Gut microbiota signature response to fecal microbiota transplantation and uses thereof in the assessment and treatment of cancer patients undergoing immunotherapy

Abstract

Immune checkpoint inhibitors (ICI) have transformed the treatment landscape across multiple solid tumors. The microbiome and immune shift, along with the clinical impact of fecal microbiota transplantation (FMT) with (ICI therapies, remain largely unknown. The present application discloses methods and uses of specific bacterial species for assessing the response of cancer patients to FMT and ICI therapies, and for treating cancer patients.

Description

[0001] GUT MICROBIOTA SIGNATURE RESPONSE TO FECAL MICROBIOTA TRANSPLANTATION AND USES THEREOF IN THE ASSESSMENT AND TREATMENT OF CANCER PATIENTS UNDERGOING IMMUNOTHERAPY CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present application claims the benefit of U.S. Provisional Application No. 63 / 790,137 filed on April 17, 2025, and U.S. Provisional Application No. 63 / 846,436 filed on July 18, 2025, which are incorporated herein by reference in their entirety.

[0003] TECHNICAL FIELD

[0004] The present disclosure generally relates to the field on oncology, and more particularly to the assessment and treatment of cancer patients undergoing immunotherapies such as checkpoint inhibitor therapies.

[0005] BACKGROUND ART

[0006] Immune checkpoint inhibitors (ICI) have transformed the treatment landscape across multiple solid tumors, including melanoma1-4, non-small cell lung cancer (NSCLC)5-9, and renal cell carcinoma (RCC)10-12. The development of dual checkpoint blockade and combination regimens incorporating chemotherapy or targeted therapies to ICI has further improved survival outcomes in these patient populations. Despite these advances, a substantial proportion of patients exhibit primary or acquired resistance13, and treatment is frequently complicated by immune-related adverse events (irAEs), especially in the setting of combination therapy1415. These unresolved challenges highlight the need for innovative strategies capable of safely enhancing immunotherapy efficacy across tumor types.

[0007] Over the past decade, the gut microbiota has emerged as a key modulator of response to ICI16-20. Multiple translational and clinical studies have identified distinct microbial signatures associated with improved outcomes21 22. In contrast, gut microbiome disruption, also known as dysbiosis, whether related to cancer23or antibiotic use24, has consistently been linked to resistance to ICI25. These findings have catalyzed the development of microbiota-centered interventions aimed at restoring gut microbiome homeostasis or eubiosis, and improving immunotherapy outcomes26.

[0008] Among these strategies, fecal microbiota transplantation (FMT) (particularly from healthy donors, and not autologous FMT)27, a strategy first approved for the treatment of recurrent or refractory Clostridium difficile infection28-31, has provided compelling proof-of-concept. Early phase trials demonstrated that FMT from ICI responders was safe and let to encouraging response rates in patients with melanoma refractory to anti-PD-13233, further validated in signalfinding phase II trials from healthy donor volunteers in the first-line setting in melanoma andNSCLC34-37. More recently, the first randomized controlled trial (RCT) in RCC (the TACITO trial)38showed that FMT significantly improved progression-free survival compared with placebo. Ongoing randomized studies in melanoma, including PICASSO (NCT04988841) and ME.17 / Canbiome2 (NCT06623461), are expected to further define the clinical impact of FMT against standard-of-care ICI regimens.

[0009] However, important questions surrounding the clinical utility, deployment, and mechanism surrounding FMT in oncology remain unanswered. In the model of C. difficile, one important determinant of FMT success relies on efficacious engraftment of bacterial strains from the donor — also termed donor engraftment39. A meta-analysis including several FMT trials in the context of C. difficile, inflammatory bowel disease, and multidrug resistant infections, suggested that the total number of engrafted bacteria from the donors was a determinant of FMT success4041. Conversely, early FMT studies in immuno-oncology were not able to demonstrate that donor strain engraftment was responsible for FMT-mediated benefit, as both responders and nonresponders achieved effective strain-level engraftment from donors37. Despite these advances, the patient-, donor-, clinical- and microbiome-level factors that shape response to FMT remain incompletely understood. In parallel, heterogeneity in trial design, particularly with respect to preparative regimens such as polyethylene glycol and / or antibiotic exposure, further complicates cross-study comparisons and the interpretation of both clinical and microbiological outcomes. These knowledge gaps highlight the need for a comprehensive and integrative appraisal of the existing clinical and metagenomic evidence surrounding the clinical development of FMT in immuno-oncology. A cohesive synthesis of existing data may also help to identify the key ecological and clinical principles that should guide patient selection, trial design, and the development of next-generation microbiome-based interventions.

[0010] Trials have shown that fecal microbiota transplantation (FMT) plus immune checkpoint inhibitors (ICI) could improve aPD-1 response in melanoma. The microbiome and immune shift, along with the clinical impact of FMT with immune checkpoint therapies such as aPD-1 in nonsmall cell lung cancer (NSCLC) and aPD-1 +aCTLA-4 (dual ICI) in melanoma, is unknown.

[0011] The present description refers to a number of documents, the content of which is herein incorporated by reference in their entirety.

[0012] SUMMARY OF THE INVENTION

[0013] In various aspects and embodiments, the present disclosure provides the following items 1 to 113:

[0014] 1. A method for improving the antitumor response in a melanoma cancer patient treated with an immune checkpoint inhibitor (ICI) therapy, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp.

[0015] 2. The method of item 1 , wherein the at least one of the following bacterial species comprises Faecalibacterium prausnitzii.

[0016] 3. The method of item 1 or 2, wherein the at least one of the following bacterial species comprises Eubacterium rectale.

[0017] 4. The method of any one of items 1 to 3, wherein the at least one of the following bacterial species comprises Bacteroidota FGB6568 GGB1627 SGB2227.

[0018] 5. The method of any one of items 1 to 4, wherein the at least one of the following bacterial species comprises Candidatus Melainabacteria FGB2107 GGB6020 SGB8653.

[0019] 6. The method of any one of items 1 to 5, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9634 SGB15093.

[0020] 7. The method of any one of items 1 to 6, wherein the at least one of the following bacterial species comprises Firmicutes FGB1776 GGB4684 SGB6478.

[0021] 8. The method of any one of items 1 to 7, wherein the at least one of the following bacterial species comprises Ligilactobacillus salivarius.

[0022] 9. The method of any one of items 1 to 8, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

[0023] 10. The method of any one of items 1 to 9, wherein the at least one of the following bacterial species comprises Blautia caecimuris.

[0024] 11. The method of any one of items 1 to 10, wherein the at least one of the following bacterial species comprises Segatella copri.

[0025] 12. The method of any one of items 1 to 11 , wherein the at least one of the following bacterial species comprises Holdemanella porci.

[0026] 13. The method of any one of items 1 to 12, wherein the at least one of the following bacterial species comprises Butyricimonas faecihominis.

[0027] 14. The method of any one of items 1 to 13, wherein the at least one of the following bacterial species comprises Oscilibacter spp.

[0028] 15. A method for improving the antitumor response in a lung cancer patient treated with an immune checkpoint inhibitor (ICI) therapy, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB 15028,Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 39, Lachnospiraceae bacterium OFO96, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368.

[0029] 16. The method of item 15, wherein the at least one of the following bacterial species comprises Faecalibacterium prausnitzii.

[0030] 17. The method of item 15 or 16, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

[0031] 18. The method of any one of items 15 to 17, wherein the at least one of the following bacterial species comprises Gemmiger formicilis.

[0032] 19. The method of any one of items 15 to 18, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

[0033] 20. The method of any one of items 15 to 19, wherein the at least one of the following bacterial species comprises Faecalibacterium sp. CLA AA H233.

[0034] 21. The method of any one of items 15 to 20, wherein the at least one of the following bacterial species comprises Akkermansia muciniphila.

[0035] 22. The method of any one of items 15 to 21 , wherein the at least one of the following bacterial species comprises Ruminococcus sp. AF41 9.

[0036] 23. The method of any one of items 15 to 22, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3828 SGB5197.

[0037] 24. The method of any one of items 15 to 23, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9602 SGB15028.

[0038] 25. The method of any one of items 15 to 24, wherein the at least one of the following bacterial species comprises Firmicutes FGB1226 GGB58485 SGB80143.

[0039] 26. The method of any one of items 15 to 25, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15052.

[0040] 27. The method of any one of items 15 to 26, wherein the at least one of the following bacterial species comprises Firmicutes FGB3048 GGB9561 SGB14972.

[0041] 28. The method of any one of items 15 to 27, wherein the at least one of the following bacterial species comprises Colidextribacter sp. 210702 DFI 39.

[0042] 29. The method of any one of items 15 to 28, wherein the at least one of the following bacterial species comprises Lachnospiraceae bacterium OFO96.

[0043] 30. The method of any one of items 15 to 29, wherein the at least one of the following bacterial species comprises Anaerosacchariphilus sp. NSJ 68.

[0044] 31. The method of any one of items 15 to 30, wherein the at least one of the following bacterial species comprises Jutongia hominis.32. The method of any one of items 15 to 31 , wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15051.

[0045] 33. The method of any one of items 15 to 32, wherein the at least one of the following bacterial species comprises Roseburia sp. BX1005.

[0046] 34. The method of any one of items 15 to 33, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3588 SGB4808.

[0047] 35. The method of any one of items 15 to 34, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9766 SGB15383.

[0048] 36. The method of any one of items 15 to 35, wherein the at least one of the following bacterial species comprises Bacteroides finegoldii.

[0049] 37. The method of any one of items 15 to 36, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

[0050] 38. The method of any one of items 15 to 37, wherein the lung cancer is non-small cell lung carcinoma (NSCLC).

[0051] 39. The method of any one of items 1 to 38, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

[0052] 40. The method of any one of items 1 to 39, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor. 41. The method of item 40, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

[0053] 42. The method of any one of items 1 to 41, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

[0054] 43. The method of item 42, wherein the immune checkpoint inhibitor comprises ipilimumab.

[0055] 44. The method of any one of items 40 to 43, wherein (a) the immune checkpoint inhibitor comprises a combination of a PD-1 or PD-L1 inhibitor and a CTLA-4 inhibitor, and wherein the composition does not comprise Segatella copri and / or Holdemanella porci, or (b) the composition comprises Segatella copri and / or Holdemanella porci and the immune checkpoint inhibitor comprises a PD-1 or PD-L1 inhibitor as the sole immune checkpoint inhibitor.

[0056] 45. A composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp for use in improving the antitumor response in a melanoma cancer patient treated with immune checkpoint inhibitor (ICI) therapy.46. The composition for use according to item 45, wherein the at least one bacterial species comprises Faecalibacterium prausnitzii.

[0057] 47. The composition for use according to item 45 or 46, wherein the at least one of the following bacterial species comprises Eubacterium rectale.

[0058] 48. The composition for use according to any one of items 45 to 47, wherein the at least one of the following bacterial species comprises Bacteroidota FGB6568 GGB1627 SGB2227.

[0059] 49. The composition for use according to any one of items 45 to 48, wherein the at least one of the following bacterial species comprises Candidatus Melainabacteria FGB2107 GGB6020 SGB8653.

[0060] 50. The composition for use according to any one of items 45 to 49, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9634 SGB15093.

[0061] 51. The composition for use according to any one of items 45 to 50, wherein the at least one of the following bacterial species comprises Firmicutes FGB1776 GGB4684 SGB6478.

[0062] 52. The composition for use according to any one of items 45 to 51 , wherein the at least one of the following bacterial species comprises Ligilactobacillus salivarius.

[0063] 53. The composition for use according to any one of items 45 to 52, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

[0064] 54. The composition for use according to any one of items 45 to 53, wherein the at least one of the following bacterial species comprises Blautia caecimuris.

[0065] 55. The composition for use according to any one of items 45 to 54, wherein the at least one of the following bacterial species comprises Segatella copri.

[0066] 56. The composition for use according to any one of items 45 to 55, wherein the at least one of the following bacterial species comprises Holdemanella porci.

[0067] 57. The composition for use according to any one of items 45 to 56, wherein the at least one of the following bacterial species comprises Butyricimonas faecihominis.

[0068] 58. The composition for use according to any one of items 45 to 57, wherein the at least one of the following bacterial species comprises Oscilibacter spp.

[0069] 59. A composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB 15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 3 9, Lachnospiraceae bacterium OFO9 6, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / orFirmicutes FGB38642 GGB9758 SGB15368 for use in improving the antitumor response in a lung cancer patient treated with immune checkpoint inhibitor (ICI) therapy.

[0070] 60. The composition for use according to item 59, wherein the at least one of the following bacterial species comprises Faecalibacterium prausnitzii.

[0071] 61. The composition for use according to item 59 or 60, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

[0072] 62. The composition for use according to any one of items 59 to 61 , wherein the at least one of the following bacterial species comprises Gemmiger formicilis.

[0073] 63. The composition for use according to any one of items 59 to 62, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368. 64. The composition for use according to any one of items 59 to 63, wherein the at least one of the following bacterial species comprises Faecalibacterium sp. CLA AA H233.

[0074] 65. The composition for use according to any one of items 59 to 64, wherein the at least one of the following bacterial species comprises Akkermansia muciniphila.

[0075] 66. The composition for use according to any one of items 59 to 65, wherein the at least one of the following bacterial species comprises Ruminococcus sp. AF41 9.

[0076] 67. The composition for use according to any one of items 59 to 66, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3828 SGB5197.

[0077] 68. The composition for use according to any one of items 59 to 67, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9602 SGB15028.

[0078] 69. The composition for use according to any one of items 59 to 68, wherein the at least one of the following bacterial species comprises Firmicutes FGB1226 GGB58485 SGB80143.

[0079] 70. The composition for use according to any one of items 59 to 69, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15052.

[0080] 71. The composition for use according to any one of items 59 to 70, wherein the at least one of the following bacterial species comprises Firmicutes FGB3048 GGB9561 SGB14972.

[0081] 72. The composition for use according to any one of items 59 to 71 , wherein the at least one of the following bacterial species comprises Colidextribacter sp. 210702 DFI 39.

[0082] 73. The composition for use according to any one of items 59 to 72, wherein the at least one of the following bacterial species comprises Lachnospiraceae bacterium OFO96.

[0083] 74. The composition for use according to any one of items 59 to 73, wherein the at least one of the following bacterial species comprises Anaerosacchariphilus sp. NSJ 68.

[0084] 75. The composition for use according to any one of items 59 to 74, wherein the at least one of the following bacterial species comprises Jutongia hominis.

[0085] 76. The composition for use according to any one of items 59 to 75, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15051.77. The composition for use according to any one of items 59 to 76, wherein the at least one of the following bacterial species comprises Roseburia sp. BX1005.

[0086] 78. The composition for use according to any one of items 59 to 77, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3588 SGB4808.

[0087] 79. The composition for use according to any one of items 59 to 78, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9766 SGB15383.

[0088] 80. The composition for use according to any one of items 59 to 79, wherein the at least one of the following bacterial species comprises Bacteroides finegoldii.

[0089] 81. The composition for use according to any one of items 59 to 80, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

[0090] 82. The composition for use according to any one of items 59 to 81 , wherein the lung cancer is non-small cell lung carcinoma (NSCLC).

[0091] 83. The composition for use according to any one of items 59 to 82, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

[0092] 84. The composition for use according to any one of items 59 to 83, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor.

[0093] 85. The composition for use according to item 84, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

[0094] 86. The composition for use according to any one of items 59 to 85, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

[0095] 87. The composition for use according to item 86, wherein the immune checkpoint inhibitor comprises ipilimumab.

[0096] 88. The composition for use according to any one of items 84 to 87, wherein (a) the immune checkpoint inhibitor comprises a combination of a PD-1 or PD-L1 inhibitor and a CTLA-4 inhibitor, and wherein the composition does not comprise Segatella copri and / or Holdemanella porci, or (b) the composition comprises Segatella copri and / or Holdemanella porci and the immune checkpoint inhibitor comprises a PD-1 or PD-L1 inhibitor as the sole immune checkpoint inhibitor.

[0097] 89. A method for assessing the response of a melanoma cancer patient treated with a combination therapy comprising an immune checkpoint inhibitor (I Cl) therapy and fecal microbiota transplantation (FMT), the method comprising detecting bacteria species present in a stool sample from the patient after the FMT, wherein the presence of increased levels of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatellacopri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp. relative to reference levels is indicative that the patient is a responder to the combination therapy, and wherein the presence of increased levels of at least one of the following bacterial species: Firmicutes FGB4806 GGB51647 SGB4348, Coprococcus eutactus, Oscillospiraceae bacterium CLA AA H250, Akkermansia muciniphila, Frisingicoccus SGB4674, Streptococcus vestibularis, and / or Bactericides nordi relative to reference levels is indicative that the patient is a nonresponder to the combination therapy.

[0098] 90. A method for assessing the response of a lung cancer patient treated with a combination therapy comprising an immune checkpoint inhibitor (ICI) therapy and fecal microbiota transplantation (FMT), the method comprising detecting bacterial species present in a stool sample from the patient after the FMT, wherein the presence of increased levels of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB 15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 39, Lachnospiraceae bacterium OFO96, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368 relative to reference levels is indicative that the patient is a responder to the combination therapy, and wherein the presence of increased levels of at least one of the following bacterial species: Megamonas funiformis, Megasphaera SGB5856, Firmicutes FGB1354 GGB3304 SGB4367, Proteobacteria FGB2404 GGB6612 SGB9346, Escherichia coll, Bactericides stercoris, Phocaeicola massiliensis, Streptococcus salivarius, Bacteroides ovatus, Bacteroides faecis, Streptococcus vestibularis, Bactericides nordi and / or Fusobacterium nucleatum relative to reference levels is indicative that the patient is a non-responderto the combination therapy.

