Methods for determining and improving the potential efficacy of anti-cancer treatments - Patents.com

Abstract

The present invention relates to the field of anti-cancer treatment, and more particularly to a method for determining ex vivo whether a patient with a hematological malignancy is likely to benefit from allogeneic hematopoietic stem cell transplantation (allo-HSCT) by analyzing the gut microbiota in a fecal sample from said patient. The present invention also relates to treatments aimed at reducing the risk of relapse after allo-HSCT.

Description

[Technical Field]

[0001] The present invention relates to the field of anti-cancer treatment, and more particularly to a method for determining ex vivo whether a patient with a hematological malignancy is likely to benefit from allogeneic hematopoietic stem cell transplantation (allo-HSCT) by analyzing the gut microbiota in a fecal sample from said patient. The present invention also relates to treatments aimed at reducing the risk of relapse after allo-HSCT. [Background technology]

[0002] Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative treatment for hematological malignancies (Non-Patent Document 1). Relapse is the main cause of death after allo-HSCT (Non-Patent Document 2). The curative graft-versus-tumor (GVT) effect relies on the alloreactivity of donor immune cells against tumor cells (Non-Patent Document 3). GVT is primarily mediated by donor T cells, as exemplified by the higher risk of relapse associated with T cell-depleted grafts (Non-Patent Document 4) and the preventive effect of donor lymphocyte infusion (DLI) (Non-Patent Document 5). However, the benefits of allo-HSCT may be offset by graft-versus-host disease (GVHD), in which T cells target healthy host tissues (Non-Patent Document 2, Non-Patent Document 6, Non-Patent Document 7).

[0003] Chronic pulmonary GVHD (cGVHD) develops in 10% of patients during the first two years after HSCT (Non-Patent Document 8). cGVHD shares a common induction mechanism with bronchiolitis obliterans syndrome (BOS) that occurs after lung transplantation (Non-Patent Document 9). After lung transplantation, BOS is prevented with azithromycin, a second-generation macrolide (Non-Patent Document 10).

[0004] In the context of a randomized, multicenter, placebo-controlled, double-blind, superiority trial (ALLOZITHRO trial, NCT01959100) aimed at evaluating azithromycin as a pulmonary cGVHD prophylaxis, we analyzed stool and blood samples collected before and after the allo-HSCT procedure from patients enrolled in the ALLOZITHRO trial.

[0005] The gut microbiota was perturbed after allo-HSCT (Non-Patent Document 11). This dysbiosis was characterized by low alpha diversity of the gut microbiota and a predominance of Enterococcaceae (Non-Patent Document 12, Non-Patent Document 13). Antibiotics used during and before the allo-HSCT procedure affect the abundance and predominance of bacterial taxa (Non-Patent Document 14, Non-Patent Document 15, Non-Patent Document 16). Parenteral nutrition also affects the gut microbiota composition (Non-Patent Document 17). Low microbial diversity is associated with a high risk of mortality, especially due to non-relapse mortality (Non-Patent Document 18, Non-Patent Document 19). Regarding relapse, several studies have reported an association between the gut microbiota and allo-HSCT relapse. Low abundance of Blautia is associated with a higher risk of relapse (Non-Patent Document 17), whereas the presence of a cluster of operational taxonomic units (OTUs) primarily composed of Eubacterium limosum is associated with a lower risk of relapse (Non-Patent Document 20). To date, no publications have demonstrated that the presence of certain bacterial genera or taxa after allo-HSCT increases the risk of relapse and correlates with a poor antitumor immune response. The mechanisms underlying the role of the gut microbiota in antitumor responses after allo-HSCT remain unclear but may be related to gut microbiome-derived metabolites (Non-Patent Documents 21 and 22). Therefore, a better understanding of the role of the gut microbiota in antitumor responses after allo-HSCT remains necessary to define new complementary therapies for treating hematological malignancies.

[0006] In particular, we showed that the composition of the gut microbiota after allo-HSCT correlates with relapse or remission: Bacteroides species DJF_B097 (a close relative of Bacteroides stercoris) and Prevotella species DJF_RP53 (a close relative of Prevotella copri), as well as their closest relatives, are associated with complete remission, whereas Bacteroides fragilis (and related strains) is associated with a higher risk of relapse. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Copelan, EA (2006) Hematopoietic Stem-Cell Transplantation. N Engl J Med 354, 1813-1826 [Non-patent document 2] Horowitz et al. (2018) Bone Marrow Transplant 53, 1379-1389 [Non-patent document 3] Blazar et al. (2020) Nat Rev Clin Oncol 17, 475-492 [Non-patent document 4] Horowitz et al. (1990) Blood, 8 [Non-patent document 5] Schmid et al. (2007) JCO 25, 4938-4945 [Non-patent document 6] Cho et al. (2012) Biology of Blood and Marrow Transplantation 18, 1136-1143 [Non-Patent Document 7] Zeiser, R., and Blazar, BR (2017) N Engl J Med 377, 2167-2179 [Non-licensed document 8] Bergeron et al. (2018) Eur Respir J 51, 1702617

Non-licensed literature 9

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Non-licensed Document 11

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Non-licensed Document 17

[0008] By applying a multi-omics approach to quantify the abundance and diversity of bacterial taxa, viral species, and fecal metabolite levels, we decipher how the gut bacteriome, virome, and metabolomics profiles interact in patients undergoing allo-HSCT. We uncovered a network of bacteria, bacteriophages, and metabolic pathways associated with post-transplant relapse. [Means for solving the problem]

[0009] Accordingly, the present invention provides a method for predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, comprising: (i) determining the gut microbial phylotypes (OTUs (operational taxonomic units), ASVs (amplicon sequence variants or molecular species), i.e., any group of 16S rRNA gene coding sequences that have at least 97% similarity to each other and are therefore grouped together in a taxonomic entity and are represented by selected representative sequences) in the gut sample of the individual after allo-HSCT; (ii) determining the abundance or relative abundance of phylotypes comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1 (related to Bacteroides fragilis), and optionally determining the relative abundance of at least one of the OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 (related to Bacteroides sp. DJF_B097) and / or denovo9260 of SEQ ID NO: 3 (related to Prevotella sp. DJF_RP53 X); Including, The present invention relates to a method for detecting a gut microbiota comprising an OTU enriched for said OTU comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1, and optionally depleted for at least one of the OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 and / or denovo9260 of SEQ ID NO: 3, wherein individuals having a gut microbiota depleted for at least one of the OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1, have a higher risk of recurrence.

[0010] According to certain embodiments, the present invention provides a method for predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, comprising: (i) determining the gut microbial phylotypes (OTUs (operational taxonomic units), ASVs (amplicon sequence variants or molecular species), i.e., any group of 16S rRNA gene coding sequences that have at least 97% similarity to each other and are therefore grouped together in a taxonomic entity and are represented by selected representative sequences) in the gut sample of the individual after allo-HSCT; (ii) determining the abundance or relative abundance of phylotypes comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1; Including, wherein individuals having a gut microbiota enriched for said phylotype have a higher risk of recurrence.

[0011] Preferably, the individual may then undergo a treatment to alter the composition of the gut microbiota, for example using specific bacteriophages.

[0012] The "relative abundance" of a phylotype is defined as the percentage of the number of sequences grouped into that phylotype relative to the total number of filtered sequences in a given sample. A phylotype should be grouped by at least two 16S rRNA gene sequences (singletons, i.e., phylotypes containing only one sequence, are not considered).

