising a polypeptide sequence that binds to a target, obtained by a process comprising selection of a single target-binding polypeptide sequence from a plurality of polypeptide sequences. For example, the selection process can be selection of a unique clone from a plurality of clones, such as a pool of hybridoma clones, phage clones, or recombinant DNA clones. It is understood that the selected target-binding sequence can be further modified, e.g., to improve affinity for the target, humanize the target-binding sequence, improve its production in cell culture, reduce its immunogenicity in vivo, create multispecific antibodies, etc., and that antibodies comprising the modified target-binding sequence are also monoclonal antibodies of the present disclosure. In contrast to polyclonal antibody preparations, which typically include several different antibodies directed against different determinants (epitopes), each monoclonal antibody of a monoclonal antibody preparation is directed against a single determinant on an antigen. In addition to their specificity, monoclonal antibody preparations are advantageous in that they are typically uncontaminated by other immunoglobulins.

[0057]

[0090] The phrases "pharmaceutical composition" or "pharmacologically acceptable composition" refer to molecular entities and compositions that do not produce adverse, allergic, or other undesirable reactions when administered to an animal, such as a human, as appropriate. In light of the present disclosure, one of skill in the art will know how to prepare pharmaceutical compositions containing an antibody or additional active ingredient. Furthermore, it will be understood that for animal (e.g., human) administration, preparations will meet sterility, pyrogenicity, general safety, and purity standards as required by the FDA Office of Biological Standards.

[0058]

[0091] As used herein, "pharmaceutically acceptable carriers" include any and all aqueous solvents (e.g., water, alcoholic / aqueous solutions, saline, parenteral vehicles such as sodium chloride and Ringer's dextrose), non-aqueous solvents (e.g., propylene glycol, polyethylene glycol, vegetable oils, and injectable organic esters such as ethyl oleate), dispersion media, coatings, surfactants, antioxidants, preservatives (e.g., antibacterial or antifungal agents, antioxidants, chelating agents, and inert gases), isotonic agents, absorption delaying agents, salts, drugs, drug stabilizers, gels, binders, excipients, disintegrants, lubricants, sweeteners, flavoring agents, dyes, fluid and nutrient replenishers, such materials, and combinations thereof, as known to those skilled in the art. The pH and exact concentration of the various components in the pharmaceutical composition can be adjusted according to well-known parameters.

[0059]

[0092] The term "unit dose" or "dosage" refers to a physically discrete unit suitable for use in a subject, each unit containing a predetermined quantity of a therapeutic composition calculated to produce the desired response discussed herein in connection with its administration, i.e., appropriate route and treatment regimen. The number administered, both according to the number of treatments and the unit dose, depends on the desired effect. The actual dosage of the compositions of the present embodiments administered to a patient or subject can be determined by a physician and physiological factors such as the subject's weight, age, health, and sex, the type of disease being treated, the extent of disease invasion, previous or concomitant therapeutic interventions, any idiopathic disease of the patient, the route of administration, and the efficacy, stability, and toxicity of the particular therapeutic agent. For example, dosages can also include doses from about 1 μg per kg of body weight to about 1000 mg per kg of body weight per administration (such ranges including intermediate doses), or greater, and any specific doses derivable therefrom. Non-limiting examples of ranges derivable from the numbers recited herein include ranges from about 5 μg / kg body weight to about 100 mg / kg body weight, from about 5 μg / kg body weight to about 500 mg / kg body weight, etc. The practitioner responsible for administration will, in any event, determine the concentration of active ingredient(s) in the composition and appropriate dose(s) for the individual subject.

[0060]

[0093] An "anti-cancer" agent can negatively affect cancer cells / tumors in a subject by, for example, promoting the killing of cancer cells, inducing apoptosis in cancer cells, reducing the rate of proliferation of cancer cells, reducing the incidence or number of metastases, reducing tumor size, inhibiting tumor growth, reducing the blood supply to a tumor or cancer cells, promoting an immune response against cancer cells or tumors, preventing or inhibiting the progression of cancer, or extending the life expectancy of a subject with cancer.

[0061]

[0094] The term "immune checkpoint" refers to a component of the immune system that delivers inhibitory signals to that component to regulate the immune response. Known immune checkpoint proteins include CTLA-4, PD-1, and their ligands, PD-L1 and PD-L2, as well as LAG-3, BTLA, B7H3, B7H4, TIM3, and KIR. It is recognized in the art that pathways involving LAG3, BTLA, B7H3, B7H4, TIM3, and KIR constitute immune checkpoint pathways similar to those dependent on CTLA-4 and PD-1 (see, e.g., Pardoll, 2012, Nature Rev Cancer, 12:252-264; Mellman et al., 2011, Nature, 480:480-489).

[0062]

[0095] The term "PD-1 axis-binding antagonist" refers to a molecule that inhibits the interaction of a PD-1 axis binding partner with one or more of its binding partners, so as to eliminate T cell dysfunction resulting from signaling in the PD-1 signaling axis (the result being restoration or enhancement of T cell function (e.g., proliferation, cytokine production, target cell killing)). The term "PD-1 axis" refers to any component of the PD-1 immune checkpoint (e.g., PD-1, PD-L1, and PD-L2). As used herein, PD-1 axis-binding antagonists include PD-1 binding antagonists, PD-L1 binding antagonists, and PD-L2 binding antagonists.

[0063]

[0096] The term "PD-1 binding antagonist" refers to a molecule that reduces, blocks, inhibits, neutralizes, or interferes with signal transduction resulting from the interaction of PD-1 with one or more of its binding partners, such as PD-L1 and / or PD-L2. A PD-1 binding antagonist can be a molecule that inhibits the binding of PD-1 to one or more of its binding partners. In particular aspects, a PD-1 binding antagonist inhibits the binding of PD-1 to PD-L1 and / or PD-L2. For example, PD-1 binding antagonists include anti-PD-1 antibodies, antigen-binding fragments thereof, immunoadenosins, fusion proteins, oligopeptides, and other molecules that reduce, block, inhibit, neutralize, or interfere with signal transduction resulting from the interaction of PD-1 with PD-L1 and / or PD-L2. An exemplary PD-1 binding antagonist is an anti-PD-1 antibody. For example, the PD-1 binding antagonist is MDX-1106 (nivolumab), MK-3475 (pembrolizumab), CT-011 (pidilizumab), or AMP-224.

[0064]

[0097] The term "PD-L1 binding antagonist" refers to a molecule that reduces, blocks, inhibits, neutralizes, or interferes with signaling resulting from the interaction of PD-1 with one or more of its binding partners, such as PD-1 or B7-1. For example, a PD-L1 binding antagonist is a molecule that inhibits the binding of PD-L1 to its binding partners. In particular aspects, PD-L1 binding antagonists inhibit the binding of PD-L1 to PD-1 and / or B7-1. PD-L1 binding antagonists can include anti-PD-L1 antibodies, antigen-binding fragments thereof, immunoadenosins, fusion proteins, oligopeptides, and other molecules that reduce, block, inhibit, neutralize, or interfere with signaling resulting from the interaction of PD-1 with one or more of its binding partners, such as PD-1 or B7-1. For example, the PD-L1-binding antagonist reduces negative costimulatory signals mediated by or via cell surface proteins expressed on T lymphocytes, such that dysfunction of dysfunctional T cells is alleviated (e.g., effector responses to antigen recognition are enhanced). In one example, the PD-L1-binding antagonist is an anti-PD-L1 antibody. The anti-PD-L1 antibody can be YW243.55.S70, MDX-1105, MPDL3280A, or MEDI4736.

[0065]

[0098] The term "PD-L2 binding antagonist" refers to a molecule that reduces, blocks, inhibits, neutralizes, or interferes with signaling resulting from the interaction of PD-L2 with one or more of its binding partners, such as PD-1. A PD-L2 binding antagonist can be a molecule that inhibits the binding of PD-L2 to one or more of its binding partners. For example, PD-L2 binding antagonists, such as PD-L2 antagonists including anti-PD-L2 antibodies, antigen-binding fragments thereof, immunoadesins, fusion proteins, oligopeptides, and other molecules that reduce, block, inhibit, neutralize, or interfere with signaling resulting from the interaction of PD-L2 with one or more of its binding partners, such as PD-1, inhibit the binding of PD-L2 to PD-1.

[0066]

[0099] An "immune checkpoint inhibitor" refers to any compound that inhibits the function of an immune checkpoint protein. Inhibition includes reducing and completely blocking function. In particular, the immune checkpoint protein is a human immune checkpoint protein. Thus, an inhibitor of an immune checkpoint protein is, in particular, an inhibitor of a human immune checkpoint protein.

[0067]

[0100] "Subject" and "patient" refer to humans or non-humans, including primates, mammals, and vertebrates. In certain embodiments, the subject is a human.

[0068]

[0101] As used herein, the terms "treat," "treatment," "treating," or "amelioration," when used in reference to a disease, disorder, or medical condition, refer to therapeutic treatment for the condition, the purpose of which is to antagonize, alleviate, improve, inhibit, slow, or halt the progression or severity of the symptoms or condition. The term "treating" includes reducing or alleviating at least one adverse effect or symptom of the condition. Treatment is generally "effective" if one or more symptoms or clinical markers are reduced. Alternatively, treatment is "effective" if the progression of the condition is reduced or halted. That is, "treatment" includes not only the improvement of symptoms or markers, but also the halting, or at least slowing, of the progression or worsening of symptoms that would be expected in the absence of treatment. Beneficial or desired clinical results include, but are not limited to, alleviation of one or more symptoms, diminishment of the severity of the defect, stabilization (i.e., non-progression) of the tumor or malignant state, delay or slowing of tumor growth and / or metastasis, and prolongation of life expectancy compared to that expected in the absence of treatment.

[0069]

[0102] "Gut microbiota" or "gut microbiome" refers to the population of microorganisms that inhabit the intestine of a subject.

[0070]

[0103] The term "alpha diversity" is a measure of intra-sample diversity and refers to the distribution and composition pattern of all microbiota within a sample, calculated as a scalar value for each sample. "Beta diversity" is a term for inter-sample diversity and involves comparing samples such that each provides a measure of the distance or difference between each pair of samples.

[0071]

[0104] The term "relative abundance," also referred to as "relative abundance," is defined as the number of bacteria of a particular taxonomic level (phylum to species) as a percentage of the total number of bacteria of that level in a biological sample. This relative abundance can be assessed, for example, by measuring the percentage of 16S rRNA gene sequences present in a sample that are assigned to these bacteria. Relative abundance can be measured by any suitable technique known to those skilled in the art, such as 454 pyrosequencing and quantitative PCR of these specific bacterial 16S rRNA gene markers, or quantitative PCR of specific genes.

[0072]

[0105] In this document, a "good responder to a treatment," or in other words, a patient who "benefits from" the treatment, also referred to as a "responder" or "responsive" patient, refers to a patient who has cancer and who shows or will show a clinically significant reduction in cancer after receiving the treatment. Conversely, a "poor responder" or "non-responder" is a patient who does not show or will not show a clinically significant reduction in cancer after receiving the treatment. The reduction in response to a treatment can be assessed according to art-recognized standards, such as immune-related response criteria (irRC), WHO criteria, or RECIST criteria.

[0073]

[0106] The term "isolated" encompasses (1) bacteria or other entities or substances that have been separated from at least some of the components with which they were associated when originally produced (whether in nature or in an experimental setting) and / or (2) bacteria or other entities or substances that have been created, prepared, purified, and / or manufactured by the hand of man. Isolated bacteria may be separated from at least about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or more of the other components with which they were originally associated. In some embodiments, isolated bacteria are greater than about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, or greater than about 99% pure. As used herein, a substance is "pure" if it is substantially free of other components.

[0074]

[0107] The terms "purity," "purifying," and "purified" refer to bacteria or other material that has been separated from at least some of the components with which it was associated when originally produced or generated (e.g., in nature or in an experimental setting), or at any time after its initial production. A bacterium or bacterial population is considered purified if it has been isolated, such as from the material or environment containing the bacterium or bacterial population, at the time of production or after production; a purified bacterium or bacterial population can contain up to about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more than about 90% other materials and still be considered "isolated." In some embodiments, purified bacteria and bacterial populations are greater than about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, or greater than about 99% pure. In the case of the bacterial compositions provided herein, one or more bacterial types present in the composition can be purified independently from one or more other bacteria produced in and / or present in the material or environment containing the bacterial types. Bacterial compositions and their bacterial components are generally purified from residual habitat products. II. Purified bacterial populations

[0075]

[0108] Embodiments of the present disclosure relate to compositions containing short-chain fatty acids, such as compositions containing butyrate, and purified bacterial populations (e.g., bacterial populations containing short-chain fatty acids, such as butyrate-producing bacterial populations) for the treatment of cancer, such as cancer, in subjects receiving or having received an immune checkpoint inhibitor. In some embodiments, the subject is administered prebiotics and / or probiotics to enrich for butyrate-producing bacteria. In certain aspects, the subject undergoes a dietary change to enrich for butyrate-producing bacteria.

[0076]

[0109] In certain embodiments, the present disclosure provides probiotic compositions and live bacterial products comprising bacterial populations beneficial to immune checkpoint therapy responses. The probiotic composition may comprise bacteria of the phylum Firmicutes. The bacterial population may belong to the class Clostridia, particularly the order Clostridales, and one or more bacterial populations may belong to the family Clostridiaceae, family Ruminococcaceae (e.g., particularly the genus Ruminococcus or Faecalibacterium), family Micrococcaceae (e.g., particularly the genus Rothia), family Lachnospiraceae, and / or family Veillonellaceae. In a further aspect, the bacterial population may belong to the phylum Tenericutes, particularly the class Mollicutes. The bacteria may belong to the genus Peptoniphilus, particularly the species P. asaccharolyticus, P. gorbachii, P. harei, P. ivorii, P. lacrimalis, and / or P. olsenii. Further exemplary bacterial populations for probiotic compositions may include bacterial populations belonging to the genus Porphyromonas, particularly the species Porphyromonas pasteurii, Clostridium fungatei, Phascolactomacterium, or Phascolactomacterium faecium.

[0077]

[0110] For example, bacterial populations of the genus Ruminococcus include species such as Ruminococcus albus, Ruminococcus bromii, Ruminococcus callidus, Ruminococcus flavefaciens, Ruminococcus champanerensis, Ruminococcus fecis, Ruminococcus gobroii, Ruminococcus gnavus, and Ruminococcus hansenii. The bacterial population of the genus Faecalibacterium may include bacteria of the species Faecalibacterium prausnitzii.

[0078]

[0111] Exemplary bacterial populations of the genus Rothia can include bacteria of the species R. aeria, R. amarae, R. dentocariosa, R. endophytica, R. mucilaginosa, R. nasimurium, and / or R. terrae.

[0079]

[0112] Exemplary bacterial populations for incorporation into probiotic compositions include bacterial populations belonging to the phylum Firmicutes, class Clostridia, family Ruminococcaceae, species Faecalibacterium prausnitzii, genus Ruminococcus, species Porphyromonas pasteuri, family Veillonellaceae, species Clostridium fungatei, genus Phascolactobacterium, species Phascolactobacterium faecium, genus Peptoniphilus, family Micrococcaceae, class Mollicutes, and / or genus Rothia.

[0080]

[0113] In certain embodiments, the probiotic composition or live bacterial product does not include a bacterial population of the genus Bacteroides, in particular the order Bacteroidales, such as B. thetaiotaomicron, B. fragilis, B. vulgatus, B. distasonis, B. ovatus, B. stercoris, B. merdae, B. uniformis, B. eggerithii, or B. caccae. In particular, the probiotic composition does not contain bacterial populations of the genus Gardnerella or the species Collinsella stercoris, Desulfovibrio alaskensis, Bacteroides mediterraneensis, Prevotella histicola, or Gardnerella vaginalis.

[0081] [Table 2] TIFF2025160204000004.tif226149 TIFF2025160204000005.tif226149 TIFF2025160204000006.tif226149 TIFF2025160204000007.tif226149 TIFF2025160204000008.tif226149 TIFF2025160204000009.tif226149 TIFF2025160204000010.tif226149 TIFF2025160204000011.tif226149 TIFF2025160204000012.tif226149 TIFF2025160204000013.tif226149 TIFF2025160204000014.tif226149 TIFF2025160204000015.tif226149 TIFF2025160204000016.tif226149 TIFF2025160204000017.tif226149 TIFF2025160204000018.tif226149 TIFF2025160204000019.tif226149 TIFF2025160204000020.tif226149 TIFF2025160204000021.tif226149 TIFF2025160204000022.tif226149 TIFF2025160204000023.tif226149 TIFF2025160204000024.tif226149 TIFF2025160204000025.tif226149 TIFF2025160204000026.tif226149 TIFF2025160204000027.tif226149 TIFF2025160204000028.tif226149 TIFF2025160204000029.tif226149 TIFF2025160204000030.tif226149 TIFF2025160204000031.tif226149 TIFF2025160204000032.tif226149 TIFF2025160204000033.tif226149 TIFF2025160204000034.tif226149 TIFF2025160204000035.tif226149 TIFF2025160204000036.tif226149 TIFF2025160204000037.tif226149 TIFF2025160204000038.tif226149 TIFF2025160204000039.tif226149 TIFF2025160204000040.tif226149 TIFF2025160204000041.tif226149 TIFF2025160204000042.tif226149 TIFF2025160204000043.tif226149 TIFF2025160204000044.tif226149 TIFF2025160204000045.tif226149 TIFF2025160204000046.tif226149 TIFF2025160204000047.tif226149 TIFF2025160204000048.tif226149 TIFF2025160204000049.tif226149 TIFF2025160204000050.tif226149 TIFF2025160204000051.tif226149 TIFF2025160204000052.tif226149 TIFF2025160204000053.tif226149 TIFF2025160204000054.tif226149 TIFF2025160204000055.tif226149 TIFF2025160204000056.tif226149 TIFF2025160204000057.tif226149 TIFF2025160204000058.tif226149 TIFF2025160204000059.tif226149 TIFF2025160204000060.tif226149 TIFF2025160204000061.tif226149 TIFF2025160204000062.tif226149 TIFF2025160204000063.tif226149 TIFF2025160204000064.tif226149 TIFF2025160204000065.tif226149 TIFF2025160204000066.tif226149 TIFF2025160204000067.tif226149 TIFF2025160204000068.tif226149 TIFF2025160204000069.tif226149 TIFF2025160204000070.tif226149 TIFF2025160204000071.tif226149 TIFF2025160204000072.tif226149 TIFF2025160204000073.tif226149 TIFF2025160204000074.tif226149 TIFF2025160204000075.tif226149 TIFF2025160204000076.tif226149 TIFF2025160204000077.tif226149 TIFF2025160204000078.tif226149 TIFF2025160204000079.tif226149 TIFF2025160204000080.tif226149 TIFF2025160204000081.tif226149 TIFF2025160204000082.tif226149 TIFF2025160204000083.tif226149 TIFF2025160204000084.tif226149 TIFF2025160204000085.tif226149 TIFF2025160204000086.tif226149 TIFF2025160204000087.tif226149 TIFF2025160204000088.tif226149 TIFF2025160204000089.tif226149 TIFF2025160204000090.tif226149 TIFF2025160204000091.tif226149 TIFF2025160204000092.tif226149

[0082]

[0114]

Table 3

[0083]

[0115]

Table 4

[0084]

[0116] The present disclosure also provides pharmaceutical compositions comprising cultures of one or more of the microorganisms described above. Thus, the bacterial species are present in a viable form, whether dried, lyophilized, or sporulated, which can preferably be adapted for suitable administration, for example, in tablet or powder form, potentially with an enteric coating, for oral treatment.

[0085]

[0117] In certain embodiments, the composition is formulated for oral administration. Oral administration can be achieved using chewable formulations, dissolving formulations, capsule / coating formulations, multi-layered lozenges (for individual active ingredients and / or active ingredients and excipients), sustained / sustained release formulations, or other suitable formulations known to those skilled in the art. Although the term "tablet" is used herein, the formulation may take a variety of physical forms, generally referred to by other terms, such as lozenges, pills, capsules, etc.