[0099] 91. The method of item 90, wherein the lung cancer is non-small cell lung carcinoma (NSCLC).

[0100] 92. The method of any one of items 89 to 91 , wherein the step of detecting bacterial species is performed about 2 weeks to about 2 months after the FMT.

[0101] 93. The method of item 92, wherein the step of detecting bacterial species is performed about 1 month after the FMT.

[0102] 94. The method of any one of items 89 to 93, wherein the step of detecting bacterial species is performed by metagenomic sequencing.95. The method of any one of items 89 to 94, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

[0103] 96. The method of any one of items 89 to 95, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor. 97. The method of item 96, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

[0104] 98. The method of any one of items 89 to 97, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

[0105] 99. The method of item 98, wherein the immune checkpoint inhibitor comprises ipilimumab.

[0106] 100. A method for determining whether a cancer patient is a responder to a combination therapy comprising (i) an immune checkpoint inhibitor (ICI) therapy and (ii) fecal microbiota transplantation (FMT), a live biotherapeutic product (LBP) or a microbiome therapy, the method comprising: (a) measuring the number of bacterial species in a stool sample from the patient prior to initiation of the combination therapy; (b) measuring the number of bacterial species in a stool sample from the patient after initiation of the combination therapy; (c) comparing the loss of bacterial species after initiation of the combination therapy to a control; and (d) determining if the patient is a responder to the combination therapy based on the comparison.

[0107] 101. The method of item 100, wherein the control is the loss of bacterial species measured in a stool sample from a non-responder patient or a population of non-responder patients, and wherein (i) a higher loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a responder to the combination therapy; and (ii) a similar or lower loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a non-responder to the combination therapy.

[0108] 102. The method of item 100, wherein the control is the loss of bacterial species measured in a stool sample from a responder patient or a population of responder patients, and wherein (i) a similar or higher loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a responder to the combination therapy; and (ii) a lower loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a non-responder to the combination therapy.

[0109] 103. A method for determining whether a candidate live biotherapeutic product (LBP) or microbiome therapy is effective for the treatment of cancer when administered with an immune checkpoint inhibitor (ICI) therapy, the method comprising (a) measuring the number of bacterial species in a stool sample from a subject prior to administration of the candidate LBP or microbiome therapy; (b) administering the LBP or microbiome therapy and the ICI therapy to the subject; (c) measuring the number of bacterial species in a stool sample from the subject after the administration of the candidate LBP or microbiome therapy and the ICI therapy; (d) comparingthe loss of bacterial species after the administration of the LBP or microbiome therapy and the ICI therapy to a control; and (e) determining if the candidate live biotherapeutic product (LBP) or microbiome therapy is effective for the treatment of cancer based on the comparison.

[0110] 104. The method of item 103, wherein the control is the loss of bacterial species measured in a stool sample from a subject or a population of subject administered with an effective LBP or microbiome therapy, and wherein (i) a similar or higher loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is effective; and (ii) a lower loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is not effective.

[0111] 105. The method of item 103, wherein the control is the loss of bacterial species measured in a stool sample from a subject or a population of subject administered with an ineffective LBP or microbiome therapy, and wherein (i) a higher loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is effective; and (ii) a similar or lower loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is not effective.

[0112] 106. The method of any one of items 100 to 105, wherein the bacterial species that are lost comprises bacteria of the Enterocloster, Lachnoclostridium and / or Enterocloster genus.

[0113] 107. The method of any one of items 100 to 106, wherein the bacterial species that are lost comprises Enterocloster citroniae, Enterocloster bolteae, Enterocloster aldenensis, Clostridium innocuum, Clostridium saudiense, Clostridium spiroforme, Clostridium leptum, Clostridium sp Marseille P3244 SGB29302, Ruminococcus gnavus, Dialister invisus, Lachnoclostridium An 138 SGB4774, Lachnoclostridium An131 SGB4775) and / or Sellimonas intestinalis, for example Enterocloster citroniae, E. bolteae, Clostridium innocuum, C. saudiense, C. spiroforme, Ruminococcus gnavus, Dialister invisus, and / or Sellimonas intestinalis.

[0114] 108. The method of any one of items 100 to 107, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

[0115] 109. The method of any one of items 100 to 108, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor. 110. The method of claim 109, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

[0116] 111. The method of any one of items 100 to 108, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

[0117] 112. The method of item 111, wherein the immune checkpoint inhibitor comprises ipilimumab.

[0118] 113. The method of any one of items 100 to 112, wherein the cancer is gastrointestinal cancer (e.g., gastric cancer, colorectal cancer), lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer (renal cell carcinoma), liver cancer (hepatocellular carcinoma), esophageal cancer(esophageal squamous cell carcinoma) or melanoma, for example lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer, or melanoma.

[0119] Other objects, advantages and features of the present invention will become more apparent upon reading of the following non-restrictive description of specific embodiments thereof, given by way of example only with reference to the accompanying drawings.

[0120] BRIEF DESCRIPTION OF DRAWINGS

[0121] In the appended drawings:

[0122] FIG. 1 shows the trial design for the FMT-LUMINate clinical trial. S; stool sample. B; blood sample. PEG; PEG Bowel preparation. FMT; fecal microbiota transplantation. ICI; immune checkpoint inhibitors.

[0123] FIG. 2 shows that FMT improves the clinical activity of anti-PD-1 in NSCLC and anti-PD-1 / CTLA-4 in melanoma. Objective response rate per RECIST 1.1 of ICI in combination with FMT.

[0124] FIG.3 shows the results of a beta-diversity PCoA analysis demonstrating a shift between R and NR post-FMT in melanoma and NSCLC.

[0125] FIGs.4A-4D show that differential abundance using linear discriminant analysis (LEfSe) post-FMT in melanoma (FIGs. 4A-4B) and NSCLC (FIGs. 4C-4D) reveals specific bacteria enriched in responders versus non-responders.

[0126] FIGs. 5A-5B show differential abundance using linear discriminant analysis (LEfSe) in post-FMT samples in patients with melanoma who experienced severe irAE pre- relative to 1-month post-FMT.

[0127] FIG. 6 shows a flow cytometry analysis of human PBMCs in responder (R) melanoma patients post-FMT. TCM; T central memory cells. TEM; T effector memory cells.

[0128] FIG. 7A-7B show that surrogate markers of gut barrier integrity including human read analysis in the feces and LBP and sST2 in the plasma recapitulate patient phenotype in NSCLC (FIG. 7A) and melanoma (FIG. 7B). *p<0.05 **p<0.01.

[0129] FIGs 8A-J.show the association between loss of baseline bacteria and clinical efficacy.

[0130] FIG. 8A. Representative barplot of absolute number of species-level genome bins (SGBs) from one non-responder and FIG. 8B. one responder melanoma patient. FIG. 8C. Absolute lost SGBs in post-FMT samples according to response status for FMT-LUMINate NSCLC and melanoma (n=122 samples from n=32 patients) and for FIG. 8D. NSCLC (n=54 samples for n=15 patients) and melanoma subgroups (n=68 samples for n=17 patients), p-value computed by Wilcoxon test. Patients with matching metagenomics form the donors included in this analysis. FIG. 8E. Top 50 lost SGBs post-FMT in responders for NSCLC and melanoma. FIG. 8F. Pooled analysis of post-FMT samples according to response in FMT-LUMINate, MIMIC, Davar et al., Baruch et al (n=352 samples from n=69 patients), p-value computed by Wilcoxon test. FIG. 8G. Spearman rcorrelation between total SGBs loss, as determined by metagenomics and total species lost, as determined by qPCR (n=104 samples for n=28 patients). FIG. 8H. Culturomics analysis of four patients pre- and post-FMT and comparison of species engrafted from the donor (using the corresponding metagenomics donor sample) compared to species lost post-FMT, p-value computed by non-parametric Mann-Whitney test. FIG. 8I. Experimental setting: Post-FMT sample from R with NSCLC orally transferred in antibiotic SPF-treated mice. Two weeks later, MCA-205 sarcoma cells were inoculated and anti-PD-1 or isotype control was injected intraperitoneally every 3 days. As of day 3, mice also received oral gavage with bacterial cocktail or NaCI every 3 days. FIG. 8J. Tumor kinetics for n=5 mice per group. Mean + / - SEM are represented. SGB; species-level genome bins. 1w; 1 week. 1m; 1 month, 2m; 2 months. SPF; specific pathogen-free. ISO; iso-PD-1. aPD1; anti-PD-1. WT; wildtype. ATB; antibiotics. FMT; fecal microbiota transplantation. D; day. R; responder. NR; non-responder.

[0131] FIG. 9A. Absolute SGBs engrafted from the donor in non-responders compared to responders using shotgun metagenomic sequencing (n=122 samples for n=32 patients), p-value computed by Wilcoxon test. FIG.9B. Absolute the SGBs count over time in responders compared to non-responders; top plot represents in the sum of all the SGB categories. FIG. 9C. Bacterial biomass using qPCR analysis of the 16s rRNA gene according to response and timepoint (n=70 samples for n =24 patients), and FIG.9D. according to SGBs loss from baseline category obtained using the median value from the metagenomics analysis (n=38 samples for n=22 patients). Absolute lost SGBs in patients post-FMT according to FIG. 9E. donor and FIG. 9F. donor cluster. Absolute lost SGBs in non-responders compared to responders in FIG.9G. MIMIC (n=32 samples for n=12 patients), FIG. 9H. Baruch et al., (n=30 samples for n=10 patients) FIG. 9I. Davar et al. (n=168 samples for n=15 patients), p-values for FIG. 9E-H computd by Wilcoxon test. FIG. 9J.

[0132] Heatmap representation of metagenomics and qPCR with Kendall score (n=118 samples for n=31 patients). FIG. 9K. Species lost post-FMT as determined by qPCR between non-responders and responders, p-value calculated by Non-parametric Mann-Whitney test (n= 91 R and n=13 NR for n=28 patients). FIG. 9L. Culturomics analysis of the number of species isolated from n=4 patients at baseline (n=4 samples) compared to post-FMT (n=4 samples) FIG. 9M. culturomics analysis of the number of bacteria initially present in the patient’s baseline sample and no longer detected in the post-FMT sample. R; responder. NR; non-responder.

[0133] FIGs 10A-G show metabolic and immune profiling post-FMT according to clinical efficacy. FIG. 10A. Longitudinal plasma analysis of quinolinic acid and kynurenine metabolites measured by HP-LC, for each metabolite two representations are shown: smooth trajectories for responder (blue) and non-responder (red) groups overtime points, with 95% confidence intervals (shaded areas), p-values indicate statistical significance of each smooth term. Pointwise differences between groups (responder - non-responder) with 95% confidence intervals (gray shaded area). Pink horizontal segments indicate time periods where confidence intervals excludezero, representing significant between-group differences. Metabolite names are displayed above each panel. Data were fitted using Generalized Additive Mixed Models with Tweedie distribution and random subject intercepts (n=100 samples for n=35 patients, see Methods). FIG. 10B.

[0134] MetaCyc representation of the tryptophan pathway comparing baseline to post-FMT in NSCLC responders (n=62 samples from 15 patients) and melanoma responders (n=71 samples from n=15 patients), p-values computed by the wald test from DESeq2 analysis. Spearman r correlation of flow cytometry analysis from NSCLC and melanoma patient PBMCs post-FMT according to low vs. high median SGBs loss from baseline for FIG. 10C. Frequency of CD69+CD8+T cells and FIG. 10D. CD127|OWCD25h'shCD4+T regulatory cells. Spearman correlation of flow cytometry analysis from NSCLC and melanoma patient PBMCs post-FMT according to low vs. high plasma quinolinic acid for FIG. 10E. PD-1+CD8+T cells and FIG. 10F. PD-1+effector memory CD8+T cells and FIG. 10G. Effector memory T regulatory cells from (n=50 samples). TRPSYN-PWY; L-tryptophan biosynthesis. PWY-6629; superpathway of L-tryptophan biosynthesis. Abslog2FC; absolute log 2-fold change. Lo; low. Hi; high. R; responder. NR; nonresponder.

[0135] FIG. 11 A. Tryptophan metabolite according to SGBs loss calculated from the patient’s baseline (n=46 samples for n=23 patients). FIG. 11B. Olink proteomics assay UMAP representation in patients with high percentage of bacterial loss according to baseline, defined by the median, compared to low percentage of bacterial loss according to baseline. FIG. 11C.

[0136] Volcano plot of significantly enriched proteins post-FMT compared to baseline (n=35 samples for n=15 patients for patients with high loss and n=37 samples for n=16 patients for patients with low loss).

[0137] FIGs. 12A-C show the characteristics of included FMT trials in patients with solid tumors. Diagram depicting the study designs and clinical settings of the evaluated FMT trials in solid tumor oncology in combination with immune checkpoint inhibitors. Patient cohorts encompassed solid tumors (predominantly melanoma, renal cell carcinoma, and non-small cell lung cancer), with all patients receiving concurrent anti-PD-1 -based therapy as either monotherapy or in combination regimens. The FMT-Luminate NSCLC Cohort and the FMT-Luminate Melanoma Cohort corresponds to the cohorts studied in Example 1. NSCLC; non-small cell lung cancer. MSI; microsatellite unstable.

[0138] FIGs. 13A-E show the efficacy and toxicity outcomes of FMT in combination with ICI in patients with solid tumors. FIG. 13A: Distribution of the included FMT trials across different solid tumor types (Melanoma, renal cell carcinoma, non-small cell lung cancer (NSCLC), gastrointestinal, and microsatellite instability tumor). FIG. 13B: Forest plot shows the objective response rate (ORR) across the evaluated studies. FIG. 13C: Forest plot shows the proportion of patients experiencing grade 3 or higher immune-related adverse events (irAE). FIGs. 13D-E: Bar graphs comparing the proportion of response status (R vs. NR) stratified by patient demographicsand clinical characteristics, including sex (male / female), BMI (normal weight / overweight), age, and the occurrence of grade 3 or higher irAEs. Group differences were assessed using Fisher’s exact test. R; responder. NR; non-responder.

[0139] FIGs. 14A-F show post-FMT alterations in microbiome composition and longitudinal dynamics of strain engraftment and species elimination. FIG. 14A: Principal coordinate analysis (PCoA) shows the beta diversity among donor and pre-FMT / post-FMT samples stratified by response status (R vs. NR). Boxplots representing the distance to centroid for donor, pre-FMT, and post-FMT groups. FIG. 14B: The box plot compares the individual mean Bray-Curtis dissimilarity to donor distance between R and NR in post-FMT samples. Bray-Curtis dissimilarity to donor fold change over time. FIG. 14C: The number of shared strains over one time post-FMT in responders (R) and non-responders (NR) compared to baseline. FIG. 14D: The donor strain engraftment rate post-FMT in responders (R) and non-responders (NR) compared to baseline.

[0140] FIG. 14E: Number of eliminated SGBs post-FMT between R and NR groups. FIG. 14F: Number of eliminated SGBs assessed in the post-ICI control group (PRIMM dataset). SGB; species genome bin.

[0141] FIG. 15 depicts a heatmap showing the global elimination of the top 50 core gut bacterial taxa following FMT. The heatmap represents the dataset-specific elimination rates of the top 50 consistently eliminated taxa across independent cohorts. A taxon was longitudinally defined as "eliminated" if its relative abundance dropped to absolute zero in > 50% of a patient’s post-FMT follow-up points. Elimination rates and Iog2 fold-changes were exclusively calculated for patients who initially harbored the taxon at baseline (pre-FMT abundance > 0). Datasets lacking baseline (pre-FMT) detection of a given taxon are shaded in grey. Taxa marked with a triangle symbol (A) and diamond symbol (♦) indicate exact taxonomic matches with “resistance to ICI” or “dysbiosis” and “SIG122” species, respectively. Green bar denotes the overall global prevalence. The bar plot quantifies the severity of bacterial elimination, representing the median Iog2 fold-change at a 1-month sample to reflect the magnitude of ecological elimination.

[0142] FIGs. 16A-D show a multi-cohort differential abundance and microbiome diversity analysis. FIG. 16A: Boxplots comparing alpha diversity between responders (R) and non-responders (NR). FIG. 16B: Longitudinal assessment of alpha diversity from baseline (pre-FMT) across post-FMT follow-up timepoints. FIG. 16C: Post-FMT distance to centroid for individual trial cohorts. FIG. 16D: Heatmap representing differentially abundant taxa enriched in responders (UP in Responders) versus non-responders (UP in Non-responders). Significance was evaluated utilizing multiple statistical frameworks. To evaluate consistency and mitigate method-specific biases, a consensus approach was employed utilizing eight distinct differential abundance (DA) methods: DESeq2 (with poscounts and ZINB adjustment; with TMM normalization), limma-voom (with TMM and ZINB; with TMM), ANCOM-BC, and MaAsLin3. Standardized Mean Difference (SMD) was additionally calculated to quantify effect sizes. The heatmap visualizes taxasignificantly enriched in responders (orange) and non-responders (purple) across the evaluated DA tools and independent clinical cohorts. Left bar annotations indicate the prevalence and phylum-level classification of each identified taxon.

