Therapeutic bacterial compositions

Abstract

The present invention relates to bacterial compositions useful in the treatment of cancer. In particular, the compositions can be used as a combination therapy with immune checkpoint therapy. The present invention also relates to a method for identifying a subject who will respond to therapy with an immune checkpoint inhibitor, comprising determining the abundance of bacteria in a biological sample from the subject.

Description

[Technical Field]

[0001] The present invention is based on the discovery that the gut microbiome in subjects who respond to treatment with an immune checkpoint inhibitor differs from the gut microbiome in subjects who do not respond to treatment with an immune checkpoint inhibitor, and therefore the gut microbiome can be used as either a diagnostic or a therapeutic source for immune checkpoint inhibitor treatment. Specifically, the present invention relates to a composition comprising isolated bacteria selected from at least two species, wherein the bacteria from a first species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from a second species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:2. [Background technology]

[0002] Introduction Immune suppression and its evasion by malignant cancer cells are known hallmarks of cancer. Numerous co-inhibitory receptors and their ligands, known as immune checkpoints, contribute to this process. Immune checkpoint inhibitor cancer immunotherapy has been transformative in cancer management because it can produce long-term remissions and is effective against many cancers. These checkpoints include programmed cell death 1 (PD-1), PD-L1, and CTLA-4. The introduction of PD-1 inhibitors into clinical practice has had a revolutionary effect on cancer treatment, but consistent responses and favorable long-term outcomes are observed in only a minority of patients. The majority of patients do not respond to therapy. The highest rate is for melanoma (reaching 40%), but it is much lower for other cancers. Furthermore, the majority of patients develop immune-related adverse events, necessitating the discontinuation of therapy.

[0003] Thus, there is a need for (a) biomarkers to predict response to immune checkpoint inhibitors and (b) approaches to increase the proportion of cancer patients who respond to therapy.

[0004] The PD-1 (UniProt accession number Q15116, GenBank accession number U6486) protein is encoded by the PDCD1 gene and is expressed as a 55-kDa type I transmembrane protein (Agata, 1996, Int Immunol, 8(5):765-72). PD-1 is a member of the immunoglobulin superfamily (Ishida, 1992, EMBO, 11(11):3887-95), and it is an inhibitory member of the extended CD28 / CTLA-4 family of T cell regulators. Other members of this family include CD28, CTLA-4, ICOS, and BTLA. PD-1 exists as a monomer and lacks the unpaired cysteine ​​residue characteristic of other CD28 family members (Zhang, 2004, Immunity, 20:337-47). Its cytoplasmic domain contains an immunoreceptor tyrosine-based inhibitory motif (ITIM) and an immunoreceptor tyrosine-based switch motif (ITSM), which are phosphorylated during signal transduction (Riley, 2009, Immunol Rev, 229(1):114-25).

[0005] PD-1 is expressed in B cells, T cells, and monocytes (Agata, 1996). The role of PD-1 in maintaining immunogenic self-tolerance has been demonstrated in PDCD1- / - mice, which develop autoimmune disorders (Nishimura, 1999, Immunity, 11:141-51; Nishimura, 2001, Science, 291(5502):319-22). Thus, the PD-1 pathway controls antigen responses, maintaining a balance between autoimmunity and tolerance.

[0006] There are two ligands for PD-1 that mediate its regulatory function. PD-L1 (B7-H1) is normally expressed on dendritic cells, macrophages, resting B cells, bone marrow-derived mast cells, and T cells, as well as non-hematopoietic cell lineages (reviewed in Francisco, 2010, Immunol Rev, 236:219-42). PD-L2 (B7-DC) is primarily expressed on dendritic cells and macrophages (Tseng, 2001, J Exp Med, 193(7):839-45). Ligand expression is influenced by local mediators, and ligand expression can be upregulated by inflammatory cytokines.

[0007] PD-1 is known as an immunosuppressive protein that negatively regulates TCR signaling. The interaction between PD-1 and PD-L1 can act as an immune checkpoint, resulting in, for example, a reduction in tumor-infiltrating lymphocytes, a reduction in T cell receptor-mediated proliferation, and / or immune evasion by cancerous cells. Immune suppression can be reversed by inhibiting the local interaction of PD-1 with PD-L1 or PD-L2. The effect is additive when the interaction of PD-1 with both PD-L1 and PD-L2 is blocked.

[0008] The PD-1 pathway may be exploited in cancer or infection, where tumors or viruses can evade effective immune recognition and T cells exhibit an "exhausted" phenotype.

[0009] Disruption of the PD-1:PD-L1 interaction enhances T cell activity. Inhibitory anti-PD-1 monoclonal antibodies have been shown to block the interaction between PD-1 and its ligand (Wang, 2014, Cancer Immunol Res, 2(9):846-56). In vitro T cell function can be enhanced by PD-1 blockade, as demonstrated by improved proliferation and cytokine responses in mixed lymphocyte reactions of T cells and dendritic cells. Cytotoxic T lymphocytes (CTLs) from melanoma patients have also been shown to be enhanced by PD-1 blockade in vitro using the antibody nivolumab and can become resistant to suppression by regulatory T cells (Wang, 2009, Int Immunol, 21(9):1065-1077). This antibody has been shown to be effective in patients with melanoma and non-small cell lung cancer (NSCLC). Pembrolizumab, another PD-1 blocking antibody, shows a response in NSCLC patients refractory to CTLA-4 blockade. Both nivolumab and pembrolizumab functionally block the interaction of human PD-1 with its ligands.

[0010] The gut microbiome of cancer patients is a key driver of response to immune checkpoint therapy.

[0011] Previous studies have analyzed clinical databases to identify gut microbiota associated with treatment efficacy (Frankel, Neoplasia (2017) 19:848; Gopalakrishnan, Science (2018) 359:97; Matson, Science (2018) 359:104; Routy, Science (2018) 359:91). However, a major challenge in this field has been the wide variation in the microbiome signatures identified in independent studies. Published studies differ in response criteria and cancer indicators, and factors known to affect microbiome analysis, such as sample collection, storage, and processing, and geographic location, also vary. Therefore, it has been difficult to understand which are the true signatures amid the noise between studies. [Prior art documents] [Chartered documents]

[0012]

Patent Document 1

Patent document 2

Patent document 3

Non-licensed literature

[0013] [Non-licensed document 1] Agata, 1996, Int Immunol, 8(5):765-72 [Non-licensed document 2] Ishida, 1992, EMBO, 11(11): 3887-95 [Non-licensed document 3] Zhang, 2004, Immunity, 20:337-47

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[0014] Thus, there is a need to provide effective treatments for cancer and biomarkers that predict response to treatment, and the present invention aims to address this need. [Means for solving the problem]

[0015] Summary of the Invention The present invention is based on the discovery that the gut microbiome in subjects who respond to treatment with immune checkpoint inhibitors differs from the gut microbiome in subjects who do not respond to treatment with immune checkpoint inhibitors, and thus the gut microbiome can be used as either a diagnostic or a source of therapy for immune checkpoint inhibitor treatment.

[0016] Therefore, the present invention provides the following: - a composition comprising a particular bacterium as defined herein, which has been identified in patients who respond to treatment with an immune checkpoint inhibitor and which may be used as a treatment for a disease, including the treatment of cancer, the treatment of an infectious disease, or may be used as a vaccine adjuvant; - a combination therapy comprising a composition having a specific bacterium as defined herein and immune checkpoint inhibitor treatment; and - providing certain bacteria as defined herein as diagnostic agents for immune checkpoint inhibitor treatment, to identify patients who will benefit from immune checkpoint inhibitor treatment, and also to identify patients who may receive bacterial or other therapy, e.g., prior to the administration of checkpoint inhibitor therapy; Numerous embodiments are contemplated, including but not limited to:

[0017] These and other related aspects and embodiments of the invention are further described herein.

[0018] The present inventors have identified a microbiome biomarker signature that is highly predictive of response to treatment with immune checkpoint inhibitors. This is of great significance to the field and provides the basis for: predictive biomarkers for checkpoint inhibitor therapy; live bacterial therapeutic (LBT) therapy; and live bacterial therapeutic combination therapy using immune checkpoint inhibitors (e.g., anti-PD-1, anti-PD-L1, or anti-CTLA-4 drugs for cancer treatment) to increase the proportion of patients who respond to checkpoint inhibitors. In particular, the present inventors have identified multiple bacterial species present in the gut microbiome that exhibit modulated abundance, which is indicative of response to treatment with immune checkpoint inhibitors. Therefore, detecting modulated abundance of these bacteria can be used to distinguish responders from non-responders to checkpoint inhibitor therapy. Furthermore, administration of such live bacteria as a drug is predicted to convert patients who do not respond to checkpoint inhibitors into responders.

[0019] The bacteria identified and described herein may be used individually to determine response and / or provide treatment, or combinations of the bacteria may be provided to increase the discriminatory power of diagnostic methods and provide non-invasive diagnostic and treatment methods for response versus non-response.

[0020] The present inventors have identified specific gut bacteria associated with checkpoint inhibitor response. Thus, the present invention provides gut bacteria that can be used to modulate the microbiome in patients, e.g., cancer patients, to improve therapeutic response to immune checkpoint inhibitors. The studies in this disclosure used a cohort of melanoma patients undergoing therapy with an anti-PD-1 drug or combination therapy with an anti-PD-1 plus anti-CTLA-4 drug. Gut microbiome samples collected before immune checkpoint therapy were characterized in these patients via metagenomic whole-genome shotgun sequencing. Significant differences were observed in the composition of the gut microbiome in responders to immune checkpoint blockade therapy (e.g., to PD-1-based therapy) compared with non-responders, with increased or decreased abundance of specific bacteria in the responder gut microbiome compared with non-responders prior to treatment. In particular, the bacteria described herein were found to be more abundant in responders. Thus, these bacteria and subsets thereof find use in compositions that can be used either alone or in combination with immune checkpoint inhibitor treatment for the treatment of diseases, including cancer. Furthermore, these bacteria can be used as biomarkers, i.e., as diagnostic agents to distinguish responders to checkpoint inhibitor, e.g., PD-1 inhibitor therapy, from non-responders to immune checkpoint inhibitor treatment.

[0021] This study demonstrates that patients with a "favorable" gut microbiome (modulated, e.g., having a high relative abundance of one or more bacteria described herein) have enhanced antitumor immune responses. In contrast, patients with an "unfavorable" gut microbiome (containing a low relative abundance of the B1-B15 species described herein) have impaired antitumor immune responses. These findings highlight the possibility that concurrent modulation of the gut microbiome may significantly enhance the efficacy of checkpoint blockade treatments. Based on these findings, methods for disease management (e.g., cancer treatment and diagnosis) are provided herein. Also provided herein are methods for using the compositions described herein as predictive biomarker compositions to identify patients with a favorable response to immune checkpoint blockade. Furthermore, the compositions described herein have immunostimulatory properties. Thus, although disease treatment is not limited to cancer, the compositions provide for the treatment of other diseases, e.g., diseases that benefit from immunostimulatory treatments, e.g., non-cancer immunotherapies.

[0022] In a first aspect, the present invention therefore relates to a composition comprising one or more bacterial isolates, in particular bacterial populations, belonging to one or more bacterial species selected from Table 1. The present invention therefore relates to a composition comprising a bacterium selected from one or more bacteria selected from Table 1. In particular, the present invention therefore relates to a composition comprising one or more bacterial isolates having a 16S rDNA sequence selected from SEQ ID NOs: 1-15.

[0023] The composition can comprise or consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 isolated bacteria selected from various species, i.e., bacteria having a 16S rDNA selected from SEQ ID NOs: 1-29, or a sequence having at least 95%, 97%, 98%, 98.7%, 99% or 100% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-29 (e.g., selected from SEQ ID NOs: 1-15), or a sequence having at least 95%, 97%, 98%, 98.7% or 99% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15.

[0024] In one embodiment, the composition thus comprises or consists of an isolated bacterium selected from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 bacterial species, wherein the bacterium comprises a 16S rDNA sequence selected from SEQ ID NOs: 1-29 (e.g., 1-15) or a sequence with at least 95%, 97%, 98%, 98.7%, 99% or 100% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15.

[0025] In one embodiment, the composition comprises or consists of isolated bacteria selected from at least two species, wherein the bacteria from a first species comprises a 16S rDNA sequence having at least 95%, 97%, 98%, 98.7%, 99% or 100% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from a second species comprises a 16S rDNA sequence having at least 95%, 97%, 98%, 98.7%, 99% or 100% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:2.

[0026] In one embodiment, the composition comprises or consists of isolated bacteria selected from at least nine species, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-29 (e.g., 1-15) or a sequence with at least 95%, 97%, 98%, 98.7%, 99%, or 100% sequence identity to a sequence selected from SEQ ID NOs: 1-29 (e.g., 1-15). In one embodiment, the nine species include bacteria comprising the 16S rDNA sequence set forth in SEQ ID NO: 1 or a sequence with at least 95%, 97%, 98%, 98.7%, 99%, or 100% sequence identity to the 16S rDNA sequence set forth in SEQ ID NO: 1, and bacteria comprising the 16S rDNA sequence set forth in SEQ ID NO: 2 or a sequence with at least 95%, 97%, 98%, 98.7%, 99%, or 100% sequence identity to the 16S rDNA sequence set forth in SEQ ID NO: 2.

