Methods for determining and improving potential efficacy of anti-cancer treatments

Abstract

The present invention relates to the field of anti-cancer treatments, and more specifically, to a method for determining, in vitro, whether a patient is likely to benefit from allogeneic hematopoietic stem cell transplantation (allo-HSCT) by analyzing the gastrointestinal microbiota in an excrement sample from the patient with hematological malignancies; the invention also relates to treatments intended to reduce the risk of recurrence following allo-HSCT.

Description

[0001] The present invention relates to the field of anti-cancer treatment, and more particularly, to a method for in vitro determination of whether a patient suffering from a hematological malignancy is likely to benefit from allogeneic hematopoietic stem cell transplantation (allo-HSCT) by analyzing the gut microbiota in excreted samples from the patient; the present invention also relates to a treatment aimed at reducing the risk of relapse after allo-HSCT.

[0002] Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative treatment for hematological malignancies (Copelan, E.A. (2006) Hematopoietic Stem-Cell Transplantation. N Engl J Med 354, 1813–1826). Relapse is the leading cause of death after allo-HSCT (Horowitz et al. (2018) Bone Marrow Transplant 53, 1379–1389). The curative graft-versus-tumor (GVT) effect depends on the alloreactivity of donor immune cells against tumor cells (Blazar et al. (2020) Nat Rev Clin Oncol 17, 475–492). GVT is mainly mediated by donor T cells, as shown by the higher risk of relapse associated with T cell-depleted grafts (Horowitz et al. (1990) Blood, 8) and the prophylactic effect of donor lymphocyte infusion (DLI) (Schmid et al. (2007) JCO 25, 4938–4945). However, the benefits of allo-HSCT may be offset by graft-versus-host disease (GVHD), which is one of the major complications of allo-HSCT, in which T cells target healthy tissues of the host (Horowitz et al. (2018); Cho et al. (2012) Biology of Blood and Marrow Transplantation 18, 1136–1143; Zeiser, R., and Blazar, B.R. (2017) N Engl J Med 377, 2167–2179).

[0003] 10% of patients develop chronic graft-versus-host disease of the lung (cGVHD) within the first 2 years after HSCT (Bergeron et al. (2018) Eur Respir J 51, 1702617) and share a common triggering mechanism with bronchiolitis obliterans syndrome (BOS) (Barker et al. (2014) N Engl J Med 370, 1820–1828), which occurs after lung transplantation. After lung transplantation, the second-generation macrolide azithromycin prevents BOS (Vos et al. (2011) European Respiratory Journal 37, 164–172).

[0004] In the context of a randomized, multicenter, placebo-controlled, double-blind superiority study (the ALLOZITHRO trial, NCT01959100) designed to evaluate azithromycin as a prophylaxis for pulmonary cGVHD, the inventors analyzed excreta and blood samples collected from patients included in the ALLOZITHRO trial before and after allo-HSCT procedures.

[0005] The gastrointestinal microbiota is perturbed after allo-HSCT (Shono, Y., and van den Brink, M.R.M. (2018) Nat Rev Cancer 18, 283–295). This dysbiosis is characterized by lower α-diversity of the gastrointestinal microbiota and dominance of the Enterococcaceae family (Jenq et al. (2012) Journal of Experimental Medicine 209, 903–911; Peled et al. (2020) N Engl J Med 382, 822–834). Antibiotics used during and before surgery affect the abundance of bacterial taxa and bacterial dominance (Shono et al. (2016) Sci. Transl. Med.; Taur et al. (2012) Clinical Infectious Diseases 55, 905–914; Weber et al. (2017) Biology of Blood and Marrow Transplantation 23, 845–852). Total parenteral nutrition also affects gastrointestinal microbiota composition (Jenq et al. (2017) Biology of Blood and Marrow Transplantation 21, 1373–852). Low microbial diversity is associated with a higher risk of death, especially driven by non-relapse mortality (Peled et al. (2020) NEJM 328, 822-834; Taur et al. (2014) Blood 124, 1174–1182). Considering relapse, few studies have reported a link between the gastrointestinal microbiota and allo-HSCT relapse. Low abundance of Blautia is associated with a higher risk of relapse (Jenq et al. (2015)), while the presence of a cluster of operational taxonomic units (OTUs) mainly composed of Eubacterium limosum is associated with a lower risk of relapse (Peled et al. (2007) JCO 35, 1650–4945). To date, no publication has demonstrated that the presence of bacterial genera or taxa after allo-HSCT will increase the risk of relapse and be associated with an impaired anti-tumor immune response.The underlying mechanism by which the gut microbiota contributes to the anti-tumor response after allo-HSCT remains unclear but may be associated with metabolites derived from the gut microbiota (Postler, T.S., and Ghosh, S. (2017) Cell Metabolism 26, 110–130; Yang et al. (2017) Cell Host & Microbe 22, 757-765.e3). Thus, there is still a need to better understand the role of the gut microbiota in the anti-tumor response after allo-HSCT to identify novel complementary therapies for treating hematological malignancies.

[0006] Specifically, the inventors showed that the gut microbiota composition after allo-HSCT is associated with relapse or remission: Bacteroides sp. DJF_B097 (related to Bacteroides stercoris) and Prevotella sp. DJF_RP53 (related to Prevotella stercoris) and their closest bacterial strains are associated with complete remission, while Bacteroides fragilis (and related strains) is associated with a higher risk of relapse.

[0007] By applying multi-omics methods to quantify the abundance and diversity of bacterial taxa, viral species, and excreted metabolite levels, the inventors revealed how the gut bacteriome, virome, and metabolomic profiles of patients undergoing allo-HSCT interact. They highlighted the bacterial, bacteriophage, and metabolic pathway networks associated with post-transplant relapse.

[0008] Accordingly, the present invention relates to a method for predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, the method comprising the steps of:

[0009] (i) determining the gut microbial phylotype (OTU stands for operational taxonomic unit, ASV stands for amplicon sequence variant or molecular species; i.e., any set of 16S rRNA gene coding sequences that share at least 97% similarity with each other and are thus grouped together as a taxonomic entity; and represented by a selected representative sequence) in an intestinal sample of the individual after allo-HSCT;

[0010] (ii) Determine the abundance or relative abundance of the phylotype of a nucleotide fragment comprising a sequence having at least 97%, preferably 98%, 99% or 100% identity with denovo9506 of SEQ.ID.N°1 (belonging to Bacteroides fragilis), and optionally determine the relative abundance of at least one of the OTUs of a nucleotide fragment comprising a sequence having at least 97%, preferably 98%, 99% or 100% identity with denovo3073 of SEQ.ID.N°2 (belonging to Bacteroides sp. DJF_B097) and / or denovo9260 of SEQ.ID.N°3 (belonging to Prevotella sp. DJF_RP53 X);

[0011] Wherein, an individual having a gut microbiota enriched in said OTU and optionally depleted in at least one of the OTUs has a higher risk of recurrence, said OTU comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity with denovo9506 of SEQ.ID.N°1, and at least one of the OTUs comprising a nucleotide fragment of a sequence having at least 97%, preferably 98%, 99% or 100% identity with denovo3073 of SEQ.ID.N°2 and / or denovo9260 of SEQ.ID.N°3.

[0012] According to a specific embodiment, the present invention relates to a method for predicting the response of an individual to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, the method comprising the following steps:

[0013] (i) Determine the gut microbial phylotypes (OTU represents operational taxonomic unit, ASV represents amplicon sequence variant or molecular species; that is, any set of 16S rRNA gene coding sequences that share at least 97% similarity with each other and are thus grouped together as a taxonomic entity; and are represented by a selected representative sequence) in the intestinal sample of said individual after allo-HSCT;

[0014] (ii) Determine the abundance or relative abundance of the phylotype of a nucleotide fragment comprising a sequence having at least 97%, preferably 98%, 99% or 100% identity with denovo9506 of SEQ.ID.N°1;

[0015] Wherein, an individual having a gut microbiota enriched in said phylotype has a higher risk of recurrence.