[0143] FIGs. 17A-E show the correlation of baseline diversity with strain sharing and elimination rates. FIG. 17A: Elimination dynamics of the post-ICI group in the TACITO dataset. FIG. 17B:

[0144] Strain sharing rate and elimination rate separated by baseline characteristics (age, sex, and BMI category). FIGs. 17C-E: Spearman correlation plots evaluating the relationship between baseline Shannon diversity and strain sharing / elimination rates, distinctively mapped for R and NR groups.

[0145] FIGs. 18A-C show microbiome diversity and strain dynamics stratified by disease type and therapy line. FIG. 18A: Baseline alpha diversity (Shannon index) across different tumor types (Melanoma, NSCLC, RCC) and lines of therapy (1st line vs. 2nd line). FIG. 18B: PERMANOVA and PCoA evaluating beta diversity at pre-FMT and post-FMT. FIG. 18C: The number of eliminated SGBs and shared strains post-FMT compared between R and NR. Samples are subgrouped by disease type and line of therapy.

[0146] DISCLOSURE OF INVENTION

[0147] The use of the terms "a" and "an" and "the" and similar referents in the context of describing the technology (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0148] The terms "comprising", "having", "including", and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to") unless otherwise noted.

[0149] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.

[0150] The use of any and all examples, or exemplary language (“e.g.”, "such as") provided herein, is intended merely to better illustrate embodiments of the claimed technology and does not pose a limitation on the scope unless otherwise claimed.

[0151] No language in the specification should be construed as indicating any non-claimed element as essential to the practice of embodiments of the claimed technology.

[0152] Herein, the term "about" has its ordinary meaning. The term “about” is used to indicate that a value includes an inherent variation of error for the device or the method being employed to determine the value, or encompass values close to the recited values, for example within 10% of the recited values (or range of values).

[0153] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it wereindividually recited herein. All subsets of values within the ranges are also incorporated into the specification as if they were individually recited herein.

[0154] Where features or aspects of the disclosure are described in terms of Markush groups or list of alternatives, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member, or subgroup of members, of the Markush group or list of alternatives.

[0155] Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in stem cell biology, cell culture, molecular genetics, immunology, immunohistochemistry, protein chemistry, and biochemistry).

[0156] Unless otherwise indicated, the recombinant protein, cell culture, and immunological techniques utilized in the present disclosure are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook etal., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T. A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D. M. Glover and B. D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F. M. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-lnterscience (1988, including all updates until present), Ed Harlow and David Lane (editors) Antibodies: A Laboratory Manual, Cold Spring Harbour Laboratory, (1988), and J. E. Coligan et al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates until present).

[0157] In the studies described herein, the present inventors have demonstrated significant enrichment and loss of several bacterial species post-FMT in cutaneous melanoma and NSCLC patients undergoing ICI therapy who responded to the therapy (responders) relative to nonresponders. Also, enrichment in Segatella copri and Holdemanella porci post-FMT was associated with grade 3 or higher immune-related toxicity in patients treated with the combination anti-PD-1 and anti-CTLA-4, but not in those treated with anti-PD-1 monotherapy.

[0158] Accordingly, the present disclosure provides a method for improving the antitumor response in a melanoma cancer patient treated with an immune checkpoint inhibitor (ICI) therapy, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp.The present disclosure also provides a method for treating melanoma in a patient, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp. In an embodiment, the method further comprises administering an effective amount of an immune checkpoint inhibitor (ICI) to the patient.

[0159] The present disclosure also provides a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp for use in improving the antitumor response in a melanoma cancer patient treated with immune checkpoint inhibitor (ICI) therapy.

[0160] The present disclosure also provides a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp for use in treating melanoma in a patient. In an embodiment, the composition is administered with an effective amount of an ICI.

[0161] The present disclosure also provides a combination therapy comprising (a) a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp, and (b) at least one immune checkpoint inhibitor. In an embodiment, the combination therapy is for the treatment of cancer, for example melanoma.

[0162] The present disclosure also provides a method for improving the antitumor response in a lung cancer patient treated with an immune checkpoint inhibitor (ICI) therapy, the method comprising administering to the patient an effective amount of a composition comprising bacteriaof at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB 15028, Oscillospiraceae GGB9616 SGB 15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 3 9, Lachnospiraceae bacterium OFO9 6, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051 , Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368.

[0163] The present disclosure also provides a method for treating lung cancer in a patient, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 3 9, Lachnospiraceae bacterium OFO9 6, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051 , Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368. In an embodiment, the method further comprises administering an effective amount of an immune checkpoint inhibitor (I Cl) to the patient.

[0164] The present disclosure also provides a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB 15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 39, Lachnospiraceae bacterium OFO96, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368 for use in improving the antitumor response in a lung cancer patient treated with immune checkpoint inhibitor (ICI) therapy.

[0165] The present disclosure also provides a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB 15368, Faecalibacteriumsp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 39, Lachnospiraceae bacterium OFO96, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368 for use in treating lung cancer in a patient. In an embodiment, the composition is administered with an effective amount of an ICI.

[0166] The present disclosure also provides a combination therapy comprising (a) a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB 15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 3 9, Lachnospiraceae bacterium OFO9 6, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368; and (b) at least one immune checkpoint inhibitor. In an embodiment, the combination therapy is for the treatment of cancer, for example lung cancer (e.g., NSCLC).

[0167] In an embodiment, the method comprises the administration of a plurality of bacterial species in the sample, e.g., at least 2, 3, 4, or 5 bacterial species. In an embodiment, the composition comprises bacteria of one of the above-noted bacterial species. In another embodiment, the composition comprises bacteria of at least 2 of the above-noted bacterial species. In an embodiment, the composition comprises one of the above-noted bacterial species. In another embodiment, the composition comprises at least 3 of the above-noted bacterial species. In another embodiment, the composition comprises at least 4 of the above-noted bacterial species. In another embodiment, the composition comprises at least 5 of the abovenoted bacterial species. In another embodiment, the composition comprises at least 6 of the above-noted bacterial species. In another embodiment, the composition comprises at least 7 of the above-noted bacterial species.

[0168] The term “immune checkpoint inhibitor” (ICI) or “immune checkpoint blocker” (ICB) as used herein refers to an agent that block or inhibit the activity of a negative regulator of the immune response. In an embodiment, the ICI blocks or inhibits the activity of T cells (e.g., CTLs and / or CD4 helper T cells) and / or of NK cells. Examples of such negative regulators of the immune response (i.e., immune checkpoint) include Adenosine A2A receptor (A2AR), B7-H3(CD276), B7-H4 (VTCN1), B and T Lymphocyte Attenuator (BTLA or CD272), Cytotoxic T-Lymphocyte-Associated protein 4 (CTLA-4, CD152), CD47 / SIRPa, Indoleamine 2,3-dioxygenase (IDO), Killer-cell Immunoglobulin-like Receptor (KIR), Lymphocyte Activation Gene-3 (LAG3), nicotinamide adenine dinucleotide phosphate NADPH oxidase isoform 2 (NOX2), poliovirus receptor-related immunoglobulin (PVRIG), carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), Programmed Death 1 (PD-1) receptor, PD-L1, PD-L2, T-cell Immunoglobulin domain and Mucin domain 3 (TIM-3), V-domain Ig suppressor of T cell activation (VISTA), and Sialic acid-binding immunoglobulin-type lectin 7 (SIGLEC7 or CD328) and SIGLEC9 (CD329). In an embodiment, the immune checkpoint inhibitor is an inhibitor of CTLA-4, PD-1 or PD-L1. Examples of immune checkpoint inhibitors includes anti-PD-1 antibodies / blockers (e.g., Tislelizumab, Penpulimab, Pidilizumab, Sintilimab, Toripalimab, Retifanlimab, Dostarlimab, Nivolumab, Cemiplimab, Pembrolizumab, Spartalizumab, Camrelizumab, JTX-4014, INCMGA00012 (MGA012), AMP-224, AMP-514), anti-PD-L1 antibodies / blockers (e.g., Durvalumab, Avelumab, Atezolizumab, KN035, CK-301 , AUNP12, CA-170, BMS-986189), anti-CTLA-4 antibodies (e.g., Tremelimumab, Ipilimumab), anti-LAG-3 antibodies (e.g., Relatlimab, LAG525 (IMP701), REGN3767 (R3767), Bl 754,091, tebotelimab (MGD013), eftilagimod alpha (IMP321), FS118), anti-TIM-3 antibodies (MBG453, Sym023, TSR-022), anti-B7-H3 / H4 antibodies (e.g., MGC018, FPA150), adenosine signaling pathway inhibitors including A2AR inhibitors (e.g., Inupadenant (EGS100850), Etrumadenant (AB928), Imaradenant (AZD4635), Ciforadenant, NIR178, CS3005, PBF-999, INCB106385, CPI-444), CD73 antagonists / anti-CD73 antibodies (e.g., Mupadolimab (CPI-006), Oleclumab (MEDI9447), Uliledlimab, AB680, BMS-986179, NZV930, AK119, SYM024, INCA00186, ORIC-533, IPH5301 , PSB-1248937), and CD39 antagonists (TTX-030, IPH5201, SRF617), anti-NKG2A antibodies (Monalizumab), anti-PVRIG (e.g., COM701), anti-CEACAM1 antibodies (e.g., CM24), and CD47 blockers / inhibitors (Evorpacept (ALX148), Hu5F9-G4 (5F9), TTI-662, RRx-001) (see, e.g., Marin-Acevedo et al., Next generation of immune checkpoint inhibitors and beyond, Journal of Hematology & Oncology, volume 14, Article number: 45 (2021); Xia et al., CD39 / CD73 / A2AR pathway and cancer immunotherapy, Molecular Cancer, volume 22, Article number: 44 (2023)). The chemical structures and sequences of the above-noted immune checkpoint inhibitors are incorporated herein by reference.

[0169] In an embodiment, the immune checkpoint inhibitor is an inhibitor of PD-1, such as an anti-PD-1 antibody. In an embodiment, the immune checkpoint inhibitor is an inhibitor of PD-L1, such as an anti-PD-L1 antibody. In an embodiment, the immune checkpoint inhibitor is an inhibitor of CTLA-4, such as an anti-CTLA-4 antibody. In an embodiment, a combination of ICIs is used, for example a combination of a CTLA-4 inhibitor and a PD-1 inhibitor (e.g., Ipilimumab with nivolumab), or a combination of a CTLA-4 inhibitor and a PD-L1 inhibitor (e.g., Durvalumab with tremelimumab).In an embodiment, the composition comprising bacteria is in the form of a live biotherapeutic product (LBP). A “live biotherapeutic product” or “LBP”, as used herein, refers to a biological product that contains live organisms (e.g., bacteria) and may be used for the prevention, treatment, or cure of a disease. The bacteria may be naturally occurring, recombinant, or clonally selected. The live biotherapeutic product may be dried and remain alive for an extended period of time (e.g. 1-2 years). The live biotherapeutic product may comprise a single bacterial species or strains, or a mixture of 2, 3, 4, 5 or more bacterial species or strains.

[0170] In an embodiment, the bacteria are coated with or encapsulated in a suitable (e.g., biocompatible) material or a mixture thereof, such as lipids, polymers, or proteins. The bacteria may be formulated in an enterically coated tablet or capsule (soft or hard gelatin capsule). Enteric capsules can be formed of any material(s) which permit the total or partial dissolution of the capsule when exposed to medium having a mildy acidic, neutral or basic pH. In embodiments, the enteric capsule can be formed partially or totally from fatty acids, waxes, shellac, plastics, plant fibers, enteric polymers or mixtures thereof. Enteric materials which can be employed (either to produce intrinsically enteric dosage forms, or in alternative embodiments, enteric coatings) include, but are not limited to methacrylate polymers, methyl acrylate-methacrylic acid copolymers, methacrylic acid-methyl methacrylate copolymers, polyvinyl acetate phthalate, shellac, sodium alginate, zein, dextrins, amylose starch and starch derivatives, and cellulose and cellulose derivatives including hydroxy propyl methyl cellulose phthalate, hydroxy propyl methyl cellulose acetate succinate, cellulose acetate succinate, cellulose acetate trimellitate, cellulose acetate phthalate, or mixtures thereof.

[0171] The composition may also include one or more pharmaceutically acceptable excipients. Exemplary pharmaceutically acceptable excipients for the purposes of pharmaceutical compositions disclosed herein include, but are not limited to, binders, disintegrants, superdisintegrants, lubricants, diluents, fillers, flavors, glidants, sorbents, solubilizers, chelating agents, emulsifiers, thickening agents, dispersants, stabilizers, suspending agents, adsorbents, granulating agents, preservatives, buffers, coloring agents and sweeteners or combinations thereof.

[0172] Examples of binders include microcrystalline cellulose, hydroxypropyl methylcellulose, carboxyvinyl polymer, polyvinylpyrrolidone, polyvinylpolypyrrolidone, carboxymethylcellulose calcium, carboxymethylcellulose sodium, ceratonia, chitosan, cottonseed oil, dextrates, dextrin, ethylcellulose, gelatin, glucose, glyceryl behenate, galactomannan polysaccharide, hydroxyethyl cellulose, hydroxyethylmethyl cellulose, hydroxypropyl cellulose, hypromellose, inulin, lactose, magnesium aluminum silicate, maltodextrin, methylcellulose, poloxamer, polycarbophil, polydextrose, polyethylene glycol, polyethylene oxide, polymethacrylates, sodium alginate, sorbitol, starch, sucrose, sunflower oil, vegetable oil, tocofersolan, zein, or combinations thereof.Examples of disintegrants include hydroxypropyl methylcellulose (HPMC), low substituted hydroxypropyl cellulose (L-HPC), croscarmellose sodium, sodium starch glycolate, lactose, magnesium aluminum silicate, methylcellulose, polacrilin potassium, sodium alginate, starch, or combinations thereof. Examples of a lubricant include stearic acid, sodium stearyl fumarate, glyceryl behenate, calcium stearate, glycerin monostearate, glyceryl palmitostearate, magnesium lauryl sulfate, mineral oil, palmitic acid, myristic acid, poloxamer, polyethylene glycol, sodium benzoate, sodium chloride, sodium lauryl sulfate, talc, zinc stearate, potassium benzoate, magnesium stearate or combinations thereof.

[0173] Examples of diluents include talc, ammonium alginate, calcium carbonate, calcium lactate, calcium phosphate, calcium silicate, calcium sulfate, cellulose, cellulose acetate, corn starch, dextrates, dextrin, dextrose, erythritol, ethylcellulose, fructose, fumaric acid, glyceryl palmitostearate, isomalt, kaolin, lactitol, lactose, magnesium carbonate, magnesium oxide, maltodextrin, maltose, mannitol, microcrystalline cellulose, polydextrose, polymethacrylates, simethicone, sodium alginate, sodium chloride, sorbitol, starch, sucrose, sulfobutylether b-cyclodextrin, tragacanth, trehalose, xylitol, or combinations thereof.

[0174] Various useful fillers or diluents include, but are not limited to calcium phosphate, dibasic anhydrous, calcium phosphate, dibasic dihydrate, calcium phosphate tribasic, calcium sulphate, cellulose powdered, silicified microcrystalline cellulose, cellulose acetate, compressible sugar, confectioner's sugar, dextrates, dextrin, dextrose, fructose, kaolin, lactitol, lactose, lactose monohydrate, magnesium carbonate, magnesium oxide, maltodextrin, maltose, mannitol, microcrystalline cellulose, polydextrose, simethicone, sodium alginate, sodium chloride, sorbitol, starch, pregelatinized starch, sucrose, trehalose and xylitol, or mixtures thereof.

[0175] Examples of suitable lubricants include, but are not limited to, magnesium stearate, calcium stearate, zinc stearate, stearic acid, talc, glyceryl behenate, polyethylene glycol, polyethylene oxide polymers, sodium lauryl sulfate, magnesium lauryl sulfate, sodium oleate, sodium stearyl fumarate, DL-leucine, colloidal silica, and others as known in the art. In some embodiments a lubricant is magnesium stearate.

[0176] Various useful glidants include, but are not limited to, tribasic calcium phosphate, calcium silicate, cellulose, powdered, colloidal silicon dioxide, magnesium silicate, magnesium trisilicate, starch and talc, or mixtures thereof.

[0177] Pharmaceutically acceptable surfactants include, but are limited to both non-ionic and ionic surfactants suitable for use in pharmaceutical dosage forms. Ionic surfactants may include one or more of anionic, cationic or zwitterionic surfactants. Various useful surfactants include, but are not limited to, sodium lauryl sulfate, monooleate, monolaurate, monopalmitate, monostearate or another ester of olyoxyethylene sorbitane, sodium dioctylsulfosuccinate (DOSS), lecithin, stearic alcohol, cetostearylic alcohol, cholesterol, polyoxyethylene ricin oil, polyoxyethylene fatty acid glycerides, and poloxamer.In certain embodiments, the bacteria are lyophilised. In some cases, the bacteria are lyophilised in a process in which the bacteria are not exposed to temperatures greater than about 100°C, greater than about 70°C, greater than about 50°C or greater than about 30°C.

[0178] The terms “treating”, or “treatment” refers to any indicia of success in the therapy or amelioration of an injury, disease, pathology or condition, including any objective or subjective parameter such as abatement; remission; diminishing of symptoms or making the injury, pathology or condition more tolerable to the patient; slowing in the rate of degeneration or decline; making the final point of degeneration less debilitating; improving a patient’s physical or mental well-being. The treatment or amelioration of symptoms can be based on objective or subjective parameters; including the results of a physical examination, neuropsychiatric exams, and / or a psychiatric evaluation. The term "treating" and conjugations thereof, may include prevention of an injury, pathology, condition, or disease. In embodiments, treating is preventing. In embodiments, treating does not include preventing.