[0027] In another aspect, the invention relates to a pharmaceutical composition as described herein, a pharmaceutical carrier, and optionally an immune checkpoint inhibitor.

[0028] In another aspect, the invention relates to the compositions described herein for use in treating disease (e.g., certain cancers or infectious diseases). The compositions can also be used as vaccine adjuvants, which can be used to enhance vaccine responses and can be administered in conjunction with a vaccine.

[0029] In another aspect, the invention relates to the compositions described herein in increasing the effectiveness of anti-cancer treatment with immune checkpoint inhibitors.

[0030] In another aspect, the present invention relates to a method for treating cancer comprising modulating the level / abundance of one or more bacteria selected from the bacteria in Table 1 in a subject.

[0031] In another aspect, the invention relates to a kit comprising a composition described herein and optionally including an anti-cancer treatment comprising an immune checkpoint inhibitor.

[0032] In another aspect, the invention relates to a method for identifying a subject that will respond to therapy with an immune checkpoint inhibitor, the method comprising determining the abundance of one or more bacteria selected from the bacteria in Table 1 in a biological sample comprising gut flora from said subject, wherein an increase in the abundance of one or more bacteria selected from the bacteria in Table 1 indicates that the subject will respond to therapy with an immune checkpoint inhibitor.

[0033] In another aspect, the invention relates to the use of a bacterium selected from one or more bacteria selected from the bacteria in Table 1 in identifying patients who will respond to therapy with an immune checkpoint inhibitor.

[0034] In another aspect, the present invention provides a method for producing a pharmaceutical composition comprising: a sealable container configured to receive a biological sample; polynucleotide primers for amplifying a 16S rDNA polynucleotide sequence to form an amplified 16S rDNA polynucleotide sequence from at least one gut-associated bacterium, wherein the amplified 16S rDNA sequence has at least 95%, 97%, 98%, 98.7%, 99%, or 100% sequence identity to a polynucleotide sequence selected from SEQ ID NO:1 through SEQ ID NO:29 (e.g., 1-15); Detection reagents for detecting the amplified 16S rDNA sequences; instructions for use The present invention relates to a kit comprising:

[0035] In another aspect, the invention relates to a food or vaccine combination therapy for boosting a vaccine response comprising the compositions described herein.

[0036] In another aspect, the invention relates to a method for identifying a fecal donor, e.g., for cancer treatment, comprising evaluating a fecal sample from a subject for the presence of one or more bacteria selected from Table 1, and identifying the fecal donor based on the presence and / or abundance of one or more bacteria selected from Table 1.

[0037] In another aspect, the present invention relates to the use of one or more bacteria selected from Table 1 in a method for identifying a donor for FMT therapy, e.g., for the treatment of cancer.

[0038] In another aspect, the present invention relates to a method for treating a fecal transplant prior to administration to a subject, the method comprising supplementing the fecal transplant with one or more isolated bacteria selected from Table 1.

[0039] In another aspect, the present invention relates to a method for screening / identifying a fecal donor, comprising evaluating a fecal sample from a subject for the presence of one or more bacteria associated with response to cancer; and identifying the fecal donor based on the presence and / or abundance of the one or more bacteria. [Brief explanation of the drawings]

[0040] [Figure 1]A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responder to immunotherapy was predicted from baseline stool samples based on machine learning predictions using the abundance of bacteria in defined signatures. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 91.16%. The x-axis is group. B) As in A, except each study was considered separately. Frankel accuracy: 84.62%, Gajewski accuracy: 89.74%, Melresist accuracy: 93.18%, Wargo accuracy: 100%. The x-axis is group. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions based on the same microbiome signatures. AUC=0.98. The x-axis is 1-specificity, and the y-axis is sensitivity. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.958, AUC gajewski 0.978, AUC melresist 0.983, AUC wargo 1. The x-axis is 1-specificity and the y-axis is sensitivity. [Figure 2]A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of 15 bacterial species in Consortium 1. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 77.55%. B) As in A, except each study was considered separately. Frankel accuracy: 79.49%, Gajewski accuracy: 66.67%, Melresist accuracy: 81.82%, and Wargo accuracy: 84%. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 1. AUC = 0.8. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.867, AUC gajewski 0.725, AUC melresist 0.879, AUC wargo 0.773. [Figure 3] A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of nine bacterial species in Consortium 2. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 74.15%. B) As in A, except each study was considered separately. Frankel accuracy 76.92%, Gajewski accuracy 69.23%, Melresist accuracy 75%, and Wargo accuracy 76%. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 2. AUC = 0.75. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.831, AUC gajewski 0.676, AUC melresist 0.788, AUC wargo 0.734. [Figure 4]A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of 12 bacterial species in Consortium 3. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 74.15%. B) As in A, except each study was considered separately. Frankel accuracy: 76.92%, Gajewski accuracy: 66.67%, Melresist accuracy: 81.82%, and Wargo accuracy: 68%. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 3. AUC = 0.773. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.844, AUC gajewski 0.685, AUC melresist 0.862, AUC wargo 0.76. [Figure 5] A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of nine bacterial species in Consortium 4. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 71.43%. B) As in A, except each study was considered separately. Frankel accuracy: 76.92%, Gajewski accuracy: 64.1%, Melresist accuracy: 77.27%, and Wargo accuracy: 64%. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 4. AUC = 0.737. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.781, AUC gajewski 0.667, AUC melresist 0.791, AUC wargo 0.708. [Figure 6]A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of nine bacterial species in Consortium 5. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 68.71%. B) As in A, except each study was considered separately. Frankel accuracy: 71.79%, Gajewski accuracy: 58.97%, Melresist accuracy: 70.45%, and Wargo accuracy: 76%. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 3. AUC = 0.69. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.75, AUC gajewski 0.596, AUC melresist 0.766, AUC wargo 0.675. [Figure 7] A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of nine bacterial species in Consortium 6. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 69.39%. B) As in A, except each study was considered separately. Frankel accuracy: 69.23%, Gajewski accuracy: 58.97%, Melresist accuracy: 77.27%, and Wargo accuracy: 72%. C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 3. AUC = 0.71. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.767, AUC gajewski 0.577, AUC melresist 0.81, AUC wargo 0.708. [Figure 8]Receiver operating characteristic (ROC) curve for the NSCLC cohort showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 1. The NSCLC dataset is metagenomic sequences obtained from Routy and Zitvogel et al. (2018, Science 359:91-97) and classified using Microbiotica's high-precision platform. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC=0.744. [Figure 9]Isolated bacteria induce dendritic cell maturation and cytokine production. The ability of isolated bacteria to activate dendritic cells (DCs) was determined by co-culturing them with human monocyte-derived dendritic cells at a multiplicity of infection (MOI) of approximately 10:1 under anaerobic conditions. Subsequent dendritic cell maturation was determined by the expression levels of the maturation markers CD86 (A) and CD83 (B) as determined by flow cytometry. To normalize across different donors and experiments, data are presented as fold changes in mean fluorescence intensity (MFI) compared to the LPS control. DC expression of CD86 (C) and CD83 (D) after treatment with Consortium 5 (MOI of approximately 10:1) was similarly determined by flow cytometry. The MFI of these markers after stimulation with Consortium 5 is indicated by the white bars. DC expression of CD86 (E) and CD83 (F) after treatment with Consortium 6, 7, 8, and 9 was determined by flow cytometry. The MFIs of these markers are shown in white bars. (G) IL-12 and IL-10 production by DCs after treatment with isolated bacteria alone (MOI of approximately 10:1) or consortia 5 and 6 (MOI of approximately 10:1) was determined by ELISA. MOI 10, N = 5 (five donors in five independent experiments). Data are presented as the ratio of IL-12 to IL-10. LPS (10 ng / ml), poly I:C (20 μg / ml), and Salmonella typhimurium (MOI of approximately 10:1) are strong inducers of DC activation and served as positive controls for all assays (gray bars). Unstimulated or immature (Imm) DCs are shown for comparison (gray bars). Results are the mean ± SEM of five (A, B), two (C, D, E, and F), and three (G) independent experiments. [Figure 10]Dendritic cells treated with isolated bacteria activate cytotoxic CD8+ T lymphocytes (CTLs). After co-culture with isolated bacteria or control stimuli as shown in Figure 9, human monocyte-derived DCs were washed and co-cultured with purified allogeneic CD8+ T cells for 6 days. CTL activation was determined by analyzing the expression of granzyme B (A), IFN-γ (B), and perforin (C) using intracellular staining and flow cytometry. To normalize across different donors and experiments, data are presented as the fold change in the percentage of positive cells compared to the LPS control. LPS (10 ng / ml), poly I:C (20 μg / ml), and Salmonella typhimurium (MOI of approximately 10:1) are strong inducers of DC activation and served as positive controls for all assays (gray bars). Unstimulated or immature (Imm) DCs are shown for comparison (gray bars). Results are the mean ± SEM of seven donors in four independent experiments. [Figure 11] Dendritic cells treated with consortiums 6, 7, 8, and 9 activate cytotoxic CD8+ T lymphocytes (CTLs). After coculture with consortiums 6, 7, 8, and 9 or control stimuli as shown in Figure 9, human monocyte-derived DCs were washed and cocultured with purified allogeneic CD8+ T cells for 6 days. CTL activation was determined by analyzing the expression of granzyme B (A), IFN-γ (B), and perforin (C) using intracellular staining and flow cytometry. Data are shown as the percentage of positive cells. LPS (10 ng / ml), poly I:C (20 μg / ml), and Salmonella typhimurium (MOI of approximately 10:1) are potent inducers of DC activation and served as positive controls for all assays (gray bars). Unstimulated or immature (Imm) DCs are shown for comparison (gray bars). Results are the mean ± SEM of duplicates from one representative experiment. [Figure 12]Dendritic cells treated with Consortium 5 activate cytotoxic CD8+ T lymphocytes (CTLs). After coculture with Consortium 5 or a control stimulus as shown in Figure 11, human monocyte-derived DCs were washed and cocultured with purified allogeneic CD8+ T cells for 6 days. CTL activation was determined by analyzing the expression of granzyme B (A), IFN-γ (B), and perforin (C) using intracellular staining and flow cytometry. Data are shown as the percentage of positive cells. LPS (10 ng / ml), poly I:C (20 μg / ml), and Salmonella typhimurium (MOI of approximately 10:1) are strong inducers of DC activation and served as positive controls for all assays (gray bars). Unstimulated or immature (Imm) DCs are shown for comparison (gray bars). Results are the mean ± SEM of duplicates from one representative experiment. [Figure 13] Isolated bacteria and consortia 6, 7, 8, and 9 confer tumor-killing ability to induced CTLs. CD8+ T cells primed by bacteria / consortium-treated DCs (as in Figures 10 and 11) were evaluated for their ability to kill SKOV-3 cells. Cytolysis was determined by measuring the decrease in electrical impedance of SKOV-3 cells. Data are presented as the percentage of SKOV-3 cell lysis after 72 hours of coculture with CD8+ T cells. LPS (10 ng / ml), poly I:C (20 μg / ml), and Salmonella typhimurium (MOI of approximately 10:1) are strong inducers of DC activation and served as positive controls for all assays (gray bars). Unstimulated or immature (Imm) DCs are shown for comparison (gray bars). Results are the mean ± SEM of three independent experiments. [Figure 14]Consortium 5 and Blautia spp. confer tumor-killing ability to induced CTLs. CD8+ T cells primed with DCs treated with Consortium 5 or Blautia spp. (as in Figure 12) were evaluated for their ability to kill SKOV-3 cells. Cytolysis was determined by measuring the decrease in electrical impedance of SKOV-3 cells. Data are presented as the percentage of SKOV-3 cell lysis after 72 hours of coculture with CD8+ T cells. LPS (10 ng / ml), poly I:C (20 μg / ml), and Salmonella typhimurium (MOI of approximately 10:1) are strong inducers of DC activation and served as positive controls for all assays (gray bars). Unstimulated or immature (Imm) DCs are shown for comparison (gray bars). Results are the mean ± SEM of duplicates from one representative experiment. [Figure 15] Bacterial isolates have variable abilities to induce IFN-α production by plasmacytoid dendritic cells (pDCs). IFN-α production by plasmacytoid dendritic cells (pDCs) after overnight incubation with heat-killed bacteria (MOI of approximately 10:1) was determined by ELISA. 10 ng / ml IL-3 and 10 μg / ml CpG (gray bars) were also performed as negative and positive controls, respectively. Results are the mean ± SEM of three donors in two independent experiments. [Figure 16]In vivo efficacy in a mouse cancer model. Prior to tumor implantation, SPF C57BL / 6N female mice were administered antibiotics in their drinking water (kanamycin (0.4 mg / ml), colistin (850 U / ml), metronidazole (0.215 mg / ml), vancomycin (0.045 mg / ml), and gentamicin (0.035 mg / ml)) for 7 days (days -9 to -2). Mice were then reconstituted with human donor feces obtained from a melanoma patient (20 mg) via oral gavage on day -1. 5 × 105 MCA-205 murine fibrosarcoma cells were implanted subcutaneously in the flank on day 0. Starting on day -1, the mice were treated twice a week for 3 weeks. Consortium 5 (n=8) and Consortium 6 (n=8) were administered by oral gavage once (total dose of approximately 1 x 10 CFU / dose) and compared with animals treated with vehicle control. Anti-PD-1 antibody (RMP1-14) was administered twice (10 mg / kg i.p.) for 2 weeks beginning on day 6. Plots show tumor growth over time, measured by volume, in response to vehicle control, anti-PD1, and Consortium 5 (A) or Consortium 6 (B). Data are mean tumor size ± SEM and are from at least three (A) and two (B) experiments. [Figure 17] A) All 147 patients from four melanoma studies were divided into responders and non-responders according to clinical outcome. The probability of non-responders to immunotherapy was predicted from baseline fecal samples based on machine learning predictions using the abundance of nine bacterial species in Consortium 10. A cutoff of 0.5 was used to determine the accuracy of the predictions. The accuracy was 75.51%. B) As in A, except each study was considered separately. Frankel accuracy: 74.36%, Gajewski accuracy: 71.79%, Melresist accuracy: 77.27%, and Wargo accuracy: C) Receiver operating characteristic (ROC) curve of the combined melanoma dataset showing the false positive rate as a function of the true positive rate based on machine learning predictions from Consortium 3. AUC = 0.81. D) As in C, except each study was considered separately. Random forest out-of-bag error was used to prevent overoptimism and improve generalizability. AUC frankel 0.85, AUC gajewski 0.725, AUC melresist 0.826, AUC wargo 0.805. DETAILED DESCRIPTION OF THE INVENTION

[0041] In the figure, Con stands for consortium. The consortium is shown in Table 3.