[0086]

[0118] The compositions of the present disclosure are preferably formulated for oral administration, although other routes of administration may be employed, including, but not limited to, subcutaneous, intramuscular, intradermal, transdermal, intraocular, intraperitoneal, transmucosal, intravaginal, intrarectal, and intravenous.

[0087]

[0119] The desired dose of the compositions of the present disclosure may be administered in multiple (eg, 2, 3, 4, 5, 6, or more) subdoses administered at appropriate intervals throughout the day.

[0088]

[0120] In one aspect, the disclosed compositions can be prepared as a capsule. The capsule (i.e., carrier) can be a hollow, generally cylindrical capsule formed from a variety of materials, such as gelatin, cellulose, carbohydrates, etc.

[0089]

[0121] In another embodiment, the disclosed compositions can be prepared as suppositories. Suppositories can include bacteria and one or more carriers, such as, but not limited to, polyethylene glycol, acacia, acetylated monoglyceride, carnauba wax, cellulose acetate phthalate, corn starch, dibutyl phthalate, sodium docusate, gelatin, glycerin, iron oxide, kaolin, lactose, magnesium stearate, methylparaben, medicated glaze, povidone, propylparaben, sodium benzoate, sorbitan monooleate, sucrose talc, titanium dioxide, white wax, and coloring agents.

[0090]

[0122] In some embodiments, the disclosed probiotics can be prepared as tablets. Tablets can include bacteria and one or more tableting agents (i.e., carriers), such as dibasic calcium phosphate, stearic acid, croscarmellose, silica, cellulose, and cellulose coatings. Tablets can be formed using a direct compression process, although those skilled in the art will recognize that tablets can be formed using a variety of techniques.

[0091]

[0123] In other aspects, the disclosed probiotics can be formulated as a food or beverage, or alternatively, as an additive to a food or beverage, where an appropriate amount of bacteria is added to the food or beverage, with the food or beverage serving as the carrier.

[0092]

[0124] The probiotic compositions of the present disclosure may further include one or more prebiotics known in the art, such as lactitol, inulin, or a combination thereof.

[0093]

[0125] In some embodiments, the compositions of the embodiments include one or more bacterial species that produce short-chain fatty acids. In certain aspects, the bacterial species produce butyrate. For example, the bacterial population may include one or more bacterial species of the order Clostridiales. The bacteria of the order Clostridiales may be substantially in spore form. In certain aspects, the bacterial species is from the family Ruminococcaceae, Christenseneraceae, Clostridiaceae, or Coriobacterialaceae. In some embodiments, the bacteria of the order Clostridiales include a first family and a second family. In some embodiments, the first family is selected from the group consisting of Ruminococcaceae, Christenseneraceae, Clostridiaceae, and Coriobacterialaceae, and the second family is not the same as the first family. Examples of bacterial species are Faecalibacterium prausnitzii, Ruminococcus albus, Ruminococcus brommii, Ruminococcus callidus, Ruminococcus flavefaciens, Ruminococcus champanerensis, Ruminococcus faecius, Ruminococcus gobroii, Ruminococcus gnavus, Ruminococcus hansenii, Ruminococcus hydrogenotrophicus, Ruminococcus lactalis, Ruminococcus luti, and Ru. Examples of suitable bacterial species include, but are not limited to, Mycobacterium obeum, Ruminococcus palustris, Ruminococcus pasteurii, Ruminococcus productus, Ruminococcus cinckii, Ruminococcus turkes, Subdoligranulum variabile, Butyrivibrio fibrisolvens, Roseburia intestinalis, Anerostipes cassae, Blautia obeum, Eubacterium nodatum, and Eubacterium oxidoreducens. In certain aspects, the bacterial species is Faecalibacterium prausnitzii. In certain embodiments, the bacterial population does not include a bacterial species of the class Bacteroidetes or the family Prevotellaceae.

[0094]

[0126] In some embodiments, the bacterial population includes bacteria of the order Clostridiales in an amount effective or sufficient to produce one or more metabolites capable of enhancing immune checkpoint therapy in a subject. In some embodiments, the one or more metabolites include a short-chain fatty acid. In particular aspects, the short-chain fatty acid is butyrate. In some embodiments, the Clostridiales produce one or more short-chain fatty acids (e.g., butyrate) in an amount effective to increase local short-chain fatty acid concentrations by 2-fold, 4-fold, 5-fold, 10-fold, 50-fold, 100-fold, 1000-fold, or more than 1000-fold.

[0095]

[0127] In some embodiments, the probiotic composition may further comprise a food or nutritional supplement effective to stimulate the growth of Clostridiales bacteria present in the gastrointestinal tract of a subject. In some embodiments, the nutritional supplement is produced by bacteria associated with a healthy human gut microbiome. In certain embodiments, the nutritional supplement is produced by Clostridiales bacteria. In certain embodiments, the nutritional supplement comprises a short-chain fatty acid. In certain embodiments, the short-chain fatty acid is selected from butyric acid, propionic acid, or a combination thereof. In certain embodiments, the short-chain fatty acid is butyric acid.

[0096]

[0128] Accordingly, certain embodiments of the present disclosure relate to administering a butyric acid prodrug or salt to a subject currently receiving or receiving an immune checkpoint inhibitor. For example, the butyric acid can be sodium butyrate, arginine butyrate, ethyl butyryl lactate, tributyrin, 4-phenylbutyric acid, AN-9, or AN-10. Butyric acid prodrugs and salts are described in International Publication No. 96 / 15660 and U.S. Patent Application No. 5,763,488, the disclosures of which are incorporated herein by reference. Other orally available butyric acid prodrugs and salts that can be administered include, but are not limited to, isobutyramide, 1-octyl butyrate, orthonitrobenzyl butyrate, 3-monoacetone glucose monobutyrate, 1-monoacetone mannose monobutyrate, xylitol monobutyrate, isobutyramide, 4-phenylbutyric acid, and 4-phenylacetic acid. Each of these compounds releases butyric acid or a butyric acid analog into the bloodstream upon administration. One or more isoforms of butyric acid can include butyl butyric acid, amyl butyric acid, isobutyl butyric acid, benzyl butyric acid, a-methylbenzyl butyric acid, hexyl butyric acid, heptyl butyric acid, pentyl butyric acid, methyl butyric acid, and 2-hydroxy-3-methylbutanoic acid.

[0097]

[0129] In a further embodiment, the present disclosure relates to a method for obtaining a microbiome profile, the method comprising the steps of: i) obtaining a sample from a subject (e.g., a human subject); ii) isolating one or more bacterial species from the sample; iii) isolating one or more nucleic acids from at least one of the bacterial species; iv) sequencing the isolated nucleic acids; and v) comparing the sequenced nucleic acids to a reference nucleic acid sequence. When performing a method requiring genotyping, any genotyping assay can be used. For example, this can be done by sequencing the 16S or 23S ribosomal subunit, or by metagenomic shotgun DNA sequencing in conjunction with metatranscriptomics. The biological sample may be selected from the group comprising whole blood, plasma, urine, tears, semen, saliva, oral mucosa, interstitial fluid, lymphatic fluid, spinal fluid, amniotic fluid, glandular fluid, sputum, feces, sweat, mucus, vaginal secretions, cerebrospinal fluid, hair, skin, fecal material, wound exudate, wound homogenate, and wound fluid. In certain aspects, the sample is fecal material or an oral sample.

[0098]

[0130] In some embodiments, a microbiome profile is identified as suitable for immune checkpoint therapy. A suitable microbial profile will have a high relative abundance of one or more bacterial species from the phylum Firmicutes, class Clostridiales, order Clostridiales, family Ruminococcaceae, genus Ruminococcus, genus Hydrogenoanellobacterium, genus Faecalibacterium, phylum Actinobacteria, class Coriobacteriales, order Coriobacteriales, family Coriobacteriales, domain Archaea, phylum Cyanobacteria, phylum Euryarchaeota, or family Christensenellaceae. A suitable microbial profile will have a low relative abundance of bacteria from the genus Diaryster, family Veillonellaceae, phylum Bacteroidetes, class Bacteroidetes, order Bacteroidales, or family Prevotellaceae. Thus, a suitable microbial profile will have a high relative abundance of one or more bacterial species from the phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, genus Ruminococcus, genus Hydrogenoanellobacterium, phylum Actinobacteria, class Coriobacteria, order Coriobacteriales, family Coriobacteriaceae, domain Archaea, phylum Cyanobacteria, phylum Euryarchaeota, or family Christensenellaceae, and a low abundance of one or more bacterial species from the genus Diaryster, family Veillonellaceae, phylum Bacteroidetes, class Bacteroidetes, order Bacteroidales, and / or family Prevotellaceae. III. Immune checkpoint blockade

[0099]

[0131] The present disclosure provides methods of enhancing the efficacy of immune checkpoint blockade by modulating a subject's microbiome, such as by administering a composition comprising a short-chain fatty acid, such as butyrate, and / or a population of short-chain fatty acid-producing bacteria, such as one or more butyrate-producing bacteria. Immune checkpoints either strengthen or weaken signals (e.g., costimulatory molecules). Inhibitory immune checkpoint molecules that can be targeted by immune checkpoint blockade include adenosine A2A receptor (A2AR), B7-H3 (also known as CD276), B- and T-lymphocyte attenuator (BTLA), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4; also known as CD152), indoleamine 2,3-dioxygenase (IDO), killer cell immunoglobulin (KIR), lymphocyte activation gene 3 (LAG3), programmed death 1 (PD-1), T-cell immunoglobulin domain and mucin domain 3 (TIM-3), and V-domain IgT-cell activation inhibitor (VISTA). In particular, immune checkpoint inhibitors target the PD-1 axis and / or CTLA-4.

[0100]

[0132] Immune checkpoint inhibitors can be small molecules, ligands or receptors, or drugs such as recombinant forms of antibodies, such as human antibodies (e.g., International Patent Publication No. 2015016718; Pardoll, Nat Rev Cancer, 12(4):252-64, 2012; both of which are incorporated herein by reference). Known inhibitors of immune checkpoint proteins or analogs thereof can be used, and in particular, chimeric, humanized, or human forms of antibodies can be used. As known to those of skill in the art, alternative and / or equivalent names may be used for certain antibodies referred to in this disclosure. In the context of the present invention, such alternative and / or equivalent names are interchangeable. For example, lambrolizumab is also known under the alternative and equivalent names MK-3475 and pembrolizumab.

[0101]

[0133] It is contemplated that any immune checkpoint inhibitor known in the art to stimulate an immune response may be used. This includes inhibitors that directly or indirectly stimulate or enhance antigen-specific T lymphocytes. These immune checkpoint inhibitors include, but are not limited to, drugs targeting immune checkpoint proteins and pathways involving PD-L2, LAG3, BTLA, B7H4, and TIM3. For example, LAG3 inhibitors known in the art include soluble LAG3 (IMP321 or LAG3-Ig, disclosed in WO2009044273), as well as murine or humanized antibodies that block human LAG3 (e.g., IMP701, disclosed in WO2008132601), or fully human antibodies that block human LAG3 (such as those disclosed in EP2320940). Another example is provided by the use of blocking agents against BTLA, including, but not limited to, antibodies that block the interaction of human BTLA with its ligands (such as 4C7, disclosed in WO 2011014438). Yet another example is provided by the use of agents that neutralize B7H4, including, but not limited to, antibodies against human B7H4 (such as those disclosed in WO 2013025779 and WO 2013067492) or soluble recombinant forms of B7H4 (such as those disclosed in U.S. Patent No. 20120177645). Yet another example is provided by agents that neutralize B7-H3, including, but not limited to, antibodies that neutralize human B7-H3 (e.g., MGA271, disclosed as BRCA84D and derivatives in U.S. Patent Application No. 20120294796). Yet another example is provided by agents that target TIM3, including, but not limited to, antibodies that target human TIM3 (e.g., antibodies disclosed in WO2013006490A2 or F38-2E2, an anti-human TIM3 blocking antibody disclosed by Jones et al., J Exp Med., 2008, 205(12):2763-79). A. PD-1 axis antagonist

[0102]

[0134] T cell dysfunction or anergy occurs concomitantly with the inducible and persistent expression of the inhibitory receptor, programmed death-1 polypeptide (PD-1). Accordingly, provided herein is therapeutic targeting of PD-1 and other molecules that signal through interactions with PD-1, such as programmed death-ligand 1 (PD-L1) and programmed death-ligand 2 (PD-L2). PD-L1 is overexpressed in many cancers and is often associated with poor prognosis (Okazaki T et al., Intern. Immun., 2007, 19(7):813). Accordingly, provided herein is an improved method of treating cancer by combining inhibition of PD-L1 / PD-1 interactions with modulating the microbiome.

[0103]

[0135] For example, PD-1 axis-binding antagonists include PD-1 binding antagonists, PDL1 binding antagonists, and PDL2 binding antagonists. Alternative names for "PD-1" include CD279 and SLEB2. Alternative names for "PDL1" include B7-H1, B7-4, CD274, and B7-H. Alternative names for "PDL2" include B7-DC, Btdc, and CD273. In some embodiments, PD-1, PDL1, and PDL2 are human PD-1, human PDL1, and human PDL2.

[0104]

[0136] In some embodiments, the PD-1 binding antagonist is a molecule that inhibits the binding of PD-1 to its ligand binding partner. In a detailed aspect, the PD-1 ligand binding partner is PDL1 and / or PDL2. In another embodiment, the PDL1 binding antagonist is a molecule that inhibits the binding of PDL1 to its ligand binding partner. In a detailed aspect, the PDL1 binding partner is PD-1 and / or B7-1. In another embodiment, the PDL2 binding antagonist is a molecule that inhibits the binding of PDL2 to its ligand binding partner. In a detailed aspect, the PDL2 binding partner is PD-1. The antagonist can be an antibody, antigen-binding fragment thereof, immunoadenosin, fusion protein, or oligopeptide. Exemplary antibodies are described in U.S. Pat. Nos. 8,735,553, 8,354,509, and 8,008,449, all of which are incorporated herein by reference. Other PD-1 axis antagonists for use in the methods provided herein are known in the art, such as those described in U.S. Patent Application Nos. 20140294898, 2014022021, and 20110008369, all of which are incorporated herein by reference.

[0105]

[0137] In some embodiments, the PD-1-binding antagonist is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody). In some embodiments, the anti-PD-1 antibody is selected from the group consisting of nivolumab, pembrolizumab, and CT-011. In some embodiments, the PD-1-binding antagonist is an immunoadexin (e.g., an immunoadexin comprising the extracellular portion or PD-1-binding portion of PDL1 or PDL2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence). In some embodiments, the PD-1-binding antagonist is AMP-224. MDX-1106-04, MDX-1106, ONO-4538, BMS-936558, and nivolumab, also known as OPDIVO®, are anti-PD-1 antibodies described in WO 2006 / 121168. Pembrolizumab, also known as MK-3475, Merck 3475, lambrolizumab, Keytruda®, and SCH-900475, is an anti-PD-1 antibody described in WO 2009 / 114335. CT-011, also known as hBAT or hBAT-1, is an anti-PD-1 antibody described in WO 2009 / 101611. AMP-224, also known as B7-DCIg, is a PDL2-Fc fusion soluble receptor described in WO 2010 / 027827 and WO 2011 / 066342. Additional PD-1 binding antagonists include CT-011, MEDI0680, also known as AMP-514, and pidilizumab, also known as REGN2810.

[0106]

[0138] In some embodiments, the immune checkpoint inhibitor is a PD-L1 antagonist, such as durvalumab, also known as MEDI4736, atezolizumab, also known as MPDL3280A, or avelumab, also known as MSB00010118C. In certain aspects, the immune checkpoint inhibitor is a PD-L2 antagonist, such as rHIgM12B7. In some aspects, the immune checkpoint inhibitor is a LAG-3 antagonist, such as, but not limited to, IMP321 and BMS-986016. The immune checkpoint inhibitor may be an adenosine A2a receptor (A2aR) antagonist, such as PBF-509.

[0107]

[0139] In some embodiments, an antibody described herein (such as an anti-PD-1 antibody, anti-PDL1 antibody, or anti-PDL2 antibody) further comprises a human or murine constant region. In a still further aspect, the human constant region is selected from the group consisting of IgG1, IgG2, IgG2, IgG3, and IgG4. In an even more detailed aspect, the human constant region is IgG1. In an even further aspect, the murine constant region is selected from the group consisting of IgG1, IgG2A, IgG2B, and IgG3. In even more detailed aspects, the antibody has reduced or minimal effector function. In even more detailed aspects, the minimal effector function results from production in a prokaryotic cell. In even more detailed aspects, the minimal effector function results from an "effectorless Fc mutation" or aglycosylation.

[0108]

[0140] Thus, antibodies used herein can be non-glycosylated. Glycosylation of antibodies is typically N-linked or O-linked. N-linked refers to the attachment of a carbohydrate moiety to the side chain of an asparagine residue. The tripeptide sequences asparagine-X-serine and asparagine-X-threonine, where X is any amino acid except proline, are recognition sequences for enzymatic attachment of a carbohydrate moiety to the asparagine side chain. Thus, the presence of these tripeptide sequences in a polypeptide creates a potential glycosylation site. O-linked glycosylation refers to the attachment of one of the sugars N-acetylgalactosamine, galactose, or xylose to a hydroxyamino acid, most commonly serine or threonine, although 5-hydroxyproline or 5-hydroxylysine can also be used. Glycosylation sites can be conveniently removed from an antibody by altering the amino acid sequence to remove one of the tripeptide sequences described above (N-linked glycosylation sites). The alteration can be made by substituting an asparagine, serine, or threonine residue within the glycosylation site with another amino acid residue (e.g., glycine, alanine, or a conservative substitution).

[0109]

[0141] The antibodies or antigen-binding fragments thereof can be produced using methods known in the art, for example, by a process comprising culturing a host cell containing nucleic acid encoding any of the previously described anti-PDL1, anti-PD-1, or anti-PDL2 antibodies or antigen-binding fragments in a form suitable for expression under conditions suitable for producing such antibodies or fragments, and recovering the antibodies or fragments. B.CTLA-4

[0110]

[0142] Another immune checkpoint that can be targeted in the methods provided herein is cytotoxic T lymphocyte-associated protein 4 (CTLA-4), also known as CD152. The complete cDNA sequence of human CTLA-4 has GenBank accession number L15006. CTLA-4 is found on the surface of T cells and acts as an "off" switch upon binding to CD80 or CD86 on the surface of antigen-presenting cells. CTLA4 is a member of the immunoglobulin superfamily expressed on the surface of helper T cells and transmits inhibitory signals to T cells. CTLA4 is similar to the T cell costimulatory protein CD28; both molecules bind to CD80 and CD86, also known as B7-1 and B7-2, respectively, on antigen-presenting cells. CTLA4 transmits inhibitory signals to T cells, whereas CD28 transmits stimulatory signals. Intracellular CTLA4 is also found in regulatory T cells and may be important for their function. Activation of T cells via the T cell receptor and CD28 leads to increased expression of CTLA-4, an inhibitory receptor for the B7 molecule.

[0111]

[0143] In some embodiments, the immune checkpoint inhibitor is an anti-CTLA-4 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), an antigen-binding fragment thereof, an immunoadesin, a fusion protein, or an oligopeptide.

[0112]

[0144] Methods well known in the art can be used to generate anti-human CTLA-4 antibodies (or VH and / or VL domains derived therefrom) suitable for use in the present methods. Alternatively, art-recognized anti-CTLA-4 antibodies can be used. For example, the anti-CTLA-4 antibodies disclosed in U.S. Patent Application No. 8,119,129, WO 01 / 14424, WO 98 / 42752; WO 00 / 37504 (CP675,206, tremelimumab, also known as tremelimumab; formerly known as ticilizumab), U.S. Patent Application No. 6,207,156; Hurwitz et al., 1998 can be used in the methods disclosed herein. The teachings of each of the aforementioned publications are incorporated herein by reference. Antibodies that compete with any of these art-recognized antibodies for binding to CTLA-4 can also be used. For example, humanized CTLA-4 antibodies are described in International Patent Application Publication No. WO 2001014424, International Patent Application Publication No. WO 2000037504, and US Pat. No. 8,017,114, all of which are incorporated herein by reference.