[0179] “Treating” or “treatment” as used herein (and as well-understood in the art) also broadly includes any approach for obtaining beneficial or desired results in a subject’s condition, including clinical results. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of the extent of a disease, stabilizing ( i.e. , not worsening) the state of disease, prevention of a disease’s transmission or spread, delay or slowing of disease progression, amelioration or palliation of the disease state, diminishment of the reoccurrence of disease, and remission, whether partial or total and whether detectable or undetectable. In other words, "treatment" as used herein includes any cure, amelioration, or prevention of a disease. Treatment may prevent the disease from occurring; inhibit the disease’s spread; relieve the disease’s symptoms, fully or partially remove the disease’s underlying cause, shorten a disease’s duration, or do a combination of these things.

[0180] "Treating" and "treatment" as used herein include prophylactic treatment. Treatment methods include administering to a subject a therapeutically effective amount of an active agent. The administering step may consist of a single administration or may include a series of administrations. The length of the treatment period depends on a variety of factors, such as the severity of the condition, the age of the patient, the concentration of active agent, the activity of the compositions used in the treatment, or a combination thereof. It will also be appreciated that the effective dosage of an agent used for the treatment or prophylaxis may increase or decrease over the course of a particular treatment or prophylaxis regime. Changes in dosage may result and become apparent by standard diagnostic assays known in the art. In some instances, chronic administration may be required. For example, the compositions are administered to the subject in an amount and for a duration sufficient to treat the patient. In embodiments, the treating or treatment is no prophylactic treatment.The composition may comprise any suitable (pharmaceutically active or therapeutically effective) amount / dose of bacteria. In an embodiment, a pharmaceutically active or therapeutic effective dose of bacteria comprises at least about 105, 106, 107, 108, 109, 1O10, 1011, 1012, or 1013colony-forming unit (cfu). In another embodiment, a pharmaceutically active or therapeutic effective dose comprises at most about 105, 106, 107, 108, 109, 1O10, 1011, 1012, or 1013cfu. In a further aspect, a pharmacologically active therapeutic effective dose is from 108cfu to 1014cfu, from 109cfu to 1013cfu, from 1O10cfu to 1012cfu, from 109cfu to 1014cfu, from 109cfu to 1012cfu, from 109cfu to 1011cfu, from 109cfu to 1O10cfu, from 1O10cfu to 1014cfu, from 1O10cfu to 1013cfu, from 1011cfu to 1014cfu, from 1011cfu to 1013cfu, from 1012cfu to 1014cfu, or from 1013cfu to 1014cfu.

[0181] In another aspect, the present disclosure provides a method for assessing the response of a melanoma cancer patient treated with a combination therapy comprising an immune checkpoint inhibitor (ICI) therapy and fecal microbiota transplantation (FMT), the method comprising detecting bacteria species present in a stool sample from the patient after the FMT, wherein the presence of increased levels of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp. relative to reference levels is indicative that the patient is a responder to the combination therapy, and wherein the presence of increased levels of at least one of the following bacterial species: Firmicutes FGB4806 GGB51647 SGB4348, Coprococcus eutactus, Oscillospiraceae bacterium CLA AA H250, Akkermansia muciniphila, Frisingicoccus SGB4674, Streptococcus vestibularis, and / or Bactericides nordi relative to reference levels is indicative that the patient is a non-responder to the combination therapy.

[0182] In another aspect, the present disclosure provides a method for assessing the response of a lung cancer patient treated with a combination therapy comprising an immune checkpoint inhibitor (ICI) therapy and fecal microbiota transplantation (FMT), the method comprising detecting bacterial species present in a stool sample from the patient after the FMT, wherein the presence of increased levels of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 3 9, Lachnospiraceae bacterium OFO9 6, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis,Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368 relative to reference levels is indicative that the patient is a responder to the combination therapy, and wherein the presence of increased levels of at least one of the following bacterial species: Megamonas funiformis, Megasphaera SGB5856, Firmicutes FGB1354 GGB3304 SGB4367, Proteobacteria FGB2404 GGB6612 SGB9346, Escherichia coli, Bactericides stercoris, Phocaeicola massiliensis, Streptococcus salivarius, Bacteroides ovatus, Bacteroides faecis, Streptococcus vestibularis, Bactericides nordi and / or Fusobacterium nucleatum relative to reference levels is indicative that the patient is a nonresponder to the combination therapy.

[0183] The reference level may be a level corresponding to an average or median level calculated based of the levels measured in several reference or control subjects (e.g., a predetermined or established standard level). The reference level may be a pre-determined “cut-off’ value recognized in the art or established based on levels measured in one or a group of control subjects. The reference level may be adjusted or normalized for age, sex, race, or other parameters. It will also be understood that the reference levels according to the disclosure may be, in addition to predetermined levels or standards, levels measured in other samples (e.g. from healthy / normal subjects) tested in parallel with the experimental sample.

[0184] In an embodiment, the reference levels are levels measured in a corresponding stool sample from a healthy subject. In an embodiment, the reference levels are levels measured in a corresponding stool sample from a non-responder. In another embodiment, the reference levels are levels measured in a corresponding stool sample from a responder.

[0185] In another aspect, the present disclosure provides a method for determining whether a cancer patient (e.g., a solid cancer patient) is a responder to a combination therapy comprising (i) an immune checkpoint inhibitor (ICI) therapy and (ii) fecal microbiota transplantation (FMT), a live biotherapeutic product (LBP) or a microbiome therapy, the method comprising: (a) measuring the number of bacterial species in a stool sample from the patient prior to initiation of the combination therapy; (b) measuring the number of bacterial species in a stool sample from the patient after initiation of the combination therapy; (c) comparing the loss of bacterial species after initiation of the combination therapy to a control; and (d) determining if the patient is a responder to the combination therapy based on the comparison. In an embodiment, the control is the loss of bacterial species measured in a stool sample from a non-responder patient or a population of non-responder patients, and wherein (i) a higher loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a responder to the combination therapy; and (ii) a similar or lower loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a non-responder to the combination therapy. In another embodiment, the control is the loss of bacterial speciesmeasured in a stool sample from a responder patient or a population of responder patients, and wherein (i) a similar or higher loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a responder to the combination therapy; and (ii) a lower loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a non-responder to the combination therapy.

[0186] The present disclosure also provides a method for determining whether a candidate live biotherapeutic product (LBP) or microbiome therapy is effective for the treatment of cancer (e.g. a solid cancer) when administered with an immune checkpoint inhibitor (ICI) therapy, the method comprising (a) measuring the number of bacterial species in a stool sample from a subject prior to administration of the candidate LBP or microbiome therapy ; (b) administering the LBP or microbiome therapy and the ICI therapy to the subject; (c) measuring the number of bacterial species in a stool sample from the subject after the administration of the candidate LBP or microbiome therapy and the ICI therapy; (d) comparing the loss of bacterial species after the administration of the LBP or microbiome therapy and the ICI therapy to a control; and (e) determining if the candidate live biotherapeutic product (LBP) or microbiome therapy is effective for the treatment of cancer based on the comparison. In an embodiment, the control is the loss of bacterial species measured in a stool sample from a subject or a population of subject administered with an effective LBP or microbiome therapy, and wherein (i) a similar or higher loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is effective; and (ii) a lower loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is not effective. In another embodiment, the control is the loss of bacterial species measured in a stool sample from a subject or a population of subject administered with an ineffective LBP or microbiome therapy, and wherein (i) a higher loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is effective; and (ii) a similar or lower loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is not effective.

[0187] In some embodiments, the bacterial species that are lost comprise bacteria of one or more of the genus depicted in FIG. 15. In some embodiments, the bacterial species that are lost comprise one or more of the bacterial species depicted in FIG. 15. In embodiments, the bacterial species that are lost comprises bacteria of the Enterocloster, Lachnoclostridium, Ruminococcus, Dialister, Sellimonas and / or Enterocloster genus, for example bacteria of the Enterocloster, Lachnoclostridium, and / or Enterocloster genus. In further embodiments, the bacterial species that are lost comprise Enterocloster citroniae, Enterocloster bolteae, Enterocloster aldenensis, Clostridium innocuum, Clostridium saudiense, Clostridium spiroforme, Clostridium leptum,Clostridium sp Marseille P3244 SGB29302, Ruminococcus gnavus, Dialister invisus, Lachnoclostridium An138 SGB4774, Lachnoclostridium An131 SGB4775) and / or Sellimonas intestinalis, for example Enterocloster citroniae, E. bolteae, Clostridium innocuum, C. saudiense, C. spiroforme, Ruminococcus gnavus, Dialister invisus, and / or Sellimonas intestinalis.

[0188] In an embodiment, the loss of at least 2 bacterial species is measured / determined. In an embodiment, the loss of at least 3 bacterial species is measured / determined. In an embodiment, the loss of at least 3 bacterial species is measured / determined. In an embodiment, the loss of at least 4 bacterial species is measured / determined. In an embodiment, the loss of at least 5, 6, 7, 8, 9 or 10 bacterial species is measured / determined.

[0189] The assessment of the presence and level (amount) of the bacterial species in the sample may be performed using any known methods in the art, including molecular techniques such as polymerase chain reaction (PCR), 16S rRNA sequence, and whole-genome sequencing (WGS), immunological methods such as ELISA and flow cytometry, and mass spectrometry. Automated systems for the identification of bacteria are also commercially available (e.g., BD PHOENIX® and VITEK®). In an embodiment, the assessment of the presence and level (amount) of the bacterial species in the sample is performed by metagenomic sequencing (e.g., shotgun metagenomic sequencing).

[0190] In an embodiment, the stool sample is obtained about 1 weeks to about 3 months post-FMT. In an embodiment, the stool sample is obtained about 2 weeks to about 2 months post-FMT. In an embodiment, the stool sample is obtained about 3 weeks to about 6 weeks post-FMT. In an embodiment, the stool sample is obtained about 4 weeks (i.e., about 1 month) post-FMT.

[0191] In an embodiment, the immune checkpoint inhibitor is an inhibitor of PD-1, such as an anti-PD-1 antibody (e.g., pembrolizumab or nivolumab). In an embodiment, the immune checkpoint inhibitor is an inhibitor of PD-L1, such as an anti-PD-L1 antibody. In an embodiment, the immune checkpoint inhibitor is an inhibitor of CTLA-4, such as an anti-CTLA-4 antibody (e.g., ipilimumab). In an embodiment, a combination of ICIs is used, for example a combination of a CTLA-4 inhibitor and a PD-1 inhibitor (e.g., Ipilimumab with nivolumab), or a combination of a CTLA-4 inhibitor and a PD-L1 inhibitor (e.g., Durvalumab with tremelimumab).

[0192] In some embodiments, the cancer is a solid cancer, and more particularly gastrointestinal cancer (e.g., gastric cancer, colorectal cancer), lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer (renal cell carcinoma), liver cancer (hepatocellular carcinoma), esophageal cancer (esophageal squamous cell carcinoma) or melanoma, for example lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer, or melanoma.

[0193] MODE(S) FOR CARRYING OUT THE INVENTION

[0194] The present invention is illustrated in further details by the following non-limiting examples.Example 1: Clinical activity of FMT in combination with anti-PD-1 in patients with NSCLC and in combination with anti-PD-1 plus anti-CTLA-4 in patients with melanoma

[0195]

[0196] Overview. 20 patients (pts) with advanced NSCLC treated with first-line (1L) aPD-1 (pembrolizumab) and 20 with advanced melanoma treated with 1L dual anti-CTLA4 (ipilimumab) + anti-PD1 (nivolumab) ICI were recruited. FMT from 1 healthy donor (HD) was administered orally to each pt post bowel prep. 1 week later ICI was initiated (FIG. 1). A total of 11 different HD were used. The primary endpoint was objective response rate (ORR). The gut microbiome was profiled using shotgun metagenomics as described previously (Routy etal., Nature Medicine 2023). The taxonomic classification was conducted using the MetaPhlAn4 pipeline as described previously (Routy et al., Nature Medicine 2023). Metabolomic profiling was performed using HPLC. Olink proteomics (96 markers) on plasma and flow cytometry (40 markers) on PBMCs were assessed.

[0197] Study participants and clinical trial design. The FMT-LUMINate trial (NCT04951583) is a multicenter, open-label, phase II trial that included patients with advanced non-small cell lung cancer (NSCLC), cutaneous melanoma, and uveal melanoma who were being treated with first-line ICI therapy. Patients were >18 years of age, with histologically confirmed diagnosis of either NSCLC, cutaneous melanoma, or uveal melanoma and measurable disease as per RECIST 1.1 criteria. An ECOG score of 0-2 was required. Previous treatment with palliative surgery or radiation were allowed. For the melanoma cohort, previous adjuvant anti-PD-1 was allowed provided the last dose occurred >6 months prior to enrollment. For the melanoma cohort, previous treatment with BRAF / MEK inhibitors were allowed. Major exclusion criteria included previous treatment with anti-PD-1 in the NSCLC cohort, a history of autoimmune diseases, active bowel disease, the use of daily corticosteroids >10 mg of prednisone or equivalent, symptomatic brain metastases, or leptomeningeal disease. Patients were enrolled and treated at five academic centers in Canada: Centre Hopitalier de I’Universite de Montreal (CHUM; Quebec), the London Regional Cancer Program (LRCP; Ontario), Centre hospitalier de I’Universite de Quebec (CHUQ; Quebec), McGill University Health Centre (MUHC; Quebec), Lakeridge Health (Lakeridge; Ontario). The research ethics board at each institution approved the conduct of the trial in accordance with the current federal regulations to include the Canadian Food and Drug Regulations (C.05.001); the US Code of Federal Regulations (21CFR part 56); International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use Good Clinical Practice Guidelines (ICH GCP); and the Declaration of Helsinki.

[0198] Patient inclusion and exclusion criteria for enrollment. Inclusion criteria are summarized below:

[0199] Age 18 years or older;• Confirmed histological diagnosis of either: advanced cutaneous melanoma, unresectable or advanced uveal melanoma, or advanced non-small cell lung cancer (NSCLC);

[0200] • Stage IV or unresectable disease;

[0201] • No prior anti-PD1 treatment with the exception of those received adjuvant therapy (see point 5);

[0202] • For patients with newly diagnosed advance melanoma who relapsed after adjuvant immunotherapy, patients can be included in this study if they relapsed >6 months after their last dose of immunotherapy given in the adjuvant setting;

[0203] • For patients with NSCLC, tumor PD-L1 expression level > / = 50%

[0204] • Evaluable disease as per iRECIST or RECIST;

[0205] • ECOG performance status of 0-2;

[0206] • Ability to ingest capsules;

[0207] • Patients receiving systemic steroids at physiologic doses are permitted to enroll provided the dose not exceed 10 mg prednisone daily or equivalent permitted);

[0208] • Negative pregnancy test for women of child-bearing potential; and

[0209] • Highly effective contraception (any method above 97% success rate)for both male and female subjects throughout the study and for at least 60 days after last treatment administration if the risk of conception exists.

[0210] Exclusion criteria are summarized below:

[0211] • Pregnant or breastfeeding, or expecting to conceive or father children within the projected duration of the trial, starting with the pre-screening or screening visit through 120 days after the last dose of trial treatment.

[0212] • Current or recent [in the last 90 days] long-term exposure to high dose oral or IV corticosteroids

[0213] o Patients who require intermittent use of bronchodilators, local steroid injections, or short-term corticosteroids for any reason including but not limited to brain metastases treatment / prophylaxis are permitted to enroll at the discretion of the sponsor.

[0214] • Has a diagnosis of immunodeficiency (e.g. HIV, transplantation) or receiving systemic steroid therapy (>10mg prednisone daily or equivalent) or any other form of active immunosuppressive therapy.

[0215] • Presence of a chronic debilitating intestinal disease (e.g. Malabsorption, colonic tumor) • Use of probiotics during FMT. Probiotics must be discontinued a minimum of 24 hours before FMT administration and patients are not permitted to take probiotics during the course of immunotherapy treatment.• Use of antibiotics within 2 weeks of enrollment in the study.

[0216] • Presence of absolute contra-indications to FMT administration

[0217] o Toxic megacolon

[0218] o Severe dietary allergies (e.g. shellfish, nuts, seafood)

[0219] o Active inflammatory bowel disease

[0220] • Expected to require any other form of systemic or localized anti-neoplastic therapy while on study. Palliative radiation therapy is permitted at the discretion of the sponsor.

[0221] • In the last year, has a known history of a malignancy requiring anti-neoplastic treatment.

[0222] o This time requirement does not apply to patients who underwent successful definitive resection of basal or squamous cell carcinoma of the skin, superficial bladder cancer, in situ cancers including cervical cancer, breast cancer, melanoma, or other in situ cancers.

[0223] • Symptomatic (CNS) metastases

[0224] • Leptomeningeal involvement (leptomeningeal enhancement on MRI / CT imaging and / or positive CSF cytology).

[0225] • Has an uncontrolled autoimmune disease that requires active immunosuppressive agents;

[0226] o Patients with vitiligo, type I diabetes, well controlled hypothyroidism due to Hashimoto disease, resolved childhood asthma / atopy are not excluded.

[0227] • A history of (non-infectious) pneumonitis that required steroids or current pneumonitis.