[0042] Detailed Description The present invention will now be further described. In the following text, various aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects, unless expressly stated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous.

[0043] Generally, the nomenclature used in connection with, and the techniques of, microbiology, cell and tissue culture, pathology, molecular biology, cancer immunology, genetics, and protein and nucleic acid chemistry and hybridization described herein are well known and commonly used in the art. The methods and techniques of the present disclosure are generally performed according to conventional methods well known in the art and described in the various general and more detailed references cited and discussed throughout the specification, unless otherwise indicated. See, e.g., Green and Sambrook et al., "Molecular Cloning: A Laboratory Manual," 4th ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (2012).

[0044] The nomenclature used in connection with, and the laboratory procedures and techniques of, analytical chemistry, microbiology, bioinformatics, and medicinal and drug discovery chemistry described herein are those well known and commonly used in the art.

[0045] The present invention relates to bacterial compositions, each comprising or consisting of one or more bacterial isolates (e.g., a consortium of defined bacterial isolates) from one or more species disclosed herein. The compositions have immunostimulatory properties and are therefore therapeutic compositions useful in the treatment of disease. In some embodiments, the compositions are mixtures of bacterial isolates selected from more than one species identified in Table 1.

[0046] The composition is not a fecal microbiota transplant (FMT), but contains a mixture of defined bacterial isolates that does not contain fecal material.Therefore, preparations containing a defined bacterial mixture are generally considered to be safer treatments than FMT.The advantage of this composition is that it contains only fully defined and characterized bacteria, and does not contain undefined or unnecessary components that may be present in donor feces, thereby making it possible to standardize therapeutic compositions and increasing the safety of the composition.

[0047] FMT relies on a fecal sample from a human donor that is administered directly to the recipient, for example, via colonoscopy, and the bacteria present in the fecal sample are not isolated prior to administering FMT to the recipient. Although FMT is widely used, there are several disadvantages associated with FMT. The composition of the FMT material is highly donor-dependent and therefore inconsistent. Despite donor screening, it is difficult to determine the bacterial load of the sample. Donors must also be screened for pathogens and the risk of colonization with drug-resistant bacteria must be assessed. In certain embodiments described below, the present invention also relates to enhancing FMT therapy with one or more bacterial isolates from one or more species disclosed herein, and to methods for screening / identifying fecal donors.

[0048] The compositions described herein include isolated bacteria. The term "isolated" refers to bacteria that have been isolated from their natural environment. Isolated bacteria, e.g., isolated bacterial strains, are substantially free of other cellular material, chemicals, and / or fecal material. Thus, as used herein, the term "isolated" bacteria refers to bacteria that have been separated from one or more undesirable components (e.g., another bacterium or bacterial strain), one or more components of a growth medium, and / or one or more components of a sample (e.g., a fecal sample). In some embodiments, the bacteria are substantially isolated from a source such that other components of the source are not detectable. As used herein, the term "species" refers to a taxonomic entity conventionally defined by genomic sequence and / or phenotypic characteristics. A "strain" is a specific example of a species that has been isolated and purified according to conventional microbiology techniques. It is understood that the terms "bacterium" and "bacterial isolate" refer to a plurality of bacteria that are a bacterial population.

[0049] In one embodiment, the bacteria of the composition are metabolically inactive prior to administration. For example, the bacteria are freeze-dried. In one embodiment, the composition comprises vegetative bacterial cells and does not comprise bacterial spores. In one embodiment, the composition comprises vegetative bacterial cells and / or bacterial spores. In one embodiment, the composition comprises vegetative bacterial cells and does not comprise or is substantially devoid of bacterial spores. In one embodiment, the composition comprises less than about 0.5%, less than about 1%, less than about 2%, less than about 3%, less than about 4%, or less than about 5% spores.

[0050] The composition is preferably a live bacterial therapeutic, bacteriotherapy, or live bacterial biopharmaceutical. As described herein, a live bacterial product (also referred to as a bacterial composition, live bacterial consortium, bacterial mixture, or bacterial consortium) comprises one or more bacterial strains from one or more bacterial species described herein. The term live bacterial therapy is used interchangeably with bacteriotherapy herein and defines a therapy that uses live bacteria to restore health or alleviate disease / disease symptoms or increase response to therapy.

[0051] The bacterial compositions of the invention provide an immunostimulatory effect. In some embodiments, the bacterial compositions induce or stimulate an immunotherapeutic effect, such as an anti-cancer effect (e.g., cancer cell inhibition or cytotoxicity), when administered to a subject. In some embodiments, the bacterial compositions induce or stimulate an immune response that provides an anti-cancer effect or other beneficial therapeutic effect when administered to a subject, as further described herein.

[0052] As described herein, a composition can include one or more bacterial species selected from the bacterial species listed in Table 1. The ability of a particular bacterium or combination of bacterial species in a live bacterial product to induce a beneficial effect, i.e., an immunostimulatory effect, e.g., an anti-cancer effect, can be assessed using any method known in the art, such as an in vitro assay (e.g., using cell culture) or an in vivo study. Suitable assays are provided in the Examples.

[0053] In some embodiments, the live anti-cancer bacterial product induces specific immune cell populations (e.g., CD8+ T cells, Th17, Th1 cells). The abundance of a specific population of cells (e.g., CD8+ T cells, Th17, Th1 cells) can be assessed by any method known in the art, such as by detecting a cell marker indicative of the cell type, assessing the direct or indirect activity of the cell type, and / or measuring the production of one or more cytokines produced by the specific cell type. In some embodiments, the live anti-cancer bacterial product induces CD8+ T cells (or "CD8+ T cells"). As will be appreciated by those skilled in the art, a combination of bacterial species and / or multiple strains from one or more species described herein can be selected and combined to produce a live anti-cancer bacterial product that induces CD8+ T cells.

[0054] In one embodiment, the isolated bacteria, e.g., isolated bacterial strains from species listed herein, may be viable bacteria that, when administered to a subject, are capable of colonizing the gastrointestinal tract of said subject.

[0055] The inventors have shown that combining bacteria from different species can provide therapeutic compositions that find use as combination therapies with checkpoint inhibitors. In a first aspect, the present invention relates to compositions comprising isolated bacteria (e.g., bacterial strains) selected from one or more of the bacterial species B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 shown in Table 1, or a subset thereof. Accordingly, the present invention relates to compositions comprising one or more bacterial isolates (e.g., bacterial populations) having a 16S rDNA sequence selected from SEQ ID NOS: 1-19 (e.g., 1-15). Accordingly, the present invention relates to compositions comprising or consisting of one or more bacterial isolates of the species shown in Table 1.

[0056] Table 1 below lists 15 different bacterial species from which the isolated bacteria present in the compositions are selected. Reference to exemplary 16S rDNA sequences characterizing each species is also provided in Table 1. As used herein, the term 16S rDNA sequence or 16S rDNA refers to a DNA nucleic acid sequence, i.e., a nucleic acid molecule, that encodes a 16S rRNA nucleic acid sequence, i.e., a nucleic acid molecule. The nucleic acid sequences referenced below are listed in Table 2. Also, as further described below, the bacteria of the compositions described herein and bacteria of other embodiments can have a 16S rDNA sequence with specific sequence identity to the SEQ ID NOs listed below.

[0057] [Table 1A]

[0058] [Table 1B]

[0059] Aspects and embodiments of the invention described herein are defined by reference to the species names and / or SEQ ID NOs. set forth in Table 1. In some cases, various exemplary sequences for the same species, e.g., corresponding to various exemplary strains belonging to the same species, are provided in Table 1. When multiple sequences are provided for a species, the sequences share a high degree of sequence identity, e.g., various strains defined by SEQ ID NOs: 2 and 17 have at least 99% sequence identity, SEQ ID NOs: 4 and 16 have at least 99% sequence identity, SEQ ID NOs: 6 and 18 have at least 99% sequence identity, and SEQ ID NOs: 12 and 20 have at least 99% sequence identity.

[0060] In the aspects and embodiments described herein, for each of B1 to B15, any of the sequences defined above (SEQ ID NOS: 1 to 29) may be used. Thus, when multiple sequences are provided for a single species, any of these sequences may be used.

[0061] It is understood that the inventors provide compositions having specific bacterial species with immunostimulatory effects (e.g., anti-cancer effects). It is also understood that for each species, various strains, i.e., the strains identified above or other strains belonging to the same species, can be used. It should be understood that closely related bacterial strains (e.g., defined by 16S rDNA sequences) with similar or identical biological properties can also be included. In some embodiments, the bacterial strains provided herein can be replaced with bacterial strains with similar or identical biological properties.

[0062] In some embodiments, the anti-cancer / live bacterial composition comprises one or more bacterial strains of one or more of the 15 listed species shown in Table 1. In some embodiments, the anti-cancer / live bacterial composition comprises one or more bacterial strains of more than one of the 15 listed species (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 species).

[0063] In one embodiment, the composition comprises or consists of 15 isolated bacteria, e.g., bacteria from each of the 15 bacterial species listed in Table 1, e.g., with respect to the 16S rDNA sequences shown in Table 1 or sequences having a particular percentage identity thereto as described below, or with respect to the species names shown above.

[0064] The present invention also relates to compositions comprising or consisting of bacteria selected from a subset of the bacterial species listed in Table 1; for example, compositions comprising or consisting of various bacteria selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of the bacterial species listed in Table 1, with respect to the 16S rDNA sequences shown in Table 1 or sequences having a particular percentage of identity thereto as explained below, or with respect to the species names shown above. All combinations are envisioned.

[0065] Thus, in one embodiment, a composition comprises or consists of at least one isolated population of bacteria belonging to one or more of the species in Table 1. For example, the composition comprises or consists of bacteria selected from 2, 3, 6, 9, or 12 bacterial species listed in Table 1. These may be selected from the consortia shown in Table 3 (e.g., consortia 2, 4, 5, 6, and 10). The bacteria may be defined by reference to their 16S rDNA as shown in the sequence identifiers in Table 1. Thus, various bacteria selected from the bacteria listed in Table 1 may be combined in a single composition.

[0066] For example, a composition may comprise or consist of isolated bacteria selected from at least two species, e.g., up to three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, or fifteen species set forth in Table 1, e.g., with respect to the sequences set forth in Table 1. For example, a composition may comprise or consist of isolated bacteria from nine bacterial species listed in Table 1. In one example, a composition may comprise or consist of isolated bacteria from nine species (i.e., consortia 2, 4, 5, 6, and 10) set forth in Table 3. Bacteria may be defined by reference to their 16S rDNA sequences set forth in the sequence identifiers in Table 1.

[0067] In one embodiment, the composition comprises or consists of isolated bacteria selected from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 species listed in Table 1, e.g., with respect to the SEQ ID NOs shown in Table 1. In one embodiment, the composition comprises or consists of isolated bacteria selected from a consortium in Table 3. In one embodiment, the composition comprises or consists of isolated bacteria having a 16S rDNA selected from the SEQ ID NOs shown in Table 1. Bacteria may be defined by reference to their 16S rDNA as shown in the sequence identifiers in Table 1. Sequences with specific sequence identity percentages as shown herein are also within the scope of the invention.

[0068] In one embodiment, the composition comprises an isolated bacterium selected from at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, or at least 15 species listed in Table 1, e.g., with respect to the sequences shown in Table 1, e.g., with respect to the SEQ ID NOs shown in Table 1. In one embodiment, the composition comprises an isolated bacterium selected from at least 9 species shown in Table 1. Sequences having a particular percentage of sequence identity as shown herein are also within the scope of the invention.

[0069] In one embodiment, the composition comprises or consists of isolated bacteria selected from no more than 2, no more than 3, no more than 4, no more than 5, no more than 6, no more than 7, no more than 8, no more than 9, no more than 10, no more than 11, no more than 12, no more than 13, no more than 14, or no more than 15 species listed in Table 1, e.g., with respect to the sequences shown in Table 1, e.g., with respect to the SEQ ID NOs shown in Table 1. Sequences having a particular percentage of sequence identity as set forth herein are also within the scope of the invention.