[0016] Preferably, said individual can subsequently receive treatment to alter the gut microbiota composition using, for example, specific bacteriophages.

[0017] The "relative abundance" of a phylotype is defined as the percentage of the number of sequences assigned to that phylotype out of the total number of filtered sequences in a given sample. A phylotype should be assigned to at least two 16S rRNA gene sequences (disregarding monomers, i.e., phylotypes containing only one sequence).

[0018] The gastrointestinal microbial composition and the abundance and relative abundance of a given phylotype can be determined by classical and appropriate methods known to those skilled in the art. In a specific embodiment, the determination is performed on total DNA extracted from human excreta or mucosal or tissue samples by techniques well-known to those skilled in the art (such as shotgun metagenomic sequencing or 16S rRNA gene sequencing). It can also be determined by quantitative PCR techniques using specific probes that target specific phylotype sequences or bacterial isolates and strains to which the phylotype belongs, and any sequence having at least 97%, preferably 98%, 99% or 100% identity with the nucleic acid sequence of the phylotype.

[0019] Each phylotype is represented at different levels of relative abundance, with some phylotypes having a relative abundance ranging from 0.01% to 0.1% of the total number of sequences, and others having a relative abundance ranging from 0.5% to 35% of the total number of sequences. Preferably, in the method of the present invention, the relative abundance of phylotypes is evaluated by applying a detection threshold of 1% of the total number of sequences (per sample).

[0020] According to another embodiment, the present invention relates to a method for predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, the method comprising the following steps:

[0021] (i) determining the gastrointestinal microbial phylotype composition in an intestinal sample of the individual after allo-HSCT;

[0022] (ii) determining the presence or absence of a phylotype comprising a nucleotide fragment having at least 97%, preferably 98%, 99% or 100% identity with denovo9506 of SEQ.ID.N°1 by applying a detection threshold of 1% of the total number of sequences (per sample) to define the presence or absence of the phylotype, or by applying quantitative PCR detection using a probe targeting a specific phylotype or bacterial isolate;

[0023] wherein an individual having a gastrointestinal microbiota showing the presence of the phylotype has a higher risk of relapse.

[0024] In another embodiment, the present invention also relates to a method for predicting an individual's response to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, the method comprising the following steps:

[0025] (i) Determine the gastrointestinal microbiota phylotypes in the intestinal samples of the individual after allo-HSCT (OTU stands for operational taxonomic unit, ASV stands for amplicon sequence variant or molecular species; i.e., any set of 16S rRNA gene coding sequences that share at least 97% similarity with each other and are thus grouped together in a taxonomic entity; and are represented by a selected representative sequence);

[0026] (ii) Determine 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 with denovo3073 of SEQ.ID.N°2 and / or denovo9260 of SEQ.ID.N°3;

[0027] Wherein an individual having a gastrointestinal microbiota enriched in at least one or two of said phylotypes is a good responder to allo-HSCT.

[0028] According to another embodiment, the present invention relates to a method for predicting the response of an individual to allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of hematological malignancies, the method comprising the following steps:

[0029] (i) Determine the composition of gastrointestinal microbiota phylotypes in the intestinal samples of the individual after allo-HSCT;

[0030] (ii) Determine 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 with denovo3073 of SEQ.ID.N°2 and / or denovo9260 of SEQ.ID.N°3 by applying a detection threshold of 1% of the total number of sequences (per sample) to define the presence or absence of phylotypes, or by applying quantitative PCR detection using probes targeting specific phylotypes or bacterial isolates;

[0031] Wherein an individual having a gastrointestinal microbiota exhibiting the presence of at least one or both of said phylotypes is a good responder to allo-HSCT.

[0032] As used herein, "hematological malignancies" is a general term for various specified blood cancers; the WHO classifies such cancers according to their presumed cell of origin, genetic abnormalities, and clinical features (see Khoury, J.D. et al. The 5th edition of the world health organization classification of haematolymphoid tumours: 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 haematolymphoid tumours: Lymphoid neoplasms. Leukemia 36, 1720–1748 (2022)). Non-limiting examples of hematological malignancies are acute myeloid leukemia, acute lymphoblastic leukemia, myelodysplastic neoplasms, myeloproliferative neoplasms, B and T non-Hodgkin lymphomas and lymphoproliferative disorders, Hodgkin lymphoma.

[0033] Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a procedure in which a portion of the peripheral blood stem cells or bone marrow stem cells from a healthy donor are obtained and prepared for intravenous infusion.

[0034] Thus, the method of the present invention allows determination of whether a patient with a hematological malignancy is likely to benefit from allogeneic hematopoietic stem cell transplantation (allo-HSCT) by analyzing the gastrointestinal microbiota in an intestinal sample, such as a mucosal sample obtained after biopsy or a fecal sample from the patient; this can be evaluated at any time after allo-HSCT.

[0035] In the context of the present invention, "relapse" refers to the recurrence (return) of a disease, a hematological malignancy, or the signs and symptoms or biological abnormalities of the disease (also known as minimal residual disease (MRD)) after a period of improvement.

[0036] A patient who is a "good responder to allo-HSCT" refers to a patient affected by a blood cancer and who has received or will receive an allogeneic hematopoietic stem cell transplantation (allo-HSCT) and who will show a clinically significant improvement after receiving said anti-cancer treatment; the clinically significant improvement can be evaluated by clinical examinations (weight, general condition, pain and palpable masses, if any), biomarkers and imaging studies (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 standardized criteria well known to those skilled in the art.

[0037] The gastrointestinal microbiota refers to the population of microorganisms living in the gut of any organism belonging to the animal kingdom; in the present invention, said organism is preferably a human. The gastrointestinal microbial composition evolves continuously throughout life and is the result of different environmental influences.

[0038] Dysbiosis refers to a harmful imbalance of the gastrointestinal microbial composition that can occur under certain specific circumstances.

[0039] Intestinal samples can be collected at any time point after an allogeneic hematopoietic stem cell transplantation (allo-HSCT).

[0040] The germline-type recombinant bacterial 16S rRNA gene sequences of the present invention and named "denovo#", the 16S rRNA gene sequences share the same or similar nucleotide bases (at least 97% sequence identity in the targeted 16S region). Each phylotype is affiliated with a bacterial species when sharing 98% or higher sequence similarity with the sequences described in public databases, whether isolated or not, cultured or uncultured.

[0041] Alternatively, a method for predicting an individual's response to an allogeneic hematopoietic stem cell transplantation (allo-HSCT), particularly in the context of a hematological malignancy, can be performed by detecting an increase or decrease in at least one metabolite of the individual.

[0042] More specifically,

[0043] – A decrease in 2-hydroxydodecanedioate (ester) and / or 2-methylmalonyl carnitine in an individual's intestinal sample is associated with a higher risk of relapse;

[0044] - A decrease and / or an increase in homocarnosine, arabinose, glycerophosphoserine, glycerophosphocholine, 1-stearoyl-GPI, glycerophosphoinositol, and / or 1-palmitoyl-GPI in a plasma or gut sample of an individual, and / or an increase in N-methylalanine and / or dopamine 3-O-sulfate, are associated with a favorable response to allo-HSCT.

[0045] The present invention also relates to the treatment of patients diagnosed as having a risk of relapse, the treatment being for altering their gastrointestinal microbiota.