[0228] • Has serious concomitant illnesses, such as: impaired cardiovascular function or clinically significant cardiovascular disease (uncontrolled congestive heart failure requiring treatment (NYHA grade > 3), uncontrolled hypertension, acute myocardial cardiac ischemia or unstable angina < 2 months prior to study entry, and severe cardiac arrhythmia), and active inflammatory bowel disorders.

[0229] • Has an active infection requiring systemic therapy.

[0230] • Patient has received a live vaccine within 4 weeks prior to the first dose of treatment o Note: Seasonal influenza vaccines for injection are generally inactivated flu vaccines and are allowed; however intranasal influenza vaccines (e.g., Flu-Mist®) are live attenuated vaccines, and are not allowed.

[0231] o Has known psychiatric or substance abuse disorders that would interfere with cooperation with the requirements of the trial.

[0232] Fecal microbiota transplantation. Patients underwent a standard bowel preparation with 4,000 ml PEG3350 liquid solution the night before a single FMT was performed via capsules that contained 80-100 g of feces from screened healthy donors. Feces collection and encapsulation were conducted under the supervision of the Department of Infectious Diseases at St Joseph’sHospital in London, Ontario. Each set of capsules contained the feces from a single donor. Major exclusion criteria for healthy donors included any known transmissible pathogen, history of medical illness, any history of major infection such as COVID-19, monkeypox, hepatitis and recent travel. Patients were required to consume 36-40 capsules under supervision and within 2 h of defrosting the capsules, followed by a 30-min period of observation. If a patient required antibiotics for an active infection throughout the study protocol, a “salvage” FMT was performed within 14 days of the last dose of antibiotics. In the event that the treating oncologist or investigator deemed that the salvage FMT would interfere with patient safety (i.e., antibiotics administered during a severe AE (SAE) period). Patients were not required to undergo a second bowel preparation in the event of salvage FMT. In the event of prophylactic antibiotics (i.e., Pneumocystis jirovecii prophylaxis), salvage FMT was not required.

[0233] Capsule preparation. FMT capsules were prepared according to an established protocol as described in previous studies (Routy et al., Nature Medicine 2023). Briefly, feces donations (80-100 g) were processed separately without pooling by mixing in 0.9% normal saline and glycerol and were filtered using a stomacher bag. The filtrate was centrifuged and the final sediment (containing 1013colony-forming units per ml of microbes) was mixed to incorporate residual liquid to allow pipetting into capsules as previously described (Routy et al., Nature Medicine 2023).

[0234] Immune checkpoint inhibitor therapy and assessments. Immune checkpoint inhibitor therapy was publicly funded. For the NSCLC cohort, ICI was pembrolizumab 2mg / kg every 3 weeks as per the standard-of-care. For the melanoma cohort, ICI was ipilimumab 3mg / kg IV plus nivolumab 1mg / kg IV every 3 weeks x 4 cycles followed by nivolumab 3 mg / kg every 2 weeks or nivolumab 6 mg / kg IV every 4 weeks. The first cycle of ICI was administered within 7 d after FMT. Therapy continued at the discretion of the treating oncologist until unacceptable toxicity, completion of 2 years, progression, or death. Routine imaging was completed every 3 months for the first 2 years. Acceptable imaging included computed tomography or magnetic resonance imaging at the discretion of the treating oncologist who was encouraged to use the same modality of imaging throughout whenever possible. The assessment of treatment response was conducted as per RECIST v.1.1 criteria and iRECIST when applicable at 3 months, 6 months, and 12 months. Assessment of treatment response at all other timepoints were done by investigator-assessed RECIST. The ORR was defined as the number of patients with a CR and PR and designated as R for the translational studies. All other patients, to include those with SD and PD as best response, were designated as NR.

[0235] Data and sample collection. At each clinic visit, patient data to include demographics, clinical and imaging assessments and adverse event recording were collected per protocol using a secure, password protected, electronic data capture web-based tool (Medidata Rave Unified Life Science Platform). Patient fecal and blood samples were collected at the following timepoints:before FMT (baseline), the first cycle of ICI therapy (1 week), and the second cycle of ICI therapy (1 month), 2 months, 3 months, 6 months, 9 months, and 1 year. A complete clinical assessment was conducted at each visit with routine lab-work per standard-of-care. Patients were permitted to withdraw consent at any time with no impact on treatment although analysis of previously obtained samples was retained.

[0236] Assessment and management of adverse events. FMT and ICI-related toxicities were evaluated using the CTCAE v.5.0 grading scale. Toxicities were attributed to FMT only if the investigator deemed the AE to be related to FMT and if the AE occurred before the first cycle of ICI. Adverse events were managed by the treating investigator in accordance with routine clinical practice guidelines. Any patient who experienced a severe adverse event had ICI therapy suspended until resolved or discontinued at the discretion of the treating investigator. All adverse events were reviewed by the trial Sponsor to ensure consistency in attribution and grading.

[0237] Outcomes and sample size. The primary outcome of the study was the ORR in the NSCLC cohort, defined as the proportion of patients whose best objective response was either a CR or PR. Best objective response was determined as the best response recorded from the first dose of study treatment until the last tumor assessment prior to initiation of subsequent therapy. Secondary outcomes included disease control rate, defined as the proportion of patients achieving CR, PR, or SD lasting more than 6 months; ORR in the melanoma cohort; and safety of FMT in combination with ICI. Additional secondary outcomes to be reported subsequently include progression-free survival (PFS) and overall survival (OS) at 12 months in both the NSCLC and cutaneous melanoma cohorts. Microbiome engraftment was evaluated by acquired donorhost similarity using the Bray-Curtis dissimilarity index. An exploratory endpoint was safety and preliminary activity of FMT in the uveal melanoma cohort, which will be reported separately. Per protocol, each cohort was to be analysed separately.

[0238] Metagenomics analysis - sequencing and processing. A total of 199 fecal samples collected from 40 patients enrolled underwent whole genome shotgun sequencing. Stool aliquots were preserved at -20 °C in DNA / RNA Shield Buffer (Zymo) until processing. DNA extraction was performed using the DNeasy PowerSoil Pro Kit (Qiagen), following the manufacturer’s protocol. Sequencing libraries were prepared using the Illumina® DNA Prep (M) Tagmentation kit (Illumina), following the manufacturer’s instructions, and purified with a 0.7* ratio of Agencourt AMPure XP beads. Sequencing was conducted on a NovaSeq 6000 S4 flow cell (Illumina) at the University of Trento’s in-house sequencing facility (Trento, Italy). Raw reads were processed through a quality control pipeline available at https: / / qithub.com / SeqataLab / preprocessinq. Briefly, reads were filtered out if they had low quality (Q < 20), were shorter than 75 bp, or contained two or more ambiguous bases. Host-derived reads (human genome hg19 and Illumina spike-in phiX174) were also removed. On average, each sample yielded approximately 48 million high-quality reads. 5 samples failed internal quality control and were excluded from downstreamanalyses. Taxonomic profiling was carried out using MetaPhlAn 4 (database version vJun23_CHOCOPhlAnSGB_202307. From the 194 samples that passed quality control 12 were excluded from the downstream analysis: 10 of excluded samples occurred during unscheduled visits and 2 excluded samples were collected after salvage FMT. Since the salvage FMT usually was not from the same original donor, all samples occurring after the patient received salvage FMT were excluded.

[0239] Metagenomics analysis - statistical analysis. All statistical analysis and visualization were done in R. Microbiome sequencing data were processed and analyzed using the phyloseq package. Taxonomic assignments and abundance tables were imported into a phyloseq object, including corresponding sample metadata. Alpha diversity analysis was performed using the vegan package, calculating richness and Shannon index, comparisons with a p-value < 0.05 were considered significant. Principal component analysis was done with the packages prcomp and factoextra, visualizing both the variance explained by the principal components and the top contributing taxa. PERMANOVA analysis with 999 permutations using the adonis function from the vegan package. Comparisons with a p-value < 0.05 were considered significant. Strain sharing between the patients and donors was calculated using a StrainPhlAn 4 with an in-house database for strain identification. Bray-Curtis dissimilarity was calculated between the samples and their corresponding donor samples using the distance function from phyloseq. These distances were then adjusted to their corresponding baseline sample to observe the fold change over time. Linear discriminant analysis Effect Size (LEfSe) was performed to identify microbial taxa that differentiate between subject groups according to their response, cohort or toxicity development using the package yingtools2. Features with LDA scores > 2 and p-values <0.05 were considered significant. Taxonomic abundance patterns between groups were visualized via heatmaps generated by the ComplexHeatmap package. The analysis was based on differentially abundant taxa between conditions using a Wilcoxon rank-sum test. Taxa with p-values < 0.05 were retained for visualization.

[0240] Metagenomics analysis - Calculation ofSGBs categories. For all patients with available corresponding donor shotgun metagenomic sequencing data, SGBs in each sample were categorized as follows: Baseline for the SGBs were only unique to the patient at their baseline sample, Donor for the SGBs present in the sample matched unique SGBs from their respective donor sample but were not found in the baseline sample, Both for unique SGBs that were found in both the baseline sample and the matching donor sample, New for SGBs not present either in the donor or the baseline and Lost when SGBs that were identified at Baseline were no longer present in the sample. To calculate the proportion of lost SGBs, we divided the Lost by the total amount of SGBs present at baseline.

[0241] Metagenomics analysis - Pathway analysis. Shotgun metagenomic samples were also processed with the HMP Unified Metabolic Analysis Network (HUMAnN3) pipeline (v.3.8). Thestratified pathway abundances was evaluated from the MetaCyc database. Pathways with zero total abundance across all samples were filtered out. Differential abundance analysis between baseline and post-FMT was performed using DESeq2.

[0242] High throughput culturomics. High throughput culturomics was done as previously described (Diop K, et al. Gut Pathog 17:21, 2025). A total of 0.3 g of stool from enrolled patients was resuspended in 1 mL of sterile 1* PBS. From this suspension, seven serial dilutions were prepared, and 50 pL of each dilution was plated onto 5% Columbia agar supplemented with sheep blood (COS; Thermo Fisher, Montreal, Canada). Plates were incubated aerobically and anaerobically in an anaerobic chamber (5% H2, 5% CO2, and 90% N2) at 37 °C for up to 72 hours. Anaerobic conditions were achieved using sealed Zip bags (Becton Dickinson, Mississauga, Ontario, Canada) containing GasPak™ anaerobic generators (Becton Dickinson). In parallel, enrichment cultures were performed by inoculating 200 pL of stool suspension into BD BACTEC™ Lytic / 10 Aerobic (Becton Dickson Mississauga, Ontario, Canada) for aerobic conditions. For anaerobic conditions, BD BACTEC™ Lytic / 10 Anaerobic / F (Becton Dickinson) and Yeast Casitone Fatty Acids Broth with Carbohydrates (YCFAC) were used. All broth cultures, were supplemented with 5% defibrinated sheep blood and 5% filtered rumen fluid (0.22 pm). At multiple time points (24 h, 3, 7, 10, 15, 21, and 30 days), serial dilutions from enrichment broths were plated on COS agar and incubated under the same aerobic and anaerobic conditions. Following incubation, isolated colonies were subcultured onto fresh COS agar and incubated at 37 °C for an additional 72 hours. Colonies were then purified and subjected to identification. Bacterial identification was performed using matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS). For each isolate, a double spot was deposited onto a 96-spot MSP target plate, overlaid with 1 pL of a saturated a-cyano-4-hydroxycinnamic acid matrix solution (prepared in 50% acetonitrile and 2.5% trifluoroacetic acid), and allowed to dry. Spectra were acquired using a MicroFlex LT / SH mass spectrometer (Bruker) and analyzed via FlexControl v3.4 and MALDI Biotyper Compass v4 software. Identification was achieved by comparison to the Bruker MBT Compass BDAL reference library and an in-house spectral database. Colonies yielding identification scores >1.9 were considered reliably identified at the species level. Isolates with scores <1.9 were further analyzed by whole-genome sequencing for taxonomic assignment. Bacterial species detected in patient samples at baseline were compared to those identified one month post-FMT. Due to lack of donor samples available for culturomics, we used the corresponding donor shotgun metagenomics sequencing sample. Species detected at baseline but absent at the one-month post-FMT timepoint were classified as lost. Conversely, bacterial species present in both the donor sample and the recipient one month post-FMT — but not detected at in the patient at baseline — were defined as engrafted from the donor. Engraftment from the donor vs. lost from baseline were compared using the Non-parametric Mann-Whitney test using GraphPad Prism (version 10.4.2).Metabolomic analysis. Fifty pL plasma was mixed with 500 pL of ice-cold extraction solution (methanol / water, 9:1, v / v, at -20 °C) containing isotopically labeled internal standards. The mixture was vortexed for 5 minutes at 2500 rpm to ensure thorough homogenization and efficient extraction of endogenous metabolites. Samples were then centrifuged at 15,000 g for 10 minutes at 4 °C. The resulting supernatants were collected and aliquoted into multiple fractions to be analyzed by different Liquid chromatography coupled with mass spectrometers (LC / MS). Targeted detection of polyamines and bile acids was performed by LC-MS / MS using a 1290 Ultra-High-Performance Liquid Chromatography (UHPLC) system (Agilent Technologies) coupled to a 6470 triple quadrupole mass spectrometer (QQQ 6470) (Agilent Technologies). For polyamine quantification, 10pL of extract were injected into a Kinetex C18 analytical column (150 mm x 2.1 mm, 2.6 pm particle size; Phenomenex), equipped with a C18 guard column (5 mm x 2.1 mm). The column was maintained at 40 °C using a Peltier oven. Chromatographic separation was achieved using a binary mobile phase consisting of (A) water with 0.1% heptafluorobutyric acid (HFBA) and (B) acetonitrile with 0.1% HFBA, both freshly prepared. The initial mobile phase composition was 95% A and 5% B, followed by a linear gradient to 30% B over 7 minutes. The column was washed with 90% B for 2.25 minutes and then re-equilibrated with 5% B for 4 minutes. The autosampler was maintained at 4 °C. MS parameters included a gas temperature of 350 °C, gas flow rate of 12 L / min, and capillary voltage of 2.5 kV. For bile acid analysis, 10 pL of sample were injected onto a Column Poroshell 120 EC-C8 1200bars (100 mm x 2.1 mm, 1.9 pm particle size; Agilent technologies), protected by an XDB-C18 guard column (5 mm x 2.1 mm, 1.8 pm). The column was also maintained at 40 °C. The mobile phase consisted of (A) water with 0.2% formic acid and (B) acetonitrile / isopropanol (1:1, v / v), both freshly prepared. Initial conditions were 70% A and 30% B, followed by a shift to 38% B over 2 minutes, maintained for an additional 2 minutes. A rapid gradient from 38% to 60% B was applied over 30 seconds. The column was washed with 98% B for 2 minutes and re-equilibrated at 30% B for 1.5 minutes. Autosampler temperature was kept at 4 °C. Instrument parameters were set with a gas temperature of 310 °C, gas flow rate of 9 L / min, and capillary voltage of 4.5 kV. In addition, pseudo-targeted metabolomic profiling was performed using ultra-high-performance liquid chromatography coupled with high-resolution accurate mass spectrometry (UHPLC-HRAM) on a Dionex U3000 UHPLC system coupled to a Q-Exactive Orbitrap mass spectrometer (ThermoFisher Scientific), as previously described (Durand S, et al. Methods Cell Biol 164:137-156, 2021 ; Abdellatif M, et al., Sci Transl Med 13, 2021). All targeted treated data were merged and cleaned with a dedicated R package (@Github / Kroemerlab / GRMeta).

[0243] Metabolite longitudinal data were analyzed using Generalized Additive Models (GAMs) with the mgcv R package (version 1.9-3) using the gam function. Each metabolite was fitted with a GAM model that included the two groups (R and NR) present in the study. Before applying any model data were cleaned by removing individuals with only one time point of analysis andduplicated samples. After cleaning the dataset, this metabolite analysis comprised 100 samples from 35 patients, each having at least two time points sampled (from 2 to 4). Data normalization was performed before model fitting. Metabolite intensities were transformed using square root normalization with the sqrt function from R base (version 4.5). Individuals GAMs were fitted for each metabolite with the following model structure:

[0244] a. Smooth terms for time with group-specific evolution over time using factor smooth interactions

[0245] b. Random effects for individual subjects

[0246] c. Tweedie distribution to account for the data distribution characteristics d. Restricted Maximum Likelihood (REML) estimation method

[0247] Model performance was assessed by calculating root mean squared error to evaluate model accuracy deviance explained and R-squared to assess how much the model explained the signal variance, along with residual analysis and concurvity assessment. Temporal evolution was evaluated through model outputs and p-value. Between-group differences were calculated using the difference_smooths function from the gratia package (version 0.10.0) to compute differences between R and NR smooth curves with 95% confidence intervals. Figures displaying temporal evolution were created using smooth estimates calculated with the smooth_estimates function from the gratia package. Both trajectory and difference plots were generated using the ggplot function from ggplot2 package (version 3.5.2) and combined using plot_grid function from cowplot package (version 1.1.3). P-values were displayed using the ggsignif function from ggsignif package (version 0.6.4). Data manipulation was performed using dplyr package (version 1.1.4) and tidyverse (version 2.0.0). All analyses were conducted in R (version 4.5).