[0070] In one embodiment, the composition comprises or consists of isolated bacteria selected from 2 to 4, 2 to 5, 2 to 6, 2 to 7, 2 to 8, 2 to 9, 2 to 10, 2 to 11, 2 to 12, 2 to 13, 2 to 14, or 2 to 15 species shown in Table 1, e.g., with respect to the sequences shown in Table 1, e.g., with respect to the SEQ ID NOs shown in Table 1. Sequences having a particular percentage of sequence identity as shown herein are also within the scope of the invention.

[0071] In one embodiment, the composition comprises an isolated bacterial mixture comprising or consisting of 2 to 15 bacterial strains having at least 90%, at least 95%, at least 97%, at least 98%, at least 98.7%, or at least 99% sequence identity to a 16s rDNA sequence selected from SEQ ID NOs: 1 to 15 (e.g., SEQ ID NOs: 16 to 29). Exemplary compositions are shown herein, for example, in Table 3.

[0072] Those skilled in the art will understand that bacterial species for use in the compositions and methods of the present invention selected from Table 1 can have sequences shown in Tables 1 and 2, or sequences with a particular percentage of identity to those sequences, and retain biological activity, i.e., activity against cancer / effectiveness in enhancing the efficacy of therapy using immune checkpoint inhibitors.

[0073] In one embodiment, the composition can be as described above, but does not include bacteria of any other species (i.e., species not listed in Table 1), or the composition includes only trace or biologically irrelevant amounts of bacteria from another species. By biologically irrelevant is meant bacteria that have no effect on the treatment of cancer. Thus, in one embodiment, the composition consists of the bacteria described.

[0074] In one embodiment, the composition does not include other bacterial species belonging to the genera listed in Table 1.

[0075] In one embodiment, the composition may include other bacterial species belonging to the genera listed in Table 1, but does not include bacterial species from genera not listed in Table 1. In one embodiment, the composition may include other bacterial species belonging to different genera.

[0076] Methods for determining sequence identity are known in the art. It is known that clades, operational taxonomic units (OTUs), species, and strains are, in some embodiments, identified by their 16S rDNA sequences. Relatedness can be determined by percent identity, which can be determined using methods known in the art.

[0077] Bacterial species and strains used in the compositions described herein can be identified based on the 16S nucleic acid sequence (full length or a portion thereof, e.g., the V region). The 16S ribosomal DNA gene encodes the DNA component of the 30S subunit of the bacterial ribosome. It is widely present in all bacterial species. Various bacterial species have one to multiple copies of the 16S rRNA gene. 16S rRNA gene sequencing is by far one of the most common methods for targeting housekeeping genes to study bacterial phylogeny and genus / species classification. Therefore, bacteria can be taxonomically classified based on the sequence of a gene encoding the 16S nucleic acid sequence, e.g., ribosomal DNA (rDNA) in bacteria. This gene sequence is also referred to as the ribosomal DNA sequence (rDNA). Bacterial 16S rDNA is approximately 1500 nucleotides long and is used to reconstruct the evolutionary relationships and sequence similarities between one bacterial isolate and another using phylogenetic approaches. 16S rDNA sequences are used for phylogenetic reconstruction because they are generally highly conserved but contain specific hypervariable regions that harbor sufficient nucleotide diversity to distinguish most microbial genera and species.

[0078] Using well-known techniques for determining the sequence of the full-length 16S rDNA sequence or any hypervariable region of the 16S rDNA sequence, genomic DNA is extracted from a bacterial sample, the 16S rDNA (full-length region or specific hypervariable regions) is amplified using polymerase chain reaction (PCR), the PCR product is purified, and the nucleotide sequence is determined to determine the genetic composition of the 16S rDNA gene or a subdomain of this gene. If full-length 16S rDNA sequencing is performed, the sequencing method used can be, but is not limited to, Sanger sequencing. If one or more hypervariable regions, such as the V4 region, are used, sequencing can be performed using the Sanger method or next-generation sequencing methods, such as, but not limited to, Illumina (sequencing by synthesis) using barcoded primers that allow for multiplex reactions. The V1-V9 regions of 16S rDNA refer to the first nine hypervariable regions of the 16S rDNA gene, which are often used for genotyping bacterial samples. In some embodiments, at least one of V1-V9 is used to characterize a bacterial isolate.

[0079] In some embodiments, bacterial species identified as described herein are identified by sequence identity to 16S rDNA sequences known in the art and described herein. In some embodiments, selected species are identified by sequence identity to the full-length 16S rDNA sequences shown in Table 2. In some embodiments, selected species are identified by sequence identity to portions (e.g., V3 and / or V4) of the 16S rDNA sequences shown in Table 2.

[0080] As used herein, the term "homology" or "identity" generally refers to the percentage of nucleic acid residues in a sequence that are identical to the residues in the reference sequence being compared, after aligning the two sequences to achieve the maximum homology percentage, and in some embodiments, introducing gaps, if necessary, and not considering any conservative substitutions as part of the sequence identity. Thus, the percentage homology between two nucleic acid sequences is equal to the percentage identity between the two sequences. Methods and computer programs for alignment are well known. The percentage identity between two sequences can be determined using well-known mathematical algorithms.

[0081] In one embodiment, the degree of sequence identity between a query sequence and a reference sequence can be determined with the aid of a commercially available sequence comparison program. This typically involves aligning the two sequences using a default scoring matrix and default gap penalties, determining the number of exact matches, and dividing the number of exact matches by the length of the reference sequence. Suitable computer programs useful for determining identity include, for example, BLAST (blast.ncbi.nlm.nih.gov).

[0082] In various embodiments set forth herein when reference is made to a SEQ ID NO, sequences having a particular percentage of sequence identity to the full length sequence are also within the scope of the invention.

[0083] Thus, full-length or partial 16S rDNAs of bacterial species listed in Table 2 with respect to sequence identifiers in Table 1 and used in the compositions and methods of the invention have at least 90%, e.g., at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 98.7%, at least 99%, at least 99.5%, or at least 100% sequence identity to the corresponding reference 16S rDNA (i.e., SEQ ID NOS: 1-29). In some embodiments, the threshold sequence identity is at least 94.5%. In one embodiment, the sequence identity is at least 95%. In one embodiment, the sequence identity is at least 96%. In one embodiment, the sequence identity is at least 97%. In one embodiment, the sequence identity is at least 98%. In one embodiment, the sequence identity is at least 98.7%. In one embodiment, the sequence identity is at least 99%.

[0084] In one embodiment, the composition therefore comprises two or more bacteria of a bacterial species comprising a 16S rDNA sequence selected from SEQ ID NOs: 1 to 15 or comprising a 16S rDNA sequence that has at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity to a nucleic acid sequence selected from SEQ ID NOs: 1 to 15. Such sequences include SEQ ID NOs: 16 to 29, such as SEQ ID NOs: 16 to 20.

[0085] In some embodiments, the threshold sequence identity is 94.5%, 94.6%, 94.7%, 94.8%, 94.9%, 95.0%, 95.1%, 95.2%, 95.3%, 95.4%, 95.5%, 95.6%, 95.7%, 95.8%, 95.9%, 96.0%, 96.1%, 96.2%, 96.3%, 96.4%, 96.5%, 96.6%, 96.7%, 96.8%, 96.9%, 97.0%, 97.1%, 97.2%, 97.3%, 97.4%, 97.5%, 97.6%, 97.7%, 97.8%, 97.9%, 98.0%, 98.1%, 98.2%, 98.3%, 98.4%, 98.5%, 98.6%, 98.7%, 98.8%, 98.9%, 99.0%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 9 ...1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99. %, 97.2%, 97.3%, 97.4%, 97.5%, 97.6%, 97.7%, 97.8%, 97.9%, 98.0%, 98.1%, 98.2%, 98.3%, 98.4%, 98.5%, 98.6%, 98.7%, 98.8%, 98.9%, 99.0%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9% or 100%.

[0086] In one embodiment, the bacterium present in the composition belonging to the same species as the bacterium disclosed herein has at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as 97% or 98.7%, identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15 and retains efficacy in enhancing the efficacy of therapy using immune checkpoint inhibitors active against cancer. Such sequences include SEQ ID NOs: 16-29, such as SEQ ID NOs: 16-20.

[0087] In one embodiment, the composition comprises or consists of one or more of the following 15 bacteria having 16S rDNA of the following SEQ ID NOs: SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 2 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 3 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 4 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 5 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 6 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 7 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 8 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 9 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 10 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 11 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 12 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 13 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 14 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 15 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto.

[0088] The above examples are SEQ ID NOs: 16 to 29.

[0089] Thus, the composition comprises or consists of a population of bacteria belonging to one or more of the 15 bacteria with 16S rDNA set out above.

[0090] In one embodiment, the composition does not include Faecalibacterium prausnitzii (e.g., SEQ ID NO: 15). In one embodiment, the composition does not include Alistipes indicusus (e.g., SEQ ID NO: 5), Alistipes obesi (e.g., SEQ ID NO: 4 or 16), and / or Alistipes timonensis (e.g., SEQ ID NO: 10).

[0091] In one embodiment, the composition comprises a consortium shown in Table 3.

[0092] Thus, in one embodiment, the composition comprises a bacterium having a 16S rDNA sequence of SEQ ID NO: 1 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identical thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 2 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identical thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 3 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 4 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; A bacterium having a 16S rDNA sequence of SEQ ID NO: 5 or having at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7%, identity thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 6 or having at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.a bacterium having a 16S rDNA sequence with SEQ ID NO: 7 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence with SEQ ID NO: 8 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; 9 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and SEQ ID NO: 9 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Contains or consists of bacteria (Consortium 2 in Table 3) with rDNA sequences.

[0093] In one embodiment, the composition is directed to a bacterium having SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 2 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 3 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Bacteria having a 16S rDNA sequence; Bacteria having a 16S rDNA sequence of SEQ ID NO:6 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; Bacteria having a 16S rDNA sequence of SEQ ID NO:7 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; SEQ ID NO:8 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.a bacterium having a 16S rDNA sequence with SEQ ID NO: 9 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence with SEQ ID NO: 11 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 12 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; A bacterium having an rDNA sequence of SEQ ID NO: 13 or at least 90% thereto, e.g., at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, e.g., at least 97% or at least 98%.a bacterium having a 16S rDNA sequence having 7% identity thereto or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence having SEQ ID NO: 14 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; 15 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and SEQ ID NO: 15 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Containing or consisting of bacteria (Consortium 3 in Table 3) with rDNA sequences.

[0094] In another embodiment, the composition comprises a bacterium having SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 2 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; SEQ ID NO: 3 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Bacteria having a 16S rDNA sequence; Bacteria having a 16S rDNA sequence of SEQ ID NO:6 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; Bacteria having a 16S rDNA sequence of SEQ ID NO:7 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; SEQ ID NO:8 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.a bacterium having a 16S rDNA sequence with SEQ ID NO: 9 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence with SEQ ID NO: 11 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; 14 or a 16S rDNA sequence having at least 90%, e.g., at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, e.g., at least 97% or at least 98.7% identity thereto (Consortium 4 in Table 3).

[0095] In another embodiment, the composition is directed to a bacterium having SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 7 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 8 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Bacteria having a 16S rDNA sequence; Bacteria having a 16S rDNA sequence of SEQ ID NO: 9 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; Bacteria having a 16S rDNA sequence of SEQ ID NO: 13 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; SEQ ID NO: 16 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.a bacterium having a 16S rDNA sequence with SEQ ID NO: 17 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence with SEQ ID NO: 18 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and a bacterium (Consortium 5 in Table 3) having a 16S rDNA sequence of SEQ ID NO: 20 or at least 90%, e.g., at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, e.g., at least 97% or at least 98.7%, identical thereto.

[0096] In another embodiment, the composition is directed to a bacterium having SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 2 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 7 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Bacteria having a 16S rDNA sequence; Bacteria having a 16S rDNA sequence of SEQ ID NO: 9 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; Bacteria having a 16S rDNA sequence of SEQ ID NO: 13 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; SEQ ID NO: 16 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.a bacterium having a 16S rDNA sequence with SEQ ID NO: 18 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence with SEQ ID NO: 19 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and a bacterium (Consortium 6 in Table 3) having a 16S rDNA sequence of SEQ ID NO: 20 or at least 90%, e.g., at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, e.g., at least 97% or at least 98.7%, identical thereto.

[0097] In another embodiment, the composition is directed to a bacterium having SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 2 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having SEQ ID NO: 7 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 9 or having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 18 or having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and a bacterium (Consortium 7 in Table 3) having a 16S rDNA sequence of SEQ ID NO: 19 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto.

[0098] In another embodiment, the composition comprises a bacterium having a 16S rDNA sequence of SEQ ID NO: 1 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identical thereto; a 16S rDNA sequence of SEQ ID NO: 2 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identical thereto; and a bacterium (Consortium 8 in Table 3) having a 16S rDNA sequence of SEQ ID NO: 7 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto.

[0099] In another embodiment, the composition comprises or consists of a bacterium having SEQ ID NO: 1 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and a bacterium having SEQ ID NO: 2 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto (Consortium 9 in Table 3).