[0046] According to an embodiment, such treatment is carried out using bacteriophages.

[0047] Bacteriophages (phages) are a class of viruses that specifically lyse bacteria. They are widely present in soil, air, water, and living organisms. They have strong specificity and bind to specific sites on the surface of bacterial cells. Since the first discovery of bacteriophages by Frederick Twort in 1915, a growing number of studies have shown that bacteriophages have high antibacterial activity and specificity against drug-resistant bacteria and prevent the disruption of the microbial community.

[0048] Compared with antibiotic treatment, the use of bacteriophages has extremely low side effects, is much faster and more effective, 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 will not kill non-pathogenic "normal flora" bacteria, thus preserving the "colonization resistance" of hosts such as the human gut and can be used to specifically target one or more bacterial species.

[0049] Accordingly, the present invention relates to at least one phage that targets Bacteroides fragilis (directly or indirectly) for preventing cancer relapse in patients who have received allogeneic hematopoietic stem cell transplantation (allo-HSCT); preferably, the cancer is selected from the group consisting of acute myeloid leukemia, acute lymphoblastic leukemia, myelodysplastic neoplasms, myeloproliferative neoplasms, B and T non-Hodgkin lymphomas and lymphoproliferative diseases, Hodgkin lymphoma.

[0050] As used herein, a bacteriophage targeting Bacteroides fragilis refers to a bacteriophage having direct or indirect antibacterial activity against Bacteroides fragilis, that is, having the ability to kill and / or inhibit the growth or reproduction of the bacterium Bacteroides fragilis. The direct antibacterial activity can be evaluated by culturing Bacteroides fragilis according to known techniques, contacting the culture with the bacteriophage, and monitoring the growth and lysis of the bacteria after said contact. A reduction in colony size or a reduction in the total number of colonies indicates a bacteriophage having antibacterial activity against Bacteroides fragilis. The indirect effect can be evaluated 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 the growth and lysis of Bacteroides fragilis bacteria after said contact. A decrease in colony size or a decrease in the total number of colonies indicates a bacteriophage-bacterial target resulting in antibacterial activity against Bacteroides fragilis.

[0051] Efforts were made to select the following bacteriophages: (i) having lytic properties; (ii) being specific for Bacteroides fragilis; and (iii) lysing more than 70% of Bacteroides fragilis and having good resilience in the entire human gastrointestinal tract (i.e., resistance to pH changes, bile, and pancreatic salts, as usually evaluated by Van de Wiele et al., simulator of the human intestinal microbial ecosystem (SHIME), 2015).

[0052] Examples of bacteriophages having direct antibacterial activity against Bacteroides fragilis are:

[0053] - Lytic bacteriophages VA7, MTK, and UZ-1 of the Siphoviridae family;

[0054] - Bacteriophage B56-3;

[0055] - Bacteriophage B40-8;

[0056] - Bacteriophage vB_BfrS_23.

[0057] Examples of bacteriophages having indirect antibacterial activity against Bacteroides fragilis are:

[0058] - Lactococcus bacteriophage D4410;

[0059] - Lactococcus bacteriophage D4412;

[0060] - Lactobacillus bacteriophage Lrm1;

[0061] - Streptococcus bacteriophage Dp-1;

[0062] - Rhodococcus virus Poco6;

[0063] - Alpha papillomavirus 10;

[0064] - Lactococcus phage BK5-T.

[0065] The isolated bacteriophage can be administered alone or incorporated into a pharmaceutical composition.

[0066] Thus, the pharmaceutical composition of the present invention can include one, two or more isolated bacteriophages having direct or indirect antibacterial activity against Bacteroides fragilis.

[0067] Thus, the present invention also relates to a pharmaceutical composition comprising at least one phage targeting Bacteroides fragilis, which is used for preventing cancer recurrence in patients who have received allogeneic hematopoietic stem cell transplantation (allo-HSCT).

[0068] The pharmaceutical composition comprising at least one bacteriophage can be formulated into a unit dose or multi-dose formulation.

[0069] The phage targeting Bacteroides fragilis is preferably formulated into a pharmaceutical composition which also contains a pharmaceutically acceptable carrier, and the phage can be stored as a concentrated aqueous solution or a freeze-dried powder formulation. The pharmaceutical composition can contain other components as long as the other components do not reduce the effectiveness of the bacteriophage to an ineffective level for treatment. Pharmaceutically acceptable carriers are well-known, and those skilled in the pharmaceutical art can easily select a carrier suitable for a specific route of administration (Remington's Pharmaceutical Sciences, Mack Publishing Co., Easton, PA, 1985).

[0070] Suitable dosage forms of the pharmaceutical composition can be selected from the group consisting of: ointments, solutions, suspensions or emulsions, extracts, powders, granules, sprays, lozenges, tablets or capsules, and additionally include dispersants or stabilizers.

[0071] In an embodiment, the pharmaceutical composition is formulated for delivery to the intestine (e.g., the small intestine and / or the colon).

[0072] Thus, bacteriophages can also be formulated for rectal delivery to the intestine (e.g., the colon). Thus, in some embodiments, a composition comprising a bacteriophage can be formulated for delivery by suppository, colonoscopy, endoscopy, sigmoidoscopy, or enema. A pharmaceutical preparation or formulation, and particularly one for oral administration, can include additional components capable of effectively delivering the compositions of the present disclosure to the intestine (e.g., the colon). A variety of pharmaceutical preparations can be used that permit delivery of the composition to the intestine (e.g., the colon). Examples thereof include pH-sensitive compositions, and more specifically, buffered sachet formulations or enteric polymers that release their contents when the pH becomes alkaline after the enteric polymer has passed through the stomach. When a pH-sensitive composition is used to formulate a pharmaceutical preparation, the pH-sensitive composition is preferably a polymer with a pH threshold for composition breakdown between about 6.8 and about 7.5. Such a numerical range is the range in which the pH moves towards the alkaline side in the distal part of the stomach and is thus a range suitable for delivery and use in the colon. It should also be understood that each part of the intestine (e.g., the duodenum, jejunum, ileum, cecum, colon, and rectum) has a different biochemical and chemical environment. For example, parts of the intestine have different pH values, allowing for targeted delivery through compositions with specific pH sensitivities. Thus, the compositions provided herein are formulated for delivery to the intestine or a specific part of the intestine (e.g., the duodenum, jejunum, ileum, cecum, colon, and rectum) by providing a dosage form with appropriate pH sensitivity. (See, e.g., Villena et al., Int J Pharm 2015, 487(1-2):314-9).

[0073] Bacteriophages targeting Bacteroides fragilis can be administered orally; in this case, the bacteriophages can be incorporated into tablets or capsules, which will enable the bacteriophages to pass through the stomach without reduction of bacteriophage viability due to gastric acid and release fully active bacteriophages in the small intestine. In some embodiments, the composition is formulated with an enteric coating that increases the survival rate of the bacteriophage in the harsh environment of the stomach. The enteric coating resists the action of gastric juice in the stomach such that the bacteriophage incorporated therein will pass through the stomach and enter the intestine. The enteric coating can dissolve rapidly when in contact with intestinal fluid, such that the bacteriophage encapsulated in the coating will be released in the intestine. The enteric coating can be composed of polymers and copolymers known in the art, such as commercially available EUDRAGIT (Evonik Industries). (See, e.g., Zhang, AAPS PharmSciTech, (2016) 17(1), 56-67).

[0074] According to certain embodiments, a bacteriophage targeting Bacteroides fragilis or a pharmaceutical composition comprising at least one bacteriophage targeting Bacteroides fragilis is administered orally and is associated with a proton pump inhibitor (PPI), such as, for example, omeprazole, lansoprazole, dexlansoprazole, esomeprazole, pantoprazole, rabeprazole, and ilaprazole.