[0248] BioMe qPCR assay. The Precision Microbiome Profiling (PMPTM) (Bio-Me, Oslo, Norway), which is a validated quantitative PCR method for analysing the gut microbiome composition, based on TaqMan™ technology on OpenArray® format (Thermo Fisher Scientific), was used. This assay targets 108 bacterial species and subspecies (107 bacteria and 1 fungal species). Standard curves for the assays were created using reference materials quantified by fluorescence (Quant-iTTM PicoGreenTM dsDNA Reagent, Thermo Fisher Scientific). The reference materials were acquired from DSMZ (Leibniz Institute, Braunschweig, Germany) or ATCC (American Type Culture Collection, Manassas, US). Standard curves for each qPCR assay were used to convert the quantification cycle (Cq) value into number of genomic copies per pL of sample; this number was transformed into normalized absolute quantification. qPCR was performed on 220 samples in total (n=181 patients and n=39 donors) and only patient samples with corresponding SGBs category information were included in the final analysis (n=104 samples). qPCR and metagenomics relative abundance were visualized using the ComplexHeatmap package in R and a Kendall score Kendall's T was computed using the cor.testfunction in the Stat package using pairwise complete observations. Spearman r correlations were performed using Graphpad Prism.

[0249] Olink proteomics. Multiplex highthroughput proteomics using the Immuno-Oncology Panel (Olink, Uppsala, Sweden) and analyzed per manufacturer’s instructions. All Olink data are reported as Linearized Normalized Protein Expression (NPX), per manufacturer’s instructions. Olink data were visualized in R using the OlinkAnalyze package using the olink_umap_plot using manyfold approximations and projections using UMAP and the olink_volcano_plot. For volcano plots, Banjamini-Hochberg corrected p-values <0.05 were considered statistically significant.

[0250] Quantification of16S PCR. Quantitative real-time PCR (qPCR) was performed to assess the relative abundance of total bacterial DNA in stool samples by targeting the V6 region of the 16S rRNAgene. The primer pair used was 891 F and 1033R as described previously (Anhe FF et al. Gut 68:453-464, 2019). For each reaction, 400 ng of extracted DNA was combined with 500 nM of each primerand IXqPCRBIO SyGreen Blue MixHi-ROX (PCRBIO Systems). Amplification was carried out using a real-time PCR system, and threshold cycle (Ct) values were obtained. Bacterial load was estimated by comparing sample Ct values to a standard curve generated using serial dilutions of Escherichia coli genomic DNA, allowing for the approximation of bacterial DNA concentration in ng / pL. Non-parametric Mann-Whitney Test to compare SGBs loss groups and two-way ANOVA were performed for R and NR across timepoints using Graphpad Prism (v10.4.2).

[0251] PBMC Immuno-phenotyping - Spectral flow cytometry staining and acquisition. Aliquots of cryopreserved peripheral blood mononuclear cells (PBMCs), each containing 5 * 10® cells preserved in fetal calf serum with DMSO, were quickly thawed, gently washed, and resuspended in FACS buffer consisting of 1X PBS supplemented with 5% fetal calf serum (FCS) and 2 mM EDTA. To exclude non-viable cells, samples were incubated with the LIVE / DEAD™ Fixable Blue Dead Cell Stain Kit (Invitrogen) according to the manufacturer’s protocol. Surface staining was performed using fluorochrome-conjugated antibodies sourced from BD Biosciences, R&D Systems, MBL, BioLegend, Miltenyi Biotec, and Cytek. Data were acquired with a Cytek Aurora 5-laser spectral analyzer and data analysis was performed using FlowJo v10.8.1.

[0252] PBMC Immuno-phenotyping - Manual gating and high-dimensional flow cytometry analysis. Flow cytometry analysis PBMCs following a sequential gating strategy that excluded debris, cell doublets, and non-viable cells, ultimately selecting for viable CD45+immune cells. The resulting CD45+live-cell populations were exported as new *.fcs files using FlowJo v10.8.1, and these curated datasets served as input for UMAP and scyan analyses.

[0253] PBMC Immuno-phenotyping - Algorithms for dimensionality reduction: UMAP analysis. The UMAPs were computed and displayed using the Scanpy package on alive CD45+ cells.

[0254] PBMC Immuno-phenotyping - Single-cell Cytometry Annotation Network (Scyan) automatic annotation. Cell-type annotations were automatically performed using Scyan a biology-driven model that leverages prior knowledge of cell types. This approach allowed to identify nine main populations: Basophils, NK cells, B cells, Monocytes, Dendritic cells, CD4+ T cells, CD8+ T cells, double-positive (DP) T cells, and double-negative (DN) T cells. Subsequently, for each patient, separate .fcs files were generated for each of these nine populations. The percentages of all subpopulations were then determined through manual gating using FlowJo v10.8.1 software.

[0255] Murine experiments. Murine experiments were conducted using 7-week-old female wildtype C57BL / 6 SPF-reared mice obtained from Charles River. Mice received 3 days of antibiotics solution containing ampicillin (1 mg / ml), streptomycin (5 mg / ml) and colistin (1 mg / ml; Sigma-Aldrich), which was added to the sterile drinking water of mice before FMT. Three consecutive FMT, using patient’s feces (ethics approval was obtained (MP-02-2022-10121 / 21.173, MP-02-2018-7132 / 17.035 and 16.161), were performed at day D-15, D-14, and D-13. FMT was performed by thawing fecal material, and 200 pL of the suspension (lOOmg / ml) was then transferred by oral gavage. An additional 10OpL was applied on the fur of each animal. Two weeks after the first FMT, mice were implanted subcutaneously with 0.8x 106MCA-205 cells. When tumors reached 25 - 35 mm2in size, mice were treated four times intraperitoneally every 3 d with anti-PD-1 monoclonal antibody (250 pg per mouse; clone RMP1-14, Bio X Cell) or isotype control (clone 2A3, Bio X Cell). Additionally, the mice received 200pl gavage of NaCI or bacterial Cocktail group starting three days after the tumor implantation and at each anti-PD-1 or isotype control treatment. Tumor sizes were measured with manual calipers. The mice were sacrificed 2 days after the last treatment. Experimental groups were compared using the Mann-Whitney test in Graphpad Prism.

[0256] For murine experiments, bacterial cocktails were prepared as follows: Bacterial strains used in the murine experiments were isolated from patients at baseline enrolled in the FMT-LUMINate clinical trial and stored at -80°C. Bacteria were cultured from frozen stocks on fastidious anaerobe agar plates (Fisher scientific # PB0225A) for 48 hours at 37°C under anaerobic conditions. Prior to cocktail formulation, the identity of each isolate was confirmed by MALDI-TOF mass spectrometry. Following identification, bacterial colonies were harvested, resuspended in sterile NaCI, and adjusted to an optical density (OD) of 1.0, corresponding to approximately 1 x 109colony-forming units (CFU)Zml.

[0257] Cell Culture, Reagents, and Tumor Cell Lines. MCA-205 fibrosarcoma cells, class I MHC H-2b syngeneic cell lines for C57BL / 6 mice, were used for this study. The cells were cultured at 37°C in the presence of 5% CO2 in RPMI 1640 containing 10% FCS, 2 mM L-glutamine, and 100 Ul / ml penicillin / streptomycin. All reagents were purchased from Gibco-lnvitrogen (Carlsbad, CA, USA). Cell lines were checked for mycoplasma using PlasmoTest Mycoplasma Detection Kit (InvivoGen).

[0258] ResultsIn NSCLC, the overall response rate (ORR) was 80% (16 PR) with no grade >3 immune-related adverse events (irAE) (FIG. 2, Table 1). In melanoma, ORR was 75% (5 CR), with 65% developing grade >3 irAE, including 3 (15%) myocarditis (FIG. 2, Table 1). The irAE occurred earlier than previously reported (median onset of 42 days).

[0259] Table 1: Safety of FMT in Combination with ICI

[0260] Adverse events (AE) related to FMT-LUMINate KN-042 FMT and pembrolizumab NSCLC n=20 n=637 Any grade 15 (78.9%) 63% Grade >3 0 (0%) 18%

[0261] AE related to FMT and ipilimumab FMT-LUMINate CM-067

[0262] / nivolumab melanoma n=20 n=313 Any grade 19 (95%) 96% Grade >3 13 (65%) 59% Diarrhea / colitis grade >3 4 (20%) 17% Myocarditis grade >3 3 (15%) 1-15%1Liver toxicity2grade >3 5 (25%) 15% Pneumonitis grade >3 1 (5%) 1%

[0263]

[0264] Pre-FMT patient microbiome composition was distinct from HD. Post-FMT, the responders (R) and non-responders (NR) clustered separately (p=0.03) (FIGs. 3 and 4A-4D).

[0265] Post-FMT, increased polyamines and bile salts was observed in R. Elevation of exhausted T central and T effector memory cells were noted in R (FIG. 6). Conversely, proteomics analyses revealed elevated markers of immune exhaustion including CD28 and IL10 in NR. Addressing the link between microbiome and irAE, in melanoma, 6 (46%) that developed grade >3 irAE, including 2 / 3 of the myocarditis received FMT from the same donor (D5). D5 was not associated with irAE in NSCLC treated with aPD-1 alone. Microbiome analysis revealed that D5 clustered separately (p<0.01) from other HD with a high relative abundance of Prevotella copri (FIGs.5A-5B). A signal for the development of irAE in pts that engrafted P. copri was observed. Pts with grade>3 irAE exhibited higher levels of leaky gut markers such as lipopolysaccharide binding protein (LBP) (FIGs. 7A) or ST2 post-FMT (p=0.002) (FIG. 7B).

[0266] These results validate the role of FMT to improve ICI activity in NSCLC and melanoma, while differentially modulating irAE according to ICI backbone. The data also shows that the microbiome shift is distinct among R while engraftment of P. copri was associated with dual ICI irAE.To better understand the taxonomic drivers of clinical outcome as well as the global shift in the microbiome distinguishing R from NR post-FMT, changes in the absolute number of species-level genome bins (SGBs)30’31across key categories were next examined: i) present uniquely in the donor, ii) present uniquely in the patient at baseline, present in both the patient at baseline and the donor, iii) lost SGBs relative to the SGBs detected in the patient at baseline, and iv) new SGBs — that were neither detected in the patient at baseline nor derived from the donor (FIGs. 8A-B). Although the number of SGBs engrafted from the donor was similar in R vs. NR (p=0.15) (FIG. 9A), it was noted that the magnitude of SGBs loss calculated from the patient at baseline was significantly higher in R compared to NR post-FMT (p=0.016) (FIG. 8C). The magnitude of SGBs loss from baseline in R was maintained in both NSCLC (p=0.011) and melanoma subgroups (p=0.096) (FIG. 8D). No significant decrease in overall SGBs counts was observed in R overtime post-FMT (FIG. 9B). This lack in decrease in overall SGBs overtime in R was explained by an acquisition SGBs acquired from the donor (category i) or new SGBs (category iv) (FIG. 9B). It was confirmed that overall biomass did not change in R or in those with high median SGBs loss overtime by performing quantitative PCR (qPCR) of the 16S rRNA gene (FIGs. 9C-D).

[0267] The top lost SGBs in R included canonically harmful taxa associated with ICI resistance122324such as Enterocloster citroniae, E. bolteae, Clostridium innocuum, C. saudiense, C. spiroforme, Ruminococcus gnavus (now member of Mediterraneibacter genus), Dialister invisus, and Sellimonas intestinalis (FIG. 8E). Next, examining the impact of the donor on SGBs loss, heterogeneity was observed among individual donors (FIG. 9E), with a trend for a more pronounced SGBs loss in Cluster B (p=0.07) (FIG. 9F).

[0268] Similar results were observed with regards to elimination of baseline SGBs associated with R in the previously published phase I trial (MIMIC)20of FMT in combination with anti-PD-1 in patients with untreated melanoma (p=0.018) (FIG. 9G), and in the first two published studies of FMT in combination with anti-PD-1 in refractory melanoma (p=0.074 for Baruch et al18., and p=0.0015 for Davar et al.17) (FIGs. 9H-I). Accordingly, a pooled analysis of all studies validated the conclusion that loss of baseline SGBs was associated with favorable clinical responses (p<0.001) (FIG. 8F).

[0269] Next, a validated qPCR-based assay of 108 selected species2332was employed and it was confirmed that qPCR relative abundances significantly correlated with shotgun metagenomic data (FIG. 9J). This orthogonal qPCR-based approach, which unlike metagenomics is not limited by sequencing depth, demonstrated that the magnitude of species lost relative to the patient’s baseline significantly correlated with the corresponding losses detected by metagenomics (p<0.001) (FIG. 8G). Moreover, using this qPCR technique, bacterial loss post-FMT was also associated with response (FIG. 9K). To further strengthen this observation that loss of baseline SGBs were more strongly associated with response than the acquisition of donor-derived SGBs,high throughput culturomics was performed in 4 R patients at baseline and at one-month post-FMT. At baseline, a mean of 54 species, spanning total 217 strains, was isolated, compared to post-FMT with a mean of 45 species, spanning 183 strains (p=0.171) (FIG. 9L). As observed in the metagenomics results, it was confirmed that significantly more species were lost in these R patients post-FMT, defined as species no longer detected in the post-FMT sample by culturomics, compared to those engrafted from their respective donors (p=0.029) (FIG. 8H). Similar to the metagenomics results, the lost bacteria in R belonged to genera that are considered as harmful such as Enterocloster, Streptococcus, and Dialister (FIG. 9M). Taken together, these findings provide evidence that microbiome remodeling by depletion of pre-existing species may be more important to therapeutic response than engraftment of specific donor species.

[0270] Finally, to validate the relevance of lost bacterial species observed in patients, FMT was performed in antibiotic-treated specific-pathogen-free (SPF) conditions733(FIG. 8I). Antibiotic-pretreated mice were recolonized with stool sourced from an R patient with NSCLC post-FMT, which led to improved anti-PD-1 activity compared to isotype control (p=0.007). To assess the functional relevance of specific bacterial losses post-FMT, a cocktail of four bacteria isolated from the baseline sample that were depleted post-FMT (Streptococcus mutans, Clostridium innocuum, Streptococcus parasanguinis and Enterocloster lavalensis) were reintroduced by gavage into FMT-recipient mice. Anti-PD-1 activity was inhibited in the group of mice receiving FMT plus the cocktail compared to controls receiving FMT alone (p=0.015), suggesting that the loss of deleterious species plays a role to achieve the full therapeutic benefit of FMT (FIG. 8J).

[0271] The results suggest that the response to FMT may be better explained by the loss of a group of potentially deleterious bacteria rather than solely by the engraftment of individual donor strains. To investigate how bacterial loss affects the function of the global microbiome composition, untargeted plasma metabolomics was conducted to assess systemic changes in the metabolic profile post-FMT. Across all cohorts, quinolinic acid and kynurenine increased in NR over time (p<0.001) (FIG. 10A). Both quinolinic acid and kynurenine are metabolites of the tryptophan pathway and have been causatively linked to resistance to ICI34-36. In line with the notion that absence of bacterial loss was associated with resistance to therapy in the study, patients with low proportion of bacterial loss relative to their baseline had higher concentrations of plasma tryptophan post-FMT (p=0.004) (FIG. 11 A). Next, examining the metabolomic profile in the feces, it was found that metabolism of the tryptophan pathway significantly decreased over time in R, and distinct species contributed to these tryptophan pathway dynamics in NSCLC compared to melanoma, consistent with cohort-specific bacterial signatures affecting responders in each malignancy (FIG. 10B). Taken together, these results suggest functional redundancy across different taxa in both the plasma and feces, with convergence of effects on the tryptophan pathway distinguishing R from NR post-FMT.In addition to the metabolomic profile, the impact of bacterial loss on circulating inflammatory markers was examined using high throughput proteomics. Patients with high proportion of bacterial loss relative to their baseline exhibited distinct clustering of systemic circulating inflammatory proteins post-FMT, as opposed to patients with low bacterial loss, where no distinct clustering was observed (FIG. 11B). Patients with high bacterial loss exhibited significant increase in distinct circulating pro-inflammatory proteins post-FMT including IFNG, CXCL9, CXCL13, CCL20, CD8A, CD4, and CD28, while patients with low bacterial loss exhibited no distinct changes in circulating proteins post-FMT (FIG. 11C). The impact of bacterial loss on the metabolic profile and on systemic immunity was next investigated. Patients with pronounced bacterial loss exhibited an increase in the frequency of circulating CD8+CD69+T cells (FIG. 10C), but a decrease in circulating CD127|OWCD25hishCD4+regulatory T cells (FIG. 10D). Linking these findings with the metabolic profile, high plasma concentrations of quinolinic acid from the tryptophan pathway were associated with a reduced frequency of circulating CD8+lymphocyte subpopulations including PD-1+CD8+T cells (FIG. 10E) and PD-1+CD45RA" CCR7“ effector memory CD8+T cells (FIG. 10F). High quinolinic acid was also associated with an increased frequency of circulating CD127|OWCD25hisheffector memory regulatory T cells (FIG. 10C). Taken together, these results provide evidence that failure to eliminate baseline deleterious taxa may sustain an immunosuppressive metabolic and systemic immune milieu that compromises ICI responses.