[0013] Determining the gut microbial composition and the abundance and relative abundance of a given phylotype can be performed using suitable classical methods known to those skilled in the art. In a specific embodiment, the determination is performed on total DNA extracted from human fecal samples, mucosal samples, or tissue samples using techniques known to those skilled in the art, such as shotgun metagenomic sequencing or 16S rRNA gene sequencing. The determination can also be performed using quantitative PCR techniques using specific probes targeting specific phylotype sequences or related bacterial isolates and strains, as well as any sequences that have at least 97%, preferably 98%, 99%, or 100% identity to the nucleic acid sequences of the phylotypes.

[0014] Each phylotype is represented at a different level in terms of relative abundance, with the relative abundance of some phylotypes ranging from 0.01% to 0.1% of the total number of sequences, and some other phylotypes ranging from 0.5% to 35% of the total number of sequences. In the methods of the invention, the relative abundance of a phylotype is preferably assessed by applying a detection threshold of the total number of sequences (per sample).

[0015] According to another embodiment, the present invention provides a method of predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, comprising: (i) determining the gut microbial phylotype composition in a gut sample of the individual after allo-HSCT; (ii) determining the presence or absence of phylotypes comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1 by applying a detection threshold of 1% of the total number of sequences (per sample) to define the presence or absence of a phylotype, or by applying quantitative PCR detection using probes targeted to specific phylotypes or bacterial isolates; Including, wherein individuals having a gut microbiota indicative of the presence of said phylotypes have a higher risk of recurrence.

[0016] In another embodiment, the present invention provides a method of predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the setting of hematological malignancies, comprising: (i) determining the gut microbial phylotypes (OTUs (operational taxonomic units), ASVs (amplicon sequence variants or molecular species), i.e., any group of 16S rRNA gene coding sequences that have at least 97% similarity to each other and are therefore grouped together in a taxonomic entity and are represented by selected representative sequences) in the gut sample of the individual after allo-HSCT; (ii) determining the abundance or relative abundance of at least one phylotype comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 and / or denovo9260 of SEQ ID NO: 3; Including, Also disclosed are methods, wherein individuals having a gut microbiota enriched for at least one or two of the above phylotypes are good responders to allo-HSCT.

[0017] According to another embodiment, the present invention provides a method of predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, comprising: (i) determining the gut microbial phylotype composition in a gut sample of the individual after allo-HSCT; (ii) determining the presence or absence of at least one phylotype comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 and / or denovo9260 of SEQ ID NO: 3, by applying a detection threshold of 1% of the total number of sequences (per sample) to define the presence or absence of a phylotype or by applying quantitative PCR detection using probes targeted to a specific phylotype or bacterial isolate; Including, The present invention relates to a method in which individuals with a gut microbiota that exhibits the presence of at least one of the two above-mentioned phylotypes or two of the above-mentioned phylotypes are good responders to allo-HSCT.

[0018] As used herein, "hematological malignancies" is a general term referring to various blood cancers, which are classified by the WHO according to their presumed cellular origin, genetic abnormalities, and clinical features (see Khoury, JD et al. The 5th edition of the world health organization classification of hematolymphoid tumors: Myeloid and histiocytic / dendritic neoplasms. Leukemia 36, ​​1703-1719 (2022) and Alaggio, R. et al. The 5th edition of the world health organization classification of hematolymphoid tumors: Lymphoid neoplasms. Leukemia 36, ​​1720-1748 (2022)). Non-limiting examples of hematological malignancies are acute myeloid leukemia, acute lymphocytic leukemia, myelodysplastic neoplasms, myeloproliferative neoplasms, B-cell non-Hodgkin's lymphoma, T-cell non-Hodgkin's lymphoma, B-cell lymphoproliferative disorders, T-cell lymphoproliferative disorders, and Hodgkin's lymphoma.

[0019] Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a procedure in which a portion of a healthy donor's peripheral blood or bone marrow stem cells is obtained and prepared for intravenous infusion.

[0020] Thus, the method of the present invention allows determining whether a patient with a hematological malignancy is likely to benefit from allogeneic hematopoietic stem cell transplantation (allo-HSCT) by analyzing the gut microbiota in an intestinal sample, e.g., a mucosal sample obtained after biopsy, or a fecal sample from said patient, which can be assessed after any delay after allo-HSCT.

[0021] In the context of the present invention, "relapse" means the reappearance of a disease, i.e., a hematological malignancy, or the signs and symptoms or biological abnormalities of said disease (also called minimal residual disease (MRD)) after a period of improvement.

[0022] A patient who is a "good responder to allo-HSCT" is one who has a blood cancer, has undergone or will undergo allogeneic hematopoietic stem cell transplantation (allo-HSCT), and shows clinically significant improvement after receiving the anticancer treatment. Clinically significant improvement can be assessed by clinical examination (weight, general condition, pain, and palpable mass, if any), biomarkers and imaging tests (ultrasound, CT scan, PET scan, MRI), and bone marrow examination (bone marrow aspiration or biopsy). In certain embodiments, a good response to allo-HSCT is a complete remission as defined by standard criteria known to those skilled in the art.

[0023] The intestinal microbiota refers to the population of microorganisms living in the intestine of any organism belonging to the animal kingdom. In the present invention, the organism is preferably a human. The intestinal microbial composition evolves throughout life as a result of different environmental influences.

[0024] Dysbiosis refers to a harmful loss of balance in the intestinal microbial composition that can occur in several specific situations.

[0025] Intestinal samples are collected at any time point after allogeneic hematopoietic stem cell transplantation (allo-HSCT).

[0026] The phylotypes in this invention are regroupings of bacterial 16S rRNA gene sequences that share the same or similar nucleotide bases (at least 97% sequence identity for the targeted 16S region) and are referred to as "denovo#." Each phylotype pertains to bacterial species, either isolated or unisolated, cultured or uncultured, that share 98% or more sequence similarity with sequences listed in public databases.

[0027] Alternatively, methods of predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, can be performed by detecting an increase or decrease in at least one individual metabolite.

[0028] In more detail, Decreased 2-hydroxysebacic acid and / or 2-methylmalonylcarnitine in an individual's intestinal sample is associated with a higher risk of recurrence; Decreases in hercynine, arabinose, glycerophosphoserine, glycerophosphocholine, 1-stearoyl-GPI, glycerophosphoinositol, and / or 1-palmitoyl-GPI, and / or increases in N-methylalanine and / or dopamine 3-O-sulfate in plasma or intestinal samples from an individual are associated with a favorable response to allo-HSCT.

[0029] The present invention also relates to treatments for patients diagnosed as being at risk of recurrence, which alter their gut microbiota.

[0030] According to some embodiments, such treatment is carried out using bacteriophages.

[0031] Bacteriophages (phages) are a class of viruses that specifically lyse bacteria. They are widespread in soil, air, water, and living organisms. They have strong specificity and bind to specific sites on the surface of bacterial cells. Since their first discovery by Frederik Tward in 1915, a growing number of studies have demonstrated that bacteriophages have high antibacterial activity and specificity against drug-resistant bacteria, preventing damage to microbial communities.

[0032] Compared to antibiotic treatment, the use of bacteriophages is faster and more effective with far fewer side effects, does not inhibit the body's natural immunity or cause allergic reactions, and is non-invasive and non-toxic to humans, other mammals, and plants. Of particular importance, bacteriophages do not kill non-pathogenic "normal flora" bacteria, thereby preserving "colonization resistance" of reservoir hosts such as the human intestinal tract, and can be used to specifically target one or a few bacterial species.