[0113]

[0145] An exemplary anti-CTLA-4 antibody is ipilimumab (also known as 10D1, MDX-010, MDX-101, and Yervoy®) or antigen-binding fragments and variants thereof (see, e.g., WO 01 / 14424). In other embodiments, the antibody comprises the heavy and light chain CDRs or VRs of ipilimumab. Thus, in one embodiment, the antibody comprises the CDR1, CDR2, and CDR3 domains of the VH region of ipilimumab and the CDR1, CDR2, and CDR3 domains of the VL region of ipilimumab. In another embodiment, the antibody competes for binding to and / or binds to the same epitope on CTLA-4 as the above-mentioned antibodies. In another embodiment, the antibody has at least about 90% variable region amino acid sequence identity with the above-described antibody (e.g., at least about 90%, 95%, or 99% variable region identity with ipilimumab).

[0114]

[0146] Other molecules for modulating CTLA-4 include soluble CTLA-4 ligands and CTLA-4 receptors such as those described in U.S. Pat. No. 5,844,905, U.S. Pat. No. 5,885,796 and International Patent Application Publication No. WO 1995001994, and International Patent Application Publication No. WO 1998042752, all of which are incorporated herein by reference, and immunoadenosine such as those described in U.S. Pat. No. 8,329,867, which is incorporated herein by reference. C. Killer immunoglobulin-like receptors (KIRs)

[0115]

[0147] Another immune checkpoint inhibitor for use in the present disclosure is an anti-KIR antibody. Methods well known in the art can be used to generate anti-human KIR antibodies (or VH and / or VL domains derived therefrom) suitable for use in the present methods.

[0116]

[0148] Alternatively, art-recognized anti-KIR antibodies can be used. Anti-KIR antibodies may be cross-reactive with multiple inhibitory KIR receptors and may enhance the cytotoxic activity of NK cells bearing one or more of these receptors. For example, an anti-KIR antibody may bind to each of KIR2D2DL1, KIR2DL2, and KIR2DL3 and enhance NK cell activity by reducing, neutralizing, and / or antagonizing the inhibition of NK cell cytotoxicity mediated by any or all of these KIRs. In some embodiments, the anti-KIR antibody does not bind to KIR2DS4 and / or KIR2DS3. For example, monoclonal antibodies 1-7F9 (also known as IPH2101), 14F1, 1-6F1, and 1-6F5, described in International Publication No. WO 2006 / 003179, the teachings of which are incorporated herein by reference, can be used. Antibodies that compete with any of these art-recognized antibodies for binding to KIR can also be used. Additional art-recognized anti-KIR antibodies that may be used include, for example, the anti-KIR antibodies disclosed in WO 2005 / 003168, WO 2005 / 009465, WO 2006 / 072625, WO 2006 / 072626, WO 2007 / 042573, WO 2008 / 084106, WO 2010 / 065939, WO 2012 / 071411, and WO 2012 / 160448.

[0117]

[0149] An exemplary anti-KIR antibody is lirilumab (also referred to as BMS-986015 or IPH2102). In other embodiments, the anti-KIR antibody comprises the heavy and light chain complementarity determining regions (CDRs) or variable regions (VRs) of lirilumab. Thus, in one embodiment, the antibody comprises the CDR1, CDR2, and CDR3 domains of the heavy chain variable (VH) region of lirilumab and the CDR1, CDR2, and CDR3 domains of the light chain variable (VL) region of lirilumab. In another embodiment, the antibody has at least about 90% variable region amino acid sequence identity with lirilumab. IV. Treatment

[0118]

[0150] Provided herein are methods for treating or delaying the progression of cancer in an individual, comprising administering to the individual an effective or sufficient amount of a short-chain fatty acid, such as butyrate, and / or administering a population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, to a subject receiving or currently receiving immune checkpoint therapy. Also provided herein are methods for selecting subjects who will respond favorably to immune checkpoint therapy by assessing the subjects' microbial profiles and administering an immune checkpoint inhibitor to subjects identified as having a favorable microbial profile.

[0119]

[0151] In some embodiments, treatment results in a durable response in the individual, even after treatment is discontinued. The methods described herein can be used in the treatment of conditions in which enhanced immunogenicity is desirable, such as increasing tumor immunogenicity for the treatment of cancer. Also provided herein are methods of enhancing immune function in an individual, such as an individual with cancer, comprising administering to the individual an effective amount of an immune checkpoint inhibitor (e.g., a PD-1 axis-binding antagonist and / or a CTLA-4 antibody) and a short-chain fatty acid, such as butyrate, and / or a population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria. In some embodiments, the individual is a human.

[0120]

[0152] Examples of cancers contemplated for treatment include lung cancer, head and neck cancer, breast cancer, pancreatic cancer, prostate cancer, kidney cancer, bone cancer, testicular cancer, cervical cancer, gastrointestinal cancer, lymphoma, preneoplastic lesions of the lung, colon cancer, melanoma, metastatic melanoma, basal cell skin cancer, squamous cell skin cancer, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, and angiosarcoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, desmoplastic melanoma, and bladder cancer.

[0121]

[0153] In some embodiments, the individual has a cancer that is resistant (documented to be resistant) to one or more anti-cancer treatments. In some embodiments, resistance to anti-cancer treatment includes recurrence of cancer or refractory cancer. Recurrence may refer to the reappearance of cancer at the original site or a new site after treatment. In some embodiments, resistance to anti-cancer treatment includes progression of cancer during treatment with an anti-cancer treatment. In some embodiments, the cancer is at an early or late stage.

[0122]

[0154] The individual may have a cancer that expresses (e.g., has been shown to express in a diagnostic test) the PD-L1 biomarker. In some embodiments, the patient's cancer expresses a low level of the PD-L1 biomarker. In some embodiments, the patient's cancer expresses a high level of the PD-L1 biomarker. The PD-L1 biomarker can be detected in a sample using a method selected from the group consisting of FACS, Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blotting, immunodetection, HPLC, surface plasmon resonance, optical spectroscopy, mass spectrometry, HPLC, qPCR, RT-qPCR, multiplex qPCR or RT-qPCR, RNA-seq, microarray analysis, SAGE, MassARRAY, and FISH, and combinations thereof.

[0123]

[0155] In some embodiments of the methods of the present disclosure, the cancer has a low level of T cell infiltration. In some embodiments, the cancer has no detectable T cell infiltrate. In some embodiments, the cancer is a non-immunogenic cancer (e.g., non-immunogenic colorectal cancer and / or ovarian cancer). Without wishing to be bound by theory, the combination treatment may be directed at T cells (e.g., CD4 + T cells, CD8 + The priming, activation, proliferation, and / or infiltration of T cells (memory T cells) may be increased compared to before administration of the combination. A. Administration

[0124]

[0156] The treatments provided herein include administration of an immune checkpoint inhibitor, a prebiotic or probiotic composition comprising a short-chain fatty acid such as butyrate, and / or a population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria. The treatments can be administered in any suitable manner known in the art. For example, the immune checkpoint inhibitor (e.g., a PD-1 axis-binding antagonist and / or a CTLA-4 antibody), and the short-chain fatty acid such as butyrate and / or the population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, can be administered sequentially (at different times) or concurrently (at the same time). In some embodiments, one or more immune checkpoint inhibitors are in separate compositions as the probiotic therapy. In some embodiments, the immune checkpoint inhibitor is in the same composition as the probiotic composition.

[0125]

[0157] According to a preferred embodiment, the probiotic bacterial composition is formulated for oral administration. A variety of formulations are known to those skilled in the art, which may include live or dead microorganisms, and may be presented as food supplements (e.g., pills, tablets, etc.), or as functional foods such as beverages or fermented yogurt.

[0126]

[0158] The one or more immune checkpoint inhibitors and the short-chain fatty acids such as butyrate and / or the population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, can be administered by the same or different routes of administration. In some embodiments, the immune checkpoint inhibitor is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intracerebroventricularly, or intranasally. In some embodiments, the short-chain fatty acids such as butyrate and / or the population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, are administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intracerebroventricularly, or intranasally. In certain aspects, the immune checkpoint inhibitor is administered intravenously, and the short-chain fatty acids such as butyrate and / or the population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, are administered orally. Effective amounts of an immune checkpoint inhibitor and short-chain fatty acids such as butyric acid, and / or a population of short-chain fatty acid-producing bacteria such as butyric acid-producing bacteria can be administered to prevent or treat a disease. Appropriate dosages of the immune checkpoint inhibitor and / or short-chain fatty acids such as butyric acid, and / or a population of short-chain fatty acid-producing bacteria such as butyric acid-producing bacteria can be determined based on the type of disease being treated, the severity and course of the disease, the individual's clinical condition, the individual's clinical history and response to treatment, and the direction of the attending physician.

[0127]

[0159] For example, a therapeutically effective or sufficient amount of an immune checkpoint inhibitor such as an antibody and / or a short-chain fatty acid such as butyric acid administered to a human, whether administered as a single dose or multiple doses, would be in the range of about 0.01 to about 50 mg / kg of patient body weight. In some embodiments, the antibody used is administered, for example, daily at about 0.01 to about 45 mg / kg, about 0.01 to about 40 mg / kg, about 0.01 to about 35 mg / kg, about 0.01 to about 30 mg / kg, about 0.01 to about 25 mg / kg, about 0.01 to about 20 mg / kg, about 0.01 to about 15 mg / kg, about 0.01 to about 10 mg / kg, about 0.01 to about 5 mg / kg, or about 0.01 to about 1 mg / kg. In some embodiments, the antibody is administered at 15 mg / kg. However, other dosing regimens may also be useful. In one embodiment, the anti-PDL1 antibody described herein is administered to a human at a dose of about 100 mg, about 200 mg, about 300 mg, about 400 mg, about 500 mg, about 600 mg, about 700 mg, about 800 mg, about 900 mg, about 1000 mg, about 1100 mg, about 1200 mg, about 1300 mg, or about 1400 mg on day 1 of a 21-day cycle. The dose can be administered as a single dose or as multiple doses (e.g., two or three doses), such as by infusion. The progress of this therapy can be easily monitored by conventional techniques.

[0128]

[0160] For example, a therapeutically effective or sufficient amount of each of at least one isolated or purified bacterial population, or each of at least two isolated or purified bacterial populations, of a probiotic composition or live bacterial product of the embodiments administered to a human may be at least about 1 x 10 3 colony-forming units (CFU) of bacteria, or at least about 1 x 10 4 (CFU). In some embodiments, a single dose will be about 1 x 10 4 , 1×10 5 , 1×10 6 , 1×10 7 , 1×10 8 , 1×10 9 , 1×10 10 , 1×10 11 , 1×1012 , 1×10 13 , 1×10 14 , 1×10 15 , or 1 × 10 15 The composition will contain more than 1 x 10 CFU of bacteria. In particular embodiments, the bacteria are provided in spore form or as sporulated bacteria. In certain embodiments, the concentration of spores of each isolated or purified bacterial population, e.g., each species, subspecies, or strain, is greater than 1 x 10 per gram of composition or per administered dose. 4 , 1×10 5 , 1×10 6 , 1×10 7 , 1×10 8 , 1×10 9 , 1×10 10 , 1×10 11 , 1×10 12 , 1×10 13 , 1×10 14 , 1×10 15 , or 1 × 10 15 These are viable bacterial spores exceeding CFU.

[0129]

[0161] For individual tumors, solid tumors, and accessible tumors, intratumoral injection or injection into the tumor vasculature is specifically contemplated. Local, regional, or systemic administration may also be appropriate. For tumors >4 cm, the administered volume is about 4 to 10 ml (particularly 10 ml), while for tumors <4 cm, a volume of about 1 to 3 ml (particularly 3 ml) is used. Multiple injections delivered as a single dose include volumes of about 0.1 to about 0.5 ml. For example, adenoviral particles can be advantageously contacted by multiple injections into the tumor.

[0130]

[0162] Treatment regimens may vary and often depend on tumor type, tumor location, progression, and the health and age of the patient. Certain types of tumors clearly require more invasive treatment, while at the same time, certain patients cannot tolerate burdensome protocols. The clinician will be best placed to make such decisions based on the known efficacy and toxicity, if any, of the therapeutic formulation.

[0131]

[0163] In certain embodiments, the tumor being treated may not be resectable, at least initially. Treatment with a therapeutic viral construct may increase the resectability of the tumor due to marginal regression or disappearance of certain portions, particularly invasive portions. After treatment, resection may be possible. Further treatment following resection is used to eliminate microscopic residual disease at the tumor site.

[0132]

[0164] Treatments may include various "unit doses." A unit dose is defined as containing a predetermined quantity of a therapeutic composition. The quantity to be administered, as well as the particular route and formulation, are within the skill of those in the clinical arts in determining the dosage. A unit dose need not be administered as a single injection, but may comprise a continuous infusion over a set period of time. B. Further Anticancer Treatment

[0133]

[0165] In some embodiments, the compositions provided herein comprising an immune checkpoint inhibitor, a short-chain fatty acid such as butyrate, and / or a population of short-chain fatty acid-producing bacteria, such as butyrate-producing bacteria, can be administered in combination with at least one additional therapeutic agent. The additional treatment can be a cancer treatment such as radiation therapy, surgery, chemotherapy, gene therapy, DNA therapy, viral therapy, RNA therapy, immunotherapy, bone marrow transplant, nanotherapy, monoclonal antibody therapy, or a combination of the foregoing. The additional treatment can be in the form of adjuvant or neoadjuvant treatment.

[0134]

[0166] In some embodiments, the additional cancer treatment is administration of a small molecule enzyme inhibitor or an anti-metastatic agent. In some embodiments, the additional treatment is administration of a side effect mitigator (e.g., an agent intended to reduce the occurrence and / or severity of side effects of treatment, such as an anti-nausea agent). In some embodiments, the additional cancer treatment is radiation therapy. In some embodiments, the additional cancer treatment is surgery. In some embodiments, the additional cancer treatment is a combination of radiation therapy and surgery. In some embodiments, the additional cancer treatment is gamma irradiation. In some embodiments, the additional cancer treatment is a therapy targeting the PBK / AKT / mTOR pathway, an HSP90 inhibitor, a tubulin inhibitor, an apoptosis inhibitor, and / or a chemopreventive agent. The additional cancer treatment may be one or more chemotherapeutic agents known in the art.

[0135]

[0167] Various combinations can also be employed. In the example below, the immune checkpoint inhibitor, butyrate, and / or butyrate-producing bacterial population is "A" and the additional cancer treatment is "B." A / B / AB / A / BB / B / AA / A / BA / B / BB / A / AA / B / B / BB / A / B / BB / B / B / AB / B / A / BA / A / B / BA / B / A / BA / B / B / AB / B / A / AB / A / B / AB / A / A / BA / A / A / BB / A / A / AA / B / A / AA / A / B / A

[0136]

[0168] Administration of any compound or treatment of the present embodiments to a patient follows standard protocols for the administration of such compounds, taking into account the toxicity of the agent, if any. Thus, in some embodiments, there is a step of monitoring toxicity attributable to the combination therapy. 1.Chemotherapy

[0137]

[0169] A wide variety of chemotherapeutic agents can be used in accordance with this embodiment. The term "chemotherapy" refers to the use of drugs to treat cancer. "Chemotherapeutic agent" is used to connote a compound or composition administered in the treatment of cancer. These agents or drugs are categorized by their mode of action within the cell, for example, whether and at what stage they affect the cell cycle. Alternatively, agents can be characterized based on their ability to induce chromosomal and mitotic abnormalities by directly crosslinking DNA, intercalating into DNA, or affecting nucleic acid synthesis.

[0138]

[0170] Examples of chemotherapeutic agents include alkylating agents such as thiotepa and cyclophosphamide; alkyl sulfonates such as busulfan, improsulfan, and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines, including altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, and trimethylolmelamine; acetogenins (especially bullatacin and bullatacinone); camptothecin (including the synthetic analog topotecan); bryostatin; kallistatin; CC-1065 (including its synthetic analogs adozelesin, carzelesin, and bizelesin); cryptophycins (especially cryptophycin 1 and cryptophycin 8); dolastatin duocarmycins (including synthetic analogs KW-2189 and CB1-TM1); eleutherobin; pancratistatin; sarcodictyin; spongistatin; nitrogen mustards such as chlorambucil, chlornaphazine, chlorophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembicin, phenesterine, prednimustine, trofosfamide, and uracil mustard; nitrosoureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimustine; enediyne antibiotics (e.g., calicheamicins, especially calicheamicin gamma II and calicheamicin omega II); dynemicins, including dynemicin A; bisphosphonates such as clodronate;In addition to esperamicin, the neocarzinostatin chromophore and related chromoproteins include the enediyne antibiotic chromophores, aclacinomycin, actinomycin, anthramycin, azaserine, bleomycin, cactinomycin, carabicin, carminomycin, carzinophilin, chromomycin, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, doxorubicin (morpholino-doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin, and deoxydoxorubicin) ), antibiotics such as epirubicin, esorubicin, idarubicin, marcelomycin, mitomycins such as mitomycin C, mycophenolic acid, nogalamycin, olivomycin, peplomycin, potfilomycin, puromycin, chelamycin, rhodrubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, and zorubicin; antimetabolites such as methotrexate and 5-fluorouracil (5-FU); folic acid analogs such as denopterin, pteropterin, and trimetrexate; fludarabine, 6-methyl-2-propanol; Purine analogs such as thiamiprine, thiamiprine, and thioguanine; pyrimidine analogs such as ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, and floxuridine; androgens such as calsterone, dromostanolone propionate, epithiostanol, mepitiostane, and testolactone; antiadrenal agents such as mitotane and trilostane; folic acid supplements such as folinic acid; aceglatone; aldophosphamide glycosides; aminolevulinic acid; eniluracil; a Musacrine; Bestravcil; Bisantrene; Edatrexate; Defofamine; Demecolcine; Diazicon; Elformitin; Elliptinium acetate; Epothilone; Etoglucide; Gallium nitrate; Hydroxyurea; Lentinan; Lonidynin; Maytansinoids such as maytansine and ansamitocins; Mitoguazone; Mitoxantrone; Mopidanmol; Nitraerin; Pentostatin; Fenameth; Pirarubicin; Rosoxantrone; Podophyllic acid; 2-ethylhydrazide; Procarbazine; PSK polysaccharide complex; Razoxane; Rhizoxin;Schizophyllan; spirogermanium; tenuazonic acid; triazicon; 2,2',2''-trichlorotriethylamine; trichothecenes (e.g., T-2 toxin, veracrine A, roridin A, and anguidine); urethane; vindesine; dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside ("Ara-C"); cyclophosphamide; taxoids, such as paclitaxel and docetaxel; gemcitabine; 6-thioguanine; mercaptopurine; platinum coordination complexes such as cisplatin, oxaliplatin, and carboplatin; vinblastine; platinum; etoposide (VP-16); ifosfamide; mitoxantrone; vincristine; vinorelbine; novantrone; teniposide; edatrexate; daunomycin; aminopterin; xeloda; ibandronate; irinotecan (e.g., CPT-11); the topoisomerase inhibitor RFS2000; difluoromethylornithine (DMFO); retinoids such as retinoic acid; capecitabine; carboplatin, procarbazine, plicomycin, gemcitabine, navelbine, farnesyl protein transferase inhibitors, transplatin, and pharmaceutically acceptable salts, acids, or derivatives of any of the above; 2. Radiation therapy

[0139]

[0171] Other widely used agents that cause DNA damage include those commonly known as gamma rays, X-rays, and / or directed delivery of radioisotopes to tumor cells. Other forms of DNA damaging agents are also contemplated, such as microwaves, proton beam irradiation (U.S. Patent Nos. 5,760,395 and 4,870,287), and UV irradiation. All of these agents most likely affect widespread damage to DNA, DNA precursors, DNA replication and repair, and chromosome assembly and maintenance. X-ray doses range from daily administrations of 50-200 roentgens over a prolonged period (3-4 weeks) to single doses of 2000-6000 roentgens. Dosage ranges for radioisotopes vary widely and depend on the half-life of the isotope, the type of radiation emitted, and uptake by neoplastic cells. 3. Immunotherapy

[0140]

[0172] Those skilled in the art will understand that immunotherapy can be used in combination with or in conjunction with the methods described herein. In the context of cancer treatment, immunotherapeutics generally rely on the use of immune effector cells and molecules that target and destroy cancer cells. Rituximab (RITUXAN®) is an example of an immunotherapy. The immune effector can be, for example, an antibody specific for a marker on the surface of tumor cells. The antibody can be used alone as a therapeutic effector or can recruit other cells that carry out cell killing. Antibodies can also be conjugated to drugs or toxins (such as chemotherapeutic agents, radionuclides, ricin A chain, cholera toxin, pertussis toxin, etc.) and can also be used as targeting agents. Alternatively, the effector can be a lymphocyte bearing a surface molecule that interacts directly or indirectly with a tumor cell target. Various effector cells include cytotoxic T cells and NK cells.