[0272] Example 2: Meta-analysis of prospective FMT trials in solid tumor immunotherapy Study characteristics

[0273] The PRISMA74method was utilized for the clinical level systematic review and meta-analysis, resulting in 12 evaluable clinical cohorts from n=11 distinct studies included in the meta-analysis. Across all included studies, a total of 220 patients who received FMT in combination with anti-PD-1-based ICI. Study characteristics and results are presented in FIGs. 12A-C. All studies included were prospective interventional studies. The majority were single-arm, early-phase trials conducted in refractory disease settings, while four studies evaluated FMT in the first-line settings, including one randomized 1:1 placebo-controlled study in metastatic renal cell carcinoma (TACITO). Tumor types represented across studies included melanoma (n=4 studies), renal cell carcinoma (n=2 studies), non-small cell lung cancer (n=2 studies), colorectal cancer, upper gastrointestinal malignancies such as gastric cancer and esophageal squamous cell carcinoma, hepatocellular carcinoma, MSI-enriched tumors including small bowel adenocarcinoma, pancreatic adenocarcinoma, and endometrial adenocarcinoma, as well as mixed solid tumor populations. All cohorts received anti-PD-1 -based therapy either as monotherapy (n=7 studies) or in combination with anti-CTLA-4 (n=1 study) or targeted therapy (n=3 studies).FMT bowel preparation and route of administration

[0274] All but one study included some form of bowel preparation, most frequently polyethylene glycol (PEG) alone (n=7 studies), and n=3 studies with PEG plus antibiotics. One study used antibiotics alone as bowel preparation. Routes of FMT administration included colonoscopic instillation (n=2 studies), oral capsule delivery (n=6 studies), or a combination of both (n=3 studies).

[0275] Donor screening

[0276] Across the 11 / 12 clinical cohorts with available information, donor selection followed two main strategies: use of rigorously screened healthy volunteers (n=5 studies) or use of patients who had previously derived clinical benefit (best response of either OR or PR) from ICI (n=5 studies) (FIGs. 12A-C). Healthy donor FMT was used in MIMIc, PERFORM, FMT-LUMINate, Zhang et al., and Du et al. In contrast, Baruch et al., Davar et al., Park et al., Baruch et al., and TACITO et al. used responder-derived donor stool. Across studies, donor qualification generally combined a clinical interview or questionnaire, review of medical and exposure history, and microbiological testing of blood and / or stool. MIMIc, FMT-LUMINate, and PERFORM all drew from the same healthy donor pool in London, Ontario, and used the same donor recruitment, eligibility, and screening procedures. Donor recruitment began through public campaigns, followed by email pre-screening, telephone review, detailed medical history assessment, laboratory testing, and final physician evaluation. Eligible donors were required to have a body mass index of 18.5-30 kg / m2and were excluded for major medical illness, metabolic disease, gastrointestinal or liver disease, prior malignancy, psychiatric illness, recent major infection including SARS-CoV-2 or Monkeypox (as of FMT-LUMINate), recent antibiotic exposure, recent hospitalization, recent travel, sexual exposure risks, or any evidence of transmissible infection. Screening was extensive and included blood, stool, urine, rectal, throat, and nasal testing for multidrug-resistant organisms and a broad panel of pathogens, including HIV, HTLV, hepatitis viruses, CMV, EBV, syphilis, strongyloidiasis, schistosomiasis, amebiasis, chlamydia, gonorrhea, MRSA. SARS-CoV-2, Monkeypox screening was employed as of FMT-LUMINate. Du et al., and Zhang et al. also used healthy donors, with limited details on the screening procedures employed for donor selection.

[0277] Among the cancer responder donor studies, Baruch et al. used two melanoma donors who had achieved a OR for at least 1 year on anti-PD-1 monotherapy; thus this study was a strict CR-only donor model. TACITO was similarly restrictive: donor candidates were identified among patients with advanced RCC who had experienced complete response to PD-1 inhibitors, and from five candidate donors, two ultimately passed screening and were qualified for donation. Screening included a dedicated questionnaire on communicable-disease risk, recent antibiotics, systemic illness, family history of gastrointestinal cancer or inflammatory bowel disease, and potentially excreted medications, followed by blood and stool testing for transmissible diseases;screening was repeated every 2 months, and each donation was preceded by an additional symptom / exposure questionnaire plus SARS-CoV-2 nasopharyngeal swab after March 2020. Davaret al., used seven melanoma donors, including four with CR and three with PR, all selected for durable objective response to prior anti-PD-1 -based therapy; their median donor progression-free survival was 56 months, with a range of 45-70 months. For Davar et al., candidate donors underwent serial stool sampling and extensive infectious studies, and blood and stool specimens were screened for 32 viral, bacterial, fungal, and protozoan agents before FMT. Park et al. likewise used anti-PD-1 responder donors. Six donors were involved, four with hepatocellular carcinoma, one each with gastric cancer, one with esophageal squamous cell carcinoma. Cancertype matching between donor and recipient was attempted when possible. Four patients were in complete response and two in partial response for at least 6 months. Finally, Baruch et al. 2024 used a single donor with metastatic MSI-H colorectal cancer who had achieved CR on anti-PD-1 therapy.

[0278] Recipient and donor demographics and clinical outcomes.

[0279] Median recipient age across cohorts ranged from 55 to 76 years. Sex distribution varied by cohort and tumor type, with a predominance of male patients (from 55 to 100%) in most studies. More than 80-95% of patients had ECOG 0-1 at baseline. Body mass index was inconsistently reported but was available in some first-line cohorts, where both normal-weight and overweight / obese patients were represented. Disease stage was advanced in the majority of participants. For donor demographics, the healthy donor median age was generally younger compared to the cancer donor median age. Sex distribution among responder-donors varied, with some cohorts including both male and female donors.

[0280] Across all twelve cohorts in 191 patients evaluable for efficacy outcomes (FIG. 13A), a combined ORR of 37% (95% Cl 22-55%) (FIG. 13B) and DCR of 63% (95% Cl 44-79%) were observed, with study weights evening distributed (approximately 6-10% per cohort). Given the differences in ORR between previously untreated and refractory cohorts, and known response rate differences among these populations, a subgroup analysis according to line of therapy was performed. In previously untreated cohorts (n=101), an ORR of 63% (95% Cl 53-72%) and DCR of 79% (95% Cl 69-87%) were observed. In refractory cohorts (n=90), an ORR of 18% (95% Cl 11-28%) and DCR of 43% (95% Cl 26-63%) were observed. For toxicity, across twelve cohorts in 195 patients evaluable for toxicity, a combined rate of grade > 3 irAE of 24% (95% Cl 14-37%) was observed (FIG. 13C). Taken together, these results highlight an encouraging response rate in the front-line setting, as well as encouraging DCR in patients with refractory cancers. The global level of grade 3 or higher irAEs is consistent with previous reports of ICI alone.

[0281] A key open question in the field is whether recipient features associate with the clinical outcome to FMT. For this analysis, data where individual patient-level information was available for each outcome measure was used. There was no association with recipient sex, age, BMI, andresponse (FIG. 13D) or the development of grade 3 or higher irAEs following FMT. While previous work has linked the development of irAE with response in the context of standard-of-care ICI therapy, such an association in the context of FMT was not observed, with a similar proportion of responders and non-responders experiencing grade 3 or higher irAE (FIG.13E). It was next asked whether donor type (healthy vs. cancer) could impact FMT outcomes. For this analysis, in order to reduce bias with regard to line of therapy as outcomes were consistently improved in first-line studies (p<0.001), this analysis was limited to first-line cohorts only, as most studies using cancer donors were conducted in refractory settings, whereas first-line studies predominantly used healthy donors. Both cancer and healthy donor FMT led to similar proportions of response and toxicity. Lastly, whether FMT was administered in a single dose, or multiple doses, had no bearing on the response or toxicity.

[0282] Mega-analysis of host microbiome features associated with response following FMT

[0283] The FMT clinical trial participant (recipients) and donor metagenomes of publicly available data were compiled. Across n=6 studies with available data, 610 samples from n=109 recipients and n=78 samples from n=20 donors were analysed.

[0284] Recipient microbiome features associated with response were first synthesized. Given the complexity of analysing studies with different time points, analyses were pooled using the mean relative abundance value of all of the post-FMT samples for a given recipient, an approach previously used to treat multiple longitudinal timepoints from the same patient75, in addition to a timepoint-based analysis. Alpha diversity, which measures the number of microbial taxa within a community, tended to be improved for responders compared to non-responders at baseline (p=0.043), also seen in the post-FMT pooled analysis (p=0.099) (FIG. 16A), but there was no significant difference at the post-FMT timepoint (FIG. 16B).

[0285] For beta diversity, three complementary approaches were used: (i) the Jaccard dissimilarity index, which captures differences in microbial community composition based on presence-absence (selected to better reflect the baseline elimination signal identified as a candidate biomarker in phase II studies); (ii) Jaccard-based distance-to-centroid, which guantifies within-group heterogeneity and the extent to which microbiome composition differs between groups; and (iii) Bray-Curtis dissimilarity to donor, chosen given its use in phase I and II studies to assess the degree of convergence of recipient microbiomes toward their respective donors overtime.

[0286] Donor samples were first compared to pre-FMT recipient samples and a significant global difference in microbiome composition was observed (p=0.009) (FIG. 14A). At baseline, responders and non-responders were already distinguishable using the distance-to-centroid metric (p=0.014). Post-FMT, responders and non-responders remained significantly different, both by Jaccard dissimilarity (p=0.004) and distance-to-centroid (p<0.001). Overtime, there was a significant difference comparing the pre-FMT samples to post-FMT responder (p<0.001) andnon-responder (p<0.001) samples. We next explored whether post-FMT beta diversity patterns were consistent across studies. Post-FMT beta diversity differed significantly across studies in responders (p<0.05 across all studies, with the greatest variability observed in refractory cohorts), while non-responders showed no significant between-study variation (p>0.05 for all studies) (FIG.

[0287] 16C). Taken together, these analyses suggest a few potential conclusions. First, the donor microbiome is distinct from that of recipients at baseline, suggesting that the patients are distinct from either healthy donors or super-responder donors. Second, although responders and non-responders already exhibited differences in global microbiome composition at baseline, these differences became more pronounced following FMT. Lastly, significant global shifts in the microbiome composition occurred pre- vs post-FMT in responders as well as the non-responders. Finally, as the last beta diversity analysis, as also seen in prior studies, the degree of acquired Bray-Curtis donor-host dissimilarity was not different in the responders or non-responders (FIG.

[0288] 14B), suggesting that the degree of acquired similarity to the donor was not predictive of clinical response to FMT.

[0289] In an attempt to better understand the gut microbiome signatures in responders compared to non-responders, a multi-algorithm differential abundance (DA) approach was employed as similarly performed by Thomas et al., Nature Reviews Clinical Oncology 202325. This approach integrated eight distinct analytical methods: DESeq276, limma-voom77, ANCOM-BC78, MaAsLin379, and a standardized mean difference (SMD) meta-analysis80. First, to capture the most prominent dataset-specific signals, the top 10 differentially abundant taxa from each cohort were selected based on Wilcoxon rank-sum tests. Subsequently, to determine the directional association of these prioritized taxa, a majority rule across the eight DA methods was applied. Taxa with more 'up' than 'down' assignments across the models were stratified into the responder cluster, while taxa with more ‘down’ assignments were grouped into the non-responder cluster. This approach identified a final panel of 55 species, including 30 taxa enriched in responders and 22 taxa enriched in non-responders. Examination of dataset-level Wilcoxon patterns revealed inter-cohort heterogeneity in gut microbiome composition, with no single taxon consistently enriched in responders or non-responders across all studies (FIG. 16D). Dialister hominis, a member of the Dialister genus, was one of the taxa most reproducibly associated with non-response, appearing in 4 of 6 studies. Globally, these findings indicate that no single, consistent microbial signature distinguishes responders or non-responders across FMT studies. These observations set the stage for subsequent analyses examining whether shared patterns may instead emerge at the level of strain engraftment and loss of baseline species following FMT. Refining the candidate biomarker in FMT

[0290] In non-oncology FMT studies, one measure of FMT success has been linked to engraftment of strains from the donor41. In the MIMic, FMT-LUMINate, and TACITO trials, the degree of engraftment from the donor was not discriminative of response. Consistent with thesefindings, in the present mega-analysis, the total number of donor strains detected in recipients did not differ significantly between responders and non-responders at any assessed post-FMT timepoint, nor when analyses were performed using the mean number of strains engrafted across all available post-FMT samples per recipient (FIG. 14D). The absence of a consistent microbial signature across cohorts suggests that alternative mechanisms may underlie clinical benefit following FMT. The FMT-LUMINate trial (Example 1) is the first to describe that responders demonstrated significantly greater loss of bacterial species present in the recipient microbiome at baseline.

[0291] Regarding elimination of pre-existing baseline taxa following FMT as the candidate biomarker, in the mega-analysis, responders exhibited more profound elimination of the absolute number of baseline taxa using the mean of all post-FMT samples (p=0.012) and at 2 months (p=0.023) and at 6 months (p=0.025), with numerical increase at 1 month (p=0.084) (FIG. 14D).

[0292] It was next sought to determine whether this response-associated elimination of baseline taxa was specific to FMT rather than just a biological phenomenon observed in responder patients post ICI. To do so, publicly available shotgun metagenomics longitudinal studies were analyzed, including the PRIMM cohort84, a prospective multi-center study of 175 patients with advanced melanoma treated with anti-PD-1 therapy with serial stool sampling and shotgun metagenomic profiling (FIG. 14E), as well as the n=22 placebo arm patients of the TACITO trial38(FIG. 17A). In these two cohorts, there was no difference between the bacterial loss and outcome, further validating that this microbiome change is only observed post-FMT, and therefore unlikely to be a result of immunotherapy alone.

[0293] It was next sought to determine recipient features governing strain engraftment and loss. Recipient age, sex, or BMI did not dictate number of engrafted strains or the degree of eliminated taxa (FIG. 17B). The strain engraftment rate directly correlated with the rate of bacterial elimination (p<0.001), suggesting that donor strain engraftment was required for elimination of bacterial taxa in the recipients (FIG. 17C). Recipient baseline alpha diversity was negatively correlated with the rate of engraftment of donor strains in responders (p<0.001) (FIG. 17D).

[0294] Recipient baseline alpha diversity tended to negatively correlate with the rate of elimination in responders (p=0.074) (FIG. 17E).

[0295] Disease and study-specific features associated with the endpoints of interest were next examined. Alpha diversity at baseline was not different among the three histologies studied and according to line of therapy (FIG. 18A). Beta diversity was significantly different at baseline and maintained post-FMT according to disease type and line of therapy (FIG. 18B). Across these subgroups, engraftment was not associated with response, however, responders mostly consistently experienced higher degrees of elimination of baseline taxa, however this analysis was likely limited by small sample size (FIG.18C). Lastly, examining recipient features associated with the development of toxicity, there was no significant difference in alpha diversity, betadiversity, strain engraftment, or elimination of baseline taxa in patients with grade 3 or higher AE compared to those without toxicity using the mean of all post-FMT timepoints or at each timepoint. These findings collectively suggest that the key ecological event underlying successful FMT may not be donor microbial acquisition perse, but rather the restructuring of the recipient microbiome through selective depletion of unfavorable baseline taxa, a process associated with improved clinical outcomes but not with the development of immune-related toxicity.

[0296] Defining the baseline taxa preferentially eliminated in FMT responders

[0297] To visualize the most frequently depleted species, elimination was defined longitudinally as baseline taxa reaching zero abundance in >50% of post-FMT samples per patient, and the 50 most frequently eliminated taxa based on the number of patients exhibiting sustained loss were identified (FIG. 15). Four taxa were affiliated with the Clostridium genus, namely Clostridium innocuum, Clostridium leptum, Clostridium sp Marseille P3244 SGB29302, and an unclassified Clostridium species (SGB6179), along with related Lachnoclostridium species (An138 SGB4774 and An131 SGB4775). In addition, two top eliminated taxa belonged to the genus Enterocloster (E aldenensis, and E. cltronlae). Altogether, 26 / 50 (52%) of these most frequently depleted taxa have previously been associated with disease states (including cancer), a state of dysbiosis or resistance to ICI. These included Agathobaculum faecis (an oral-associated taxon)84, Eisenbergiella fay / 84-86, Faecalicatena contorta2545, Hydrogeniiclostridium mannosilyticumM, Mediterraneibacter gnavus7, Sellimonas Intestlnalls72, Dielma fastidiosa (member of the Erysipelotrichaceae family)84.

[0298] Although the present invention has been described hereinabove by way of specific embodiments thereof, it can be modified, without departing from the spirit and nature of the subject invention as defined in the appended claims. In the claims, the word "comprising" is used as an open-ended term, substantially equivalent to the phrase "including, but not limited to". The singular forms "a", "an" and "the" include corresponding plural references unless the context clearly dictates otherwise.

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Claims

WHAT IS CLAIMED IS:

1. A method for improving the antitumor response in a melanoma cancer patient treated with an immune checkpoint inhibitor (ICI) therapy, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp.

2. The method of claim 1, wherein the at least one of the following bacterial species comprises Faecalibacterium prausnitzii.

3. The method of claim 1 or 2, wherein the at least one of the following bacterial species comprises Eubacterium rectale.

4. The method of any one of claims 1 to 3, wherein the at least one of the following bacterial species comprises Bacteroidota FGB6568 GGB1627 SGB2227.

5. The method of any one of claims 1 to 4, wherein the at least one of the following bacterial species comprises Candidatus Melainabacteria FGB2107 GGB6020 SGB8653.

6. The method of any one of claims 1 to 5, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9634 SGB15093.

7. The method of any one of claims 1 to 6, wherein the at least one of the following bacterial species comprises Firmicutes FGB1776 GGB4684 SGB6478.

8. The method of any one of claims 1 to 7, wherein the at least one of the following bacterial species comprises Ligilactobacillus salivarius.

9. The method of any one of claims 1 to 8, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

10. The method of any one of claims 1 to 9, wherein the at least one of the following bacterial species comprises Blautia caecimuris.