[0033] Thus, the present invention relates to at least one phage (directly or indirectly) targeting Bacteroides fragilis for preventing cancer recurrence in patients who have undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT), preferably selected from the group consisting of acute myeloid leukemia, acute lymphocytic leukemia, myelodysplastic tumors, myeloproliferative neoplasms, B-cell non-Hodgkin's lymphoma, T-cell non-Hodgkin's lymphoma, B-cell lymphoproliferative disorder, T-cell lymphoproliferative disorder, and Hodgkin's lymphoma.

[0034] As used herein, a phage targeting Bacteroides fragilis refers to a bacteriophage that has direct or indirect antibacterial activity against Bacteroides fragilis, i.e., the ability to kill and / or inhibit the growth or reproduction of the bacterium Bacteroides fragilis. Direct antibacterial activity can be assessed by culturing Bacteroides fragilis according to known techniques, contacting the culture with a bacteriophage, and monitoring bacterial growth and lysis after said contact. A reduction in colony size or total number of colonies indicates a bacteriophage with antibacterial activity against Bacteroides fragilis. Indirect activity can be assessed by culturing Bacteroides fragilis according to known techniques, contacting the Bacteroides fragilis culture with a co-culture of a specific bacteriophage and a known bacterial target, and monitoring Bacteroides fragilis bacterial growth and lysis after said contact. A reduction in colony size or total number of colonies indicates bacteriophage-bacterial targeting resulting in antibacterial activity against Bacteroides fragilis.

[0035] We attempt to select phages that are (i) lytic, (ii) specific for B. fragilis, and (iii) capable of lysing more than 70% of B. fragilis and have good durability throughout the human intestinal tract (i.e., resistance to pH fluctuations, bile salts, and pancreatic salts, commonly assessed by the Simulator of the Human Intestinal Microbial Ecosystem (SHIME) Van de Wiele et al., 2015).

[0036] Examples of bacteriophages with direct antibacterial activity against Bacteroides fragilis include lytic phages VA7, MTK, and UZ-1 of the Siphoviridae family; bacteriophage B56-3; bacteriophage B40-8; bacteriophage vB_BfrS_23; is.

[0037] Examples of bacteriophages with indirect antibacterial activity against Bacteroides fragilis include Lactococcus phage D4410; Lactococcus phage D4412; Lactobacillus phage Lrm1; Streptococcus phage Dp-1; Rhodococcus virus Poco6; Alphapapillomavirus 10; Lactococcal phage BK5-T; is.

[0038] The isolated bacteriophage may be administered alone or may be incorporated into a pharmaceutical composition.

[0039] Thus, the pharmaceutical compositions of the present invention may comprise one, two, or more isolated bacteriophages that have direct or indirect antibacterial activity against Bacteroides fragilis.

[0040] Therefore, the present invention also relates to a pharmaceutical composition comprising at least one phage targeting Bacteroides fragilis for preventing cancer recurrence in patients who have undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT).

[0041] Pharmaceutical compositions comprising at least one bacteriophage can be formulated in unit dose or multi-dose formulations.

[0042] Phages targeting Bacteroides fragilis are preferably formulated as pharmaceutical compositions that also contain a pharmaceutically acceptable carrier and can be stored as concentrated aqueous solutions or lyophilized powder preparations. The pharmaceutical composition may contain other components, so long as the other components do not reduce the effectiveness of the bacteriophage to the extent that the treatment is ineffective. Pharmaceutically acceptable carriers are known, and those skilled in the pharmaceutical arts can readily select carriers appropriate for a particular route of administration (Remington's Pharmaceutical Sciences, Mack Publishing Co., Easton, PA, 1985).

[0043] Suitable formulations of the pharmaceutical composition may be selected from the group consisting of ointments, solutions, suspensions or emulsions, extracts, powders, granules, sprays, lozenges, tablets or capsules, and may further comprise dispersing agents or stabilizers.

[0044] In certain embodiments, the pharmaceutical composition is formulated for delivery to the intestine (eg, the small intestine and / or colon).

[0045] Thus, the bacteriophage may be formulated for rectal delivery to the intestine (e.g., the colon). Thus, in some embodiments, a composition comprising the bacteriophage may be formulated for delivery via suppository, colonoscopy, endoscopy, sigmoidoscopy, or enema. Pharmaceutical preparations or formulations, particularly those for oral administration, may contain additional components that enable efficient delivery of the compositions of the present disclosure to the intestine (e.g., the colon). Various pharmaceutical preparations that enable delivery of the composition to the intestine (e.g., the colon) can be used. Examples include pH-sensitive compositions, more specifically, buffered sachet formulations or enteric polymers that release their contents when the enteric polymer passes through the stomach and the pH becomes alkaline. When a pH-sensitive composition is used to formulate a pharmaceutical preparation, the pH-sensitive composition is preferably a polymer with a pH threshold such that disintegration of the composition occurs between about 6.8 and about 7.5. This range corresponds to the pH shift toward alkaline at the end of the stomach and is therefore suitable for delivery to the colon. Furthermore, it should be understood that each part of the intestine (for example, the duodenum, jejunum, ileum, cecum, colon and rectum) has a different biochemical and chemical environment. For example, each part of the intestine has a different pH, which allows targeted delivery by compositions with specific pH sensitivity. Therefore, the compositions provided herein can be formulated for delivery to the intestine or specific parts of the intestine (for example, the duodenum, jejunum, ileum, cecum, colon and rectum) by providing the formulation with appropriate pH sensitivity (see, for example, Villena et al., Int JP harm 2015, 487 (1-2): 314-9).

[0046] Phages targeting Bacteroides fragilis may be administered orally. In such cases, the phage can be incorporated into a tablet or capsule, which allows the phage to pass through the stomach without loss of viability due to gastric acidity and release of fully active phage in the small intestine. In some embodiments, the composition is formulated with an enteric coating that enhances the survival of the bacteriophage through the harsh environment of the stomach. The enteric coating resists the action of gastric fluid in the stomach, allowing the incorporated bacteriophage to pass through the stomach and enter the intestine. The enteric coating can be readily dissolved upon contact with intestinal fluid, thereby releasing the bacteriophage encapsulated in the coating into the intestinal tract. The enteric coating may consist solely of polymers and copolymers known in the art, such as, for example, commercially available EUDRAGIT (Evonik Industries) (see, e.g., Zhang, AAPS PharmSciTech, (2016) 17 (1), 56-67).

[0047] According to certain embodiments, a phage targeting Bacteroides fragilis, or a pharmaceutical composition comprising at least one phage targeting Bacteroides fragilis, is orally administered in combination with a proton pump inhibitor (PPI), such as omeprazole, lansoprazole, dexlansoprazole, esomeprazole, pantoprazole, rabeprazole, and ilaprazole.

[0048] The dosage and desired drug concentration of the pharmaceutical composition may vary depending on the specific use. The determination of the appropriate dosage or route of administration is well within the skill of an ordinary physician. Animal experiments can provide reliable guidance for determining effective doses in human therapy. Those skilled in the art can perform interspecies scaling of effective doses according to the principles described in Mordenti, J. and Chappell, W. (1989) Toxicokinetics and New Drug Development, Yacobi et al., Eds., Pergamon Press, New York 1989, pp. 42-96. Based on previous European human experience, 10 7 10 from PFU 11 A phage dose of between PFU is considered appropriate in most cases (https: / / clinicaltrials.gov / ct2 / show / NCT04737876, https: / / doi.org / 10.1016 / j.cell.2022.07.003).