[0100] Thus, in one embodiment, the composition comprises a bacterium having a 16S rDNA sequence of SEQ ID NO: 1 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identical thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 2 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identical thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 3 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 5 or at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7% identical thereto; A bacterium having a 16S rDNA sequence of SEQ ID NO: 7 or having at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.7%, identity thereto; a bacterium having a 16S rDNA sequence of SEQ ID NO: 10 or having at least 90%, for example at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, for example at least 97% or at least 98.a bacterium having a 16S rDNA sequence with SEQ ID NO: 11 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; a bacterium having a 16S rDNA sequence with SEQ ID NO: 13 or at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; 14 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; and SEQ ID NO: 14 or a 16S rDNA sequence having at least 90%, such as at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, such as at least 97% or at least 98.7% identity thereto; Contains or consists of bacteria (Consortium 10 in Table 3) with rDNA sequences.

[0101] With respect to the percentage identities recited for the above composition embodiments, in one embodiment the sequence identity is at least 98.7% or 99%. It is understood that where Table 1 provides multiple sequences for a single species, any of these sequences may be used in accordance with the above embodiments.

[0102] In one example, species used in the compositions are identified based on their 16S rDNA sequences (e.g., full-length or partial sequences). In some cases, strains of bacterial species useful in the present invention, such as strains of the species disclosed herein, can be obtained from public biological resource centers, such as ATCC (atcc.org), DSMZ (dsmz.de), or the Riken BioResource Center (en.brc.riken.jp). 16S rDNA sequences useful for identifying species or other aspects of the present invention can be obtained from public databases, such as the Human Microbiome Project (HMP) website or GenBank.

[0103] Those skilled in the art will understand that a composition can include one or more than one strain of a particular bacterial species listed in Table 1. For example, a composition of the invention includes more than one bacterial strain of a species. For example, in some embodiments, a composition of the invention includes more than one strain from the same species (e.g., more than 1, more than 2, more than 3, more than 4, more than 5, more than 6, more than 7, more than 8, more than 9, more than 10, more than 15, more than 20, more than 25, more than 30, more than 35, more than 40, or more than 45 strains). In other embodiments, a composition of the invention includes one bacterial strain of each species.

[0104] In one embodiment, the bacteria of the composition are capable of colonizing the gastrointestinal tract of a subject. In one embodiment, the bacteria of the composition are capable of persistent colonization in the gastrointestinal tract of a subject.

[0105] In one embodiment, the composition has one or more of the following characteristics: The composition has an immunostimulatory effect; the composition is effective in treating and / or preventing cancer in a subject, tissue, or cell, e.g., when used in conjunction with checkpoint inhibitor therapy; · the composition is effective in treating and / or preventing an infectious disease in a subject, tissue, or cell; administration of the composition to a subject, tissue, or cell induces an immune response in the subject and / or increases the effectiveness of an anti-cancer therapy that includes an immune checkpoint inhibitor; administration of the composition to a subject, tissue, or cell enhances a CD8+ response; administration of the composition to a subject, tissue, or cell enhances immune checkpoint blockade; administration of the composition maintains or induces tumor responsiveness at immune checkpoints; administration of the composition to a subject, tissue, or cell increases the number or activity of immune system cell types, such as T cells, B cells, dendritic cells, macrophages, neutrophils, NK cells, plasmacytoid dendritic cells, and combinations thereof; administration of the composition to a subject, tissue, or cell alters the ratio of immune cells in the subject to favor cell types capable of suppressing tumor growth, such as T cells, cytotoxic T lymphocytes, helper T cells, natural killer (NK) cells, natural killer T (NKT) cells, plasmacytoid dendritic cells, anti-tumor macrophages, B cells, dendritic cells, and combinations thereof; and / or administration of the composition to a subject, tissue, or cell alters the ratio of immune cells in the subject against cell types that are capable of protecting the tumor, such as myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), tumor-associated neutrophils (TANs), M2 macrophages, tumor-associated macrophages (TAMs), and combinations thereof; administration of the composition to a subject, tissue, or cell increases the abundance / level of bacteria in the subject that prevent cancer / tumor growth, metastasis, and / or evasion of treatment / immune response; Administration of the composition to a subject, tissue, or cell increases the abundance of bacteria in the subject, tissue, or cell that create an environment or microenvironment (e.g., metabolome) that contributes to the treatment of cancer and / or inhibits cancer / tumor growth.

[0106] The subject can be a human or can be an animal in an animal model, e.g., a mouse model. In vitro models, e.g., tissue-based or cell-based models, can also be used to test efficacy. Suitable models and assays are also provided in the Examples.

[0107] As used herein, "immune response" refers to the actions of cells of the immune system (e.g., T lymphocytes, B lymphocytes, natural killer (NK) cells, macrophages, eosinophils, mast cells, dendritic cells, neutrophils, etc.) and soluble macromolecules (including antibodies, cytokines, and complement) produced by any of these cells or the liver that result in the selective targeting of, binding to, damaging, destroying, and / or eliminating from a subject an invading pathogen, a pathogen-infected cell or tissue, or a cancerous or other abnormal cell. This can be measured by assessing appropriate markers or cell types.

[0108] As used herein, the term "immunotherapy" refers to the treatment or prevention of cancer by methods that involve inducing, enhancing, suppressing or otherwise modifying the immune response.

[0109] Bacterial isolates may be isolated and cultured as described in WO 2013 / 171515 or WO 2017 / 182796, both of which are incorporated by reference. In one embodiment, the bacterial strains are cultured and grown separately and then combined in the composition.

[0110] The bacterial isolates used in the compositions are preferably non-pathogenic strains, in other words, the bacteria preferably do not cause disease in healthy human individuals when administered to said individuals.

[0111] In one embodiment, each bacterium present in the composition is susceptible to treatment with one or more antibiotics. In other words, the bacterium is not resistant to treatment with at least one antibiotic. This allows for antibiotic treatment of an individual if one or more of the bacteria contained in the therapeutic composition administered to the individual unexpectedly cause disease in the individual. Thus, in one embodiment, the bacterium is susceptible to treatment with one or more antibiotics selected from the group consisting of β-lactams, fusidic acid, elfamycin, aminoglycosides, fosfomycin, tunicamycin, metronidazole, and / or vancomycin. In vitro and in silico methods for screening bacteria for antibiotic resistance are known in the art.

[0112] In one embodiment, the isolated bacteria included in the composition can be free of one or more genes encoding one or more virulence factors and / or preferably do not produce one or more virulence factors. A virulence factor in this context is a property that enhances the ability of the bacterium to cause disease in an individual. Virulence factors include the production by the bacterium of bacterial toxins (e.g., endotoxins and exotoxins) and the production of hydrolytic enzymes that can contribute to bacterial pathogenicity. Methods for screening bacteria for genes encoding virulence factors are known in the art.

[0113] In some embodiments, one or more of the bacterial strains are human-derived bacteria, meaning that the one or more bacterial strains were obtained or identified from a human or a sample derived from a human (e.g., a human donor). In some embodiments of the compositions provided herein, all of the bacterial strains are human-derived bacteria. In some embodiments of the compositions provided herein, the bacterial strains are derived from more than one human donor.

[0114] The bacterial strains used in the live bacterial products provided herein are typically isolated from the microbiome of a healthy individual, for example, from human feces, but in some cases may not be derived from a healthy individual. In some embodiments, the live bacterial product includes strains originating from a single individual. In some embodiments, the live bacterial product includes strains originating from multiple individuals. In some embodiments, the bacterial strains are obtained, isolated, and grown individually from multiple individuals. The individually grown bacterial compositions can then be combined to provide a composition of the present disclosure. It should be understood that the origin of the bacterial strains in the live bacterial products provided herein is not limited to the human microbiome from a healthy individual.

[0115] Isolation and characterization can be accomplished using standard methods in the art. For example, the V4-V5 region of the 16S rRNA-encoding gene can be amplified and sequenced. The sequence can then be aligned and compared to the 16S sequences provided herein for bacterial isolates. Sequencing protocols and alignment software are well known in the art.

[0116] In some cases, strains of bacterial species useful in the present invention, such as strains of the species disclosed herein, may be obtained from public biological resource centers, such as those described above.

[0117] In some embodiments, in which the compositions of the present invention comprise more than one bacterial strain or species listed herein, the individual bacterial strains or species may be for separate, simultaneous, or sequential administration. For example, the compositions may comprise bacteria from all or a subset of the species listed in Table 1, or the bacterial strains or species may be selected from those listed in Table 1 but stored separately and administered separately, simultaneously, or sequentially. In some embodiments, more than one bacterial strain or species are stored separately but mixed together before use.

[0118] As described herein, the bacterial compositions of the present invention have a therapeutic effect when administered to a subject and can be used in the treatment or prevention of cancer. Thus, the compositions described herein are therapeutic compositions. Thus, the present invention also encompasses pharmaceutical compositions comprising the bacterial compositions described herein, which can include additional components, such as vaccines.

[0119] In one embodiment, the composition may include pharmaceutically acceptable excipients, carriers, buffers, stabilizers, or other substances known to those skilled in the art. Such substances should be non-toxic and should not interfere with the effectiveness of the isolated bacteria present in the therapeutic composition. The exact nature of the pharmaceutically acceptable excipients or other substances will depend on the route of administration, which may be oral or rectal. Many methods for the preparation of therapeutic compositions are known to those skilled in the art.

[0120] The bacterial compositions of the present invention may include prebiotics, pharmaceutically acceptable carriers, insoluble dietary fiber, buffers, osmotic agents, antifoaming agents, and / or preservatives. Specific examples of excipients that may be included in the compositions are disclosed below.

[0121] Prebiotics can provide nutrients for the isolated bacteria present in the bacterial composition, supporting their early growth and establishment after administration to an individual. Any prebiotic known in the art can be used. Non-limiting examples of prebiotics include oligosaccharides, such as fructooligosaccharides, such as oligofructose and inulin, mannan oligosaccharides and galactooligosaccharides, water-soluble oligofructose-enriched inulin, and water-soluble dietary fiber. Insoluble dietary fiber can be included in the therapeutic composition as a carrier, for example, to provide protection during transportation or storage. A buffer can be included in the bacterial composition to promote the viability of the isolated bacteria present. An antifungal agent can be included in the bacterial composition as a preservative.

[0122] In one embodiment, a therapeutic bacterial composition may contain no other active ingredients (including other isolated bacteria) other than the bacterial isolates described herein, and may optionally contain prebiotics. Thus, the active ingredients of a therapeutic composition may consist of the group of bacterial isolates described herein and optional prebiotics.

[0123] The bacterial compositions of the present invention may be administered to a subject in a variety of ways (including in the form of a capsule, tablet, gel or liquid), as described in more detail elsewhere herein.

[0124] The bacterial compositions of the present invention can be administered orally or rectally to a subject. When the composition is for oral administration, it can be in the form of a capsule or tablet. When the therapeutic composition is for rectal administration, it can be in the form of an enema, tablet, or capsule. The preparation of suitable capsules, tablets, and enemas is well known in the art. The capsule or tablet can include an enteric coating to protect the capsule or tablet from stomach acid. For example, the capsule or tablet can be enteric-coated, pH-dependent, sustained-release, and / or gastric-acid-resistant. Such capsules and tablets are used, for example, to minimize dissolution of the capsule or tablet in the stomach while allowing dissolution in the small intestine. When intended for oral administration, the composition can be in solid or liquid form, with semi-solid, semi-liquid, suspension, and gel forms being included within the forms considered herein as either solid or liquid.

[0125] As a solid composition for oral administration, the composition can be formulated into powder, granules, compressed tablets, pills, capsules, chewing gum, wafers, etc. Such solid compositions typically contain one or more inert diluents. Additionally, one or more of the following may be present: binders such as carboxymethylcellulose, ethylcellulose, microcrystalline cellulose, or gelatin; excipients such as starch, lactose, or dextrin; disintegrants such as alginic acid, sodium alginate, corn starch, etc.; lubricants such as magnesium stearate; glidants such as colloidal silicon dioxide; sweeteners such as sucrose or saccharin; flavorings such as peppermint, methyl salicylate, or citrus flavoring; and coloring agents. When the composition is in the form of a capsule (e.g., a gelatin capsule), it can contain, in addition to materials of the above type, a liquid carrier such as polyethylene glycol, cyclodextrin, or fatty oil.

[0126] When intended for oral administration, the composition may contain one or more of a sweetener, a preservative, a dye / coloring agent, and a flavoring agent. In a composition for administration by injection, one or more of a surfactant, a preservative, a wetting agent, a dispersing agent, a suspending agent, a buffer, a stabilizer, and an isotonic agent may also be included.

[0127] The bacterial composition can include a pharmaceutically acceptable carrier or vehicle, which can be in particulate form, so that the composition is in tablet or powder form, for example. The term "carrier" refers to a diluent, adjuvant, or excipient with which the composition is administered. Such pharmaceutical carriers can be liquids, such as water and oils (including oils of petroleum, animal, vegetable, or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil, etc.). Carriers can be saline, acacia gum, gelatin, starch paste, talc, keratin, colloidal silica, urea, etc. Additionally, auxiliary substances, stabilizers, thickeners, lubricants, and coloring agents can be used. In one embodiment, the composition and pharmaceutically acceptable carrier are sterilized. Saline solutions and aqueous dextrose and glycerol solutions can also be used as liquid carriers, particularly for injectable solutions. Suitable pharmaceutical carriers also include excipients, such as starch, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glycerol monostearate, talc, sodium chloride, dried skim milk powder, glycerol, propylene, glycol, water, ethanol, etc. The composition, if desired, can also contain minor amounts of wetting or emulsifying agents, or pH buffering agents.