[0141]

[0173] Antibody-drug conjugates have emerged as a breakthrough approach to the development of cancer therapeutics. Antibody-drug conjugates (ADCs) contain a monoclonal antibody (MAb) covalently linked to a cell-killing drug. This approach combines the high specificity of MAbs for their antigenic target with a highly potent cytotoxic drug, resulting in "armed" MAbs that deliver their payload (drug) to tumor cells with enriched levels of antigen. Targeted delivery of the drug also minimizes its exposure in normal tissues, resulting in reduced toxicity and an improved therapeutic index. The FDA approval of two ADC drugs, ADCETRIS® (brentuximab vedotin) in 2011 and KADCYLA® (trastuzumab emtansine or T-DM1) in 2013, validated the approach. Currently, over 30 ADC drug candidates are in various stages of clinical trials for cancer treatment. As antibody engineering and linker-payload optimization become increasingly mature, the discovery and development of new ADCs increasingly depends on the identification and validation of new targets amenable to this approach and the generation of targeting MAbs. Two criteria for ADC targets are up-regulated / high levels of expression in tumor cells and robust internalization.

[0142]

[0174] In one aspect of immunotherapy, tumor cells must have some marker suitable for targeting, i.e., not present in the majority of other cells. Many tumor markers exist, any of which may be suitable for targeting in the context of this embodiment. Common tumor markers include CD20, carcinoembryonic antigen, tyrosinase (p97), gp68, TAG-72, HMFG, sialyl Lewis antigen, MucA, MucB, PLAP, laminin receptor, erbB, and p155. An alternative aspect of immunotherapy is to combine anti-cancer effects with immunostimulatory effects. Immune stimulatory molecules also exist, including cytokines such as IL-2, IL-4, IL-12, GM-CSF, and gamma-IFN; chemokines such as MIP-1, MCP-1, and IL-8; and growth factors such as FLT3 ligand. 4.Surgery

[0143]

[0175] Approximately 60% of people with cancer will undergo some type of surgery, including preventative surgery, diagnostic or staging surgery, curative surgery, and palliative surgery. Curative surgery includes resection, which physically removes, excises, and / or destroys all or part of cancerous tissue, and may be used in conjunction with other treatments, such as the treatment of the present embodiments, chemotherapy, radiation therapy, hormone therapy, gene therapy, immunotherapy, and / or alternative therapies. Tumor resection refers to the physical removal of at least a portion of the tumor. In addition to tumor resection, surgical treatments include laser surgery, cryosurgery, electrosurgery, and microsurgical surgery (Mohs surgery).

[0144]

[0176] Removal of part or all of the cancerous cells, cancerous tissue, or cancerous tumor can result in the formation of a void in the body. Treatment can be achieved by perfusion of the area with additional anti-cancer therapy, direct injection, or local application. Such treatment can be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, and 5 weeks, or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months. These treatments can also be variable-dose treatments. 5. Other drugs

[0145]

[0177] It is contemplated that other agents may be used in combination with certain aspects of the present embodiments to improve the therapeutic efficacy of the treatment. These additional agents include agents that affect the upregulation of cell surface receptors and gap junctions, cytostatic and differentiation agents, inhibitors of cell adhesion, agents that increase the sensitivity of hyperproliferative cells to apoptosis-inducing agents, or other biological agents. Increased intercellular signaling due to an increase in the number of gap junctions will enhance the anti-hyperproliferative effect on adjacent hyperproliferative cell populations. In other embodiments, cytostatic and differentiation agents may be used in combination with certain aspects of the present embodiments to improve the anti-hyperproliferative efficacy of the treatment. It is contemplated that inhibitors of cell adhesion will improve the efficacy of the present embodiments. Examples of cell adhesion inhibitors are focal adhesion kinase (FAK) inhibitors and lovastatin. It is further contemplated that other agents that increase the sensitivity of hyperproliferative cells to apoptosis, such as the antibody c225, may be used in combination with certain aspects of the present embodiments to improve the efficacy of the treatment. [Example]

[0146]

[0178] The following examples are included to support preferred embodiments of the present invention. Those skilled in the art will appreciate that the techniques disclosed in the examples which follow represent techniques discovered by the inventors to function well in the practice of the invention, and therefore may be considered to constitute preferred modes of practice thereof. However, those skilled in the art will also appreciate, in light of this disclosure, that many changes can be made in the specific embodiments disclosed and still obtain like or similar results without departing from the spirit and scope of the invention. Example 1: Characterization of the microbiome in melanoma patients

[0147]

[0179] To better understand the role of the microbiome in responding to immune checkpoint blockade in cancer patients, microbiome samples were collected from patients with metastatic melanoma undergoing treatment with PD-1 blockade (n = 112 patients). At the start of treatment, oral (mouth) and intestinal (fecal) microbiome samples were collected, and tumor biopsies and blood samples were also collected when feasible to assess genomic alterations as well as the density and phenotype of tumor-infiltrating and circulating immune cell subsets (Figure 1A). Taxonomic profiling via 16S rRNA gene sequencing was performed on all available oral and intestinal samples, followed by metagenomic whole-genome shotgun sequencing (WGS) on a subset. Patients were classified as responders (R) or non-responders (NR) based on imaging analysis per RECIST 1.1 criteria (Schwartz et al., 2016). It is noteworthy that patients in the R group were relatively similar to those in the NR group with respect to age, sex, normotype, previous treatment, concomitant treatment, and serum LDH (Table 3). The frequencies of specific melanoma-driving mutations and total mutational burden were also similar between groups (Figure 5) (17).

[0148]

[0180] [Table 5] TIFF2025160204000152.tif23149

[0149]

[0181] We first assessed the oral and fecal microbiota landscapes in patients with metastatic melanoma via 16S sequencing (V4 region). While both communities were relatively diverse, the oral microbiome was dominated by Lactobacillales, while the fecal microbiome was dominated by Bacteroidetes (Figure 1B). Bipartite network analysis (Muegge et al., 2011) confirmed a clear separation of community structure between the oral and fecal microbiomes for both matched and aggregated samples (Figures 1C and 6), suggesting that these communities are significantly different.

[0150]

[0182] Loss of diversity (dysbiosis) is associated with long-term health conditions (Turnbaugh et al., 2008; Qin et al., 2010) and cancer (Garrett et al., 2015; Segre et al., 2015; Drewes et al., 2016) and with poor outcomes from certain forms of cancer treatment, including allogeneic stem cell transplantation (Taur et al., 2014). Based on these data, we examined the diversity of the oral and gut microbiomes in patients undergoing PD-1 blockade and found that gut microbiome diversity was significantly greater in R compared to NR using several metrics (p = 0.009, Figures 1D and 7). No significant differences were observed in the oral microbiome (p = 0.11, Figure 8). Examining the relationship between diversity and progression-free survival (PFS) in this cohort confirmed that patients with high fecal microbiome diversity had significantly longer PFS compared with patients with intermediate or low diversity (p = 0.021 and 0.041, respectively; Figures 1E-1F and 9). No differences in PFS were observed when comparing oral microbiome diversity (Figures 10A-10D).

[0151]

[0183] Because differences in microbiome composition can also influence cancer development and response to treatment (Sivan et al., 2015; Iida et al., 2013; Viaud et al., 2013; Vetizou et al., 2015), we also sought to determine whether differences existed between the oral and gut microbiomes of R and NR in response to PD-1 blockade. To investigate this, we calculated the enrichment index (ei) of operational taxonomic units (OTUs) and compared R versus NR. This confirmed that a significantly different set of bacteria was associated with response to anti-PD-1 therapy, with enrichment of Clostridiales in R and Bacteroidales in NR gut microbiomes (p<0.001, Figures 2A-2B and 11). No significant differences in enrichment were observed in the oral microbiomes of R versus NR (Figures 12A-12B). To further explore these findings, higher-level taxonomic comparisons were performed via linear discriminant analysis of effect sizes (LEfSe) (Segata et al., 2011), which again confirmed the differential abundance of bacteria in the fecal microbiomes of R versus NR in response to PD-1 blockade, with enrichment of Clostridiales / Ruminococcaceae in R and Bacteroidales in NR (Figures 2C-2D). No significant differences were observed between the oral microbiomes of R and NR, except for the high abundance of Bacteroidales in NR in response to PD-1 blockade (Figures 13A-13B). Paired comparisons of bacterial taxa at all levels were then performed by response. In addition to confirming previous taxonomic distinctions, these analyses identified the species Faecalibacterium prausnitzii as significantly enriched in R (Figure 2E, Table 4).Metagenomic WGS further revealed enrichment of Faecalibacterium species in R, along with other species including Akkermansia species, while Bacteroides thetaiotaomicron, Escherichia coli, and Acerotorunculus colihominis were enriched in NR (Figure 2F, Table 4). Notably, the limited number of longitudinally tested samples showed that the gut microbiome remained relatively stable over time (Figures S14A-S14C).

[0152]

[0184] Based on these insights, we next asked whether the composition and abundance of bacteria within the gut microbiome and / or oral microbiome could predict the response to PD-1 blockade in our cohort. To do this, we clustered all identified OTUs into clusters of related OTUs (crOTUs) via phylogenetic tree construction from sequence alignment data (Peled et al., 2017). This technique involves comparing the abundance of different potential bacterial groupings based on 16S sequence similarity and helps address the rare distribution of OTU abundances observed in the absence of this approach (Figures 15A-15B). We then performed unsupervised hierarchical clustering of crOTU abundances within the gut microbiome and oral microbiome without input of response data. Results confirmed that patients could be separated into two significantly distinct clusters: one cluster (cluster 1) composed entirely of R and the other cluster (cluster 2) composed of a mixture of R and NR (p = 0.02), enriched for Clostridiales in cluster 1 and Bacteroidales in cluster 2 (Figures 3A-3B). Evaluating PFS in each of these clusters subsequently confirmed that patients in cluster 1 had a significantly shorter time to progression when receiving PD-1 blockade compared with patients in cluster 2 (p = 0.02) (Figure 3C). To better understand the compositional differences in these clusters, paired comparisons of the gut microbiota were performed and identified a pattern very similar to that seen in clustering by response: enriched for Clostridiales / Ruminococcaceae in cluster 1 and enriched for Bacteroidales in cluster 2 (Figure 3D; Table 5). Analysis of crOTUs in the oral microbiome did not reveal a clear relationship with treatment response (FIGS. 16A-16B).

[0153]

[0185] To explore the association between specific bacterial taxa and treatment response, we compared PFS to anti-PD-1 therapy based on the median relative abundance of these taxa in the gut microbiome, as they were associated with the "top hits" consistently observed across analyses (F. prausnitzii in R and Bacteroidales in NR). Patients with high abundance of F. prausnitzii had significantly prolonged PFS compared with patients with low abundance (p = 0.03). Conversely, patients with high abundance of Bacteroidales had significantly shortened PFS compared with patients with low abundance (p = 0.05) (Figure 3E). This is consistent with recently published data in a small cohort of patients receiving CTLA-4 blockade, which showed that patients with a high abundance of Faecalibacterium in the gut microbiome had significantly longer PFS compared with patients with a high abundance of Bacteroides (Chaput et al., 2017). Additionally, Cox proportional hazards analysis in our cohort confirmed that the strongest predictors of response to PD-1 blockade were fecal microbiome alpha diversity (HR = 3.94; 95% CI = 1.02-12.52), F. prausnitzii abundance (HR = 2.92; 95% CI = 1.08-7.89), and crOTU clusters (HR = 3.80; 95% CI = 1.09-13.21). These effects remained significant in multivariate analyses after adjusting for prior immunotherapy treatment (Table 6).

[0154]

[0186] [Table 6] TIFF2025160204000154.tif113149

[0155]

[0187] [Table 7] TIFF2025160204000156.tif22149

[0156]

[0188] Next, seeking insight into mechanisms by which the gut microbiome may influence response to anti-PD-1 therapy, we first performed functional genomic profiling of gut microbiome samples via metagenomic WGS in R versus NR to treatment. Organism-specific gene hits were assigned to Kyoto Encyclopedia of Genes and Genomes (KEGG) ontologies (KOs). Based on these annotations, the metagenome for each sample was reconstructed into metabolic pathways using the MetaCyc hierarchy of pathway classifications (Caspi et al., 2008; Kanehisa et al., 2000). Unsupervised hierarchical clustering of the relative abundance of both KOs and predicted pathways identified three groups of patient samples with response rates of 72.7%, 57.1%, and 42.9%. Comparison of gene function abundance across these groups showed changes in metabolic function, with R dominating anabolic functions, including amino acid biosynthesis, which may promote host immunity ( Blacher et al., 2017 ) ( Figure 3F ), ​​while NR dominated catabolic functions ( Figure 3F , 17 , Table 7 ).

[0157]

[0189] Given that preclinical models provide clear evidence that the differential composition of the gut microbiome can influence therapeutic responses to PD-1 blockade at the level of the tumor microenvironment (Sivan et al., 2015), we investigated the relationship between the gut microbiome and systemic and antitumor immune responses in a cohort of patients receiving PD-1 blockade. To do this, we compared tumor-associated immune infiltrates via multiparameter IHC and found a high density of CD8+ in baseline samples from R versus NR. +We observed T lymphocytes (p = 0.04), consistent with previous reports (Figures 4A and 18A-18F) (Tumeh et al., 2014; Chen et al., 2016). Next, Spearman's rank correlation was performed using paired comparisons between specific bacterial taxa enriched in the gut microbiomes of R and NR patients and immune markers in the tumor microenvironment. This confirmed a strong positive correlation between the abundance of Ruminococcus / Faecalibacterium in the gut and cytotoxic T cell infiltrates in tumors, and a strong negative correlation for Bacteroidales (Figures 4B-4C and 19-20). Analysis of the systemic immune response via flow cytometry and cytokine assays revealed that patients with a high abundance of Ruminococcus in the gut had a higher level of effector CD4 T cells in the systemic circulation. + T cells and effector CD8 + While patients with elevated levels of T cells and preserved cytokine responses to PD-1 blockade, those with a high abundance of Bacteroidales in the gut microbiome exhibited elevated levels of regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) in the systemic circulation, blunting cytokine responses (Figures 4D and 21-22). To better understand the impact of compositional differences in the gut microbiome on antigen processing and presentation within the tumor microenvironment, we performed multiplex IHC targeting the myeloid compartment (Tsujikawa et al., 2017). In these studies, patients with a high abundance of Faecalibacterium in their gut microbiomes had a higher density of markers for immune cells and antigen processing and presentation compared to patients with a high abundance of Bacteroidales (Figures 4E-4F and 23-24), suggesting a possible mechanism by which the gut microbiome may modulate antitumor immune responses (Sivan et al., 2015).

[0158]

[0190] Placing these insights in the context of published literature, we propose that the gut microbiome modulates responses to anti-PD-1 therapy treatment (Figure 4G). Specifically, we propose that patients with a "favorable" gut microbiome (high diversity and high abundance of Ruminococcaceae / Faecalibacterium) have enhanced systemic and antitumor immune responses mediated by enhanced antigen presentation at the lymph node and tumor level, as well as preserved effector T cell function in the periphery and tumor microenvironment. In contrast, patients with an "unfavorable" gut microbiome (low diversity and high abundance of Bacteroidetes) have impaired systemic and antitumor immune responses mediated by limited intratumoral infiltration by both lymphoid and myeloid elements, diminished antigen presentation capacity, and skewing of immunoregulatory cellular and humoral elements, including Tregs and MDSCs, in the periphery. These findings highlight the potential for parallel modulation of the gut microbiome to significantly enhance the efficacy of checkpoint blockade.

[0159]

[0191] [Table 8] TIFF2025160204000158.tif56149

[0160]

[0192] [Table 9] TIFF2025160204000160.tif210149 TIFF2025160204000161.tif210149 TIFF2025160204000162.tif210149 TIFF2025160204000163.tif214149 TIFF2025160204000164.tif210149 TIFF2025160204000165.tif213149 TIFF2025160204000166.tif210149 TIFF2025160204000167.tif212149 TIFF2025160204000168.tif212149 TIFF2025160204000169.tif210149 TIFF2025160204000170.tif208149 TIFF2025160204000171.tif210149 TIFF2025160204000172.tif212149 TIFF2025160204000173.tif209149 TIFF2025160204000174.tif202149

[0161]

[0193] [Table 10] Example 2: Materials and Methods

[0162]

[0194] Patient Cohort: This study enrolled an initial cohort of 112 patients with metastatic melanoma. These patients were treated with anti-PD1 immune checkpoint blockade therapy at the University of Texas (UT) MD Anderson Cancer Center between April 2015 and March 2016 and signed voluntary informed consent for the collection and analysis of tumor, blood, and microbiome samples under an Institutional Review Board (IRB)-approved protocol. Patients diagnosed with uveal melanoma (n = 10), patients receiving anti-PD1 in combination with targeted agent therapy or adoptive T cell transfer therapy (n = 8), or patients in whom response could not be determined (n = 6) were excluded from this analysis. Electronic medical records were independently reviewed by three investigators to assign clinical response groups and record other clinical parameters (Table 3). The primary outcome of clinical response (responder) (R) was defined by radiographic evidence of complete response (CR), partial response (PR), or stable disease (SD) according to RECIST 1.1 criteria for at least 6 months. Lack of clinical response (non-responder) (NR) was defined by progression (PD) on serial CT scans or by a sustained clinical benefit of less than 6 months (minimal benefit).

[0163]

[0195] Microbiome Sample Collection: Oral samples were collected at scheduled pre-treatment visits using a Catch-All Sample Collection Swab (Epicentre, Madison, WI). All patients also received an outpatient OMNIgene GUT kit (OMR-200), a fecal sample collection kit from DNA Genotek that required return. Importantly, this kit helps maintain the stability of the microbiome profile at room temperature for up to 60 days. All samples were frozen at -80°C prior to DNA extraction and analysis.

[0164]

[0196] The final cohort consisted of oral samples collected from 87 patients (52 of whom were R and 34 were NR) and fecal samples collected from 43 patients (30 of whom were R and 13 were NR). All samples except for two oral and two fecal samples were collected at baseline. These samples were included as baseline surrogates in a subset analysis of longitudinal samples that showed no change after treatment intervention in this cohort.

[0165]

[0197] Tumor and Blood Sample Collection: Matched pretreatment tumor samples (n = 23) available at the time point were obtained from the MD Anderson Cancer Department of Pathology archives and the Institutional Tissue Bank. After samples were quality-controlled for percent tumor viability by MD Anderson pathologists, we included 17 samples from R and 6 samples from NR. At baseline, blood samples (n = 11) collected and stored for research studies (protocols previously listed) were also considered for study inclusion, resulting in 8 R and 3 NR samples.

[0166]

[0198] DNA extraction and bacterial 16S sequencing: Preparation and sequencing were performed in collaboration with the Center for Metagenomics and Microbiome Research (CMMR) at Baylor College of Medicine. The 16S rRNA gene sequencing method was adapted from methods developed for the NIH-Human Microbiome Project (Framework for Human Microbiome Research, 2012).