[0049] The Bacteroides fragilis-targeting phage or pharmaceutical composition comprising the Bacteroides fragilis-targeting phage can be administered as a single application, periodic applications, or continuous applications.

[0050] In another embodiment, the present invention relates to a probiotic composition comprising one or more isolated bacterial strains of a species selected from the group consisting of Bacteroides stercolis and the closely related phylotype Bacteroides sp. DJF_B097 and / or Prevotella copri and the closely related phylotype Prevotella sp. DJF_RP53 for preventing cancer recurrence in patients who have undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT).

[0051] In some embodiments, the bacterial strains are isolated. Any of the bacterial strains described herein may be isolated and / or purified from a source, such as a culture or a microbiome sample (e.g., feces). The bacterial strains used in the compositions provided herein are generally isolated from the microbiome of a healthy individual. Also as used herein, the term "purified" refers to a bacterial strain that has been separated from one or more components, such as contaminants, or a composition comprising such a bacterial strain. In some embodiments, the bacterial strain is substantially free of contaminants. In some embodiments, one or more bacterial strains of a composition may be purified independently from one or more other bacteria produced and / or present in the culture or sample containing the bacterial strain. In some embodiments, the bacterial strain is isolated or purified from the sample and then cultured under conditions suitable for bacterial replication, for example, under anaerobic culture conditions. Bacteria grown under conditions suitable for bacterial replication can then be isolated / purified from the culture in which they were grown.

[0052] Any of the compositions described herein, including probiotic compositions comprising the compositions, may contain bacterial strains in any form, for example, in an aqueous form such as a liquid or suspension, embedded in a semi-solid form, powdered form, or freeze-dried form. In some embodiments, the composition or bacterial strains in the composition are lyophilized. In some embodiments, a subset of the bacterial strains in the composition are lyophilized. Methods for lyophilizing compositions, particularly compositions comprising bacteria, are known in the art. See, e.g., U.S. Pat. No. 3,261,761, U.S. Pat. No. 4,205,132, PCT Publication Nos. WO 2014 / 029578 and WO 2012 / 098358, which are incorporated herein by reference in their entireties. The bacteria may be lyophilized as a combination, and / or the bacteria may be lyophilized separately and combined prior to administration. The bacterial strain may be combined with a pharmaceutical excipient before being combined with other bacterial strains, or multiple lyophilized bacteria may be combined in lyophilized form and the combined bacterial mixture may then be combined with a pharmaceutical excipient. In some embodiments, the bacterial strain is a lyophilized cake. In some embodiments, the composition comprising one or more bacterial strains is a lyophilized cake.

[0053] Here again, the probiotic composition is formulated for delivery to the intestine (eg, the small intestine and / or colon).

[0054] In a preferred embodiment, the probiotic composition can be administered in the form of a suppository or orally, as described above.

[0055] According to another embodiment, the present invention relates to a nutritional composition comprising at least one metabolite involved in a metabolic subpathway selected from the group consisting of fatty acid metabolism (also BCAA metabolism), phospholipid metabolism, lysophospholipids, secondary bile acid metabolism, and xanthine metabolism.

[0056] Preferably, the metabolite is selected from the group consisting of:

[0057] TIFF2025540247000002.tif123170

[0058] In certain embodiments of the present invention, a pharmaceutical composition, a probiotic composition, and a nutritional composition comprising at least one phage targeting Bacteroides fragilis can be administered in combination to prevent cancer recurrence in patients who have undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT). [Brief explanation of the drawings]

[0059] [Figure 1] Figure 1 shows the gut bacterial profile after allogeneic hematopoietic stem cell transplantation. Bar plots show the top 10 absolute permutation multivariate analysis of variance (PERMANOVA) coefficients for genera (A) and phylotypes (here OTUs) (B) for each enterotype. [Figure 2] Figure 1 shows the correlation between enterotype and metabolite levels and viral frequency after allogeneic hematopoietic stem cell transplantation. Dot plots summarize the enrichment factors (EFs) and p-values ​​of enriched subpathways from differentially detected metabolite levels in stool (A) and plasma (B). Enrichment factors were computed using the over-representation method and p-values ​​using the hypergeometric distribution test. [Figure 3] Principal coordinate analysis of viral reads and viral cluster composition. Principal coordinate analysis using a UniFrac distance matrix from bacteriophage-derived reads only. Samples were clustered using hierarchical K-means. Driving species were computed with permutation multivariate analysis of variance (PERMANOVA). [Figure 4]Figure 1 shows correlations between bacterial taxa, metabolic pathways, and viruses after allogeneic hematopoietic stem cell transplantation. (A) Summary of correlation network illustrating correlations between metabolic pathways, viral species, and enterotypes of top driver phylotypes. (B) Network illustrating correlations between metabolic pathways, viral species, and enterotypes of top driver phylotypes associated with relapse or complete remission. For visualization purposes, phylotypes are listed with the corresponding species name. Staph: Staphylococcus; Lact: Lactococcus; Mycobact: Mycobacterium. [Figure 5] Figure 1 shows a graph of viral genera associated with enterotypes of the top driver phylotypes. The tile plot shows the number of species associated with the phylotypes and which belong to the same genus. [Figure 6] Graph showing enterotype according to randomization group and relapse at 12 months. Bar plots show the frequency of complete response or relapse samples at 12 months according to enterotype. [Figure 7] Figure 1 shows a multivariate polynomial regression model assessing the association between enterotype and clinical variables. The multivariate model includes variables associated with enterotype. Gray dots and lines indicate statistically significant coefficients. Coefficients are compared with enterotype 1 and the unlisted group for each variable. GVHD: graft-versus-host disease; CsA: cyclosporine; MMF: mycophenolate mofetil; MTX: methotrexate; HSC: hematopoietic stem cells. [Figure 8]Specific gut microbiota associated with post-transplant azithromycin intake and relapse. (A) Dot plot showing principal coordinate analysis (PCoA) computed from the Bray-Curtis distance matrix. Boxes indicate the centroids of each group. Bar plots represent the top 15 phylotypes (PERMANOVA coefficients) driving differences between groups. Panels compare patients who relapsed with those who achieved complete response at 12 months. (B) Dot plot showing PERMANOVA coefficients for phylotypes found in the top 15 coefficients for both relapse / complete response and azithromycin / placebo enterotypes. (C) Forest plot showing hazard ratios and 95% confidence intervals for relapse according to the Fine and Gray competing risks model for relapse, using non-relapse-related death as a competing risk. This analysis was performed after computing the mean OTU abundance for each patient using the phylotypes shown in panel C. For visualization purposes, only significant phylotypes are shown. (D) Dynamic microbial signatures associated with relapse at 12 months. (E) Violin plots and regression curves with 95% CIs showing microbial signatures in samples associated with relapse and complete response. (F) Correlation analysis between phylotypes associated with relapse. Spearman coefficients and p-values ​​adjusted using the Benjamini-Hochberg method are shown. [Figure 9] Graphs showing parallel changes in bacterial phylotypes and their associated bacteriophages associated with relapse or complete remission. (A) Dot plots and regression lines with 95% confidence intervals showing the frequency of phylotypes associated with relapse or complete remission during the first few months after allogeneic hematopoietic stem cell transplantation (HSCT). (B) Abundance of Bacteroides fragilis-associated bacteriophages in patients who relapsed after allo-HSCT. [Figure 10]Figure 1 shows the association of gut microbiota with plasma metabolites and T cell status. (A) Heatmap showing the subsets of T cell status associated with enterotype. Here, the percentage of status clusters among phenotypic subsets is examined. A Kruskal-Wallis test was performed, and p-values ​​for multiple comparisons were adjusted using the false discovery rate method. Only clusters with a mean frequency of >0.5% were retained. (B) Dot plots showing statistically significant associations between taxa associated with relapse and T cell subsets. Here, a linear regression model was evaluated. Only status clusters containing at least 1% of the phenotypic T cell subsets were retained for analysis. T cell subsets were defined as the dependent variable in the equation. Only significant false discovery rate-adjusted p-values ​​for the beta coefficients are shown. Prevotella species DJF_RP53 was not associated with T cell status and is therefore not shown. PB: peripheral blood, Treg: regulatory T cells, EM: effector memory, EMRA: effector memory CD45RA+, CM: central memory, SCM: stem cell memory, Non-conv: non-conventional, MAIT: mucosal-associated invariant T cells. DETAILED DESCRIPTION OF THE INVENTION [Example]