[0075] The dosage of the pharmaceutical composition and the desired drug concentration can vary according to the particular use. Determining the appropriate dosage or route of administration is entirely within the skill of the ordinary physician. Animal experiments can provide reliable guidance for determining effective dosages for human therapies. Those of ordinary skill in the art can perform interspecies scaling of effective dosages 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, pp42 - 96. Based on previous human experience in Europe, in most cases, a phage dosage between 10 7 and 10 11 PFU is suitable ( https: / / clinicaltrials.gov / ct2 / show / NCT04737876 , https: / / doi.org / 10.1016 / j.cell.2022.07.003 ).

[0076] The bacteriophage targeting Bacteroides fragilis or the pharmaceutical composition comprising the bacteriophage targeting Bacteroides fragilis can be administered as a single use, on a regular basis, or as a continuous use.

[0077] According to another embodiment, the present invention relates to a probiotic composition comprising isolated bacterial strains of one or more species selected from the group consisting of Bacteroides stercoris and related phylotype Bacteroides genus DJF_B097, and / or Prevotella copri and related phylotype Prevotella genus DJF_RP53, the probiotic composition being for preventing cancer recurrence in patients who have received allogeneic hematopoietic stem cell transplantation (allo - HSCT).

[0078] In some embodiments, the bacterial strain is isolated. Any of the bacterial strains described herein can be isolated and / or purified from, for example, a culture or a microbiota sample (e.g., excreted material). The bacterial strains used in the compositions provided herein are typically isolated from the microbiome of a healthy individual. Also as used herein, the term "purified" refers to a bacterial strain or composition that has been isolated from one or more components, such as contaminants. In some embodiments, the bacterial strain is substantially free of contaminants. In some embodiments, one or more bacterial strains of the composition can be purified independently from one or more other bacteria present in the culture or sample containing the bacterial strain. In some embodiments, the bacterial strain is isolated or purified from a sample and then cultured under conditions suitable for bacterial replication, such as 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.

[0079] Any of the compositions described herein (including probiotic compositions comprising the composition) can comprise the bacterial strain in any form, such as an aqueous form (e.g., a solution or suspension), an embedded semi-solid form, a powder form, or a lyophilized form. In some embodiments, the composition or the bacterial strain of the composition is lyophilized. In some embodiments, a subset of the bacterial strains in the composition is lyophilized. Methods for lyophilizing compositions, particularly compositions comprising bacteria, are known in the art. See, for example, US 3,261,761; US 4,205,132; PCT publications WO 2014 / 029578 and WO2012 / 098358, the entire contents of which are incorporated herein by reference. The bacteria can be lyophilized as a composition, and / or the bacteria can be lyophilized individually and combined prior to administration. The bacterial strain can be combined with a pharmaceutical excipient and then combined with another bacterial strain, or multiple lyophilized bacteria can be combined in a lyophilized form, and once combined, the bacterial mixture can subsequently 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.

[0080] Again, the probiotic composition is formulated for delivery to the gut (e.g., the small intestine and / or the colon).

[0081] In a preferred embodiment, the probiotic composition can be administered in the form of a suppository or orally, as described above.

[0082] According to another embodiment, the present invention relates to a nutritional composition comprising at least one metabolite involved in a metabolite sub-pathway selected from the group consisting of fatty acid metabolism (also known as BCAA metabolism), phospholipid metabolism, lysophospholipids, secondary bile acid metabolism, and xanthine metabolism.

[0083] Preferably, the metabolite is selected from the group consisting of:

[0084]

[0085]

[0086] In a particular embodiment of the present invention, a pharmaceutical composition, a probiotic composition, and a nutritional composition comprising at least one phage targeting Bacteroides fragilis can be combined and administered for the prevention of cancer recurrence in patients who have received allogeneic hematopoietic stem cell transplantation (allo-HSCT). BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 - Gastrointestinal bacterial characteristics after allogeneic hematopoietic stem cell transplantation: Bar graphs show the top 10 absolute permutational multivariate analysis of variance (PERMANOVA) coefficients for genera (A) and phylotypes (here OTUs) (B) in each enterotype.

[0088] Figure 2 - Correlation between enterotype and metabolite levels and virus frequency after allogeneic hematopoietic stem cell transplantation: Dot plots summarize the enrichment factors (EFs) and p-values of enriched sub-pathways for metabolite levels detected differently from feces (A) and plasma (B). Enrichment factors were calculated using the overrepresentation method, and P-values were calculated using the hypergeometric test.

[0089] Figure 3 - Principal coordinate analysis of virus reads and virus cluster composition: Principal coordinate analysis using the UniFrac distance matrix from bacterial phages was performed only on reads. Samples were clustered using hierarchical Kmeans. Permutational multivariate analysis of variance (PERMANOVA) was used to calculate the driving species.

[0090] Figure 4 - Correlations among bacterial taxa, metabolic pathways, and viruses after allogeneic hematopoietic stem cell transplantation: (A) The summarized correlation network illustrates the correlations among metabolic pathways, virus species, and gut-type top driver phylotypes. (B) The network illustrates the correlations among metabolic pathways, virus species, and gut-type top driver phylotypes associated with relapse or complete remission. For visualization purposes, the corresponding species names are used to describe the phylotypes. Staph: Staphylococcus; Lact: Lactococcus, Mycobact: Mycobacterium.

[0091] Figure 5 - Virus genera associated with gut-type top driver phylotypes: The checkerboard plot shows the number of species associated with the phylotype and belonging to the same genus.

[0092] Figure 6 - Gut types according to randomization and 12-month relapse: The bar plot shows the sample frequencies of complete response or relapse at 12 months according to gut type.

[0093] Figure 7 - Multinomial regression model assessing the association between gut type and clinical variables.

[0094] The multivariable model includes variables related to gut type. Gray dots and lines show the statistically significant coefficients. The coefficients are related to gut type 1 and the groups not mentioned in each variable. GVHD: graft-versus-host disease, CsA: cyclosporine, MMF: mycophenolate mofetil, MTX: methotrexate, HSC: hematopoietic stem cell.

[0095] Figure 8 - Specific gastrointestinal microbiota associated with azithromycin intake and post-transplant relapse

[0096] (A) The dot plot depicts principal coordinate analysis (PCoA) calculated from the Bray-Curtis distance matrix. Squares show the centroid of each group. The bar plot represents the top 15 phylotypes (PERMANOVA coefficients) driving the differences between groups. The panel compares patients who relapsed at 12 months and patients with a complete response.

[0097] (B) The dot plot shows the PERMANOVA coefficients of the phylotypes found among the top 15 coefficients in relapse / complete response and azithromycin / placebo gut types.

[0098] (C) Forest plot showing the hazard ratio and 95% confidence interval for recurrence according to the Fine and Gray competing risks model, where death unrelated to recurrence is the competing risk. After calculating the mean OTU abundance for each patient, the analysis was performed using the phylotypes shown in panel C. For visualization purposes, only the important phylotypes are shown.

[0099] (D) Dynamic microbial signatures associated with 12-month recurrence. Violin plots with 95% CIs and regression curves show the microbial signatures in samples associated with recurrence and complete response.

[0100] (E) Bar plot depicting the taxonomic contributions to the microbial signatures.

[0101] (F) Correlation analysis between phylotypes associated with recurrence. Spearman coefficients and p-values adjusted using the Benjamini-Hochberg method are shown.