[0167]

[0199] Briefly, bacterial genomic DNA was extracted using the MO BIO PowerSoil DNA Isolation Kit (MO BIO Laboratories, USA). The 16S rDNA V4 region was amplified by PCR and sequenced using a 2 × 250 bp paired-end protocol on the MiSeq platform (Illumina, Inc., San Diego, CA), resulting in near-perfectly overlapping paired-end reads. The primers used for amplification contained adapters for MiSeq sequencing and single-end barcodes (Caporaso et al., 2012), allowing for pooling and direct sequencing of PCR products.

[0168]

[0200] Using open-reference OTU picking (Edgar, 2010; Rognes et al., 2016; Caporaso et al., 2010), quality-filtered sequences with >97% identity were clustered into bins known as operational taxonomic units (OTUs) and classified at the species level against the 16S sequence database, NCBI 16S ribosomal RNA, using the ncbi-blast+ package 2.5.0. Phylogenetic classification was obtained from the NCBI taxonomy database. The relative abundance of each OTU was determined for all samples. Taxonomic classification was verified using the Greengenes, SILVA, and RDP databases.

[0169]

[0201] Phylogenetic trees were empirically constructed using the FastTree algorithm (Price et al., 2010) in the QIIME software package, as previously described (Peled et al., 2016). Briefly, every node in the tree was considered a cluster of related OTUs (crOTU), where the abundance of each crOTU was the sum of the abundances of its member OTUs. Trees were constructed from sequence alignments of all OTUs observed in both the oral microbiome (1,152 OTUs, 97.5% of 1,182) and the intestinal microbiome (1,434 OTUs, 98.6% of 1,455). The resulting crOTU trees for the oral and intestinal microbiomes contained 1,152 and 1,434 nodes, respectively.

[0170]

[0202] Taxonomic alpha diversity was estimated using the inverse Simpson index. Rarefaction limits were set based on the minimum number of reads in all analyzed oral (13,000) and fecal (8,000) samples, where:

number

[0171]

[0203] Bipartite networks comparing and contrasting oral and gut microbiota: Bipartite networks were constructed using the make_biparitite_network.py script in QIIME with default parameters (Caporaso et al., 2010) and then visualized in Cytoscape using an edge-weighted Spring embedded layout. Two networks were generated using all oral and fecal samples (Figure 6), and only paired samples (Figure 1A) when both samples were obtained from the same patient.

[0172]

[0204] An enrichment index (ri) was used to visualize the differences in the oral and gut microbiomes between R and NR. The representation of each species in R (riR) and in NR (riNR) was quantified as the proportion of samples within each group that had a non-zero abundance for a particular species. The value of ri ranged from 0 (the OTU was not found in any sample within the group) to 1 (the OTU was found in all samples within the group).

[0173]

[0205] The OTU enrichment index (ei) was used to quantify and compare the enrichment of each OTU in R versus its enrichment in NR, where ei = (riR - riNR) / (riR + riNR). This index ranges in value from -1 to +1. If a species is identified in all R samples but not in any NR samples, ei = +1. The opposite is true for ei = -1.

[0174]

[0206] Using the distribution ei score, all species were classified into three sets: Set 1: differentially enriched in R, Set 2: found in both groups, and Set 3: differentially enriched in NR. Within each set, all OTUs were sorted by abundance and then visualized as a heatmap of log(10)-transformed OTU abundances in all samples given as columns. OTU abundance thresholds (low, medium, and high) were derived from the distribution of abundances for all OTUs.

[0175]

[0207] Statistical evaluation of biomarkers using LEfSe: The LEfSe analysis method first compares the abundance of all bacterial clades between R and NR in the oral and gut microbiomes using a Kruskal-Wallis test with a predefined α of 0.05. Significantly different vectors resulting from the comparison of abundances between groups (e.g., the relative abundance of the genus Faecalibacterium) are used as inputs to linear discriminant analysis (LDA), which yields effect sizes (Figure 2B). A key advantage of LEfSe over traditional statistical tests is that it yields effect sizes in addition to p-values. This allows for sorting the results of multiple tests by the magnitude of differences between groups. For hierarchically organized bacterial clades, a larger number of comparisons are required at the genus and species levels compared to the phylum and class levels. Due to the difference in the number of hypotheses considered at different levels, there may be a lack of correlation between p-values ​​and effect sizes.

[0176]

[0208] Metagenomic Whole Genome Shotgun (WGS) Sequencing: This was also performed in collaboration with CMMR and Metagenopolis (MGP). Briefly, metagenomic sequencing data provides species-level resolution for bacterial and microbial collections, particularly the near-complete genomic content of samples, also referred to as pangenomes (sequencing depth directly relates to the amount of coverage of the pangenome, particularly the dataset).

[0177]

[0209] Whole-genome shotgun (WGS) sequencing utilizes the same extracted bacterial genomic DNA used for 16S rRNA gene composition analysis. However, WGS sequencing achieves high-depth sequencing by utilizing a powerful sequencing platform. Individual libraries were constructed from each sample and loaded onto the HiSeq platform (Illumina) for sequencing using a 2 × 100 bp paired-end read protocol. Quality filtering, trimming, and demultiplexing steps were performed using an in-house pipeline developed by assembling publicly available tools, including Casava v1.8.3 (Illumina) for fastq generation, Trim Galore and cutadapt for adapter and quality trimming, and PRINSEQ for sample demultiplexing.

[0178]

[0210] Gut microbiota analysis was performed using a quantitative metagenomics pipeline developed at MGP. This method allows for analysis of the microbiota at the gene and species levels. High-quality reads were selected and cleaned to remove possible human contaminants. These were mapped and counted using the MetaHIT hs_9.9M gene catalog (Li et al., 2014) using an in-house METEOR Studio pipeline using a two-step procedure: first, using uniquely mapped reads, and then attributing shared reads (mapping distinct genes from the catalog) according to their mapping ratio using unique reads. Mapping was performed using a >95% identity threshold to account for gene variability and the nonredundant nature of the catalog.

[0179]

[0211] After a size reduction step (correcting for different sequencing depths) and normalization (RPKM) on 14M reads, a gene frequency profile matrix was obtained and used as a reference standard to perform the analysis using MetaOMineR, a suite of R packages developed at MGP and specialized in the analysis of large-scale quantitative metagenomic datasets.

[0180]

[0212] The hs_9.9M gene catalog was clustered into 1438 MGSs (MetaGenomic Species, a group of >500 genes whose abundances covary across hundreds of samples and therefore belong to the same microbial species (Nielson et al., 2014)). Taxonomic annotation of MGSs was performed using gene homology with previously sequenced organisms (using blastN against databanks by nt and databanks by wgs). MGS signal across samples was calculated as the mean or median signal of 50 marker genes. MGS frequency profile matrices were constructed using the MGS mean signal after normalization (sum of MGS frequencies across samples = 1).

[0181]

[0213] Reads whose genomic coordinates overlap with known KEGG orthologs were tabulated, and KEGG modules were calculated stepwise, with completion determined for each detected species and metagenome if 65% of the reaction steps were present. Pathways were constructed for each taxon and metagenome by calculating the minimum set of MinPaths resulting from the gene orthologs present.

[0182]

[0214] Pathway Metagenomic Database (PGDB): A database was created for each WGS sample using the PathoLogic program [PMID: 26454094] from Pathway Tools software. The input to the program was generated using predicted gene functions based on KEGG orthology and their assignment to taxonomic lineages in the metagenome. Therefore, if the same function (KO group) had several taxonomic annotations, each annotation was considered a separate gene. The KEGG definition of the KO group was used as the gene function, and the primary name of the KO group, if available, was used as the gene name. EC numbers were assigned to genes according to the KO group annotation by their number in KEGG. Metabolic reconstructions were performed in automatic mode to automatically predict transport reactions as described in the Pathway Tools manual with optional help. The organism class for PGDB was used as the [DOMAIN] value of "TAX-2 (bacteria)" and the [CODON-TABLE] value was set to 1. Pathway Tools was used to aggregate and compare the generated PGDBs, which are available upon request.

[0183]

[0215] Statistical Analysis: Alpha diversity was compared between R and NR using the Wilcoxon rank-sum test or the Mann-Whitney (MW) test. All patients were classified into high, moderate, or low diversity groups based on the tertiles of distribution. Paired comparisons of taxonomic abundance were performed by both response and cluster using the MW test. Within each level (phylum, class, order, family, genus, and species), taxa with low abundance (<0.1%) and low variability (<0.001) were excluded. Corrections for multiple comparisons were performed using the false discovery rate method with an alpha level of 0.05. Effect sizes were estimated for each taxon as U / √n, where U is the test statistic for the MW test and n is the total sample size (n = 43) (Fritz et al., 2012), and a volcano plot was created with log10 (FDR-corrected p-value) on the y-axis and median-corrected effect size on the x-axis. In addition, patients were also classified as having high or low abundance of Faecalibacterium prausnitzii or Bacteroidales based on the median abundance of these taxa in the gut microbiome. Kaplan-Meier estimates were estimated for each group and compared using the log-rank test. Hazard ratios were estimated using Cox proportional hazards models.

[0184]

[0216] In general, the MW test was used for comparisons between dichotomous outcome variables (R versus NR), and the Spearman correlation test was used to compare continuous variables. In addition, Fisher's exact test was used to compare proportions between dichotomous variables. Hypothesis testing was performed using both one-sided and two-sided tests, as appropriate, with a significance level of 95%. All analyses were performed using R and GraphPad Prism (La Jolla, CA).

[0185]

[0217] Immunohistochemistry: Briefly, sections (4 μm thick) were prepared from formalin-fixed, paraffin-embedded (FFPE) tissues. The presence of tumor was confirmed by a pathologist on hematoxylin / eosin-stained slides (H&E). Slides were then stained for CD3 (n = 17) (DAKO, Santa Clara, CA, 1:100), CD8 (n = 21) (Thermo Scientific, Waltham, MA, 1:100), PD-1 (n = 16) (Abcam, Cambridge, UK, 1:250), PD-L1 (n = 15) (1:100, Cell Signaling, Danvers, MA), GzmB (n = 17), RORγT (n = 14) (1:800, EMD Millipore, Billerica, MA), and FoxP3 (n = 16) (1:50, BioLegend, San Diego, CA) using a Leica Bond RX automated slide stainer (Leica Biosystems, Buffalo Grove, IL) and counterstained with hematoxylin. The stained slides were then scanned using an automated Aperio Slide Scanner (Leica), and the density of immune infiltrates in the tumor area was quantified using a modified version of the default "Nuclear v9" algorithm, expressed as positive counts per mm for CD3, CD8, PD-1, FoxP3, and RORγT, and as an H-score for PD-L1, where the percentage of positive cells was multiplied by their intensity on a scale of 1 to 3, allowing for a score between 1 and 300.

[0186]

[0218] Flow cytometry was performed on peripheral blood mononuclear cells (PBMCs). PBMCs were stained for CD3 (UCHT1, BioLegend), CD4 (SK3, eBioscience, Thermo Scientific), CD8 (RPAT8, BD Biosciences, Mississauga, Canada), FoxP3 (PCH101, eBioscience), CD127 (HIL-7R~7M21, BD Biosciences), CD19 (HIB-19, BioLegend), CD14 (61D3, eBioscience), HLA-DR (L243, BD Biosciences), CD33 (WM53, BD Biosciences), CD56 (NCAM1, BD Biosciences), and CD11b (ICRF44, BD Biosciences). Acquisition was performed on a Fortessa flow cytometer (BD Biosciences). Analyses were performed using FlowJo version 10 (Tree Star Inc., Ashland, OR).

[0187]

[0219] Multiplex immunohistochemistry: 12-marker sequential multiplex bone marrow immunohistochemistry was performed as previously described (Tsujikawa, Cell Reports, 2017). Briefly, FFPE sections were subjected to sequential cycles of staining, scanning, and destaining, and scanned with an Aperio slide scanner (Leica) using AEC as the chromogen. After staining and scanning for nuclei, CD68, tryptase, CSF1R, DC-SIGN, CD66b, CD83, CD163, HLA-DR, PD-L1, CD3 / CD20 / CD56, and CD45 with hematoxylin, CD45-positive regions were extracted from all images using ImageScope, and images were aligned, overlapped, and segmented using CellProfiler. Image layers were pseudocolor processed using FCS Express for analysis and quantification.

[0188]

[0220] Cytokine multiplexing: Plasma levels of 41 cytokines were assessed using a multiplex bead assay (Bio-Rad, Hercules, CA). Cytokines, chemokines, and soluble mediators quantified included IL-1b, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, eotaxin, basic FGF, G-CSF, GM-CSF, IFN-γ, IP-10, MIP-1a, PDGF-bb, MIP-1b, RANTES, TNF-α, VEGF, IL-2ra, HGF, TRAIL, IL-17A, IL-17F, IL-23, SDF1 / CXCL12, CCL22, MCP-1 / CCL2, Gro-α / CXCL1, ENA78 / CXCL5, EGF, TGF-β1, TGF-β2, and TGF-β3. Example 3: Modulation of the gut microbiome enhances anti-tumor responses in a mouse melanoma model

[0189]

[0221] Next, we sought to use insights gained from human studies to investigate the hypothesis that modulating the gut microbiome could enhance antitumor responses in mouse melanoma models. Studies were conducted to determine whether modulating the gut microbiome by enriching for short-chain fatty acid-producing bacteria would enhance antitumor responses.

[0190]

[0222] These studies used a mouse tumor model with a common driver mutation (BRAF) found in melanoma. Cells were implanted into genetically identical mice (C57BL6) purchased from two different sources (Taconic farms versus Jackson laboratories) that differ significantly in their microbiomes (Taconic mice were enriched for short-chain fatty acid-producing bacteria). - / -We implanted tumor cells into Taconic and Jackson mice and observed substantial differences in tumor growth (Figure 33A) and survival (Figure 33B) in these genetically identical mice with different microbiomes. Interestingly, co-housing Taconic and Jackson mice (Figure 33C) (because mice are normally coprophagous) or modulation of the gut microbiome by fecal transplantation abolished these differences, suggesting that modulation of the gut microbiome can alter tumor growth in BRAF mutant tumor models. Because mice are coprophagous, co-housing results in the emergence of a fusion microbiome derived from both individual microbiomes. 16S sequencing performed on singly housed versus co-housed Taconic and Jackson mice confirmed significantly different microbiomes in singly and co-housed mice (Figures 33D and 33E), confirming modulation of the gut microbiome in these mice.

[0191]

[0223] Based on data from human patients, we investigated whether oral administration of butyrate (a short-chain fatty acid) enhances antitumor responses by providing sufficient substrate to promote a favorable gut microbiome. In these studies, butyrate was orally administered to Taconic and Jackson mice. In these studies, butyrate treatment was associated with enhanced antitumor responses (Figure 33F), further suggesting that modulation of the gut microbiome can enhance antitumor responses.

[0192]

[0224] These studies are therefore important because they provide novel data on the diversity and composition of the oral and gut microbiomes in patients with metastatic melanoma receiving systemic therapy and confirm the existence of differential bacterial "signatures" in responders versus non-responders to immune checkpoint blockade (specifically, PD-1-based therapy). Example 4: Fecal microbiota transplantation

[0193]

[0225] Fecal microbiome transplantation (FMT) with a favorable gut microbiome in germ-free (GF) mice reduces tumor growth (FMT1): To explore the causal link between a "favorable" gut microbiome and the response to immune checkpoint blockade, fecal microbiome transplantation (FMT) experiments were performed in germ-free (GF) recipient mice. V600E A well-established, injection-based mouse syngeneic melanoma model driven by expression of PD-1 and loss of PTEN (BP cells) was used. The first experiment (FMT1) sought to determine whether FMT could affect tumor growth. GF mice (n = 3 per group) were implanted by oral gavage with feces from responders (R-FMT group) or non-responders (NR-FMT group) to anti-PD-1 therapy. Control mice were implanted with PBS alone. Two weeks after FMT, each mouse received 8 x 10 BP cells. 5 The mice were subcutaneously injected and tumor growth was monitored twice weekly (Figure 25A). Blood and fecal pellets were collected at different time points during the experiment.

[0194]

[0226] Results from FMT1 confirmed a significant delay in tumor growth in the R-FMT group by day 14 compared to mice implanted with feces derived from NR to anti-PD-1 (p=0.04, Figure 1B).

[0195]

[0227] Next, we explored the systemic effects of FMT on the immune system using multicolor fluorescence-activated cell sorting (FACS) analysis. We found that mice receiving R-FMT had a higher percentage of natural effector cells (CD45 ) in the spleen compared with mice in the NR-FMT group. + CD11b + Ly6G + ), and a low frequency of suppressor myeloid cells (CD11b + CD11c + It is noteworthy that the GF recipient mice had a specific subpopulation of the innate immune compartment (expressing IL-1 and IL-2) (Figure 26). These data suggested that a specific subpopulation in the innate immune compartment plays a role in the antitumor response elicited by transplantation of a favorable gut microbiota in GF recipient mice.

[0196]

[0228] This conclusion was further confirmed by quantitative confocal imaging of intestinal and tumor sections from mice subjected to FMT. Indeed, tumors from mice subjected to R-FMT expressed CD45 + T cells and CD8 + The density of T cells was higher in mice receiving NR-FMT than in mice receiving NR-FMT (Figures 27A and 27C, upper panels). Furthermore, R-derived FMT significantly increased the expression of CD45 T cells in the intestinal tract. + Immune cells and CD8 + The number of T cells was locally increased compared to NR-FMT (Figures 27B and 27C, lower panels).

[0197]

[0229] Additionally, taxonomic characterization using 16S sequencing revealed striking differences in the gut microbiomes of germ-free mice before and after fecal microbiota transplantation (FMT), with a significant increase in Bacteroidetes and Clostridiales taxa. As expected, control mice receiving PBS harbored significantly different taxa in their gut microbiomes compared with mice receiving feces from responders (R-FMT) or non-responders (NR-FMT) to anti-PD-1 therapy. Furthermore, the gut microbiomes of mice receiving R-FMT and NR-FMT remained substantially stable over the time course after transplantation.

[0198]

[0230] FMT of a favorable gut microbiome in GF mice reduces tumor growth and enhances response to anti-PD-L1 therapy (FMT2): In a second FMT experiment (FMT2), we asked whether a microbiome from an R patient could enhance response to immunotherapy when transplanted into a mouse model of melanoma. To address this question, GF mice were transplanted by oral gavage with feces from responders (n=2, R-FMT group) or non-responders (n=3, NR-FMT group) to anti-PD-1 therapy. Control mice (n=2) were transplanted with PBS alone. Two weeks after FMT, each mouse received 8x10 BP cells. 5 The tumor growth was monitored twice weekly. 3 Once this was reached, mice were treated with anti-PD-L1 antibody administered by intraperitoneal injection (Figure 28A).

[0199]

[0231] Results from FMT2 confirmed a significant delay in tumor growth in the R-FMT group compared to mice transplanted with NR-derived feces to anti-PD-1 by day 14 (p=0.04, Figure 28B). Importantly, mice transplanted with R-FMT also exhibited an improved response to anti-PD-L1 therapy compared to mice transplanted with NR-derived feces (NR-FMT) (Figure 28C).

[0200]

[0232] Next, we determined the mechanisms by which the gut microbiome may influence systemic and antitumor immune responses. FACS analysis of immune infiltrates in tumors and spleens revealed that mice receiving R-FMT had a higher percentage of tumor-infiltrating CD45 cells compared with mice in the NR-FMT group. + The results confirmed the presence of myeloid cells (Figure 29A). Notably, tumor immune infiltrates derived from R-FMT contained a higher percentage of natural effector cells (CD45 + CD11b + Ly6G + ), and a low frequency of suppressor myeloid cells (CD11b + CD11c + These data correlated specific subpopulations of the innate immune compartment with the antitumor responses induced by FMTs derived from R patients, both at the peripheral (denoted as FMT1) and tumor levels.