[0060] I. Materials and Methods cohort Stool samples were collected serially from patients enrolled in the multicenter, randomized, double-blind, placebo-controlled, phase 3 superiority trial ALLOZITHRO (NCT01959100). This included 27 patients in the azithromycin group (n = 73 samples) and 28 patients in the placebo group (n = 75 samples). The first and last samples were collected the week before and 6 weeks after allo-HSCT, respectively. The majority of samples (n = 92) were assessed for all omics, including 43 and 49 patients in the placebo and azithromycin cohorts, respectively. Patient characteristics in the placebo and azithromycin cohorts were similar. Nutritional support and concomitant antibiotics were also similar between groups.

[0061] Bacteriome Sample processing and 16S rRNA gene sequencing. Patient fecal samples were frozen in aliquots (150 mg), homogenized, and lysed using both mechanical (10 min bead-beating) and chemical techniques as previously described (Doi:10.1038 / nature08821). Total DNA was extracted using the MoBio Power Fecal DNA Isolation Kit according to the manufacturer's recommendations. DNA quality and quantity were assessed using a spectrophotometer (Nanodrop 1000, Thermoscientific). Sequencing was then performed on the GeT-PlaGe platform at Genopole (Toulouse-Midi-Pyrénées, France) using Illumina's MiSeq technology targeting the V3-V4 region of the 16S rRNA gene with the following primers: V3fwd-TACGGRAGGCAGCAG (where R is A or G) and V4rev-TACCAGGGTATCTAAT.

[0062] Phylotype identification pipeline. Raw sequences were analyzed using the open-source software package Quantitative Insights Into Microbial Ecology (QIIME) (Caporaso et al. (2010) Nat Methods 7, 335-336). After primer and barcode trimming, sequences were quality filtered (minimum length = 300 bp, minimum quality threshold = 20, chimera removal) and clustered into operational taxonomic units (OTUs) using uclust at a similarity level of 97%. Samples that were amplified but had fewer than 500 reads were removed. The most abundant member of each OTU was selected as a representative sequence and assigned to different taxonomic levels using the RDP Naive Bayes classifier and RDP Seqmatch programs (Cole et al. (2009) Nucleic Acids Res 37, D141-14). Estimates of phylotype richness and diversity were calculated based on the rarefied OTU table (n = 3000 reads) using the number of observed OTUs, Shannon's index, and Simpson's index. An average of 14677 reads were obtained per sample (ranging from 3056 to 24545 reads).

[0063] Virome DNA and RNA virome analysis using shotgun next-generation sequencing. Fecal samples (solid phase) were resuspended in phosphate-buffered saline (PBS), diluted (50%), and then centrifuged at 2500 g for 20 min. To enrich for viral particles by reducing host background, stool supernatants were filtered through 0.45 μm filters (Corning Costar Spin-X centrifuge filters), and 315 μl aliquots of the filtrate were pretreated by incubation with different nucleases: TURBO DNase (Invitrogen, Carlsbad, CA); Baseline-ZERO DNase (Ambion, Foster City, CA); Benzonase (NEB); and RNase A (Promega) at 37°C for 30 min prior to extraction. Total nucleic acids were extracted using a NucliSENS easyMAG (Biomerieux) according to the manufacturer's protocol. 25 μL of the extract was used for DNA library preparation. Depletion of methylated host DNA was performed using the NEBNext™ Microbiome DNA Enrichment Kit (NEB) according to the manufacturer's instructions. DNA was then purified using zymo DNA Clean (Zymo) and eluted in 7.5 μL of sterile water. DNA libraries were prepared using the Nextera XT Library Preparation Kit (Illumina). For RNA library preparation, the Trio RNA Kit (TECAN) was used according to the manufacturer's instructions. Library sequencing was performed on an Illumina HiSeq X (16 lanes) using 150 / 150 bp paired-end sequencing.

[0064] Species Identification Pipeline. Raw reads were cleaned using TRIMMOMATIC (Bolger, AM, Lohse, M., and Usadel, B. (2014) Bioinformatics 30, 2114-2120). Duplicate reads were removed using Dedupe (Gregg, F., and Eder, D. (2022). Dedupe. https: / / github.com / dedupeio / dedupe). Taxonomic assignment was performed using Kraken2 with the Refseq virus, bacterial, and human databases (Wood, DE, Lu, J., and Langmead, B. (2019) Genome Biol 20, 257). Reads assigned to viruses by Kraken were verified using Blastn against the Refseq virus database. Reads with discordant assignments between Kraken and Blast were removed. 5 × 10 6 Samples with less than 0.5 reads per million (RPM) were excluded. Data with RPM less than 0.5 were assimilated to 0. Variables were filtered according to the average RPM of seven negative controls (Miller et al. (2019) Genome Res. 29, 831-842). The DNA read database and RNA read database were then merged to obtain the final count database.

[0065] Fecal metabolomics Sample processing. Samples were processed and analyzed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) at Metabolon (Durham, USA). Data acquisition, quality control, and metabolite identification and quantification were performed as previously described (Michonneau, D. (2019). Nature Communications, 10, 5695).

[0066] Pathway identification. To assign metabolic pathways, the list of identified metabolites was manually compared with the Metabolon Human Metabolome Database (Wishart et al. (2007) HMDB: the Human Metabolome Database. Nucleic Acids Research 35, D521-D526) and the PubChem database (Kim et al. (2021). PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Research 49).

[0067] Metabolite data preprocessing. Uncharacterized metabolites were excluded from the analysis. Metabolite quantification was normalized to the dry weight of the extracted fecal material. Missing values ​​(metabolites below the quantification threshold) were imputed to 50% of the minimum value for the corresponding metabolite, and metabolites with missing values ​​>50% were excluded. For drug-related metabolites, missing values ​​were imputed to 1% of the minimum value, and missing metabolites were excluded.