[0128] The composition may be in the form of one or more dosage units. In one embodiment, the dosage unit contains at least 1 x 10 3 , at least 1 x 10 4 , at least 1 x 10 5 , at least 1 x 10 6 , at least 1 x 10 7 , at least 1 x 10 8 , at least 1 x 10 9 , at least 1 x 1010 , at least 1 x 10 11 , at least 1 x 10 12 , at least 1 x 10 13 or 1 x 10 13 The bacterial isolate or composition may contain more than colony forming units (cfu) of vegetative bacterial cells. In some embodiments, the dosage unit comprises a pharmaceutically acceptable excipient, an enteric coating, or a combination thereof. The bacterial isolate or composition may be provided in an appropriate dose.

[0129] Treatments or specific processes may be applied to improve the stability or viability of the bacterial isolates in the composition. The bacterial composition may be applied in dry or wet form. The bacterial composition may be lyophilized. A lyophilized therapeutic composition may contain one or more stabilizers and / or cryoprotectants. A lyophilized bacterial composition may be reconstituted using a suitable diluent prior to administration to an individual.

[0130] The present invention also relates to a pharmaceutical composition comprising one or more bacteria selected from the bacterial species in Table 1 or a composition described herein, further comprising an effective amount of an immune checkpoint inhibitor.

[0131] Immune checkpoints are regulatory pathways within the immune system involved in maintaining immune homeostasis (e.g., regulating the duration and magnitude of an immune response, self-tolerance) to minimize cellular damage resulting from an aberrant immune response. Inhibitors of immune checkpoints, referred to herein as "immune checkpoint inhibitors," specifically inhibit immune checkpoints and can have stimulatory or inhibitory effects on the immune response.

[0132] In one embodiment, the immune checkpoint inhibitor is an antibody or fragment thereof, an interfering nucleic acid molecule, or another chemical moiety.

[0133] Many checkpoint inhibitors are known in the art and many treatments, including antibody treatments, have been approved by regulatory authorities, while other treatments, including treatments with monoclonal antibodies or antibody fragments, such as single domain antibodies, have shown efficacy in a wide range of cancers.

[0134] In one embodiment, the immune checkpoint inhibitor inhibits PD-1 activity, i.e., acts as a PD-1 antagonist.

[0135] By "PD-1 antagonist" or "PD-1 inhibitor" is meant any chemical or biological molecule that blocks the binding of PD-L1 expressed on cancer and / or immune cells to PD-1 expressed on immune cells (T cells, B cells, or NKT cells), and preferably also blocks the binding of PD-L2 expressed on cancer and / or immune cells to PD-1 expressed by immune cells.

[0136] In one embodiment, the immune checkpoint inhibitor is a PD-1 inhibitor, a PD-L1 inhibitor, or a PD-L2 inhibitor, such as an anti-PD-1 antibody, an anti-PD-L1 antibody, or an anti-PD-L2 antibody. In one embodiment, the immune checkpoint inhibitor is an anti-PD-1 antibody. In one embodiment, the immune checkpoint inhibitor is an anti-PD-1 antibody or an anti-PD-L1 antibody optionally selected from nivolumab (MDX-1106, MDX-1106-04, ONO-4538, or BMS-936558), pembrolizumab (also known by the trade name KEYTRUDA®, formerly lambrolizumab®, Merck 3745, MK-3475, or SCH-900475), cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, or toripalimab.

[0137] In one embodiment, the immune checkpoint inhibitor is a cytotoxic T-lymphocyte-associated protein 4 (CTLA-4 inhibitor), i.e., it inhibits the activity of CTLA-4. CTLA-4 (CD152), which has NCBI gene ID: 1493, is a B7 / CD28 family member that inhibits T cell function. CTLA-4 mAb or CTLA-4 ligand can prevent CTLA-4 from binding to its native ligand, thereby blocking CTLA-4 from transmitting negative regulatory signals in T cells and enhancing T cell responsiveness to various antigens. In this aspect, results from in vivo and in vitro studies are substantially consistent.

[0138] The CTLA-4 inhibitor may be a CTLA4 antibody, optionally ipilimumab or tremelimumab.

[0139] In one embodiment, the immune checkpoint inhibitor is an anti-TGIT agonist, an anti-LAG3 agonist, or an anti-TIM3 agonist, e.g., an antibody. The checkpoint targets listed herein are not limiting, and one of skill in the art will understand that other checkpoint targets are also within the scope of the present invention and may be inhibited.

[0140] It should further be understood that multiple immune checkpoint inhibitors may be used in the methods, compositions, and kits disclosed herein.

[0141] In some embodiments, the cancer immunotherapeutic agent comprises an anti-cancer vaccine (also referred to herein as a cancer vaccine). Cancer vaccines generally act to increase the immune response against cancer cells. For example, a cancer vaccine comprises a cancer antigen and acts to induce or stimulate an immune response against cells bearing the cancer antigen. The induced or stimulated immune response can include an antibody (humoral) immune response and / or a T cell (cell-mediated) immune response.

[0142] Unless otherwise specified, the term PD-1, as used herein, refers to human PD-1. The terms "programmed death 1," "programmed cell death 1," "protein PD-1," "PD-1," "PD1," "PDCD1," "hPD-1," and "hPD-1" are used interchangeably and include variants, isoforms, and species homologs of human PD-1. The term PD-1 antibody or PD-1 antibody fragment refers to a molecule capable of specifically binding to the human PD-1 antigen and antagonizing PD-1 action. The human PD-1 amino acid sequence can be found at NCBI locus number NP_005009. The human PD-L1 amino acid sequence and human PD-L2 amino acid sequence can be found at NCBI locus numbers NP_054862 and NP_079515, respectively.

[0143] As used herein, the term "antibody" broadly refers to any immunoglobulin (Ig) molecule or antigen-binding portion thereof comprising four polypeptide chains: two heavy (H) chains and two light (L) chains, or any functional fragment, mutant, variant, or derivative thereof that retains the essential epitope-binding characteristics of an Ig molecule. Such mutant, variant, or derivative antibody formats are known in the art. An antibody can be monospecific or multispecific, e.g., bispecific. An antibody can be administered in combination with another antibody therapy, e.g., another antibody targeting a checkpoint inhibitor, or in combination with another anti-cancer therapy, e.g., chemotherapy, as well as targeted therapy, surgery, and / or radiation therapy.

[0144] In a full-length antibody, each heavy chain comprises a heavy chain variable region or domain (abbreviated herein as HCVR) and a heavy chain constant region. The heavy chain constant region comprises three domains: CH1, CH2, and CH3. Each light chain comprises a light chain variable region or domain (abbreviated herein as LCVR) and a light chain constant region. The light chain constant region comprises one domain, CL.

[0145] Heavy and light chain variable regions can be further subdivided into regions of hypervariability called complementarity-determining regions (CDRs), interspersed with more conserved regions called framework regions (FRs). Each heavy and light chain variable region is composed of three CDRs and four FRs, arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4.

[0146] Immunoglobulin molecules can be of any type (e.g., IgG, IgE, IgM, IgD, IgA, and IgY), class (e.g., IgG1, IgG2, IgG3, IgG4, IgAI, and IgA2) or subclass.

[0147] The term antibody as used herein refers to antibody fragments, such as F(ab')2, Fab, Fv, scFv, heavy chain only antibodies, single domain antibodies (V H , V L , V HH ) or antibody mimetic proteins. Various antibody formats, including single-domain antibodies, have been shown to exhibit efficacy against checkpoint inhibitors (e.g., Yu S et al., "Nanobodies targeting immune checkpoint molecules for tumor immunotherapy and immunoimaging," Int J Mol Med., 2021;47(2):444-454).

[0148] The scFv fragment (approximately 25 kDa) is V H and V L It consists of two variable domains: V H Domain and V L The domains are non-covalently associated through hydrophobic interactions and tend to separate. However, stable fragments can be engineered by linking the domains with flexible hydrophilic linkers to create single-chain Fvs (scFvs). The smallest antigen-binding fragment is the single variable region fragment, or V H Domain or VL The V domain is a fragment of a single domain antibody. Binding to the light / heavy chain partners is not required for target binding. Such fragments are used in single domain antibodies. Thus, single domain antibodies (approximately 12-15 kDa) have V H Domain or V L domain.

[0149] An antibody can be human, humanized, or chimeric. A chimeric antibody is a recombinant protein that contains the variable domains, including the complementarity-determining regions (CDRs), of an antibody derived from one species, preferably a rodent antibody, while the constant domains of the antibody molecule are derived from the constant domains of a human antibody.

[0150] A humanized antibody is a recombinant protein in which the CDRs from an antibody of one species, e.g., a rodent antibody, have been transferred from the heavy and light chain variable regions of the rodent antibody into human heavy and light chain variable domains (e.g., framework region sequences). The constant domains of the antibody molecule are derived from the constant domains of a human antibody. In certain embodiments, a limited number of framework region amino acid residues from the parent (rodent) antibody can be substituted into the human antibody framework region sequences.

[0151] Checkpoint inhibitors are not limited to antibodies. In one embodiment, the immune checkpoint inhibitor is an interfering nucleic acid molecule, optionally an siRNA molecule, an shRNA molecule, or an antisense RNA molecule.

[0152] In one embodiment, the immune checkpoint inhibitor is a small molecule or a proteolysis targeting chimera (PROTAC), an alternative scaffold protein, a biologic, or other immune checkpoint inhibitor. In one embodiment, the immune checkpoint inhibitor is an interfering nucleic acid molecule. In one embodiment, the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule. In one embodiment, the immune checkpoint inhibitor is a small molecule or a proteolysis targeting chimera (PROTAC) or other immune checkpoint inhibitor. Examples of small molecules that can be used as checkpoint inhibitors are provided by studies on sulfamonomethoxine and sulfamethizole. Exemplary small molecule compounds that inhibit PD-L1 are disclosed in U.S. Patent No. 9,850,225, which is incorporated by reference. A small molecule currently in human clinical trials is a molecule called Ca-170, which inhibits both the PD-L1 pathway and the V-domain Ig suppressor of T-cell activation (VISTA) pathway.

[0153] In one embodiment, the immune checkpoint inhibitor is a peptide inhibitor. An example is the peptide antagonist (D)PPA-1, which blocks PD-1 / PD-L1 interaction in vivo and reduces tumor growth (Chang HN et al., "Blocking of the PD-1 / PD-L1 Interaction by a D-Peptide Antagonist for Cancer Immunotherapy." Angew. Chem. Int. Ed., 2015;54:11760-11764). Other peptide inhibitors are PL120131 (Magiera-Mularz K. et al., "Bioactive Macrocyclic Inhibitors of the PD-1 / PD-L1 Immune Checkpoint." Angew. Chem. Int. Ed., 2017;56:13732-13735) and TPP-1 (Li C., Zhang N. et al., "Peptide Blocking of PD-1 / PD-L1 Interaction for Cancer Immunotherapy." Cancer Immunol. Res., 2018;6:178-188), which have been shown to act as competitive inhibitors of PD-L1.

[0154] In another aspect, there is provided a bacterial composition described herein for use in the treatment of a disease (e.g., cancer). In another aspect, there is provided a use of a bacterial composition described herein in the manufacture of a medicament for the treatment or prevention of a disease (e.g., cancer).

[0155] In another aspect, a method for treating or preventing disease is provided, comprising administering to a subject a bacterial composition described herein. In another aspect, a method for treating or preventing disease in a subject is provided, comprising modulating the level, e.g., increasing the level / relative abundance, of one or more bacteria selected from B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15, or a subset thereof, as set forth in Table 1. In one embodiment, the subset comprises or consists of bacteria selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 bacterial species as set forth in Table 1. Modulating the level of one or more bacteria in a subject enhances an immune response by the subject, and / or inhibits immune evasion by the cancer, and / or increases the effectiveness of anti-cancer treatment with immune checkpoint inhibitors. In one embodiment, the method comprises administering a composition described herein.

[0156] As explained below, the level / abundance can be compared to a reference value derived from a reference subject or population of subjects.

[0157] In one embodiment, the disease is cancer. In one embodiment, the cancer is melanoma. "Melanoma" is taken to mean a tumor arising from the melanocytic system of the skin and other organs. Non-limiting examples of melanoma are Harding-Passey melanoma, juvenile melanoma, lentigo maligna melanoma, malignant melanoma, acral lentiginous melanoma, amelanotic melanoma, benign juvenile melanoma, Cloudman melanoma, S91 melanoma, nodular melanoma, subungual melanoma, cutaneous melanoma, uveal / intraocular melanoma, and superficial spreading melanoma.

[0158] The compositions of the invention are particularly useful for the treatment of cancers that are treatable by checkpoint inhibitors.

[0159] In one embodiment, the cancer is associated with cells (e.g., exhausted T cells, B cells, monocytes, etc.) that express abnormally high levels of PD-1. Other cancers include those characterized by elevated expression of PD-1 and / or its ligands PD-L1 and / or PD-L2.