[0201]

[0233] In NR-FMT mice, tumors expressing RORγT + An increased frequency of Th17 cells was also detected (Figures 30A and 30B), consistent with observations made in tumors derived from patients who did not respond to PD-1 blockade. Furthermore, mice receiving NR-FMT also had high levels of regulatory CD4 in the spleen. + FOXP3 + T cells (Figure 30C) and CD4 + IL-17 + This was also confirmed by the presence of IL-14, suggesting that the host immune response was impaired.

[0202]

[0234] Mass cytometry (CyTOF) analysis using dimensionality reduction by t-SNE was performed on tumors from mice and confirmed the upregulation of PD-L1 in the tumor microenvironment of mice treated with R-FMT versus NR-FMT (Figure 31A), suggesting the development of a "hot" tumor microenvironment. CyTOF analysis also confirmed that significantly different myeloid subpopulations preferentially infiltrated tumors in R-FMT or non-responder NR-FMT (Figure 31B).

[0203]

[0235] Longitudinal sampling and microbiome characterization of fecal pellets from the FMT2 experiment showed similar results to FMT1, with relative stability in the gut microbiome over time.

[0204]

[0236] Microbiome stability in germ-free mice after FMT engraftment: 16S sequencing was performed on longitudinal fecal pellets collected from germ-free mice receiving feces from patients who responded to PD-1 blockade versus those who did not. Examination of phylogenetic taxa at the order level and comparison of baseline pellets with pellets collected 2 weeks after FMT completion revealed successful engraftment. Furthermore, persistence of the most abundant order over time suggested that the engrafted microbiome was stable throughout the entire experiment.

[0205]

[0237] Based on the hypothesis that certain bacteria in the R-FMT mouse flora help slow tumor growth and certain bacteria in the NR-FMT mouse flora help stimulate tumor growth, we compared OTUs present only in R-FMT or NR-FMT mice (or OTUs missing from control mice) in representative mice from each group in the two FMT experiments. We also examined OTUs that showed consistent transfer results in both FMT experiments.This analysis was performed on Acetobacterium elongatum, Alistipes timonensis, Anaerocolumna jejuensis, Anaerocolumna xylanovorans, Bacteroides fragilis, Bacteroides nordii, Bacteroides stercoris, Blautia faecalis, Blautia gliceracea, Blautia hansenii, Blautia obeum, Blautia schinkii, Caproiciproducens galactitorivorans, and galactitolivorans, Christensenella minuta, Clostridium ardenense, Clostridium alkalicellulosi, Clostridium amygdalinum, Clostridium oroticum, Clostridium polysaccharolyticum, Clostridium xylanolyticum, Coprobacillus cateniformis, Emergensia zimonensis, Eubacterium hallii, Extiibacter muris, Faecalis bacterium prausnitzii, Ifubacter massiliensis, Neglecta zimonensis, Novibacillus thermophilus, Oscillibacter ruminantium, Papilibacter sinamivorans, Parabacteroides johnsonii johnsonii), Parasporobacterium paucivorans, Peptococcus niger, Pseudoflavonifractor capillosus, Robinsoniella peoriensis, Ruminococcus gobroii, Ruminococcus gnavus, Ruminococcus torches, and Slachia pyriformis were found exclusively in R-FMT mice in both experiments.

[0206]

[0238] FMT from different R and NR donors in germ-free GF mice confirmed the link between R microbiota and reduced tumor growth (FMT3). To validate the finding that a "favorable" gut microbiome suppresses tumor growth when transplanted into GF mice, we performed experiments similar to FMT1 using feces from different R and NR patients. In this third experiment (FMT3), GF mice were transplanted by oral gavage with feces from one responder (n = 1, R-FMT group) or one non-responder (n = 1, NR-FMT group) to anti-PD-1 therapy. Control mice were transplanted with PBS alone (n = 3, control group). Two weeks after FMT, each mouse received 2.5 × 10 BP cells. 5 The mice were subcutaneously injected and tumor growth was monitored twice weekly (Figure 32A). Blood and fecal pellets were collected at different time points during the experiment.

[0207]

[0239] Results from FMT3 showed that by day 14, tumor growth in the R-FMT group was significantly delayed compared to mice implanted with feces derived from NR versus anti-PD-1 (Figure 32B). Importantly, this difference was maintained over time until endpoint (Figure 32C). Example 5: Methods for fecal microbiota transplantation

[0208]

[0240] Fecal Microbiota Transplantation (FMT): All animal studies were approved by the Animal Care and Use Committee at MD Anderson Cancer Center, UT, and adhered to the Guide for Care and Use of Laboratory Animals. B6 germ-free mice for mouse studies were purchased from the gnotobiotic facility at Baylor College of Medicine (Houston). All mice were transported in autoclaved cages and housed in the MD Anderson Cancer Center mouse facility. All cages, bottles with stoppers, and animal drinking water were autoclaved before use. Food and bedding were irradiated twice and inspected to ensure sterility before use in experiments. Within each treatment category, control mice received only pre-reconstituted PBS. All other mice from the experimental groups received FMT derived from R or NR donors, with each donor sample delivered to one, two, or three mice. Using a 100 μm strainer, 200 μl of clarified supernatant was obtained from a 0.1 g / μl suspension of human feces and administered orally to mice three times over a one-week period, followed by a one-week rest period to allow for microbiome establishment. Mice were then injected with the BP syngeneic tumor cell line (day 14), and tumors grew to approximately 250–500 mm. 3 Once the animals reached maturity, they were treated with an anti-PD-L1 monoclonal antibody (a purified, endotoxin-low, functional formulation of B7-H1, CD274, Leinco Technologies Inc.). Tumor growth / survival was assessed. Fecal samples, blood, spleens, and tumors were harvested and processed for further analysis.

[0209]

[0241] Flow cytometry of mouse tumors and spleens: Tumors were isolated, minced into small pieces, and digested in RPMI containing collagenase A (2 mg / mL; Roche, product number 11088793001) and DNase I (40 units / mL; Sigma-Aldrich, product number D5025) for 1 hour with agitation at 37°C. The cell suspension was passed through a cell strainer, washed in 2% RPMI supplemented with 2 mM EDTA per L, and resuspended in FACS buffer (PBS containing 2% heat-inactivated FBS supplemented with 2 mM EDTA). Spleens were disrupted in FACS buffer, and red blood cells were lysed by incubation in ACK buffer (Gibco) for 2 minutes at room temperature. The pellet was then washed and resuspended in FACS buffer. For analysis of cell surface markers, the following antibodies were used: CD45 (30-F11, BD Biosciences), CD11b (M1 / 70, eBioscience), CD11c (HL3, BD Pharmigen), Ly6G (RB6-8C5, eBioscience), Ly6C (AL-21, BD Bioscience), and F4 / 80 (BM8, eBioscience). Cells were labeled with LIVE / DEAD viability stain (Life Technologies), and samples were collected on an LSR Fortessa X20 flow cytometer (BD). Doublets were identified and excluded by plotting FSC area versus FSC height, and data were analyzed using FlowJo software (Tree Star).

[0210]

[0242] Immunofluorescence for FFPE samples: Xenograft tumors, mouse intestines, and spleens were first harvested and fixed in 10% buffered formalin (at room temperature for 4 hours), then switched to 70% ethanol and stored at 4°C. Tissues were embedded in paraffin, and 5 μm sections were mounted on positively charged slides. Tissues were deparaffinized and antigen retrieval was performed in pH 6.0 citrate buffer (Dako) using a microwave oven. Sections were blocked in blocking buffer (5% goat serum / 0.3% BSA / 0.01% Triton in PBS) followed by incubation with primary antibodies overnight at 4°C. Sections were washed and then incubated with Alexa-conjugated secondary antibodies (1:500, Molecular Probes) at room temperature for 1 hour. Coverslips were washed three times in PBS / 0.01% Triton and then incubated in Hoechst dye (1:5000, Invitrogen) for 15 minutes at room temperature. After three washes in PBS, samples were mounted in ProLog Diamond mounting media (Molecular Probe). Images were captured using a Nikon A1R+ confocal microscope equipped with a four-solid-state laser system and a 20x objective.

[0211]

[0243] CyTOF: Tumors were manually dissociated and digested with Liberase TL (Roche) and DNase I for 30 min at 37°C and passed through a 70 μm mesh filter. Samples were then centrifuged using a discontinuous gradient of Histopaque 1119 (Sigma-Aldrich) and RMPI medium. A maximum of 2.5 × 10 cells per sample was collected. 6Single cell suspensions were Fc receptor blocked and stained with a surface antibody mixture for 30 min at 4°C. Metal-conjugated antibodies were purchased from Fluidigm or conjugated using the X8 polymer antibody labeling kit according to the manufacturer's protocol (Fluidigm). 2.5 μM 194 Samples were stained with Pt-cisplatin (Fluidigm) for 1 minute and washed twice with 2% FCS PBS. Cells were barcoded using the palladium mass tag barcoding method according to the manufacturer's protocol (Fluidigm) and combined after washing twice with 2% FCS PBS. Cells were then fixed and permeabilized using a FoxP3 transcription factor staining kit according to the manufacturer's protocol (eBioscience). Samples were then stained for 30 minutes at room temperature with a mixture of antibodies against intracellular targets. Samples were washed twice with 2% FCS PBS and then incubated overnight in 1.6% PFA / 100 nM iridium / PBS before acquisition using a Helios mass cytometer (Fluidigm).

[0212]

[0244] Mass cytometry data were bead-normalized and debarcoded using Fluidigm software. Total viable CD45+ cells were manually gated using FlowJo. t-SNE analysis of total viable CD45+ cells was performed using the Cyt package in Matlab. Data were inversely hyperbolic transformed using a factor of 4 and randomly downsampled to 50,000 events per sample before t-SNE analysis. Samples from each group were then merged, and t-SNE plots for each experimental group were generated by displaying an equal number of randomly downsampled events (50,000) from each treatment group.

[0213]

[0245] [Table 11] TIFF2025160204000178.tif171149

[0214]

[0246] All of the methods disclosed and claimed herein can be executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those skilled in the art that variations can be applied to the methods, and to the steps or sequence of steps of the methods, described herein without departing from the concept, spirit, and scope of the invention. More specifically, it will be apparent that certain agents that are chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutions and modifications apparent to those skilled in the art are deemed to be within the spirit, scope, and concept of the invention, as defined by the appended claims. References The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference. The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference. A framework for human microbiome research. Nature 486, 215-221, 2012. Caporaso et al., The ISME journal 6, 1621-1624, 2012. Caspi et al., Nucleic Acids Research 36, D623-D631, 2008. Chen et al., Cancer Discovery 2016. Cooper et al., Cancer Immunology Research 2015. Fritz et al., Journal of experimental psychology: General 141, 2, 2012. Hurwitz et al., Proc Natl Acad Sci USA 95(17): 10067-10071, 1998. International Publication No. WO00 / 37504 International Publication No. WO01 / 14424 International Publication No. WO01 / 14424 International Publication No. WO1995 / 001994 International Publication No. WO1998 / 042752 International Publication No. WO2000 / 037504 International Publication No. WO2001 / 014424 International Publication No. WO2005 / 003168 International Publication No. WO2005 / 009465 International Publication No. WO2006 / 00317 International Publication No. WO2006 / 072625 International Publication No. WO2006 / 072626 International Publication No. WO2006 / 121168 International Publication No. WO2007 / 042573 International Publication No. WO2008 / 084106 International Publication No. WO2008132601 International Publication No. WO2009 / 101611 International Publication No. WO2009 / 114335 International Publication No. WO2009044273 International Publication No. WO2010 / 027827 International Publication No. WO2010 / 065939 International Publication No. WO2011 / 0008369 International Publication No. WO2011 / 014438 International Publication No. WO2011 / 066342 International Publication No. WO2012 / 071411 International Publication No. WO2012 / 160448 International Publication No. WO2013 / 006490 International Publication No. WO2013 / 025779 International Publication No. WO2013 / 067492 International Publication No. WO2014 / 022021 International Publication No. WO2015 / 016718 International Publication No. WO96 / 15660 International Publication No. WO98 / 42752 Jones et al., J Exp Med. 205(12):2763-79, 2008. Kanehisa et al., Nucleic Acids Res 28, 27-30, 2000. Li et al., Nat Biotech 32, 834-841, 2014. Mellman et al., Nature 480:480- 489, 2011. Muegge et al., Science 332, 970-974, 2011. Nielsen et al., Nat Biotech 32, 822-828, 2014. Okazaki T et al., Intern. Immun. 19(7):813, 2007. Pardoll, Nature Rev Cancer 12:252-264, 2012. European Patent Publication No. 2320940 Peled et al., Journal of Clinical Oncology 0, JCO.2016.2070.3348. Price et al., PLOS ONE 5, e9490, 2010. Qin et al., Nature 464, 59-65, 2010. Schwartz et al., RECIST 1.1. European Journal of Cancer 62, 132-137, 2016. Segata et al., Genome Biology 12, R60, 2011. Shannon et al., BMC Bioinformatics 14, 217 10.1186 / 1471-2105-14-217, 2013. Sivan et al., Science (New York, NY) 350, 1084-1089, 2015. Structure, function and diversity of the healthy human microbiome. Nature 486, 207-214, 2012. Taur et al., Blood 124, 1174-1182, 2014. Tsujikawa et al., Cell reports, 2017. Tumeh et al., Nature 515, 568-571, 2014. Turnbaugh et al., Nature 457, 480-484, 2009. U.S. Patent No. 4,870,287 U.S. Patent No. 5,760,395 U.S. Patent No. 5,763,488 U.S. Patent No. 5,885,796 U.S. Patent No. 5,844,905 U.S. Patent No. 6,207,156 U.S. Patent No. 8,008,449 U.S. Patent No. 8,017,114 U.S. Patent No. 8,119,129 U.S. Patent No. 8,329,867 U.S. Patent No. 8,354,509 U.S. Patent No. 8,735,553 U.S. Patent Publication No. 2012 / 0177645 US Patent Publication No. 2012 / 0294796 U.S. Patent Publication No. 2014 / 0294898 Vetizou et al., Science (New York, NY) 350, 1079-1084, 2015.

[0215]

[0001] This application claims the benefit of U.S. Provisional Patent Application Nos. 62 / 400,372, filed September 27, 2016; 62 / 508,885, filed May 19, 2017; and 62 / 557,566, filed September 12, 2017, each of which is incorporated herein by reference in its entirety.

Claims

1. 1. A composition comprising at least one isolated or purified population of bacteria belonging to one or more of the families Ruminococcus, Clostridiaceae, Lachnospiraceae, Micrococcaceae, and / or Veillonellaceae.

2. A composition comprising at least two isolated or purified populations of bacteria belonging to one or more of the families Ruminococcaceae, Clostridium family, Lachnospiraceae, Micrococcaceae, and / or Veillonellaceae.

3. Each of the bacterial populations is at least 10 ３ 3. The composition of claim 1 or 2, wherein the composition is present at a concentration of CFU.

4. 3. The composition of claim 1 or 2, which is a live bacterial product or a live biotherapeutic product.

5. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations are provided as bacterial spores.

6. 3. The composition of claim 1 or 2, wherein the at least one bacterial population or the at least two isolated or purified bacterial populations belong to the order Clostridiales, family XII and / or the order Clostridiales, family XIII.

7. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations belong to the family Ruminococcaceae and / or Clostridiaceae.

8. 3. The composition of claim 1 or 2, wherein the population of bacteria belonging to the family Ruminococcus is further defined as a population of bacteria belonging to the genus Ruminococcus.

9. 9. The composition of claim 8, wherein the population of bacteria belonging to the genus Ruminococcus is further defined as a population of bacteria belonging to the species Ruminococcus bromii.

10. 3. The composition of claim 1 or 2, wherein the population of bacteria belonging to the family Ruminococcaceae is further defined as a population of bacteria belonging to the genus Faecalis bacterium.

11. 11. The composition of claim 10, wherein the population of bacteria belonging to the genus Faecalibacterium is further defined as a population of bacteria belonging to the species Faecalibacterium prausnitzii.

12. 3. The composition of claim 1 or 2, wherein the population of bacteria belonging to the Micrococcaceae family is further defined as a population of bacteria belonging to the genus Rothia.

13. 3. The composition of claim 1 or 2, further comprising a population of bacteria belonging to the species Porphyromonas pasteri, Clostridium hungatei, Phascolactobacterium faecium, Peptoniphilus, and / or Mollicutes.

14. 3. The composition of claim 1 or 2, which is essentially free of bacterial populations belonging to the order Bacteroidales.

15. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations belong to one or more of the species, subspecies, or bacterial strains selected from the group consisting of the species in Table 1, having an enrichment index (ei) of greater than 0.

5.

16. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population, or the at least two isolated or purified bacterial populations, are selected from the group consisting of the species in Table 1 where "ei" equals 1.

17. The at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations are identified by an NCBI Taxonomy ID (NCBI Taxonomy ID). ID): 717959, 587, 758823, 649756, 44749, 671218, 1264, 1122135, 853, 484018, 46503, 54565, 290052, 216931, 575978, 433321, 1796646, 213810, 228924, 290054, 1509, 1462919, 29375, 337097, 1298596, 487174, 642492, 1735, 1297424, 742766, 46680, 132925, 411467, 13184 65, 1852367, 1841857, 169679, 1175296, 259063, 172901, 39488, 57172, 28118, 166486, 28133, 1529, 694434, 1007096, 84030, 56774, 102148, 626947, 216933, 1348613, 1472417, 100176, 824, 1471761, 1297617, 288966, 1317125, 28197, 358743, 264639, 1265, 1335, 66219, 694 73, 115117, 341220, 1732, 873513, 396504, 1796619, 45851, 2741, 105841, 86332, 1349822, 84037, 180311, 54291, 1217282, 762984, 1185412, 154046, 663278, 1543, 398512, 69825, 1841867, 1535, 1510, 84026, 1502, 1619234, 39497, 1544, 29343, 649762, 332095, 536633, 1033 731, 574930, 742818, 177412, 1121308, 419208, 1673717, 55779, 28117, 626937, 180332, 1776382, 40519, 34062, 40518, 74426, 1216062, 293826, 850, 645466, 474960, 36835, 115544, 1515, 88431, 216932, 1417852, 39492, 1583, 420247, 118967, 169435, 37658, 138595, 31971,１００８８６、１１９７７１７、２３４９０８、５３７００７、３１９６４４、１６８３８４、９１５１７３、９５１５９、１８１６６７８、６２６９４０、５０１５７１、１７９６６２０、８８８７２７、１１４７１２３、３７６８０６、１２７４３５６、１２６７、３９４９５、４０４４０３、１３４８、２５３３１４、２５８５１５、３３０３３、１１１８０６１、３５７２７６、２１４８５１、３２０５０２、２１７７３１、２４６７８７、２９３７１、６４９７６４、９０１、２９３７４、３３０４３、３９７７８、６８２４００、８７１６６５、１６０４０４、７４５３６８、４０８、１５８４、３３３３６７、４７２４６、１０９６２４６、５３３４２、４３８０３３、３５１０９１、１７９６６２２、１７７６３８４、８１７、４８２５６、７２０５５４、５００６３２、３６８４９、３０１３０２、８７９９７０、６５５８１１、２６４４６３、１５３２、２８５、９９５、２４２７５０、２９５３９、１４３２０５２、６２２３１２、１７９６６３６、１３３７０５１、３２８８１４、２８４４６、１４９２、８２０、３９４９６、５２７８６、１５４９、１７９６６１８、５８２、４６５０７、１０９３２７、１５３１、１３８２、３３０３９、３１１４６０、２３０１４３、２１６９３５、５３９、３５５１９、１６８１、３２８８１３、２１４８５３、８９０１４、１１２１１１５、１５８５９７４、２９４６６、１３６３、２９２８００、２７０４９８、２１４８５６、１４２８７７、１３３９２６、２０９８８０、１７９６２８、１１２１１０２、１０５６１２、１７９６６１５、３９７７７、２９３５３、１５７９、１６３６６５、５３４４３、２６１２９９、１３０２、１１５０２９８、９３８２８９、３５８７４２、４７１８７５、９３８２７８、１７９６６１３、１１１８０５７、１０７７１４４、１７３７、２１８２０５、１１２１２９８、６８４０６６、４３３６５９、５２６９９、２０４５１６、７０６５６２、２５３２５７、３２８８１２、１２８０、１４７８０２、５８１３４、１３３５６１３、８９１、５８５３９４、１５８２、２３５９３１、３０８９９４、１５８９、１６８２、１７３６、２８１２９、１７８００１、５５１７８８、２０５１、８５６、１１８５６２、１０１０７０、５１５６１９、４０２１５、１８７９７９、８２９７９、２９３６３、1776391, 1285191, 84112, 157688, 38304, 36850, 341694, 287, 75612, 818, 371674, 338188, 88164, 588581, 676965, 546271, 1236512, 178338, 862517, 157687, 158, 51048, 1583331, 529, 888745, 394340, 40545, 855, 553973, 938293, 93063, 708634, 179995, 1351, 476652, 1464038, 555088, 237576, 879566, 1852371, 742727, 1377, 35830, 997353, 218538, 83771, 1605, 28111, 131109, 46609, 690567, 46206, 155615, 51616, 40542, 203, 294, 1034346, 156456, 80866, 554406, 796942, 1002367, 29347, 796944, 61592, 487175, 1050201, 762948, 137732, 1211819, 1019, 272548, 1717, 384636, 216940, 2087, 45634, 466107, 1689, 47678, 575, 979627, 840, 1660, 1236517, 617123, 546, 28135, 82171, 483, 501496, 99656, 1379, 84032, 39483, 1107316, 584, 28124, 1033744, 65730 9, 536441, 76123, 1118060, 89152, 76122, 303, 1541, 507751, 515620, 38302, 53419, 726, 40324, 1796610, 988946, 1852370, 1017, 1168289, 76936, 94869, 1161098, 215580, 1125779, 327575, 549, 1450648, and 478. The composition of claim 1 or 2, comprising a 16S ribosomal RNA (rRNA) nucleotide sequence that is at least 90% identical to a bacterial 16S rRNA nucleotide sequence identified by an NCBI taxonomy ID selected from the group consisting of:

18. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population, or the at least two isolated or purified bacterial populations, is a species, subspecies, or strain of bacteria comprising a 16S rRNA gene sequence that is at least 80% identical to a sequence of SEQ ID NOs: 1-876.