[0068] Data and statistical analysis Enterotype definition. Dissimilarity matrices were computed using the Bray-Curtis and UniFrac methods for bacterial and viral data, respectively, using the "vegan" package (Oksanen et al. R. vegan: Community Ecology Package. R package version 2.6-2. https: / / CRAN.R-project.org / package=vegan). Dimensionality reduction of the dissimilarity matrices was performed using principal coordinate analysis (PCoA). Sample clustering was performed using hierarchical k-means with the package "factoextra" (Kassambara, A., and Mundt, F. Extract and Visualize the Results of Multivariate Data Analyses. R package. version 1.0.7. https: / / CRAN.R-project.org / package=factoextra). Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was used to identify major phylotypes in the clustering.

[0069] Dynamic microbiome signatures. We used the "coda4microbiome" package to identify metavariables that appear to recapitulate dynamic microbiome signatures (Calle, M.L., and Susin, A. (2022). Identification of Dynamic Microbial Signatures in Longitudinal Studies (Bioinformatics) 10.1101 / 2022.04.25.489415).

[0070] Correlation analysis. A correlation matrix was computed using all variables from the three omics datasets using the nonparametric Spearman method. To retain meaningful correlations, only correlations with an absolute rho coefficient greater than 0.3 and statistically significant (false discovery rate-adjusted p-value < 0.05) were retained.

[0071] Correspondence analysis. Correspondence analysis was performed with the "FactoMineR" package (Le, S., Josse, J., and Husson, F. (2008). J. Stat. Soft. 25.) with all antibiotics used during the allo-HSCT procedure.

[0072] Metabolomic analysis. Pathway enrichment was assessed using enrichment factors computed using overrepresentation analysis. Statistical tests for enrichment were performed using the hypergeometric distribution (Michonneau, 2019).

[0073] Network analysis. A network was constructed from the correlation matrix. Each node represents one variable, and edges indicate significant correlations. In the analysis of metabolically enriched pathways, edges indicate pathway-OTU associations. For visualization, the Fruchterman-Reingold algorithm was used.

[0074] Network module analysis. After applying Louvain clustering to the constructed network, modules were defined. Principal component analysis (PCA) was used to calculate the contribution and loading of each variable to the module. Variables included in one module were included in one PCA. Then, the loadings were calculated using the variable coordinates on the first PCA axis. Then, for each sample, the module variables were calculated by summing the individual variables weighted by the loadings.

[0075] Survival analysis. The incidence of recurrence was computed using the Fine and Gray method with a competing risks model. The starting point was the day of graft implantation (DO), with death as the competing risk event and recurrence as the event. When multiple samples from one patient were available, the mean value was applied.

[0076] Statistical testing. Given the non-Gaussian distribution of variables, two-tailed nonparametric tests were used. The nonparametric Kruskal-Wallis test was used to investigate the association between enterotypes and fecal and plasma metabolites, viruses, and T-cell subsets. Comparisons of frequency were performed using chi-squared or Fisher's tests. The Benjamini-Hochberg false discovery rate method was used to correct P values ​​for multiple testing. Polynomial regression was used to build models to test for multivariate associations between enterotypes and clinical variables. Linear regression models were built to test for multivariate associations between modules and clinical variables. Linear regression was used to evaluate the association between phylotypes and T-cell subsets: the association between mean phylotype frequency and T-cell subsets was assessed.

[0077] software The computing environment can be reproduced using GNU Guix (guix.GNU.org) using the “manifest.scm” and “channels.scm” files in the “guixconfig” directory of the git repository (Vallet, N., Michonneau, D., and Tournier, S. (2022). Toward practical transparent verifiable and long-term reproducible research using Guix. Sci Data 9, 597).

[0078] II. Results The post-transplant gut microbiota is characterized by four enterotypes We first characterized the gut microbiota after transplantation. Microbial load remained stable over time. The number of OTUs and alpha diversity, measured using the Simpson and Shannon indices, declined during the transplant procedure, primarily during the first 2 weeks after transplantation.

[0079] Next, we assessed whether the samples clustered into enterotypes. Using k-means clustering, four clusters of samples were identified. These clusters were correlated with alpha diversity: Cluster 2 showed the highest diversity, followed by Cluster 1, Cluster 3, and Cluster 4. Each cluster was characterized by the abundance of specific bacterial genera (Figure 1A) and phylotypes (Figure 1B). Enterotype 1 was characterized by a high proportion of Clostridium sp. and a low proportion of Bacteroides vulgatus. Enterotype 2 was characterized by a high proportion of Faecalibacterium prausnitzii, B. vulgatus, Fusobacterium necrophorum, and Bacteroides caccae and a low proportion of Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Bacteroides fragilis, and Clostridium species. Enterotype 3 was primarily driven by B. vulgatus, while enterotype 4 was enriched for E. faecalis, E. aerogenes, and B. fragilis (Figure 1).

[0080] Post-transplant gut bacteriome is associated with specific metabolic pathways and bacteriophage populations To explore the function of the microbiome, we analyzed the gut metabolome and detected 925 known fecal metabolites. Of these metabolites, 99 were significantly associated with the four enterotype distributions (Table 1).

[0081] TIFF2025540247000003.tif239170TIFF2025540247000004.tif250170TIFF20255402470 00005.tif253170TIFF2025540247000006.tif251170TIFF2025540247000007.tif105170

[0082] Enrichment analysis, using values ​​above 0.6 normalized to 0–1 to define enriched metabolites, revealed specific gut metabolic profiles among enterotypes (Figure 2A). Enterotypes were also associated with specific plasma metabolomics profiles (Figure 2B). All enterotypes except for enterotype 4 were associated with secondary bile acid metabolism. Enterotype 4 showed enrichment for primary bile acid and lysophospholipid metabolism. While enterotype 1 was enriched for lysophospholipid and secondary bile acid metabolism, enterotypes 2 and 3 were closely characterized by enrichment for metabolites involved in secondary bile acid and pyrimidine metabolism (Figure 2).

[0083] Bacteriophage species and fecal metabolomes are particularly associated with phylotypes enriched in enterotypes Next, we investigated viral species in fecal samples to investigate how the gut virome may affect gut microbiota homeostasis after allo-HSCT. Individual viruses were not associated with bacterial enterotypes. To assess whether groups of viruses may be associated with bacterial enterotypes, k-means clustering of samples was performed using either total viral or bacteriophage species. Clusters derived from total viral reads (Figure 3, left panel) were primarily driven by bacteriophage-restricted reads (Figure 3, right panel). These viral clusters were not associated with enterotypes.

[0084] To investigate which phylotypes drive metabolic profiles and whether these may be influenced by the gut virome, we performed a correlation analysis between the top 13 enterotype driver phylotypes, 99 metabolites associated with the enterotypes, and all viral species. This identified 270 statistically significant correlations, defined by a Spearman's rho absolute value greater than 0.3 and an adjusted p-value less than 0.05. These correlations were represented as a correlation network, and Louvain clustering was applied to identify modules of correlated variables. The metabolites associated with the phylotypes were then summarized within enriched metabolic pathways (Figure 4A). Enrichment of secondary bile acids was shared by B. uniformis (module 11), E. aerogenes, and Prevotella sp. DJF_RP53 (module 1). Sterol pathway enrichment was shared by Clostridium sp. (module 10) and F. prausnitzii (module 5). Among the 13 modules, the closely related phylotype of Fusobacterium necrophorum (module 2) was associated with 18 viral species, including 14 (78%) bacteriophages. In three modules, bacterial drivers associated with relapse or complete remission were Bacteroides fragilis, Prevotella sp. DJF_RP53, and Bacteroides sp. DJF_B097, and were characterized by specific correlations between metabolic pathways and bacteriophages (Figure 4B).