[0160] In one embodiment, the cancer is selected from cancers with high levels of cancer-associated gene mutations and / or high levels of tumor antigen expression. In another embodiment, the cancer is selected from cancers that are known to be immunogenic or that may become immunogenic upon treatment with other cancer therapies. In a further embodiment, the cancer may be selected from cancers that are commonly treated with non-immunological therapies, such as chemotherapy, in which the patient's immune system may play a role.

[0161] The cancer may be selected from solid or non-solid tumors. For example, in addition to melanoma, the cancer may be selected from another skin cancer, or from bone cancer, pancreatic cancer, head and neck cancer, cutaneous or intraocular malignant melanoma, uterine cancer, ovarian cancer, rectal cancer, cancer of the anal region, stomach cancer, testicular cancer, breast cancer, brain cancer, fallopian tube cancer, endometrial cancer, cervical cancer, vaginal cancer, vulvar cancer, esophageal cancer, small intestine cancer, cancer of the endocrine system, thyroid cancer, parathyroid cancer, adrenal gland cancer, kidney cancer, soft tissue sarcoma, urethral cancer, bladder cancer, rectal cancer, lung cancer, non-small cell lung cancer, thymoma, urothelial carcinoma, leukemia, prostate cancer, mesothelioma, adrenocortical carcinoma, lymphoma, such as Hodgkin's disease, non-Hodgkin's lymphoma, gastric cancer, and multiple myeloma.

[0162] In one embodiment, the tumor is a solid tumor. Examples of solid tumors that can be appropriately treated include breast cancer, lung cancer, colorectal cancer, pancreatic cancer, glioma, and lymphoma. Some examples of such tumors include epidermoid tumors, squamous cell tumors, such as head and neck tumors, colorectal tumors, prostate tumors, breast tumors, lung tumors (including small cell lung tumors and non-small cell lung tumors), pancreatic tumors, thyroid tumors, ovarian tumors, and liver tumors. Other examples include Kaposi's sarcoma, central nervous system tumors, neuroblastoma, capillary hemangioblastoma, meningioma, and metastatic brain tumors, melanoma, gastrointestinal and renal cancers and sarcomas, rhabdomyosarcoma, glioblastoma, preferably glioblastoma multiforme, and leiomyosarcoma. Examples of vascularized skin cancers for which the antagonists of the present invention are effective include squamous cell carcinoma, basal cell carcinoma, and skin cancers that can be treated by inhibiting the proliferation of malignant keratinocytes, e.g., human malignant keratinocytes. In one embodiment, the cancer is NSCL.

[0163] In one embodiment, the tumor is a non-solid tumor. Examples of non-solid tumors include leukemia, multiple myeloma, and lymphoma.

[0164] In one embodiment, the cancer is identified as a PD-1 positive cancer and / or a PD-L1 positive cancer or a cancer positive for another checkpoint protein, hi one embodiment, the cancer is locally advanced, unresectable, metastatic, or recurrent cancer.

[0165] Preferred cancers whose growth can be inhibited using the agents of the invention include cancers that are typically responsive to immunotherapy. Non-limiting examples of preferred cancers for treatment include melanoma (e.g., metastatic malignant melanoma), kidney cancer (e.g., clear cell carcinoma), prostate cancer (e.g., hormone-refractory prostate cancer), breast cancer, colon cancer, and lung cancer (e.g., non-small cell lung cancer).

[0166] As used herein, "treat," "treating," or "treatment" means to inhibit or alleviate a disease or disorder. For example, treatment can include postponing the onset of symptoms associated with a disease or disorder and / or reducing the severity of such symptoms that develop or are expected to develop the disease. These terms include alleviating existing symptoms, preventing additional symptoms, and alleviating or preventing the underlying causes of such symptoms. Thus, these terms refer to the imparting of a beneficial result to at least some of the mammals, e.g., human patients, being treated. Many medical treatments are effective in some, but not all, patients who receive the treatment.

[0167] The term "subject" or "patient" refers to an animal, e.g., a human, that is the object of treatment, observation, or diagnosis. By way of example only, a subject includes, but is not limited to, a mammal, including, but not limited to, a human or a non-human mammal, such as a non-human primate, mouse, cow, horse, dog, sheep, or cat. In one embodiment, the subject is a cancer patient who has already received prior anti-cancer treatment or is undergoing anti-cancer treatment. In one embodiment, the anti-cancer treatment is treatment with an immune checkpoint inhibitor. Exemplary immune checkpoint inhibitors are described herein.

[0168] The term "anti-cancer therapy" refers to any treatment regimen aimed at reducing or eliminating cancer, slowing the progression of cancer, preventing or reducing the risk of cancer metastasis, and / or reducing or preventing any one or more symptoms associated with cancer. Anti-cancer therapy as described herein includes administering an anti-cancer therapy to a subject, e.g., a subject having or at risk of having cancer.

[0169] Administration according to the above methods and uses includes oral or rectal administration.

[0170] In one embodiment, the subject has already undergone prior anti-cancer therapy using an immune checkpoint inhibitor. In one embodiment, an anti-cancer therapy comprising an immune checkpoint inhibitor is administered to the subject. This can be administered simultaneously with the composition of the present invention, either as part of the same medicament or as a second medicament. It can also be administered before or after the administration of the composition of the present invention. Other treatment schedules are also within the scope of the present invention.

[0171] In one embodiment, the immune checkpoint inhibitor is administered before, after, or simultaneously with the bacterial composition. In one embodiment, checkpoint therapy is initiated and, if no response is observed after 3-6 months, is then supplemented with treatment using the bacterial compositions described herein.

[0172] In one embodiment, the immune checkpoint inhibitor is administered after the bacterial composition. In one embodiment, the immune checkpoint inhibitor is administered by injection / infusion. In one embodiment, the injection is intravenous, intramuscular, intratumoral, or subcutaneous.

[0173] Checkpoint inhibitors that may be used according to the treatment mode are defined above. For example, the immune checkpoint inhibitor inhibits PD-1 activity, CTLA-4 activity, or PD-L1 activity. In one embodiment, the immune checkpoint inhibitor is an anti-PD-1 antibody, an anti-CTLA-4 antibody, or an anti-PD-L1 antibody. In one embodiment, the anti-PD-1 antibody, anti-CTLA-4 antibody, or anti-PD-L1 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, or toripalimab, ipilimumab, or tremelimumab.

[0174] The amount of antibody that is effective / active in treating a particular disorder or condition will depend on the nature of the disorder or condition and can be determined by standard clinical techniques. Additionally, in vitro or in vivo assays can optionally be used to help identify optimal dosage ranges. The precise dose to be used in the composition will also depend on the route of administration and the severity of the disease or disorder, and should be decided according to the judgment of the practitioner and each patient's circumstances. Factors such as age, body weight, sex, diet, time of administration, excretion rate, condition of the host, drug combinations, reaction sensitivities, and severity of the disease should be taken into consideration.

[0175] Typically, the amount is at least about 0.01% by weight of the anti-PD-1 antibody, anti-CTL-4 antibody, or anti-PD-L1 antibody of the composition. When intended for oral administration, this amount can vary to range from about 0.1% to about 80% by weight of the composition. Oral compositions can contain from about 4% to about 50% by weight of the antibody.

[0176] The antibody composition can be prepared so that one parenteral dosage unit contains about 0.01% to about 2% by mass of the antibody.

[0177] For administration by injection, the composition typically comprises about 0.1 mg / kg to about 250 mg / kg of the animal's body weight, preferably between about 0.1 mg / kg and about 20 mg / kg of the animal's body weight, and more preferably about 1 mg / kg to about 10 mg / kg of the animal's body weight. In one embodiment, the composition is administered at a dose of about 1 mg / kg to about 30 mg / kg, e.g., about 5 mg / kg to about 25 mg / kg, about 10 mg / kg to about 20 mg / kg, about 1 mg / kg to about 5 mg / kg, or about 3 mg / kg. The administration schedule can vary, for example, from once per week to once every two weeks, once per three weeks, or once per four weeks.

[0178] In one embodiment, the immune checkpoint inhibitor is an interfering nucleic acid molecule. In one embodiment, the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule. In one embodiment, the immune checkpoint inhibitor is a small molecule or a proteolysis-targeting chimeric molecule (PROTAC) or other immune checkpoint inhibitor, such as those described above.

[0179] In one embodiment, the methods and uses further comprise the administration of an anti-cancer therapy, for example, the administration of a second anti-cancer therapeutic in addition to the immune checkpoint inhibitor. The anti-cancer therapy can include a therapeutic agent or radiation therapy, including gene therapy, viral therapy, RNA therapy, bone marrow transplant, nanotherapy, targeted anti-cancer therapy, or oncolytic drugs, or a combination thereof. Examples of other therapeutic agents include other checkpoint inhibitors, anti-tumor drugs, immunogenic drugs, attenuated cancerous cells, tumor antigens, antigen-presenting cells, such as dendritic cells pulsed with tumor-derived antigens or tumor-derived nucleic acids, immunostimulatory cytokines (e.g., IL-2, IFNa2, GM-CSF), targeted small molecules and targeted biological molecules (e.g., components of signal transduction pathways, such as modulators of tyrosine kinases and inhibitors of receptor tyrosine kinases, and agents that bind to tumor-specific antigens, including EGFR antagonists), anti-inflammatory drugs, cytotoxic drugs, radiotoxic drugs, or cells transfected with genes encoding immunosuppressants and immunostimulatory cytokines (e.g., GM-CSF), and chemotherapy. In one embodiment, the composition is used in combination with surgery. In one embodiment, the composition is used in combination with stem cell transplantation therapy, including peripheral blood transplantation, bone marrow transplantation, umbilical cord blood transplantation, or skin-derived stem cell transplantation.

[0180] In one embodiment, the composition is used in combination with adoptive cell transfer (ACT). Generally, adoptive cell transfer therapy involves removing cells from a subject, specifically generating or expanding a specific cell population, optionally activating the cells, and administering the expanded cells to the subject. In some embodiments, the desired cells are immune cells capable of killing or eliminating cancer cells.

[0181] In some embodiments, adoptive cell transfer therapy uses engineered T cell receptors or chimeric antigen receptors and may also be referred to as CAR-T therapy. CAR-T cells include T cells that are harvested from a subject and genetically engineered to express a chimeric antigen receptor (CAR) on their surface. The CAR-T cell receptor is designed to recognize a specific antigen (e.g., a cancer antigen) on a cancer cell. After the CAR-T cells are infused into a subject, they recognize and kill cancer cells that express that specific antigen on their surface. In some embodiments, the CAR-T cells are autologous, meaning that the T cells are harvested and re-administered to the same subject. In some embodiments, the CAR-T cells are CD8+ T cells. In some embodiments, the CAR-T cells are allogeneic, meaning that the T cells are harvested from one subject (e.g., a donor) and administered to another subject (e.g., a recipient).

[0182] Examples of cancer antigens that can be targeted by CAR-T cells are known in the art, and the choice of cancer antigen for targeting depends on factors such as the cancer to be targeted.

[0183] In some embodiments, the anti-cancer therapy includes administering one or more costimulatory factors. In some embodiments, the costimulatory factor is a molecule that regulates the immune response by targeting one or more costimulatory molecules. In some embodiments, the costimulatory factor enhances the anti-cancer immune response, for example, by preventing downregulation of the immune response. The costimulatory factor can be administered alone in the cancer therapy or in combination with one or more cancer therapies to enhance the therapeutic effect of the cancer therapy. In some embodiments, the costimulatory factor is an antibody that targets CD-28, OX-40, 4-1BB, or CD40.

[0184] In one embodiment of the invention, the composition is administered together with a chemotherapeutic agent and / or radiation therapy. In another particular embodiment, the chemotherapeutic agent and / or radiation therapy is administered before or after administration of the composition of the invention, preferably at least 1 hour, at least 5 hours, at least 12 hours, at least 1 day, at least 1 week, at least 1 month, or more preferably at least several months (e.g., up to 3 months) before or after administration of the composition of the invention.

[0185] As used herein, a chemotherapeutic agent refers to a molecule (e.g., a drug) that specifically or preferentially kills or specifically or preferentially inhibits the growth of cancer cells. Chemotherapeutic agents can generally be classified based on the molecular target of the chemotherapeutic agent, its mechanism of action, and / or the structure of the drug. In some embodiments, the chemotherapeutic agent is an alkylating agent, a plant alkaloid, an antitumor antibiotic, an antimetabolite, a topoisomerase inhibitor, or other antitumor drug.