[0102] Figure 9 - Parallel evolution of bacterial phylotypes associated with recurrence or complete remission and their associated bacteriophages

[0103] (A) Dot plot with 95% confidence intervals and regression line showing the frequency of phylotypes associated with recurrence or complete remission during the first few months after allogeneic hematopoietic stem cell transplantation (HSCT).

[0104] (B) Abundance of Bacteroides fragilis-associated bacteriophages in patients who relapsed after Allo-HSCT

[0105] Figure 10 - Gastrointestinal microbiota is associated with plasma metabolites and T cell status.

[0106] (A) Heatmap depicting subsets of T cell status associated with enterotypes. Here, the percentage of status clusters between phenotypic subsets was investigated. The Kruskal-Wallis test was performed, and p-values were adjusted for multiple comparisons using the false discovery rate method. Only clusters with an average frequency exceeding 0.5% were retained.

[0107] (B) Dot plot indicating statistically significant associations between recurrence-associated taxa, recurrence, and T cell subsets. Here, linear regression models were evaluated. Only status clusters including at least 1% of the phenotypic T cell subsets were retained for analysis. T cell subsets were defined as the dependent variables in the equation. Only the significantly adjusted p-values of the false discovery rate β coefficients are shown. Prevotella DJF_RP53 was not shown because it was not associated with T cell status.

[0108] PB: Peripheral blood, Tregs: T regulatory cells, EM: Effector memory, EMRA: Effector memory CD45RA+, CM: Central memory, SCM: Stem cell memory, Non-conv: Non-conventional, MAITs: Mucosa-associated invariant T cells. Example

[0109] I. Materials and methods

[0110] Cohort

[0111] Excreta samples were collected over time from patients included in the multicenter, randomized, double-blind, placebo-controlled phase 3 superiority trial ALLOZITHRO (NCT01959100), including 27 patients (n = 73 samples) from the azithromycin group and 28 patients (n = 75 samples) from the placebo group. The first and last samples were collected from one week before allo-HSCT to 6 weeks after allo-HSCT. Most samples were subjected to all omics evaluations (n = 92), including 43 placebo cohorts and 49 azithromycin cohorts, respectively. The characteristics of placebo cohort and azithromycin cohort patients were similar. The nutritional support and concomitant antibiotics used in both groups were also similar.

[0112] Bacteriome

[0113] Sample processing and 16S rRNA gene sequencing. Excreta samples from patients were snap-frozen in aliquots (150 mg) and homogenized and lysed using previously described mechanical (bead beating for 10 minutes) and chemical techniques (Doi: 10.1038 / nature08821), and total DNA was extracted using the MoBio Power Fecal DNA isolation kit according to the manufacturer's recommendations. The quality and quantity of DNA were evaluated using a spectrophotometer (Nanodrop 1000, Thermo Fisher Scientific). Sequencing was then performed on the GeT-PlaGe platform in Génopole (Toulouse Midi-Pyrénées, France) using Illumina MiSeq technology targeting the V3-V4 region of the 16S rRNA gene, and the V3-V4 region of the 16S rRNA gene had the following primers: V3fwd (forward) - TACGGRAGGCAGCAG where R is A or G, and V4rev (reverse) - TACCAGGGTATCTAAT.

[0114] Phylotype identification process.The 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 trimming primers and barcodes, the 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 threshold of 97% similarity level. Samples with fewer than 500 reads after amplification were removed. The most abundant member in each OTU was selected as the representative sequence and assigned to different taxonomic levels using the RDP Naive Bayesian classifier and the RDP Seqmatch program (Cole et al. (2009) Nucleic Acids Res 37, D141–14). Estimates of phylotype richness and diversity were calculated using the number of OTUs, Shannon, and Simpson indices observed on a rarefied OTU table (n = 3,000 reads). An average of 14,677 reads per sample were obtained (range 3,056 to 24,545).

[0115] Virome

[0116] DNA and RNA virome analysis using shotgun next-generation sequencing Analysis. Excreta samples (solid phase) were resuspended and diluted (50%) in phosphate-buffered saline (PBS) and then centrifuged at 2,500 g for 20 min. To enrich viral particles by reducing host background, the fecal supernatant was filtered through a 0.45 μm filter (Corning Costar Spin-X centrifuge tube filter), and an aliquot of 315 μl of the filtrate was pretreated by incubation with the following different nucleases for 30 min at 37 °C prior to extraction: TURBO DNAse (Invitrogen, Carlsbad, California); Baseline-ZERO DNase (Ambion, Foster City, California); Benzonase (NEB); RNAse A (Promega). Total nucleic acids were extracted using NucliSENS easyMAG (Biomerieux) according to the manufacturer's protocol. For DNA library preparation, 25 μL of the extract was used. According to the manufacturer's instructions, use The Microbiome DNA Enrichment Kit (NEB) performs methylation host DNA depletion. The DNA is then purified using Zymo DNA Clean (Zymo) and eluted in 7.5 μL of sterile water. A DNA library is prepared using the Nextera XT Library Preparation Kit (Illumina). For the preparation of the RNA library, the Trio RNA Kit (TECAN) is used according to the manufacturer's instructions. The libraries are sequenced on an Illumina HiSeq X (16 channels) using 150 / 150-bp paired-end sequencing.

[0117] Species identification process. The raw reads are cleaned using TRIMMOMATIC (Bolger, A.M., Lohse, M., and Usadel, B. (2014) Bioinformatics 30, 2114–2120). Duplicate reads are removed using Dedupe (Gregg, F., and Eder, D. (2022). Dedupe. https: / / github.com / dedupeio / dedupe). Taxonomic assignments are made using Kraken2 against viral, bacterial, and human Refseq databases (Wood, D.E., Lu, J., and Langmead, B. (2019) Genome Biol 20, 257). The Kraken viral-assigned reads are verified using Blastn on the Refseq viral database. Reads with inconsistent assignments between the Kraken and Blast methods are removed. Samples with fewer than 5.10 6 reads are excluded. Data with fewer than 0.5 reads per million (RPM) are assimilated to 0. Variables are filtered according to the average RPM in the seven negative controls (Miller et al. (2019) Genome Res. 29, 831–842). The DNA and RNA read databases are then combined to obtain the final count database.

[0118] Excretory metabolomics

[0119] Sample processing. Samples are processed and analyzed by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS / MS) using Metabolon, Inc. (Durham, USA). Sample collection, quality control, and metabolite identification and quantification are performed as previously described (Michonneau, D. (2019). Nature Communications, 10, 5695).

[0120] Pathway identification. To assign metabolic pathways, the list of identified metabolites was manually compared with metabolic compartments, the 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).

[0121] Metabolite data preprocessing. Uncharacterized metabolites were excluded from the analysis. The quantification of metabolites was normalized to the dry weight of the extracted excreted material. Missing values (metabolites below the quantification threshold) were imputed with 50% of the minimum value of the corresponding metabolite, and metabolites with more than 50% missing values were excluded. For drug-related metabolites, missing values were imputed with 1% of the minimum value, and no metabolites were excluded.

[0122] Data and statistical analysis

[0123] Enterotype definition. The `vegan` package was used to calculate dissimilarity matrices for bacterial and viral data using the Bray-Curtis and UniFrac methods, respectively (Oksanen et al. R. vegan: Community Ecology Package. R package version 2.6-2. https: / / CRAN.R-project.org / package=vegan). Principal coordinates analysis (PCoA) was used for dimensionality reduction of the dissimilarity matrices. Sample clustering was performed using the package `factoextra`, by hierarchical Kmeans (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). To identify the major phylotypes in the clusters, we used permutational multivariate analysis of variance (PERMANOVA) with 999 permutations.