[0085] When bacteriophages were examined according to their genus, only Pepyhexaviruses were commonly associated with Prevotella sp. DJF_RP53 and closely related phylotypes of E. aerogenes. Associations with the unclassified Siphoviridae family were shared by Bacteroides sp. DJF-B097, E. faecalis, and closely related phylotypes of F. necrophorum (Figure 5). Among eukaryotic host viruses, the picobirnavirus species Otarine picobirnavirus was associated with closely related phylotypes of F. prausnitzii (module 5).

[0086] Enterotypes are associated with azithromycin and subsequent relapse We evaluated whether clinical data and outcomes were associated with enterotype. For some patients, assignment to one enterotype fluctuated over time. Among 38 patients with at least two samples, 22 (58%) experienced an enterotype shift during the allo-HSCT procedure. Compared with patients whose enterotype remained stable, patients whose enterotype shifted during the transplant procedure were associated with fewer relapses (complete remission at 12 months, n = 6 / 16, 38% vs. n = 17 / 22, 77%, p = 0.020). Enterotype shift was not affected by clinical variables or the type of antibiotic used. Enterotype was not affected by time since allo-HSCT.

[0087] Next, we evaluated whether enterotypes and multi-omics modules were associated with clinical variables, particularly azithromycin uptake and relapse. ALLOZITHRO treatment group (azithromycin or placebo) was associated with enterotype (p=0.037). Enterotypes 3 and 4 were equally distributed among azithromycin and placebo samples, whereas enterotype 1 was associated with 23 samples (70%) from the azithromycin group and 17 samples (68%) from the placebo group, with enterotype 2 observed (p=0.02). Post-transplant relapse of the underlying malignancy at 12 months was associated with enterotype distribution (p=0.026). Enterotype 2 was primarily associated with complete remission (95.5%), whereas enterotypes 1, 3, and 4 were associated with 10 samples (32%, p = 0.017), 12 samples (38%, p = 0.008), and 10 samples (42%, p = 0.005) from relapsed patients, respectively (Figure 6). Among other clinical variables, donor type (p = 0.046), stem cell source (p = 0.044), and GVHD prophylaxis (p = 0.038) were also associated with enterotype. Acute GVHD and chronic GVHD were not associated with enterotype.

[0088] To avoid the possibility that inferences were influenced by confounding factors, a multivariate polynomial regression model was performed using clinical variables associated with enterotype as covariates. Relapse and azithromycin intake remained associated with enterotype (Figure 7). The type of nutritional support was not associated with enterotype (p = 0.092).

[0089] Multi-omics module specifically correlates with azithromycin intake and relapse Because azithromycin (or placebo) intake and complete remission (or hematologic relapse) were associated with enterotype, we further explored whether these parameters were associated with the multi-omics modules. This univariate analysis showed that the azithromycin group was less associated with variables from module 5 and more associated with variables from module 7. Relapse and complete remission were not associated with the modules.

[0090] Multivariate analysis using variables associated with enterotype as covariates confirmed (i) the association of modules 5 and 7 with azithromycin and (ii) module 12 was significantly associated with azithromycin intake. Module 5 contained F. prausnitzii, otariine picobirnavirus, sterols, primary and secondary bile acids, and fatty acid metabolites. Module 7 was driven by B. caccae. The latter module was enriched in primary bile acid metabolites and coffee-derived metabolites. Module 12 contained E. faecalis and was inversely associated with lactococcal phage ul36.

[0091] Furthermore, module 9 was associated with recurrence in multivariate analysis. Module 9 was centered on B. fragilis and correlated with lactobacillus phage Lrm1, whereas lactococcus phages D4412 and D4410 and two metabolites from the dicarboxylic acid and branched-chain amino acid (BCAA) pathways were inversely correlated.

[0092] Azithromycin and relapse share alterations of the gut microbiota Next, we applied supervised analysis to better characterize the gut microbiota composition associated with azithromycin and relapse. Complete remission and relapse were associated with specific microbiota (PERMANOVA p=0.003). Microbiota associated with relapse were primarily characterized by closely related phylotypes of B. fragilis, E. coli, and E. aerogenes, whereas remission was associated with OTUs related to B. uniformis, Bacteroides sp. DJF_B097, B. vulgatus, B. massiliensis, and E. faecalis (Figure 8A).

[0093] The PERMANOVA coefficients for randomization group (placebo or azithromycin) and disease status (relapse or complete response) were crossed to highlight which phylotypes were associated with both variables (Figure 8B). Consistent with the higher risk of relapse in the azithromycin group in the ALLOZITHRO trial, of the 13 phylotypes, 10 (77%) were associated with relapse and azithromycin or placebo and complete response. Briefly, among the 10 phylotypes, B. fragilis, Bacteroides spp. Smarlab BioMol-2301151, and a close relative of B. eggerthii were associated with relapse and azithromycin, whereas B. vulgatus, Bacteroides spp. DJF_B097, E. faecalis, Prevotella spp. DJF_RP53, B. cacae, P. oralis, and Bacteroides spp. CCUG 39913 were associated with placebo and complete remission.

[0094] A Fine and Gray model was computed to assess which phylotypes were associated with recurrence, taking into account the time-dependent nature of recurrence outcomes and the competing risk of death. Mean phylotype frequencies were applied for patients with multiple samples. Results revealed that B. fragilis was associated with a higher risk of recurrence (HR = 1.02, 95% confidence interval (95CI): 1.02-1.03, p < 0.001). Two other phylotypes were associated with a lower risk of recurrence: Prevotella sp. DJF_RP53 (HR = 0.90, 95CI: 0.85-0.94, p < 0.001) and Bacteroides sp. DJF_B097 (HR = 0.69, 95CI: 0.57-0.82, p < 0.001) (Figure 8C).

[0095] Incorporating time into the analysis enabled us to identify a dynamic microbial signature associated with relapse (p=0.0003). B. cacae, B. uniformis, F. prausnitzii, E. faecalis, Bacteroides spp. CCUG 39913, and closely related phylotypes of B. fragilis were drivers of the relapse-prone signature, whereas Prevotella spp. DJF_RP53, E. aerogenes, B. vulgatus, Bacteroides spp. DJF_B097, B. eggersii, and Bacteroides spp. Smarlab BioMol-2301151 were associated with complete response (Figure 8D-F). The temporal frequency profiles of closely related phylotypes of Bacteroides spp. DJF_B097 and Prevotella spp. DJF_RP53 were less frequent in relapsed patients. In contrast, the B. fragilis profile was consistent with its higher frequency in relapsed patients. No correlation was observed between phylotypes, indicating that the phylotype trajectories were independent (Figures 8F and 9A). Finally, when we examined parallel changes in bacteriophages correlated with B. fragilis over time and compared them to B. fragilis abundance, we observed inverse trajectories for Lactobacillus phage Lrm1 and Lactococcus phage D4410, indicating that these bacteriophages can be used to target B. fragilis (Figure 9B).

[0096] Overall, these results indicate that azithromycin intake influences the gut microbiota: Bacteroides species DJF_B097 and Prevotella species DJF_RP53 were associated with complete response, whereas B. fragilis was associated with a higher risk of relapse.