19. The at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations are selected from the group consisting of Bacteroides coagulans, Clostridium ardenense, Clostridium aldrichii, Clostridium alkalicellulosi, Clostridium amygdalinum, Clostridium asparagiforme, Clostridium cellulosi, and the like. icellulosi), Clostridium citroniae, Clostridium clariflavum DSM 19732, Clostridium clostridioforme, Clostridium corinum, Clostridium fimetalium, Clostridium hiranonis, Clostridium fungatei, Clostridium hylemonae DSM 15053, Clostridium indolis, Clostridium lactatifermentans, Clostridium leptum, Clostridium methylpentosum, Clostridium oroticum, Clostridium papyrosolvens DSM 2782, Clostridium populeti, Clostridium propionicumpropionicum, Clostridium saccharolyticum, Clostridium skindens, Clostridium sporosphaeroides, Clostridium stercorarium, Clostridium straminisolvens, Clostridium sufflavum, Clostridium termitidis, Clostridium thermosuccinogenes thermosuccinogenes, Clostridium viride, Clostridium xylanolyticum, Desulfotomaculum guttoideum, Eubacterium rectale ATCC 33656, Eubacterium dolichum, Eubacterium eligens ATCC 27750, Eubacterium hallii hallii), Eubacterium infirmum, Eubacterium siraeum, Eubacterium tenue, Ruminococcus torques, Acetanaerobacterium elongatum, Acetatifactor muris, Acetivibrio cellulolyticus, Acetivibrio ethanol guignenseAcholeplasma brassicae 0502, Acholeplasma parvum, Acholeplasma vitulis, Acinetobacter junii, Actinobacillus porcinus, Actinomyces boudenii, Actinomyces dentalis, Actinomyces odontolyticus, Actinomyces odontolyticus, Acutalibacter muris, Aerococcus viridans, Aeromicrobium fastidiosum, Alistipes finegoldii, Alistipes obesi, Alistipes onderdonkii, Alistipes putredinis, Alistipes shahii, Alistipes shahii WAL 8301, Alistipes timonensis JC136, Alkalibacter saccharofermentans, Alkaliphilus metalliredigens QYMF, Allisonella histaminiformans, Allobaculum stercoricanis DSM 13633, Alloprevotella rava, Alloprevotella tannery tannerae, Anaerobacterium cultisolvenschartissolvens), Anaerobiospirillum tomasii, Anaerobium acetethylicum, Anaerococcus octavius ​​NCTC 9810, Anaerococcus provenciensis, Anaerococcus vaginalis ATCC 51170, Anaerococcus jejuensis jejuensis, Anaerophyllum agile, Anaerofustis stercorihominis, Anaeroglobus geminatus, Anaeromassilibacillus senegalensis, Anaeroplasma abactoclasticum, Anaerorhabdus furcosa, Anaerosporobacter mobilis mobilis, Anaerostipes butyraticus, Anaerostipes caccae, Anaerostipes hadrus, Anaerotruncus colihominis, Anaerovorax odorimutans, Anoxybacillus rupiensis, Aquabacterium rimnoticum limnoticum), Acrobacter butzleri, Arthrospira platensis, Asaccharobacter celatus, Atopobium parvumparvulum, Bacteroides caccae, Bacteroides caecimuris, Bacteroides cellulosilyticus, Bacteroides clarus YIT 12056, Bacteroides dorei, Bacteroides eggerthii, Bacteroides finegoldii, Bacteroides fragilis fragilis), Bacteroides gallinarum, Bacteroides massiliensis, Bacteroides oleiciplenus YIT 12058, Bacteroides plebeius DSM 17135, Bacteroides rodentium JCM 16496, Bacteroides thetaiotaomicron thetaiotaomicron), Bacteroides uniformis, Bacteroides xylanisolvens XB1A, Bacteroides xylanolyticus, Barnesiella intestinihominis, Beduni massiliensis, Bifidobacterium bifidum, Bifidobacterium dentium dentium), Bifidobacterium longum subsp. infantis, Blautia cesimuliscaecimuris, Blautia coccoides, Blautia faecis, Blautia glucera, Blautia hansenii DSM 20583, Blautia hydrogenotrophica, Blautia luti, Blautia lu DSM 14534, Blautia wexlerae DSM 19850, Budvicia aquatica, Butyricicoccus pullicaecorum, Butyricimonas paravirosa, Butyricimonas crossotus, Caldicoprobacter oshimai, Caloramater kolhersii coolhaasii), Caloramater proteoclasticus, Caloramater quimbayensis, Campylobacter gracilis, Campylobacter rectus, Campylobacter ureolyticus DSM 20703, Capnocytophaga gingivalis, Capnocytophaga redbeteri leadbetteri), Capnocytophaga sputigena, Casaltella massiliensis, Catabacter hongkongensis, Catenibacterium mitsuokai, Christensenella minuta, Christensenella timonensis, Chryseobacterium takurimakense, Chryseobacterium takurimakense, taklimakanense), Citrobacter freundii, Cloacibacillus porcorumporcorum, Clostridioides difficile ATCC 9689 = DSM 1296, Clostridium amylolyticum, Clostridium bowmanii, Clostridium butyricum, Clostridium cadaveris, Clostridium colicanis, Clostridium gasigenes, Clostridium lentotherum lentocellum DSM 5427, Clostridium oceanicum, Clostridium oryzae, Clostridium paraputrificum, Clostridium pascui, Clostridium perfringens, Clostridium quinii, Clostridium saccharobutylicum, Clostridium sporogenes sporogenes), Clostridium ventriculi, Collinsella aerofaciens, Comamonas testosteroni, Coprobacter fastidiosus NSB1, Coprococcus eutactus, Corynebacterium diphtheriae, Corynebacterium durum durum), Corynebacterium mycetoidesmycetoides), Corynebacterium pyruviciproducens ATCC BAA-1742, Corynebacterium tuberculostearicum, Culturomica massiliensis, Cuneatibacter caecimuris, Defluviitalea saccharophila, Delphitia acidovorans acidovorans, Desulfitobacterium chlororespirans, Desulfitobacterium metallireducens, Desulfosporosinus acididurans, Desulfotomaculum halophilum, Desulfotomaculum intricatum, Desulfotomaculum tongense tongense), Desulfovibrio desulfuricans subsp. desulfuricans, Desulfovibrio idahonensis, Desulfovibrio litoralis, Desulfovibrio piger, Desulfovibrio simplex, Desulfovibrio zostere zosterae), Desulfuromonas acetoxidans, Dethiobacter alkaliphilus AHT1, Dethiosulfatibacter aminovorans, Dialister invisus, Dialister propionicifaciens, Dielma fastidiosa, Dietzia alimentaria 72, Dorea longicatena, Dysgonomonas gadei ATCC BAA-286, Dysgonomonas mossi mossii), Eggerthella lenta, Eikenella corrodens, Eisenbergiella tayi, Emergencia timonensis, Enorma massiliensis phI, Enterococcus faecalis, Enterorhabdus muris, Ethanoligenes harvinenense Eubacterium harbinense YUAN-3, Eubacterium coprostanoligenes, Eubacterium limosum, Eubacterium oxidoreducens, Eubacterium sulci ATCC 35585, Eubacterium uniforme, Eubacterium ventriosum, Eubacterium xylanophyllum xylanophilum), Extibacter muris, Ezakiella peruensisperuensis), Faecalibacterium prausnitzii, Faecalicoccus acidiformans, Faecalitarea cylindroides, Filifactor villosus, Flavonifractor plautii, Flintibacter butyricus, Frishingicoccus caecimulis, Fucophilus fucoidanolyticus fucoidanolyticus, Fusicatenibacter saccharivorans, Fusobacterium mortiferum, Fusobacterium nucleatum subsp. vincentii, Fusobacterium simiae, Fusobacterium varium, Garciella nitrachilescens, nitratireducens), Gemella haemolysans, Gemmiger formicilis, Gordonibacter urolithinfaciens, Gracilibacter thermotolerans JW / YJL-S1, Granulicatellae elegans, Guggenheimella bovis, Haemophilus haemolyticus Helicobacter haemolyticus, Helicobacter typhlonius, Hesperia stercoliusisstercorisuis, Holdemania biformis, Holdemania massiliensis AP2, Howardella ureilytica, Hungatella effluvii, Hungatella hathewayi, Hydrogenoanaerobacterium saccharovorans, Ifubacter massiliensis Ihubacter massiliensis, Intestinibacter bartletii, Intestinimonas butyriciproducens, Irregularibacter muris, Kiloniella laminariae DSM 19542, Kroppenstedtia guangzouensis, Lachnoanaerobacillus orale orale), Lachnoanaerobaculum umeaense, Lachnoclostridium phytofermentans, Lactobacillus acidophilus, Lactobacillus algidus, Lactobacillus animalis, Lactobacillus casei, Lactobacillus delbrueckii delbrueckii), Lactobacillus fornicalis, Lactobacillus iners, Lactobacillus pentosus, Lactobacillus rogosae, Lactococcus garvieae, Lactonifactor longoviformis, Leptotrichia buccalis buccalis), Leptotrichia hofstadii, Leptotrichia hongkongensis, Leptotrichia wadayiwadei), Leuconostoc inhae, Levyella massiliensis, Loriellopsis cavernicola, Lutispora thermophila, Marinilabilia salmonicol JCM 21150, Marvinbryantia formatexigens, Mesoplasma fotulis photouris, Methanobrevibacter smithii ATCC 35061, Methanomassiliicoccus luminyensis B10, Methylobacterium extorquens, Mitsuokella jalaludinii, Mobilitarea sibirica, Mobiluncus curtisii curtisii), Mogibacterium pumilum, Mogibacterium timidum, Moorella glycerini, Moorella humiferrea, Moraxella nonliquefaciens, Moraxella osloensis, Morganella morganii, Moriella indrigenes, indoligenes), Muribaculum intestinale, Murimonas intestini, Natranaerobirga pectinivora, Neglecta zimonensistimonensis, Neisseria cinerea, Neisseria oralis, Nocardioides mesophilus, Novibacillus thermophilus, Ochrobactrum anthropi, Odoribacter splanchnicus, Olsenera profusa, Olsenera uri, uli), Oribacterium asaccharolyticum ACB7, Oribacterium sinus, Oscillibacter ruminantium GH1, Oscillibacter valericigenes, Oxobacter pfennigii, Pantoea agglomerans, Papilibacter sinamivorans cinnamivorans, Parabacteroides faecis, Parabacteroides goldsteinii, Parabacteroides gordonii, Parabacteroides merdae, Parasporobacterium paucivorans, Parasutterella exclementihominis, Parasutterella secunda secunda), Parvimonas micra, Peptococcus niger, Peptoniphilus duerdenii ATCCBAA-1640, Peptoniphilus grossensis ph5, Peptoniphilus koenoeneniae, Peptoniphilus senegalensis JC140, Peptostreptococcus stomatis, Phascolactobacterium succinatutens, Phoscolarctobacterium masiliensis, Phoscolarctob ... massiliensis, Pontibacter indicus, Porphyromonas bennonis, Porphyromonas endodontalis, Porphyromonas pasteri, Prevotella bergensis, Prevotella buccae ATCC 33574, Prevotella denticola, Prevotella eneca, Prevotella fusca JCM 17724, Prevotella loescheii, Prevotella nigressens, Prevotella oris, Prevotella pallens ATCC 700821, Prevotella stercorea DSM 18206, Prevotellamassilia timonensis, Propionispira arcuata, Proteus mirabilis mirabilis), Providencia rettgeri, Pseudobacteroides cellulosolvens ATCC35603 = DSM 2933, Pseudobutyrivibrio ruminis, Pseudoflavonifractor capillosus ATCC 29799, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas mandelii, Pseudomonas nitroreducens, Pseudomonas putida putida), Raoultella ornithinolytica, Raoultella planticola, Raoultella massiliensis, Robinsoniella peoriensis, Romboutsia timonensis, Roseburia faecis, Roseburia hominis A2-183, Roseburia intestinalis intestinalis, Roseburia inulinivorans DSM 16841, Rothia dentocariosa ATCC 17931, Ruminiclostridium thermocellum, Ruminococcus albus, Ruminococcus bromii, Ruminococcus callidus, Ruminococcus champanerensis champanellensis 18P13 = JCM 17042, Ruminococcus faecis JCM15917, Ruminococcus flavefaciens, Ruminococcus gauvreauii, Ruminococcus lactaris ATCC 29176, Rummeliibacillus picnus cnus), Saccharofermentans acetigenes, Scardovia wiggsiae, Schlegelella thermodepolymerans, Sedimentibacter hongkongensis, Selenomonas sputigena ATCC 35185, Slackia exigua ATCC 700122, Slackia piriformis YIT 12062, Solitairea canadensis, Solobacterium moorei, Sphingomonas aquatilis, Spiroplasma alleghenense, Spiroplasma chinense, Spiroplasma chrysopicola, Spiroplasma culicicola, Spiroplasma lampydicola lampyridicola, Sporobacter termitidis, Staphylococcus aureus, Stenotrophomonas maltophilia, Stomatobaculum longum, Streptococcus agalactiae ATCC 13813, Streptococcus cristatus, Streptococcus equinus, Streptococcus equinus), Streptococcus gordonii, Streptococcus lactariuslactarius, Streptococcus parauberis, Subdoligranulum variabile, Succinivibrio dextrinosolvens, Sutterella stercoricanis, Sutterella wadsworthensis, Syntrophococcus sucromutans, Syntrophomonas zaenderi, zehnderi OL-4, Terrisporobacter mayombei, Thermoleophilum album, Treponema denticola, Treponema socranskii, Tyzzerella nexilis DSM 1787, Vallitalea guaymasensis, Vallitalea pronyensis, Vampirovibrio chlorerevavorus 3. The composition of claim 1 or 2, wherein the bacterial strain belongs to a species, subspecies, or strain selected from the group consisting of: Veillonella chlorellavorus, Veillonella atypica, Veillonella denticariosi, Veillonella dispar, Veillonella parvula, Victivallis vadensis, Vulcanibacillus modesticaldus, and Weissella confusa.

20. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population, or the at least two isolated or purified populations, belong to a bacterial species selected from the species in Table 2, indicated with a response status of Responder (R).

21. 3. The composition of claim 1, wherein the at least one isolated or purified bacterial population, or the at least two isolated or purified bacterial populations, belong to a bacterial species selected from the group consisting of the species in Table 2, which are labeled with a response status of Responder (R) and have an uncorrected p-value of less than 0.

1.

22. The at least one isolated or purified population of bacteria, or the at least two isolated or purified populations of bacteria, are selected from the group consisting of SEQ ID NOs:877-926, SEQ ID NOs:927-976, SEQ ID NOs:977-1026, SEQ ID NOs:1027-1076, SEQ ID NOs:1077-1126, SEQ ID NOs:1127-1176, SEQ ID NOs:1177-1226, SEQ ID NOs:1227-1276, SEQ ID NOs:1277-1326, SEQ ID NOs:1327-1376, SEQ ID NOs:1377-1426, SEQ ID NOs:1427-1476, SEQ ID NOs:1477-1526, SEQ ID NOs:1527-1576, SEQ ID NOs:1577-1626, SEQ ID NOs:1627-1676, SEQ ID NOs:1677-1726, SEQ ID NOs:1727-1776, SEQ ID NOs:1777-1826, 3. The composition of claim 1 or 2, wherein the species, subspecies, or strain comprises a nucleotide sequence with at least 80 percent identity to a coexistence gene group (CAG) sequence selected from the group consisting of: SEQ ID NOs: 1827-1876, 1877-1926, 1927-1976, 1977-2026, 2027-2076, 2077-2126, 2127-2176, 2177-2226, 2227-2276, 2277-2326, 2327-2376, 2377-2426, 2427-2476, 2477-2526, 2527-2576, 2577-2626, and 2627-2676.

23. The at least one isolated or purified population of bacteria, or the at least two isolated or purified populations of bacteria, have a sequence identity of at least 29% to SEQ ID NOs: 877-926, at least 16.5% to SEQ ID NOs: 927-976, at least 48.5% to SEQ ID NOs: 977-1026, at least 28% to SEQ ID NOs: 1027-1076, at least 93.5% to SEQ ID NOs: 1077-1126, at least 99.5% to SEQ ID NOs: 1127-1176, at least 99.5% identity to SEQ ID NOs:1177-1226, at least 99% identity to SEQ ID NOs:1227-1276, 100% identity to SEQ ID NOs:1277-1326, at least 21.5% identity to SEQ ID NOs:1327-1376, 100% identity to SEQ ID NOs:1377-1426, at least 97% identity to SEQ ID NOs:1427-1476, at least 55.5% identity to SEQ ID NOs:1477-1526, 100% identity to SEQ ID NOs:1527-1576, SEQ ID NOs:1577-1626 at least 34% identity to SEQ ID NOs: 1627-1676, at least 14% identity to SEQ ID NOs: 1677-1726, at least 93% identity to SEQ ID NOs: 1727-1776, 100% identity to SEQ ID NOs: 1777-1826, at least 45% identity to SEQ ID NOs: 1827-1876, at least 99% identity to SEQ ID NOs: 1877-1926, at least 74% identity to SEQ ID NOs: 1927-1976, 100% identity to SEQ ID NOs: 1977-2026 identity to SEQ ID NOs: 2027-2076, 100% identity to SEQ ID NOs: 2077-2126, at least 20% identity to SEQ ID NOs: 2077-2126, at least 84% identity to SEQ ID NOs: 2127-2176, at least 35.5% identity to SEQ ID NOs: 2177-2226, at least 32.5% identity to SEQ ID NOs: 2227-2276, at least 70% identity to SEQ ID NOs: 2277-2326, 100% identity to SEQ ID NOs: 2327-2376, at least 70.5% identity to SEQ ID NOs: 2377-2426;3. The composition of claim 1 or 2, wherein the species comprises a nucleotide sequence with at least 99.5% identity to SEQ ID NOs: 2427-2476, at least 68.5% identity to SEQ ID NOs: 2477-2526, 100% identity to SEQ ID NOs: 2527-2576, at least 97.5% identity to SEQ ID NOs: 2577-2626, or 100% identity to SEQ ID NOs: 2627-2676.