11. The method of any one of claims 1 to 10, wherein the at least one of the following bacterial species comprises Segatella copri.

12. The method of any one of claims 1 to 11, wherein the at least one of the following bacterial species comprises Holdemanella porci.

13. The method of any one of claims 1 to 12, wherein the at least one of the following bacterial species comprises Butyricimonas faecihominis.

14. The method of any one of claims 1 to 13, wherein the at least one of the following bacterial species comprises Oscilibacter spp.

15. A method for improving the antitumor response in a lung cancer patient treated with an immune checkpoint inhibitor (ICI) therapy, the method comprising administering to the patient an effective amount of a composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB 15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 39, Lachnospiraceae bacterium OFO96, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368.

16. The method of claim 15, wherein the at least one of the following bacterial species comprises Faecalibacterium prausnitzii.

17. The method of claim 15 or 16, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

18. The method of any one of claims 15 to 17, wherein the at least one of the following bacterial species comprises Gemmiger formicilis.

19. The method of any one of claims 15 to 18, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

20. The method of any one of claims 15 to 19, wherein the at least one of the following bacterial species comprises Faecalibacterium sp. CLA AA H233.

21. The method of any one of claims 15 to 20, wherein the at least one of the following bacterial species comprises Akkermansia muciniphila.

22. The method of any one of claims 15 to 21, wherein the at least one of the following bacterial species comprises Ruminococcus sp. AF41 9.

23. The method of any one of claims 15 to 22, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3828 SGB5197.

24. The method of any one of claims 15 to 23, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9602 SGB15028.

25. The method of any one of claims 15 to 24, wherein the at least one of the following bacterial species comprises Firmicutes FGB1226 GGB58485 SGB80143.

26. The method of any one of claims 15 to 25, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB 15052.

27. The method of any one of claims 15 to 26, wherein the at least one of the following bacterial species comprises Firmicutes FGB3048 GGB9561 SGB 14972.

28. The method of any one of claims 15 to 27, wherein the at least one of the following bacterial species comprises Colidextribacter sp. 210702 DFI 39.

29. The method of any one of claims 15 to 28, wherein the at least one of the following bacterial species comprises Lachnospiraceae bacterium OFO96.

30. The method of any one of claims 15 to 29, wherein the at least one of the following bacterial species comprises Anaerosacchariphilus sp. NSJ 68.

31. The method of any one of claims 15 to 30, wherein the at least one of the following bacterial species comprises Jutongia horn inis.

32. The method of any one of claims 15 to 31, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15051.

33. The method of any one of claims 15 to 32, wherein the at least one of the following bacterial species comprises Roseburia sp. BX1005.

34. The method of any one of claims 15 to 33, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3588 SGB4808.

35. The method of any one of claims 15 to 34, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9766 SGB15383.

36. The method of any one of claims 15 to 35, wherein the at least one of the following bacterial species comprises Bacteroides finegoldii.

37. The method of any one of claims 15 to 36, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

38. The method of any one of claims 15 to 37, wherein the lung cancer is non-small cell lung carcinoma (NSCLC).

39. The method of any one of claims 1 to 38, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

40. The method of any one of claims 1 to 39, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor.

41. The method of claim 40, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

42. The method of any one of claims 1 to 41, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

43. The method of claim 42, wherein the immune checkpoint inhibitor comprises ipilimumab.

44. The method of any one of claims 40 to 43, wherein (a) the immune checkpoint inhibitor comprises a combination of a PD-1 or PD-L1 inhibitor and a CTLA-4 inhibitor, and wherein the composition does not comprise Segatella copri and / or Holdemanella porci, or (b) the composition comprises Segatella copri and / or Holdemanella porci and the immune checkpoint inhibitor comprises a PD-1 or PD-L1 inhibitor as the sole immune checkpoint inhibitor.

45. A composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp for use in improving the antitumor response in a melanoma cancer patient treated with immune checkpoint inhibitor (ICI) therapy.

46. The composition for use according to claim 45, wherein the at least one bacterial species comprises Faecalibacterium prausnitzii.

47. The composition for use according to claim 45 or 46, wherein the at least one of the following bacterial species comprises Eubacterium rectale.

48. The composition for use according to any one of claims 45 to 47, wherein the at least one of the following bacterial species comprises Bacteroidota FGB6568 GGB1627 SGB2227.

49. The composition for use according to any one of claims 45 to 48, wherein the at least one of the following bacterial species comprises Candidatus Melainabacteria FGB2107 GGB6020 SGB8653.

50. The composition for use according to any one of claims 45 to 49, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9634 SGB15093.

51. The composition for use according to any one of claims 45 to 50, wherein the at least one of the following bacterial species comprises Firmicutes FGB1776 GGB4684 SGB6478.

52. The composition for use according to any one of claims 45 to 51 , wherein the at least one of the following bacterial species comprises Ligilactobacillus salivarius.

53. The composition for use according to any one of claims 45 to 52, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

54. The composition for use according to any one of claims 45 to 53, wherein the at least one of the following bacterial species comprises Blautia caecimuris.

55. The composition for use according to any one of claims 45 to 54, wherein the at least one of the following bacterial species comprises Segatella copri.

56. The composition for use according to any one of claims 45 to 55, wherein the at least one of the following bacterial species comprises Holdemanella porci.

57. The composition for use according to any one of claims 45 to 56, wherein the at least one of the following bacterial species comprises Butyricimonas faecihominis.

58. The composition for use according to any one of claims 45 to 57, wherein the at least one of the following bacterial species comprises Oscilibacter spp.

59. A composition comprising bacteria of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 3 9, Lachnospiraceae bacterium OFO9 6, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368 for use in improving the antitumor response in a lung cancer patient treated with immune checkpoint inhibitor (ICI) therapy.

60. The composition for use according to claim 59, wherein the at least one of the following bacterial species comprises Faecalibacterium prausnitzii.

61. The composition for use according to claim 59 or 60, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9635 SGB15106.

62. The composition for use according to any one of claims 59 to 61 , wherein the at least one of the following bacterial species comprises Gemmiger formicilis.

63. The composition for use according to any one of claims 59 to 62, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

64. The composition for use according to any one of claims 59 to 63, wherein the at least one of the following bacterial species comprises Faecalibacterium sp. CLA AA H233.

65. The composition for use according to any one of claims 59 to 64, wherein the at least one of the following bacterial species comprises Akkermansia muciniphila.

66. The composition for use according to any one of claims 59 to 65, wherein the at least one of the following bacterial species comprises Ruminococcus sp. AF41 9.

67. The composition for use according to any one of claims 59 to 66, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3828 SGB5197.

68. The composition for use according to any one of claims 59 to 67, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9602 SGB15028.

69. The composition for use according to any one of claims 59 to 68, wherein the at least one of the following bacterial species comprises Firmicutes FGB1226 GGB58485 SGB80143.

70. The composition for use according to any one of claims 59 to 69, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15052.

71. The composition for use according to any one of claims 59 to 70, wherein the at least one of the following bacterial species comprises Firmicutes FGB3048 GGB9561 SGB14972.

72. The composition for use according to any one of claims 59 to 71 , wherein the at least one of the following bacterial species comprises Colidextribacter sp. 210702 DFI 39.

73. The composition for use according to any one of claims 59 to 72, wherein the at least one of the following bacterial species comprises Lachnospiraceae bacterium OFO96.

74. The composition for use according to any one of claims 59 to 73, wherein the at least one of the following bacterial species comprises Anaerosacchariphilus sp. NSJ 68.

75. The composition for use according to any one of claims 59 to 74, wherein the at least one of the following bacterial species comprises Jutongia hominis.

76. The composition for use according to any one of claims 59 to 75, wherein the at least one of the following bacterial species comprises Oscillospiraceae GGB9616 SGB15051.

77. The composition for use according to any one of claims 59 to 76, wherein the at least one of the following bacterial species comprises Roseburia sp. BX1005.

78. The composition for use according to any one of claims 59 to 77, wherein the at least one of the following bacterial species comprises Lachnospiraceae GGB3588 SGB4808.

79. The composition for use according to any one of claims 59 to 78, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9766 SGB15383.

80. The composition for use according to any one of claims 59 to 79, wherein the at least one of the following bacterial species comprises Bacteroides finegoldii.

81. The composition for use according to any one of claims 59 to 80, wherein the at least one of the following bacterial species comprises Firmicutes FGB38642 GGB9758 SGB15368.

82. The composition for use according to any one of claims 59 to 81 , wherein the lung cancer is non-small cell lung carcinoma (NSCLC).

83. The composition for use according to any one of claims 59 to 82, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

84. The composition for use according to any one of claims 59 to 83, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor.

85. The composition for use according to claim 84, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

86. The composition for use according to any one of claims 59 to 85, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

87. The composition for use according to claim 86, wherein the immune checkpoint inhibitor comprises ipilimumab.

88. The composition for use according to any one of claims 84 to 87, wherein (a) the immune checkpoint inhibitor comprises a combination of a PD-1 or PD-L1 inhibitor and a CTLA-4 inhibitor, and wherein the composition does not comprise Segatella copri and / or Holdemanella porci, or (b) the composition comprises Segatella copri and / or Holdemanella porci and the immune checkpoint inhibitor comprises a PD-1 or PD-L1 inhibitor as the sole immune checkpoint inhibitor.

89. A method for assessing the response of a melanoma cancer patient treated with a combination therapy comprising an immune checkpoint inhibitor (ICI) therapy and fecal microbiota transplantation (FMT), the method comprising detecting bacteria species present in a stool sample from the patient after the FMT, wherein the presence of increased levels of at least one of the following bacterial species: Faecalibacterium prausnitzii, Eubacterium rectale, Bacteroidota FGB6568 GGB1627 SGB2227, Candidatus Melainabacteria FGB2107 GGB6020 SGB8653, Oscillospiraceae GGB9634 SGB15093, Firmicutes FGB1776 GGB4684 SGB6478, Ligilactobacillus salivarius, Oscillospiraceae GGB9635 SGB15106, Blautia caecimuris, Segatella copri, Holdemanella porci, Butyricimonas faecihominis and / or Oscilibacter spp. relative toreference levels is indicative that the patient is a responder to the combination therapy, and wherein the presence of increased levels of at least one of the following bacterial species: Firmicutes FGB4806 GGB51647 SGB4348, Coprococcus eutactus, Oscillospiraceae bacterium CLA AA H250, Akkermansia muciniphila, Frisingicoccus SGB4674, Streptococcus vestibularis, and / or Bactericides nordi relative to reference levels is indicative that the patient is a nonresponder to the combination therapy.

90. A method for assessing the response of a lung cancer patient treated with a combination therapy comprising an immune checkpoint inhibitor (ICI) therapy and fecal microbiota transplantation (FMT), the method comprising detecting bacterial species present in a stool sample from the patient after the FMT, wherein the presence of increased levels of at least one of the following bacterial species: Faecalibacterium prausnitzii, Oscillospiraceae GGB9635 SGB15106, Gemmiger formicilis, Firmicutes FGB38642 GGB9758 SGB 15368, Faecalibacterium sp. CLA AA H233, Akkermansia muciniphila, Ruminococcus sp. AF41 9, Lachnospiraceae GGB3828 SGB5197, Firmicutes FGB1226 GGB58485 SGB80143, Oscillospiraceae GGB9602 SGB15028, Oscillospiraceae GGB9616 SGB15052, Firmicutes FGB3048 GGB9561 SGB14972, Colidextribacter sp. 210702 DFI 39, Lachnospiraceae bacterium OFO96, Anaerosacchariphilus sp. NSJ 68, Jutongia hominis, Oscillospiraceae GGB9616 SGB15051, Roseburia sp. BX1005, Lachnospiraceae GGB3588 SGB4808, Firmicutes FGB38642 GGB9766 SGB15383, Bacteroides finegoldii, and / or Firmicutes FGB38642 GGB9758 SGB15368 relative to reference levels is indicative that the patient is a responder to the combination therapy, and wherein the presence of increased levels of at least one of the following bacterial species: Megamonas funiformis, Megasphaera SGB5856, Firmicutes FGB1354 GGB3304 SGB4367, Proteobacteria FGB2404 GGB6612 SGB9346, Escherichia coll, Bactericides stercoris, Phocaeicola massiliensis, Streptococcus salivarius, Bacteroides ovatus, Bacteroides faecis, Streptococcus vestibularis, Bactericides nordi and / or Fusobacterium nucleatum relative to reference levels is indicative that the patient is a non-responderto the combination therapy.

91. The method of claim 90, wherein the lung cancer is non-small cell lung carcinoma (NSCLC).

92. The method of any one of claims 89 to 91 , wherein the step of detecting bacterial species is performed about 2 weeks to about 2 months after the FMT.

93. The method of claim 92, wherein the step of detecting bacterial species is performed about 1 month after the FMT.

94. The method of any one of claims 89 to 93, wherein the step of detecting bacterial species is performed by metagenomic sequencing.

95. The method of any one of claims 89 to 94, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

96. The method of any one of claims 89 to 95, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor.

97. The method of claim 96, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

98. The method of any one of claims 89 to 97, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

99. The method of claim 98, wherein the immune checkpoint inhibitor comprises ipilimumab.

100. A method for determining whether a cancer patient is a responder to a combination therapy comprising (i) an immune checkpoint inhibitor (ICI) therapy and (ii) fecal microbiota transplantation (FMT), a live biotherapeutic product (LBP) or a microbiome therapy, the method comprising: (a) measuring the number of bacterial species in a stool sample from the patient prior to initiation of the combination therapy; (b) measuring the number of bacterial species in a stool sample from the patient after initiation of the combination therapy; (c) comparing the loss of bacterial species after initiation of the combination therapy to a control; and (d) determining if the patient is a responder to the combination therapy based on the comparison.

101. The method of claim 100, wherein the control is the loss of bacterial species measured in a stool sample from a non-responder patient or a population of non-responder patients, and wherein (i) a higher loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a responder to the combination therapy; and (ii) a similar or lower loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a non-responder to the combination therapy.

102. The method of claim 100, wherein the control is the loss of bacterial species measured in a stool sample from a responder patient or a population of responder patients, and wherein (i) a similar or higher loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a responder to the combination therapy; and (ii) a lower loss of bacterial species in the stool sample from the cancer patient relative to the control is indicative that the cancer patient is a non-responder to the combination therapy.

103. A method for determining whether a candidate live biotherapeutic product (LBP) or microbiome therapy is effective for the treatment of cancer when administered with an immune checkpoint inhibitor (ICI) therapy, the method comprising (a) measuring the number of bacterial species in a stool sample from a subject prior to administration of the candidate LBP ormicrobiome therapy ; (b) administering the LBP or microbiome therapy and the ICI therapy to the subject; (c) measuring the number of bacterial species in a stool sample from the subject after the administration of the candidate LBP or microbiome therapy and the ICI therapy; (d) comparing the loss of bacterial species after the administration of the LBP or microbiome therapy and the ICI therapy to a control; and (e) determining if the candidate live biotherapeutic product (LBP) or microbiome therapy is effective for the treatment of cancer based on the comparison.

104. The method of claim 103, wherein the control is the loss of bacterial species measured in a stool sample from a subject or a population of subject administered with an effective LBP or microbiome therapy, and wherein (i) a similar or higher loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is effective; and (ii) a lower loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is not effective.

105. The method of claim 103, wherein the control is the loss of bacterial species measured in a stool sample from a subject or a population of subject administered with an ineffective LBP or microbiome therapy, and wherein (i) a higher loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is effective; and (ii) a similar or lower loss of bacterial species in the stool sample from the subject relative to the control is indicative that the candidate LBP or microbiome therapy is not effective.

106. The method of any one of claims 100 to 105, wherein the bacterial species that are lost comprises bacteria of the Enterocloster, Lachnoclostridium and / or Enterocloster genus.

107. The method of any one of claims 100 to 106, wherein the bacterial species that are lost comprises Enterocloster citroniae, Enterocloster bolteae, Enterocloster aldenensis, Clostridium innocuum, Clostridium saudiense, Clostridium spiroforme, Clostridium leptum, Clostridium sp Marseille P3244 SGB29302, Ruminococcus gnavus, Dialister invisus, Lachnoclostridium An 138 SGB4774, Lachnoclostridium An131 SGB4775) and / or Sellimonas intestinalis, for example Enterocloster citroniae, E. bolteae, Clostridium innocuum, C. saudiense, C. spiroforme, Ruminococcus gnavus, Dialister invisus, and / or Sellimonas intestinalis.

108. The method of any one of claims 100 to 107, wherein the immune checkpoint inhibitor is an antibody or an antigen-binding fragment thereof that interferes with the binding of the immune checkpoint to one of its ligand.

109. The method of any one of claims 100 to 108, wherein the immune checkpoint inhibitor comprises a Programmed Death 1 (PD-1) or Programmed Death-Ligand 1 (PD-L1) inhibitor.

110. The method of claim 109, wherein the immune checkpoint inhibitor comprises pembrolizumab or nivolumab.

111. The method of any one of claims 100 to 108, wherein the immune checkpoint inhibitor comprises a Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor.

112. The method of claim 111, wherein the immune checkpoint inhibitor comprises ipilimumab.

113. The method of any one of claims 100 to 112, wherein the cancer is gastrointestinal cancer (e.g., gastric cancer, colorectal cancer), lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer (renal cell carcinoma), liver cancer (hepatocellular carcinoma), esophageal cancer (esophageal squamous cell carcinoma) or melanoma, for example lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer, or melanoma.

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