[0097] Bacterial taxa associated with enterotype and relapse correlate with peripheral blood T cell subsets Enterotypes were also associated with the frequency of peripheral blood T cell subsets. Enterotypes 2 and 3 were associated with clusters expressing molecules associated with T cell activation or cytotoxic activity, including 2B4, KLRG1, and granzyme B. The mucosal-associated invariant T cell (MAIT) subset was also associated with enterotype 2. Enterotype 4 was associated with TIGIT+ T cells and the Eomes+ T-bet+ subset, whereas enterotype 1 was associated with the expression of co-inhibitory molecules, including ICOS, TIGIT, PD-1, and CTLA-4, as well as TOX expression in CD4+ Th1 cells (Figure 10A). This suggests that both relapse-associated enterotypes 1 and 4 are associated with exhausted T cells in the peripheral blood. Finally, we found a significant association between B. fragilis, a taxon associated with a higher risk of relapse, and exhausted T cells co-expressing TIGIT, PD-1, and TOX in central memory CD4+ Th1 and Th2 cells and CD8+ cells. Conversely, Bacteroides sp. DJF_B097, a phylotype associated with a lower risk of relapse, was associated with KLRG1+2B4+ activated effector memory CD4+ Th0 cells and TIGIT+ central memory CD4+ Th1 cells (Figure 10B).

[0098] III. Conclusion In a randomized clinical trial, early administration of azithromycin during allo-HSCT unexpectedly increased the risk of hematologic malignancy relapse (Bergeron et al. (2017) JAMA 318, 557). Here, using unsupervised and targeted approaches, we uncovered the impact of azithromycin treatment on the gut microbiota, which contributes to relapse.

[0099] Specific differences between the gut microbiota from patients treated with azithromycin or placebo and those who relapsed or remained in complete remission were characterized. Among overlapping phylotypes associated with azithromycin, placebo, relapse, and complete response samples, a close relative of Bacteroides stercolis (Bacteroides species DJF_B097) and Prevotella copri (Prevotella species DJF_RP53) was frequently found in placebo samples and associated with complete remission. B. fragilis was frequently found in azithromycin samples and significantly associated with a higher risk of relapse. This higher risk of relapse associated with B. fragilis was found in unsupervised multivariate analyses and supervised methods. A longitudinal study of the abundance of the three phylotypes showed that the phylotype frequency curves from azithromycin intake overlapped with those of relapsed patients, whereas placebo overlapped with those of complete remission patients. B. fragilis may attenuate antitumor responses by promoting regulatory pathways.

[0100] Metabolic pathways enriched in phylotypes associated with relapse or remission included (i) phospholipid and lysophospholipid metabolites in patients with a low prevalence of Bacteroides sp. DJF_B097 (which can be explained by the effect of azithromycin on these metabolites) (Van Bambeke et al. (1996) European Journal of Pharmacology 314, 203-214), (ii) pentose metabolism in patients with a high prevalence of Prevotella sp. DJF_RP53, and (iii) low BCAAs in patients with a high prevalence of B. fragilis (which can be explained by the importance of BCAAs for galactosylceramide biosynthesis) (Oh et al. (2021) Nature 600, 302-307).

[0101] Sequence Listing denovo9506 of SEQ ID NO:1 ACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGGCGCTAGCCTGAACCAGCCAAGTAGCGTGAAGGATGAAGGCTCTATGGGTCGTAAACTTCTTTTATAAGAATAAAGTGCAGTATGTAACTGTTTTGTATGTATTATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCGGGATTATTGGTTTAAAG GGAGCGTAGGTGGACTGGTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGTCAGTCTTGAGTACAGTAGAGGTGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAGATACCCTGGTA

[0102] denovo3073 ACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGTGAAGGATGACTGCCCTATGGGTTGTAAACTTCTTTTATACGGGAATAAAGTTAGCCACGTGTGGCTTTTTGTATGTACCGTATGATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCGAGCGTTATCCCGGATTATTGGTTTAAAG GGAGCGTAGGCGGGTTGTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGACTCCGATTGCGAAGGCAGCTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAGATACCCTGGTA

[0103] denovo9260 of SEQ ID NO:3 ACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGTGCAGGAAGACGGCCCTATGGGTTGTAAACTGCTTTTATAAGGGAATAAAGTGAGAGTCGTGACTCTTTTTGCATGTACCTTATGAATAAGGACCGGCTAATTCCGTGCCAGCAGCCGCGGTAATACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAG GGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGGATTAGATACCCTGGTA

[0104] Primer: V3fwd-TACGGRAGGCAGCAG (wherein R is A or G), SEQ ID NO: 4 V4rev-TACCAGGGTATCTAAT, SEQ ID NO: 5

Claims

1. 1. A method for predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), comprising: (i) determining the gut microbial phylotype in a gut sample of the individual following allo-HSCT; (ii) determining the abundance or relative abundance of phylotypes comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1, and optionally determining the relative abundance of at least one of the OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 and / or denovo9260 of SEQ ID NO: 3; Including, 1. A method according to claim 1, wherein an individual has a gut microbiota enriched for OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1, and optionally depleted for at least one of OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 and / or denovo9260 of SEQ ID NO: 3, wherein the individual has a higher risk of recurrence.

2. (i) determining the gut microbial phylotype in a gut sample of the individual following allo-HSCT; (ii) determining the abundance or relative abundance of phylotypes comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity to denovo9506 of SEQ ID NO: 1; Including, 2. The method of predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT) according to claim 1, wherein individuals with a gut microbiota enriched in said OTUs have a higher risk of relapse.

3. (i) determining the gut microbial phylotype in a gut sample of the individual following allo-HSCT; (ii) determining the relative abundance of at least one OTU comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity to denovo3073 of SEQ ID NO: 2 and / or denovo9260 of SEQ ID NO: 3; Including, 2. The method of predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT) according to claim 1, wherein the individual has a gut microbiota in which at least one or two of the OTUs are enriched, and is a good responder to allo-HSCT.

4. Phages that directly or indirectly target Bacteroides fragilis for the prevention of cancer recurrence in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT).

5. 5. The phage that directly or indirectly targets Bacteroides fragilis according to claim 4, which is selected from the group consisting of lytic phages VA7, MTK and UZ-1, bacteriophage B56-3, bacteriophage B40-8, bacteriophage vB_BfrS_23, lactococcal phage D4410, lactococcal phage D4412, lactobacillus phage Lrm1, streptococcal phage Dp-1, rhodococcal virus Poco6, alphapapillomavirus 10 and lactococcal phage BK5-T.

6. A pharmaceutical composition comprising at least one phage that directly or indirectly targets Bacteroides fragilis for preventing cancer recurrence in patients who have undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT).

7. 7. A pharmaceutical composition comprising at least one phage that directly or indirectly targets Bacteroides fragilis, as described in claim 6, comprising two or more phages that directly or indirectly target Bacteroides fragilis.

8. A pharmaceutical composition comprising at least one phage that directly or indirectly targets Bacteroides fragilis according to claim 6 or 7, which is administered orally or in the form of a suppository.

9. 9. A pharmaceutical composition comprising at least one phage that directly or indirectly targets Bacteroides fragilis according to claim 8, which is orally administered and further comprises a proton pump inhibitor.

10. A probiotic composition comprising one or more isolated bacterial strains of a species selected from the group consisting of Bacteroides stercolis and closely related phylotype Bacteroides sp. DJF_B097 and / or Prevotella copri and closely related phylotype Prevotella sp. DJF_RP53, which prevents cancer recurrence in patients who have undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT).

11. A nutritional composition comprising at least one metabolite selected from the group consisting of:

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