[0124] Dynamic microbiome characteristics.The "coda4microbiome" package is used to identify metavariables that can summarize the characteristics of dynamic microbiomes (Calle, M. L., & Susin, A. (2022). Identification of Dynamic Microbial Signatures in Longitudinal Studies (Bioinformatics) 10.1101 / 2022.04.25.489415).

[0125] Correlation analysis. Using the non-parametric Spearman method, a correlation matrix was calculated for each variable from the three omics datasets. To keep the relevant relationships, only those with an absolute rho coefficient greater than 0.3 and statistical significance (false discovery rate-adjusted p-value < 0.05) were retained.

[0126] Correspondence analysis. Correspondence analysis was performed using the `FactoMineR` package (Lê, S., Josse, J., & Husson, F. (2008). J. Stat. Soft. 25.) for all antibiotics used in the allo-HSCT procedure.

[0127] Metabolomics analysis. By Pathway enrichment was evaluated using enrichment factors calculated by the over-representation analysis method. A hypergeometric distribution was used for the enrichment d statistical test (Michonneau, 2019).

[0128] Network analysis. Networks were constructed from the correlation matrix. Each node represents a variable, and the edges depict significant correlations. In the metabolic enrichment pathway analysis, the edges depict the connections between the pathways and the OTUs. For visualization purposes, the Fruchterman-Reingold algorithm was used.

[0129] Network module analysis. Modules were defined after applying Louvain clustering to construct the network. Principal component analysis (PCA) was used to calculate the contribution and loading of each variable to the module. The variables included in a module were included in the PCA. Then, the loading was calculated using the variable coordinates on the first PCA axis. Then, for each sample, the module variable was calculated by summing the individual variables weighted by the loading.

[0130] Survival analysis.The Fine and Gray method was used to calculate the incidence of recurrence using a competing risks model. The starting point was the day of graft infusion (D0), death was considered a competing risk event, and recurrence was the event. In the case of multiple samples from the same patient, the mean was applied.

[0131] Statistical test. Considering that the variable distribution was non-Gaussian, two-sided, non-parametric tests were employed. To study the enterotypes related to fecal and plasma metabolites, viruses, and T cell subsets, the non-parametric Kruskall-Wallis test was used. Frequency comparisons were performed using the chi-2 or Fisher test. The Benjamini-Hochberg method was used to correct the P-values for multiple tests using the false discovery rate method. A multinomial regression model was established to test the multivariate association between enterotypes and clinical variables. A linear regression model was established to test the multivariate association between modules and clinical variables. To evaluate the germline types related to T cell subsets, linear regression was used: the mean germline type frequencies related to T cell subsets were evaluated.

[0132] Software

[0133] The computational environment can be reproduced using the files "manifest.scm" and "channels.scm" in the "guixconfig" directory with a git repository of GNU Guix (Guix.GNU.org) (Vallet, N., Michonneau, D., and Tournier, S. (2022). Toward practical transparent verifiable and long-term reproducible research using Guix. Sci Data 9, 597).

[0134] II. Results

[0135] The characteristics of the gastrointestinal microbiota after transplantation are four enterotypes

[0136] First, the gastrointestinal microbiota after transplantation has been characterized. The microbial load remained stable over time. The α-diversity, Simpson, and Shannon indices measured by the number of OTUs decreased during the surgery, mainly within the first two weeks after transplantation.

[0137] Next, it was evaluated whether the samples would cluster into enterotypes. Using k-means clustering, four clusters of samples were found. These clusters were related to α-diversity: cluster 2 showed the highest diversity, followed by clusters 1, 3, and 4. Each cluster was characterized by specific bacterial genera ( Figure 1A ) and germline types ( Figure 1B)Abundance. Enterotype 1 is characterized by a higher proportion of Clostridium sp and a lower proportion of Bacteroides vulgatus. Enterotype 2 is characterized by higher proportions of Faecalibacterium prausnitzii, Bacteroides vulgatus, Fusobacterium necrophorum, and Bacteroides caccae, and lower proportions of Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Bacteroides fragilis, and Clostridium sp. Enterotype 3 is mainly driven by Bacteroides vulgatus, while Enterotype 4 is enriched in Enterococcus faecalis, Enterobacter aerogenes, and Bacteroides vulgatus (Figure 1).

[0138] The gastrointestinal bacteriome after transplantation is associated with specific metabolic pathways and bacteriophage groups

[0139] To explore the functions of the microbiome, gastrointestinal metabolites were analyzed and 925 known excreted metabolites were detected. Among these metabolites, 99 were significantly associated with the four enterotype distributions (Table 1).

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] Table 1

[0146] Enrichment analysis used normalized values from 0 to 1 (above 0.6) to define enriched metabolites, revealing specific gastrointestinal metabolite profiles among the enterotypes ( Figure 2A ), which were also associated with specific plasma metabolomic profiles ( Figure 2B ). All enterotypes except Enterotype 4 were associated with secondary bile acid metabolism. Enterotype 4 showed enrichment in primary bile acid and lysophospholipid metabolism. Enterotype 1 was enriched in lysophospholipid and secondary bile acid metabolism. Enterotypes 2 and 3 were similar, characterized by enrichment in metabolites involved in secondary bile acid metabolism and metabolites of pyrimidine metabolism (Figure 2).

[0147] Bacteriophage species and excreted metabolites are specifically associated with phylotype-enriched in enterotypes

[0148] Then, to investigate how the gut virome affects gut microbiota homeostasis after allo-HSCT, the viral species in the fecal samples were studied. Individual viruses were not associated with bacterial enterotypes. To evaluate whether the virome was associated with bacterial enterotypes, samples were clustered with all viral species or bacteriophage species using the k-means method. The clusters from all viral reads (Figure 3, left panel) were mainly driven by bacteriophage-restricted reads (Figure 3, right panel). These viral clusters were not associated with enterotypes.

[0149] To investigate which phylotypes drive metabolic features and whether these features might be affected by the gut virome, a correlation analysis was performed on the top 13 gut-type-driving phylotypes, 99 metabolites associated with gut types, and all viral species. This allowed the identification of 270 statistically significant associations, defined by an absolute Spearman's rho higher than 0.3 and an adjusted p-value lower than 0.05. These associations were represented as an association network, and Louvain clustering was applied to identify modules of associated variables. Next, the metabolites associated with phylotypes were summarized as enriched metabolic pathways ( Figure 4A ). Bacteroides uniformis (module 11), Enterobacter aerogenes, and Prevotella sp. DJF_RP53 (module 1) all shared secondary bile acid enrichment. Sterol pathway enrichment was shared by Clostridium sp. (module 10) and Faecalibacterium prausnitzii (module 5). Among the 13 modules, the phylotypes associated with Fusobacterium necrophorum (module 2) were associated with 18 viruses, including 14 (78%) bacteriophages. Three modules were driven by bacteria associated with relapse or complete remission and were characterized by specific associations of Bacteroides fragilis, Prevotella sp. DJF_RP53, and Bacteroides sp. DJF_B097 with metabolic pathways and bacteriophages ( Figure 4B ).

[0150] When bacteriophages were studied according to bacteriophage genera, only Pepyhexaviruses were generally associated with phylotypes related to Prevotella sp. DJF_RP53 and Enterobacter aerogenes. Unclassified Siphoviridae shared correlations with phylotypes related to Bacteroides sp. DJF-B097, Enterococcus faecalis, and Fusobacterium necrophorum ( Figure 5 ). Among eukaryotic host viruses, species of picobirnaviruses, Otarinepicobirnavirus, were associated with phylotypes related to Faecalibacterium prausnitzii (module 5).