24. The composition of claim 1 or 2, wherein the bacteria is lyophilized or freeze-dried.

25. 3. The composition of claim 1 or 2, formulated for oral delivery.

26. 3. The composition of claim 1 or 2, wherein the composition formulated for oral delivery is a tablet or capsule.

27. 3. The composition of claim 1 or 2, wherein the tablet or capsule comprises an acid-resistant enteric coating.

28. 3. The composition of claim 1 or 2, wherein the composition comprising the at least one isolated or purified population of bacteria or the at least two isolated or purified populations of bacteria is formulated for colonoscopy, nasogastric sigmoidoscopy, or rectal administration via enema.

29. The composition of claim 1 or 2, which is lyophilized or frozen.

30. 3. The composition of claim 1 or 2, which can be reformulated for ultimate delivery including liquids, suspensions, gels, geltabs, semisolids, tablets, sachets, lozenges, capsules, or as an enteral formulation.

31. 3. The composition of claim 1 or 2, formulated for multiple administration.

32. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations comprise an antibiotic resistance gene.

33. 3. The composition of claim 1 or 2, wherein the at least one isolated or purified bacterial population or the at least two isolated or purified bacterial populations are populations of butyric acid-producing bacteria.

34. 3. The composition of claim 1, further comprising butyric acid.

35. 35. A method of treating or preventing cancer in a subject, comprising administering to said subject a composition according to any one of claims 1 to 34.

36. 36. The method of claim 35, wherein the cancer is skin cancer.

37. 36. The method of claim 35, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

38. 36. The method of claim 35, wherein the cancer is melanoma.

39. 39. The method of claim 38, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

40. 36. The method of claim 35, further comprising administering at least one additional anti-cancer treatment.

41. 41. The method of claim 40, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy.

42. 42. The method of claim 41, wherein the biological therapy is a monoclonal antibody, siRNA, miRNA, antisense oligonucleotide, ribozyme, or gene therapy.

43. 41. The method of claim 40, wherein the at least one immune checkpoint inhibitor and / or the at least one additional anti-cancer treatment is administered intratumorally, intra-arterially, intravenously, intravascularly, intrathoracically, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, percutaneously, subcutaneously, topically, stereotactically, orally, or by direct injection or perfusion.

44. A method for treating or preventing cancer in a subject, comprising administering to the subject a composition comprising a population of at least one isolated or purified bacterium belonging to one or more of the classes Clostridia, Mollicutes, Clostridiales, Ruminococcaceae, and / or Faecalibacterium.

45. 45. The method of claim 44, wherein the cancer is skin cancer.

46. 45. The method of claim 44, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

47. 45. The method of claim 44, wherein the cancer is melanoma.

48. 48. The method of claim 47, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

49. 45. The method of claim 44, further comprising administering at least one additional anti-cancer treatment.

50. 50. The method of claim 49, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy.

51. 51. The method of claim 50, wherein the biological therapy is a monoclonal antibody, siRNA, miRNA, antisense oligonucleotide, ribozyme, or gene therapy.

52. 50. The method of claim 49, wherein the at least one immune checkpoint inhibitor and / or the at least one additional anti-cancer treatment is administered intratumorally, intra-arterially, intravenously, intravascularly, intrathoracically, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, percutaneously, subcutaneously, topically, stereotactically, orally, or by direct injection or perfusion.

53. 45. The method of claim 44, wherein the subject has a tumor and is defined as a method for treating cancer.

54. 45. The method of claim 44, wherein the subject is identified as being at risk for developing cancer and the method is defined as a method for preventing cancer.

55. Administration of the composition inhibits CD8 expression in tumors ＋ 45. The method of claim 44, which results in an expansion of T lymphocytes.

56. 56. The method of claim 55, wherein the T lymphocytes are cytotoxic T lymphocytes.

57. administration of the composition increases effector CD4 in the systemic circulation or peripheral blood of the subject ＋ , CD8 ＋ 45. The method of claim 44, which results in an expansion of T lymphocytes, monocytes, and / or myeloid dendritic cells.

58. 45. The method of claim 44, wherein administration of the composition results in a depletion of B cells, regulatory T cells, and / or myeloid-derived suppressor cells in the systemic circulation or peripheral blood of the subject.

59. 45. The method of claim 44, wherein administering the composition to the subject results in increased expression of CD3, CD8, PD1, FoxP3, Granzyme B, and / or PD-L1 in the tumor immune infiltrate.

60. 45. The method of claim 44, wherein administering the composition to the subject results in a decrease in expression of RORγT in the tumor immune infiltrate.

61. administering the composition to the subject induces the expression of CD45 in the tumor ＋ , CD3 ＋ / CD20 ＋ / CD56 ＋ , CD68 ＋ , and / or HLA-DR ＋ 45. The method of claim 44, which results in cell expansion.

62. 45. The method of claim 44, wherein administering the composition to the subject results in an increase in the level of innate effector cells in the subject.

63. The natural effector cells are CD45 ＋ CD11b ＋ Ly6G ＋ 63. The method of claim 62, wherein the cell is a cell.

64. 45. The method of claim 44, wherein administering the composition to the subject results in a reduction in the level of suppressor myeloid cells in the subject.

65. Suppressor myeloid cells express CD45 ＋ CD11b ＋ CD11c ＋ 65. The method of claim 64, wherein the cell is a cell.

66. 45. The method of claim 44, wherein the composition comprises the bacterium Faecalibacterium prausnitzii.

67. 10. A method of treating cancer in a subject, comprising administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject, wherein the subject has been determined to have a favorable microbial profile in a gut microbiome having one or more of the bacterial populations of the composition of claim 1 or 2.

68. 68. The method of claim 67, wherein the cancer is skin cancer.

69. 68. The method of claim 67, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

70. 68. The method of claim 67, wherein the cancer is melanoma.

71. 71. The method of claim 70, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

72. 68. The method of claim 67, further comprising administering at least one additional anti-cancer treatment.

73. 10. A method of predicting a response to an immune checkpoint inhibitor in a patient having cancer, comprising detecting a microbial profile in a sample obtained from the patient, wherein if the microbial profile comprises one or more of the bacterial populations of the composition of claim 1 or 2, then the response to the immune checkpoint inhibitor is favorable.

74. 74. The method of claim 73, wherein the cancer is skin cancer.

75. 74. The method of claim 73, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

76. 74. The method of claim 73, wherein the cancer is melanoma.

77. 77. The method of claim 76, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

78. 74. The method of claim 73, wherein the patient is administered an immune checkpoint inhibitor if the patient is predicted to have a favorable response to the immune checkpoint inhibitor.

79. 74. The method of claim 73, wherein the microbial profile is a gut microbial profile.

80. 10. A method for treating cancer in a subject, comprising administering a therapeutically effective amount of butyric acid, an isolated or purified population of butyric acid-producing bacteria, and / or the composition of claim 1 or 2 to the subject, wherein the subject is receiving an immune checkpoint inhibitor.

81. 81. The method of claim 80, further comprising administering at least one immune checkpoint inhibitor.

82. 82. The method of claim 81, wherein more than one checkpoint inhibitor is administered.

83. 82. The method of claim 81, further comprising administering a prebiotic or probiotic.

84. 81. The method of claim 80, wherein the isolated or purified population of butyric acid-producing bacteria comprises bacteria comprising an antibiotic resistance gene.

85. 85. The method of claim 84, further comprising administering the population of butyric acid-producing bacteria comprising an antibiotic resistance gene.

86. 86. The method of claim 85, further comprising administering an antibiotic to which the antibiotic resistance gene confers resistance.

87. 81. The method of claim 80, wherein the butyric acid-producing bacterial population comprises one or more bacterial species of the order Clostridiales.

88. 88. The method of claim 87, wherein the one or more bacterial species are from the family Ruminococcaceae, Christensenellaceae, Clostridiaceae, or Coriobacteriaceae.

89. The one or more bacterial species are selected from the group consisting of Faecalibacterium prausnitzii, Ruminococcus albus, Ruminococcus brommii, Ruminococcus callidus, Ruminococcus flavefaciens, Ruminococcus champanerensis, Ruminococcus faecius, Ruminococcus gobroii, Ruminococcus gnavus, Ruminococcus hansenii, Ruminococcus hydrogenotrophicus, Ruminococcus lactalis, Ruminococcus luti, and the like. Ruminococcus tulti, Ruminococcus obeum, Ruminococcus palustris, Ruminococcus pasteurii, Ruminococcus productus, Ruminococcus schinkii, Ruminococcus turkes, Subdoligranurum variabile, Butyrivibrio fibrisolvens, 88. The method of claim 87, wherein the fungus is selected from the group consisting of: Eubacterium oxidoreducens, Eubacterium fibrisolvens, Roseburia intestinalis, Anerostipes cassae, Blautia obeum, Eubacterium nodatum, and Eubacterium oxidoreducens.

90. 88. The method of claim 87, wherein the one or more bacterial species is Faecalibacterium prausnitzii.

91. 81. The method of claim 80, wherein the population of butyric acid-producing bacteria does not include bacterial species of the family Prevotellaceae.

92. 81. The method of claim 80, wherein administering the butyric acid comprises administering a prodrug or salt of butyric acid.

93. 81. The method of claim 80, wherein the step of administering butyric acid comprises administering sodium butyrate, arginine butyrate, ethyl butyryl lactate, tributyrin, 4-phenylbutyric acid, pivaloyloxymethyl butyric acid (AN-9), or butylidene dibutyric acid (AN-10).

94. 81. The method of claim 80, wherein the butyric acid or population of butyric acid-producing bacteria is administered orally, by colonoscopy, sigmoidoscopy, rectally via an enema, or by direct injection.

95. 82. The method of claim 81, wherein the at least one immune checkpoint inhibitor is administered intravenously and the butyrate and / or the population of butyrate-producing bacteria is administered orally.

96. 82. The method of claim 81, wherein the at least one checkpoint inhibitor is selected from an inhibitor of CTLA-4, PD-1, PD-L1, PD-L2, LAG-3, BTLA, B7H3, B7H4, TIM3, KIR, or A2aR.

97. 82. The method of claim 81, wherein said at least one immune checkpoint inhibitor is a human programmed cell death 1 (PD-1) axis binding antagonist.

98. 98. The method of claim 97, wherein the PD-1 axis binding antagonist is selected from the group consisting of a PD-1 binding antagonist, a PDL1 binding antagonist, and a PDL2 binding antagonist.

99. 99. The method of claim 98, wherein the PD-1 axis binding antagonist is a PD-1 binding antagonist.

100. 100. The method of claim 99, wherein the PD-1 binding antagonist inhibits binding of PD-1 to PDL1 and / or PDL2.

101. 100. The method of claim 99, wherein the PD-1 binding antagonist is a monoclonal antibody or an antigen-binding fragment thereof.

102. 100. The method of claim 99, wherein the PD-1 binding antagonist is nivolumab, pembrolizumab, pidilizumab, KEYTRUDA®, AMP-514, REGN2810, CT-011, BMS 936559, MPDL328OA, or AMP-224.

103. 82. The method of claim 81, wherein the at least one immune checkpoint inhibitor is an anti-CTLA-4 antibody.

104. 104. The method of claim 103, wherein the anti-CTLA-4 antibody is tremelimumab, YERVOY®, or ipilimumab.

105. 82. The method of claim 81, wherein said at least one immune checkpoint inhibitor is an anti-killer cell immunoglobulin-like receptor (KIR) antibody.

106. 106. The method of claim 105, wherein the anti-KIR antibody is lirilumab.

107. 81. The method of claim 80, wherein the cancer is skin cancer.

108. 81. The method of claim 80, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

109. 81. The method of claim 80, wherein the cancer is melanoma.

110. 109. The method of claim 108, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

111. 81. The method of claim 80, further comprising administering at least one additional anti-cancer treatment.

112. 112. The method of claim 111, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy.

113. 113. The method of claim 112, wherein the biological therapy is a monoclonal antibody, siRNA, miRNA, antisense oligonucleotide, ribozyme, or gene therapy.

114. 112. The method of claim 111, wherein the at least one immune checkpoint inhibitor and / or the at least one additional anti-cancer treatment is administered intratumorally, intra-arterially, intravenously, intravascularly, intrathoracically, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, percutaneously, subcutaneously, topically, stereotactically, orally, or by direct injection or perfusion.

115. 1. A method of treating cancer in a subject, comprising administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject, wherein the subject has been determined to have a favorable microbial profile in the gut microbiome.

116. 116. The method of claim 115, wherein the cancer is skin cancer.

117. 116. The method of claim 115, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

118. 116. The method of claim 115, wherein the cancer is melanoma.

119. 119. The method of claim 118, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

120. 116. The method of claim 115, further comprising administering at least one additional anti-cancer treatment.

121. 116. The method of claim 115, wherein the favorable microbial profile is further defined as having (a) a high alpha diversity of the gut microbiome; (b) a high abundance of butyrate-producing bacteria in the gut microbiome; (c) one or more bacteria selected from the group consisting of the species in Table 1 in the gut microbiome with an enrichment index (ei) of greater than 0.5; or (d) one or more of the bacterial species in Table 2 in the gut microbiome, labeled with a response status of Responder (R).

122. 116. The method of claim 115, wherein the suitable microbial profile is further defined as the presence or high abundance of bacteria from the phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, genus Ruminococcus, genus Faecalibacterium, genus Hydrogenoanaerobacterium, phylum Actinobacteria, class Coriobacteria, order Coriobacteriales, family Coriobacteriaceae, domain Archaea, phylum Cyanobacteria, phylum Euryarchaeota, or family Christensenellaceae.

123. 116. The method of claim 115, wherein the preferred microbial profile is further defined as an absence or low abundance of bacteria from the species Escherichia coli, Anaerotrunchus colihominis, genus Dialister, family Veillonellaceae, phylum Bacteroidetes, class Bacteroidales, order Bacteroidales, or family Prevotellaceae.

124. 116. The method of claim 115, wherein the preferred microbial profile is defined as the presence or high abundance of bacteria of the order Clostridiales and the absence or low abundance of bacteria of the order Bacteroidetes.

125. The method of claim 115, wherein the suitable microbial profile is further defined as a high abundance of butyric acid-producing bacteria, wherein the butyric acid-producing bacteria include one or more species from the genus Ruminococcus or Faecalibacterium.

126. 116. The method of claim 115, wherein the subject is determined to contain a favorable microbial profile or a favorable gut microbiome by analyzing the microbiome in a patient sample.

127. 116. The method of claim 115, wherein the patient sample is a fecal sample or an oral sample.

128. 116. The method of claim 115, wherein analyzing comprises performing 16S ribosomal sequencing and / or metagenomic whole genome sequencing.

129. 1. A method of predicting a response to an immune checkpoint inhibitor in a patient having cancer, comprising: detecting a microbial profile in a sample obtained from the patient, wherein the patient is predicted to have a favorable response to the immune checkpoint inhibitor if the microbial profile comprises: (a) high alpha diversity; (b) high abundance of butyrate-producing bacteria; (c) one or more bacteria selected from the group consisting of the species in Table 1, with an enrichment index (ei) greater than 0.5; or (d) one or more of the bacterial species in Table 2, labeled with a response status of responder (R).

130. 130. The method of claim 129, wherein the cancer is skin cancer.

131. 130. The method of claim 129, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

132. 130. The method of claim 129, wherein the cancer is melanoma.

133. 133. The method of claim 132, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

134. 130. The method of claim 129, wherein the patient is administered an immune checkpoint inhibitor if the patient is predicted to have a favorable response to the immune checkpoint inhibitor.

135. 135. The method of claim 134, wherein the patient is administered a second immune checkpoint inhibitor.

136. 130. The method of claim 129, wherein the preferred microbial profile is a preferred intestinal microbial profile.

137. 130. The method of claim 129, wherein the cancer is skin cancer.

138. 138. The method of claim 137, wherein the skin cancer is melanoma or metastatic melanoma.

139. 130. The method of claim 129, wherein the immune checkpoint inhibitor is an anti-PD1 monoclonal antibody or an anti-CTLA4 monoclonal antibody.

140. 130. The method of claim 129, wherein the butyric acid-producing bacterial population comprises one or more bacterial species of the order Clostridiales.

141. 141. The method of claim 140, wherein the one or more species are from the family Ruminococcaceae, Christenseneraceae, Clostridiaceae, or Coriobacteriumceae.

142. The one or more species are selected from the group consisting of Faecalibacterium prausnitzii, Ruminococcus albus, Ruminococcus brommii, Ruminococcus callidus, Ruminococcus flavefaciens, Ruminococcus champanerensis, Ruminococcus faecius, Ruminococcus gobroii, Ruminococcus gnavus, Ruminococcus hansenii, Ruminococcus hydrogenotrophicus, Ruminococcus lactalis, Ruminococcus luti, Ruminococcus 141. The method of claim 140, wherein the selected bacterium is selected from the group consisting of Ruminococcus obeum, Ruminococcus palustris, Ruminococcus pasteurii, Ruminococcus productus, Ruminococcus sinkii, Ruminococcus turkes, Subdoligranurum variabile, Butyrivibrio fibrisolvens, Roseburia intestinalis, Anerostipes casse, Blautia obeum, Eubacterium nodatum, and Eubacterium oxidoreducens.

143. 141. The method of claim 140, wherein the one or more species is Faecalibacterium prausnitzii.

144. 141. The method of claim 140, wherein the immune checkpoint inhibitor is an anti-PD1 monoclonal antibody or an anti-CTLA4 monoclonal antibody.

145. 141. The method of claim 140, further comprising administering at least one additional anti-cancer treatment.

146. 146. The method of claim 145, wherein the at least one additional anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormone therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biological therapy.

147. 146. The method of claim 145, wherein the at least one additional anti-cancer treatment is butyric acid and / or a butyric acid-producing bacterial population.

148. 146. The method of claim 145, further comprising administering a prebiotic or probiotic composition.

149. 149. The method of claim 148, wherein the probiotic composition is a composition described in claim 1 or 2.

150. 1. A method of predicting a response to an immune checkpoint inhibitor in a patient having cancer, comprising detecting a microbial profile in a sample obtained from the patient, wherein if the microbial profile comprises: (a) a low abundance of butyrate-producing bacteria; or (b) one or more of the bacterial species in Table 2, designated with a response status of non-responder (NR), the patient is predicted to not respond favorably to the immune checkpoint inhibitor.

151. 151. The method of claim 150, further comprising administering to the patient the probiotic composition of claim 1 or 2 if the patient is predicted not to respond favorably to the immune checkpoint inhibitor.

152. 152. The method of claim 151, wherein the immune checkpoint inhibitor is administered to a patient predicted not to respond favorably to an immune checkpoint inhibitor after administration of the composition of claim 1 or 2.

153. 151. The method of claim 150, further comprising administering to said subject who is predicted not to respond favorably to immune checkpoint inhibitors an additional anti-cancer treatment that is at least one non-immune checkpoint inhibitor.

154. 154. The method of claim 153, wherein the at least one anti-cancer treatment is surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, immune checkpoint inhibitor, second immune checkpoint inhibitor, or biological therapy.

155. 154. The method of claim 153, wherein the at least one additional anti-cancer treatment is butyric acid and / or a butyric acid-producing bacterial population.

156. 154. The method of claim 153, wherein the anti-cancer treatment is a prebiotic or probiotic.

157. 157. The method of claim 156, wherein the probiotic is a composition described in claim 1 or 2.

158. 151. The method of claim 150, wherein the cancer is skin cancer.

159. 159. The method of claim 158, wherein the skin cancer is melanoma.

160. 151. The method of claim 150, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protuberans, Merkel cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcystic adnexal carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

161. 160. The method of claim 159, wherein the melanoma is metastatic melanoma, lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentiginous melanoma, or desmoplastic melanoma.

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