[0151] Enterotypes are associated with azithromycin and subsequent recurrence

[0152] Evaluate whether clinical data and outcomes are associated with enterotypes. For some patients, belonging to a particular enterotype fluctuates over time. Among 38 patients with at least 2 samples, 22 (58%) had a change in enterotype during the allo-HSCT procedure. Compared with patients with a stable enterotype, patients with an enterotype change during transplantation were associated with relapse (complete remission at 12 months, n = 17 / 22, 77% vs. n = 6 / 16, 38%, p = 0.020). Changes in enterotype were not affected by clinical variables or the type of antibiotics used. Since allo-HSCT, enterotype has no longer been affected by time.

[0153] Next, evaluate whether enterotypes and multi-omics modules are associated with clinical variables, particularly with azithromycin intake and relapse. The ALLOZITHRO treatment group (azithromycin or placebo) was associated with enterotype (p = 0.037). Enterotypes 3 and 4 were evenly distributed among azithromycin and placebo samples, while enterotype 1 was associated with 23 (70%) samples in the azithromycin group and 17 (68%) samples in the placebo group were associated with enterotype 2 (p = 0.02). The relapse of potential malignant diseases within 12 months after transplantation was associated with enterotype distribution (p = 0.026). Enterotype 2 was mainly associated with relapse of complete remission (95.5%), while enterotypes 1, 3, and 4 were associated with 10 (32%, p = 0.017), 12 (38%, p = 0.008), and 10 (42%, p = 0.005) samples from relapsed patients ( Figure 6 ). Among other clinical variables, the type of donor (p = 0.046), stem cell source (p = 0.044), and GVHD prophylaxis (p = 0.038) were also associated with enterotype. Acute and chronic GVHD were not associated with enterotype.

[0154] To avoid potential confounding factor inferences, a multivariate multinomial regression model using clinical variables associated with enterotype as covariates was performed. Relapse and azithromycin intake remained associated with enterotype ( Figure 7 ). The type of nutritional support was not associated with enterotype (p = 0.092).

[0155] Multi-omics modules are specifically associated with azithromycin intake and recurrence

[0156] Since the intake of azithromycin (or placebo) and complete remission (or hematological relapse) were associated with enterotype, these parameters were further explored for their association in multi-omics modules. This univariate analysis showed that the azithromycin group was associated with lower module 5 and higher module 7 derived variables. Relapse and complete remission were not associated with the modules.

[0157] Multivariate analysis using variables associated with the enteric type as covariates (i) confirmed the association of modules 5 and 7 with azithromycin, and (ii) revealed a significant association of module 12 with azithromycin intake. Module 5 includes Faecalibacterium prausnitzii, otariine picobirnavirus, sterols, primary and secondary bile acids, and fatty acid metabolites. Module 7 is driven by Bacteroides stercoris. The latter module is enriched for primary bile acid metabolites and coffee-derived metabolites. Module 12 includes Enterococcus faecalis, which is negatively correlated with Lactococcus phage ul36.

[0158] In addition, module 9 was associated with recurrence in the multivariate analysis. Module 9 is centered around Bacteroides fragilis and is associated with Lactobacillus phage Lrm1, while Lactococcus phages D4412 and D4410 are negatively correlated with two metabolites from the dicarboxylic acid and branched-chain amino acid (BCAA) pathways.

[0159] Azithromycin and recurrence share gastrointestinal microbiota alterations

[0160] Next, supervised analysis was employed to better characterize the gut microbiota composition associated with azithromycin and recurrence. Complete remission and recurrence were associated with specific microbiota (PERMANOVA p = 0.003). The main features of the microbiota associated with recurrence were phylotypes related to Bacteroides fragilis, Escherichia coli, and Enterobacter aerogenes, while remission was associated with OTUs related to Bacteroides uniformis, Bacteroides sp. DJF_B097, Bacteroides vulgatus, Bacteroides massiliensis, and Enterococcus faecalis ( Figure 8A ).

[0161] The PERMANOVA coefficients for the random group (placebo or azithromycin) and disease state (recurrence or complete remission) were crossed to highlight which phylotypes were associated with these two variables (Figure 8B). Consistent with the higher recurrence risk in the azithromycin group in the ALLOZITHRO trial, 10 out of 13 phylotypes (77%) were associated with recurrence and azithromycin or placebo and complete remission. In brief, among the 10 phylotypes, close relatives of Bacteroides fragilis, Bacteroides sp. 'Smarlab BioMol-2301151', and Bacteroides eggerthii were associated with recurrence and azithromycin, while Bacteroides vulgatus, Bacteroides sp. DJF_B097, Enterococcus faecalis, Prevotella sp. DJF_RP53, Bacteroides stercoris, Bacteroides oralis, and Bacteroides sp. CCUG 39913 were associated with placebo and complete remission.

[0162] After considering the time-dependent nature of this outcome and death as a competing risk, the Fine and Gray model was computed to evaluate which germline types were associated with recurrence. The mean value was applied to the germline type frequencies of patients with multiple samples. The results showed that Bacteroides 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 germline types were associated with a lower risk of recurrence: Prevotella sp. DJF_RP53 (HR = 0.90, 95CI: 0.85 - 0.94, p < 0.001), Bacteroides sp. DJF_B097 (HR = 0.69, 95CI: 0.57 - 0.82, p < 0.001) (Figure 8C).

[0163] Time-integrated analysis allowed the identification of dynamic microbial signatures associated with recurrence (p = 0.0003). Germline type-driven signatures associated with Bacteroides stercoris, Bacteroides uniformis, Faecalibacterium prausnitzii, Enterococcus faecalis, Bacteroides sp. CCUG 39913, and Bacteroides fragilis trended towards recurrence, while Prevotella sp. DJF_RP53, Enterobacter aerogenes, Bacteroides vulgatus, Bacteroides sp. DJF_B097, Eggerthella sp., and Bacteroides sp. Smarlab BioMol-2301151 were associated with complete response ( Figure 8D to 8F). The frequency profiles of germline types associated with Bacteroides sp. DJF_B097 and Prevotella sp. DJF_RP53 over time were lower in recurrent patients. In contrast, the profile of Bacteroides fragilis was consistent with higher frequencies in recurrent patients. None of the germline types were correlated with each other, indicating that their trajectories were independent (Figure 8F and Figure 9A ). Finally, the parallel evolution of bacteriophages associated with Bacteroides fragilis over time was investigated, and it was observed that the trajectories of Lactobacillus phage Lrm1 and Lactococcus phage D4410 were opposite when compared to the abundance of Bacteroides fragilis, indicating that these bacteriophages could be used to target Bacteroides fragilis ( Figure 9B ).

[0164] In summary, these results suggest that the intake of azithromycin affects the gastrointestinal microbiota. Bacteroides sp. DJF_B097 and Prevotella sp. DJF_RP53 are associated with complete response, while Bacteroides fragilis is associated with a higher risk of recurrence.

[0165] Enterotypes and bacterial taxa associated with recurrence are related to peripheral blood T cell subsets

[0166] The enterotypes are also associated with the frequencies of peripheral blood T cell subsets. Enterotypes 2 and 3 are associated with clusters of molecules expressed in association with T cell activation or cytotoxic activity, including 2B4, KLRG1, and granzyme B. The mucosal associated invariant T cell (MAIT) subset is also associated with enterotype 2. Enterotype 4 is associated with TIGIT+ T cells and the Eomes+ T-bet+ subset, while enterotype 1 is associated with co-inhibitory molecule expression, which includes ICOS, TIGIT, PD-1, CTLA-4, and enterotype 1 is also associated with TOX expression in CD4+ Th1 cells ( Figure 10A ). This suggests that both relapse-associated enterotypes 1 and 4 are associated with T cell exhaustion in peripheral blood. Finally, we revealed a significant association between Bacteroides fragilis (a taxon associated with a higher relapse risk) and exhausted T cells that co-express TIGIT, PD-1, and TOX in central memory CD4+ Th1 and Th2 cells and CD8+ cells. Conversely, the phylotype (Bacteroides DJF_B097) associated with a lower relapse risk is associated with KLRG1+ 2B4+ activated effector memory CD4+ Th0 cells and TIGIT+ central memory CD4+ Th1 cells ( Figure 10B ).

[0167] III. Conclusions

[0168] In a randomized clinical trial, the administration of early azithromycin during allo-HSCT unexpectedly increased the risk of relapse of hematological malignancies (Bergeron et al. (2017) JAMA 318, 557). In this article, by using unsupervised and targeted methods, the effects of azithromycin treatment on the gastrointestinal microbiota leading to relapse were revealed.

[0169] Specific differences in the gastrointestinal microbiota of patients treated with azithromycin or placebo and patients with relapse or complete remission were characterized. Among the overlapping phylotypes associated with azithromycin, placebo, relapse, and complete response samples, close relatives of fecal Bacteroides (Bacteroides DJF_B097) and fecal Prevotella (Prevotella DJF_RP53) were higher in placebo samples and were associated with complete remission. The content of Bacteroides fragilis in azithromycin samples was higher and was significantly associated with a higher relapse risk. Using unsupervised multivariate analysis and supervised methods, a higher relapse risk with Bacteroides fragilis was found. Longitudinal studies of the abundances of three phylotypes showed that the phylotype frequency curve of azithromycin intake covered the curve of relapse patients, while the phylotype frequency curve of placebo covered the curve of complete remission patients. Bacteroides fragilis may inhibit the anti-tumor response by promoting regulatory pathways.

[0170] Metabolic pathways enriched with germline types associated with recurrence or remission are (i) phospholipid and lysophospholipid metabolites in patients with lower Bacteroides 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 for Prevotella DJF_RP53, and (iii) lower BCAA in patients with higher levels of Bacteroides fragilis, which can be explained by their importance for galactosylceramide biosynthesis (Oh et al. (2021) Nature 600, 302–307).

[0171] Sequence Listing

[0172] denovo9506 of SEQ.ID.N°1

[0173] ACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGGCGCTAGCCTGAACCAGCCAAGTAGCGTG

[0174] AAGGATGAAGGCTCTATGGGTCGTAAACTTCTTTTATATAAGAATAAAGTGCAGTATGTATACT

[0175] GTTTTGTATGTATTATATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGA

[0176] GGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGACTGGTAAGTCAGTT

[0177] GTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGTCAGTCTTGAGTACAGTAGAGG

[0178] TGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAG

[0179] GCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAGAT

[0180] ACCCTGGTA

[0181] denovo3073 of SEQ.ID.N°2

[0182] ACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGT

[0183] GAAGGATGACTGCCCTATGGGTTGTAAACTTCTTTTATACGGGAATAAAGTTAGCCACGTGTGG

[0184] CTTTTTGTATGTACCGTATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGG

[0185] AGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGT

[0186] TGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGA

[0187] GGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGA

[0188] AGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGTATCAAACAGGATTAG

[0189] ATACCCTGGTA

[0190] denovo9260 of SEQ.ID.N°3

[0191] ACGGGAGGCAGCAGTGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGT

[0192] GCAGGAAGACGGCCCTATGGGTTGTAAACTGCTTTTATAAGGGAATAAAGTGAGAGTCGTGAC

[0193] TCTTTTTGCATGTACCTTATGAATAAGGACCGGCTAATTCCGTGCCAGCAGCCGCGGTAATACG

[0194] GAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGT

[0195] GTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACA

[0196] AAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCG

[0197] AAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGGATTA

[0198] GATACCCTGGTA

[0199] Primer:

[0200] V3fwd - TACGGRAGGCAGCAG, where R is A or G SEQ ID N°4

[0201] V4rev - TACCAGGGTATCTAAT, SEQ ID N°5

Claims

1. A method for predicting the response of an individual to allogeneic hematopoietic stem cell transplantation (allo-HSCT), the method comprising the following steps: (i) determining the gastrointestinal microbiota phylotypes in an intestinal 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 with denovo9506 of SEQ.ID.N°1, and optionally determining the relative abundance of at least one of the OTUs comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity with denovo3073 of SEQ.ID.N°2 and / or denovo9260 of SEQ.ID.N°3; wherein an individual having a gastrointestinal microbiota enriched in the OTUs comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity with denovo9506 of SEQ.ID.N°1, and optionally depleted in at least one of the OTUs comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity with denovo3073 of SEQ.ID.N°2 and / or denovo9260 of SEQ.ID.N°3 has a higher risk of relapse.

2. The method for predicting the response of an individual to allogeneic hematopoietic stem cell transplantation (allo-HSCT) according to claim 1, the method comprising the following steps: (i) determining the gastrointestinal microbiota phylotypes in an intestinal 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 with denovo9506 of SEQ.ID.N°1; wherein an individual having a gastrointestinal microbiota enriched in the OTU has a higher risk of relapse.

3. The method for predicting the response of an individual to allogeneic hematopoietic stem cell transplantation (allo-HSCT) according to claim 1, the method comprising the following steps: (i) determining the gastrointestinal microbiota phylotypes in an intestinal sample of the individual after allo-HSCT; (ii) determining the relative abundance of at least one OTU comprising nucleotide fragments of sequences having at least 97%, preferably 98%, 99% or 100% identity with denovo3073 of SEQ.ID.N°2 and / or denovo9260 of SEQ.ID.N°3; wherein an individual having a gastrointestinal microbiota enriched in at least one of the OTUs or 2 OTUs is a good responder to allo-HSCT.

4. A bacteriophage that directly or indirectly targets Bacteroides fragilis, which is used to prevent cancer recurrence in patients who have received allogeneic hematopoietic stem cell transplantation (allo-HSCT).

5. The phage directly or indirectly targeting Bacteroides fragilis as used according to claim 4, wherein, The phages are selected from the group consisting of: lytic phages VA7, MTK, and UZ-1, bacteriophage B56-3, bacteriophage B40-8, bacteriophage vB_BfrS_23, Lactococcus phage D4410, Lactococcus phage D4412, Lactobacillus phage Lrm1, Streptococcus phage Dp-1, Rhodococcus virus Poco6, alpha papillomavirus 10, and Lactococcus phage BK5-T.

6. A pharmaceutical composition comprising at least one phage that directly or indirectly targets Bacteroides fragilis, for preventing cancer recurrence in a patient who has received an allogeneic hematopoietic stem cell transplantation (allo-HSCT).

7. The pharmaceutical composition according to claim 6, which uses at least one phage directly or indirectly targeting Bacteroides fragilis, wherein, The pharmaceutical composition comprises two or more phages that directly or indirectly target Bacteroides fragilis.

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

9. The pharmaceutical composition according to claim 8, which uses at least one phage directly or indirectly targeting Bacteroides fragilis, wherein, The pharmaceutical composition is administered orally and further comprises a proton pump inhibitor.

10. A probiotic composition, the probiotic composition comprising one or more isolated bacterial strains of species selected from the group consisting of Bacteroides stercoris and related phylotypes of the genus Bacteroides DJF_B097, and / or Prevotella stercorea and related phylotypes of the genus Prevotella DJF_RP53, the probiotic composition for preventing cancer recurrence in a patient who has received an allogeneic hematopoietic stem cell transplantation (allo-HSCT).

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

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