[0186] In one embodiment, the chemotherapeutic agent is an alkylating agent, an alkyl sulfonate, an aziridine, an ethyleneimine, a methylamelamine, an acetogenin, a camptothecin, a bryostatin, a calistatin, a statin, CC-1065, cryptophycin, dolastatin, duocarmycin, eleutherobin, pancratistatin, sarcodictyin, spongistatin, nitrogen mustard, nitrosourea, antibiotics, dynemycin; bisphosphonates, esperamicin, neocarzinostatin chromophores and related chromoprotein enediyne antibiotic chromophores, aclacinomycin, actinomycin, anthramycin, azaserine, bleomycin, cactinomycin, carabicin, caminomycin, carzinophilin, chromomycin, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, adriamycin (doxorubicin), epirubicin , esorubicin, idarubicin, marcelomycin, mitomycin, mycophenolic acid, nogalamycin, olivomycin, peplomycin, porfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin, antimetabolites, folic acid analogs, purine analogs, pyrimidine analogs, androgens, anti-adrenals, folic acid supplements, aceglatone, aldophosphamide glycoside, aminolevulinic acid, eniluracil, amsacrine, bestrabucil, bisantrene, edatrexate, demecolcine, diaziconazole, eflornithine, elformithine, elliptinium acetateacetate), epothilone, etoglucide, gallium nitrate, hydroxyurea, lentinan, lonidainine, maytansinoids, mitoguazone, mitoxantrone, mopidanmol, nitracrine, pentostatin, phenamet, pirarubicin, losoxantrone, podophyllinic acid acid), 2-ethylhydrazide, procarbazine, PSK polysaccharide complex, razoxane, rhizoxin, sizofuran, spirogermanium, tenuazonic acid, triaziconazole, 2,2',2"-trichlorotriethylamine, trichothecenes, urethanes, vindesine, dacarbazine, mannomustine, mitobronitol, mitolactol, pipobroman, gacytosine, arabinoside ("Ara-C"), cyclophosphamide, thiotepa, taxoids, Abraxane (Cremophor-free), albumin-engineered nanoparticle formulations of paclitaxel and Taxotere (docetaxel), chlorambucil, Gemzar (gemcitabine), 6-thioguanine, mercaptopurine, methotrexate, platinum analogs, vinblastine, platinum, etoposide cyclosporine (VP-16), ifosfamide, mitoxantrone, vincristine, navelbine, vinorelbine, novantrone, teniposide, edatrexate, daunomycin, aminopterin, xeloda, ibandronate, irinotecan (Camptosar, CPT-11), topoisomerase inhibitor RFS2000; difluoromethylornithine (DMFO), retinoids, capecitabine, combretastatin, leucovorin (LV), oxaliplatin, binimetinib (Mektobi), encorafenib (Biraftobi), lapatinib (Tykerb), inhibitors of PKC-α, inhibitors of Raf, inhibitors of H-Ras, inhibitors of EGFR, inhibitors of VEGF-A, pharmaceutically acceptable salts, acids or derivatives thereof, and combinations thereof.

[0187] In some embodiments, compositions of the invention may be administered with two or more (eg, two or more, three or more, four or more, five or more) therapeutic agents.

[0188] In one embodiment, administration is with an agent involved in T cell activation, tumor microenvironment modifiers (TME), or tumor-specific targets.

[0189] In one embodiment, the methods and uses further comprise administering an antibiotic to the subject.

[0190] In yet another aspect, the invention provides a method of modulating an immune response in a subject, the method comprising administering to the subject a composition of the invention.

[0191] In some embodiments, the individual has a cancer that is resistant (demonstrated to be resistant) to one or more anti-cancer therapies. In some embodiments, resistance to anti-cancer therapy includes recurrence of cancer or refractory cancer. Recurrence can refer to the reappearance of cancer after treatment, at the original site or a new site. In some embodiments, resistance to anti-cancer therapy includes progression of cancer during treatment with an anti-cancer therapy. In some embodiments, the cancer is early stage or late stage.

[0192] The compositions of the present invention have immunostimulatory properties. Therefore, their use is not limited to the treatment of cancer. Due to their immunostimulatory properties, the compositions find use in the treatment of any disease requiring immunostimulation, such as non-cancer immunotherapy. Immunotherapy is collectively defined as a therapeutic approach that targets or manipulates the immune system. Ultimately, immunotherapy aims to utilize the host's adaptive and innate immune responses to achieve long-term elimination of diseased cells and can be broadly classified into passive (including adoptive and antibody-based) and active (including vaccine and allergen-specific) approaches. Passive immunotherapy involves the administration of ex vivo-generated immune elements (antibodies, immune cells) to patients and does not stimulate a host immune response, while active immunotherapy induces a patient's immune response, resulting in the development of specific immune effectors (antibodies and T cells). Immunotherapy offers a potential modality for improving the ability to prevent or treat infectious diseases (Naran et al., Front Microbiol., 2018;9:3158). Thus, in some embodiments, the disease is an infectious disease.

[0193] Recent successes in blocking PD-1 and PD-L1 in cancer therapy demonstrate a critical role for the PD-1 / PD-L1 pathway in controlling antitumor immune responses. However, signaling regulated by the PD-1 / PD-L pathway is also associated with substantial inflammatory effects that may resemble those in autoimmune responses, chronic infections, and sepsis, consistent with a role for this pathway in balancing protective immunity with immunopathology, as well as in homeostasis and tolerance (Quin et al., Front Immunol., 2019;10:2298; Rao et al., Int. J. Infect. Dis., 2017;56:223). Thus, in another aspect, the present invention provides compositions described herein for use in treating infectious diseases, e.g., compositions comprising one or more of B1-B15 in Table 1, e.g., compositions comprising one or more bacterial isolates having a 16S rDNA sequence selected from SEQ ID NOS: 1-15 or sequences having at least 97%, at least 98%, at least 98.7%, or at least 99% sequence identity thereto (e.g., SEQ ID NOS: 16-29). Methods for treating infectious diseases, comprising administering a composition of the present invention to a subject, are also provided. Also provided are compositions described herein for use in the manufacture of a medicament for treating infectious diseases.

[0194] The infectious disease may be a viral infection, a fungal infection, or a bacterial infection. The infectious disease may be a chronic infectious disease. Non-limiting examples include human immunodeficiency virus (HIV), hepatitis B (HBV), hepatitis C (HCV), JC (John Cunningham) virus / progressive multifocal leukoencephalopathy, and tuberculosis.

[0195] Treatment of infection with the compositions of the invention may be as a combination therapy, with immunotherapy, for example with immune checkpoint inhibitors, other antiviral or anti-infective agents.

[0196] In another aspect, the present invention provides a composition described herein, e.g., a composition comprising one or more of B1-B15 in Table 1, e.g., a composition comprising one or more bacterial isolates having a 16S rDNA with a sequence selected from SEQ ID NOs: 1-15, or a sequence having at least 97%, at least 98%, at least 98.7%, or at least 99% sequence identity thereto, for use as a vaccine adjuvant. Also provided is a method for increasing vaccine efficacy, comprising administering to a subject a composition described herein, e.g., a composition comprising one or more of B1-B15 in Table 1, e.g., a composition comprising one or more bacterial isolates having a 16S rDNA with a sequence selected from SEQ ID NOs: 1-15, or a sequence having at least 97%, at least 98%, at least 98.7%, at least 99%, or at least 100% sequence identity thereto (e.g., SEQ ID NOs: 16-29). The subject can receive the vaccine before, after, or simultaneously with the administration of the bacterial composition.

[0197] Administration may be in a "therapeutically effective amount," which is sufficient to show benefit to an individual. Such benefit may be at least a reduction in at least one symptom. Thus, "treatment" of a particular disease refers to a reduction in at least one symptom. The actual amount administered, as well as the rate and time course of administration, will depend on the nature and severity of what is being treated, the particular patient being treated, the individual patient's clinical condition, the site of delivery of the composition, the type of therapeutic composition, the method of administration, the scheduling of administration, and other factors known to medical practitioners. Prescription of treatment, e.g., determining dosage, is within the responsibility of general practitioners and other physicians and may depend on the severity of the symptoms and / or the progression of the disease being treated. The therapeutically effective amount or appropriate dose of a therapeutic composition of the present invention can be determined by comparing its in vitro activity with its in vivo activity in animal models. Methods for extrapolating effective dosages in mice and other test animals to humans are known. The exact dose will depend on numerous factors, including whether the therapeutic composition is for prophylaxis or treatment.

[0198] In one embodiment of the method involving administration of a composition, the method comprises the further step of detecting the presence of one or more of the administered bacterial strains in the subject after administration, e.g., detecting in said subject a 16S nucleic acid sequence, as defined herein, of at least one administered bacterial isolate.

[0199] The compositions of the present invention can be prepared by a method comprising culturing, in a suitable medium or suitable media, two or more isolated bacteria present in the composition. Suitable media and conditions for culturing bacteria to be included in the therapeutic compositions of the present invention are described in detail elsewhere herein. For example, a method for preparing a therapeutic composition according to the present invention can include: (i) culturing the first isolated bacterium; (ii) culturing the second isolated bacterium and optionally further isolated bacteria; and (iii) mixing the bacteria obtained in (i) and (ii) to prepare a therapeutic composition; may include:

[0200] The isolated bacteria to be included in the composition can be cultured in separate steps. In other words, separate cultures of each bacterium to be included in the therapeutic composition are preferably prepared. This allows the growth of each bacterium to be evaluated and the amount of each bacterium to be included in the pharmaceutical composition to be controlled as desired. The bacteria cultured in steps (i) and (ii) preferably have distinct 16S nucleic acid sequences, i.e., 16S nucleic acid sequences that share less than 99%, less than 98%, less than 97%, less than 96%, or less than 95% sequence identity.

[0201] The above method may include the step of culturing each of the isolated bacteria to be included in the composition.

[0202] The method can optionally include one or more further steps in which the bacteria are mixed with one or more additional ingredients, such as pharmaceutically acceptable excipients, prebiotics, carriers, insoluble dietary fiber, buffers, osmotic agents, antifoaming agents, and / or preservatives. Additionally or alternatively, the method can include suspending the bacteria obtained in (i) and, optionally, the bacteria obtained in (ii) in a chemostat medium or saline (e.g., 0.9% saline). The bacteria obtained in (i) and, optionally, the bacteria obtained in (ii) can be provided under a reducing atmosphere (e.g., N, CO, H, or a mixture thereof, e.g., N:CO:H). The gases can be present in a ratio appropriate for preserving the bacteria present in the therapeutic composition. For example, the reducing atmosphere can include 80% N, 10% CO, and 10% H. Additionally or alternatively, the method may comprise lyophilizing the bacteria obtained in (i) and optionally the bacteria obtained in (ii), optionally in the presence of a stabilizer and / or cryoprotectant. The method may also comprise preparing a capsule, tablet, or enema comprising the bacteria obtained in (i) and optionally the bacteria obtained in (ii). The capsule or tablet may be enteric-coated, pH-dependent, sustained-release, and / or gastric-resistant.

[0203] The compositions of the invention may also be provided in the form of a dietary supplement, a drink or other foodstuff. Thus, the invention also relates to a food product or a vaccine comprising the composition of the invention.

[0204] Also provided is an immunogenic composition for use as an adjuvant to an anti-PD-1 antibody / anti-PD-L1 antibody / anti-PD-L2 antibody-based therapy administered to a cancer patient, the immunogenic composition for this use comprising a fragment of a bacterium selected from the bacteria listed in Table 1.

[0205] Biomarkers The present invention provides microbiome biomarkers that predict tumor response to immune checkpoint inhibitor therapy in cancer patients. In particular, the present invention provides microbiome biomarker signatures that predict tumor response to immune checkpoint inhibitor therapy. As used herein, a microbiome biomarker signature is a composite biomarker signature that includes bacteria from at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of the bacterial species listed in Table 1, each of which is increased in abundance in subjects responsive to immune checkpoint inhibitor therapy. In one embodiment, the signature includes bacteria from at least 9, 10, 11, 12, 13, 14, or 15 bacterial species selected from Table 1, each of which is increased in abundance in a population of subjects responsive to immune checkpoint inhibitor therapy. Biomarker signatures are described in more detail below.

[0206] Another aspect provides a method for treating cancer in a subject, comprising administering to the subject a therapeutically effective amount of an immune checkpoint inhibitor, wherein the subject has been determined to have a favorable microbial profile in the gut microbiome, the favorable microbial profile being characterized by the presence of a biomarker / biomarker signature described herein.

[0207] Another aspect provides a method of treating cancer in a subject, wherein the subject has been determined to have an unfavorable microbial profile in the gut microbiome. The unfavorable microbial profile is characterized by the absence of a biomarker / biomarker signature described herein. The method may further include administering an anti-cancer therapy that is not an immune checkpoint inhibitor therapy. In another embodiment, the method includes administering a therapeutic bacterial composition described herein in combination with an immune checkpoint inhibitor therapy, such as a PD-1 inhibitor.

[0208] Accordingly, the present invention also relates to a method for identifying a subject that will respond to therapy with an immune checkpoint inhibitor, e.g., PD-1, comprising the step of determining the abundance of one or more of the bacteria identified in Table 1 as B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 in a biological sample from said subject comprising gut (i.e., intestinal) flora, wherein an increased abundance of one or more of B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15, such as one or more of B1, B2, B3, B4, B5, B6, B7, B8, and / or B9, is predictive that the subject will respond to therapy with an immune checkpoint inhibitor, e.g., PD-1. B1 through B15 are listed in Table 1, which includes references to sequence identifiers that define the bacteria. The corresponding sequences are listed in Table 2. In one embodiment, the subject is a patient diagnosed with cancer, e.g., melanoma.

[0209] In particular, the present invention provides a method for predicting response to immune checkpoint inhibitor therapy in a subject with cancer / a method for identifying a subject that will respond to therapy with an immune checkpoint inhibitor, comprising: a) determining the abundance of one or more species of bacteria selected from B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 in Table 1 in a biological sample obtained from the subject; b) comparing the abundance to a reference level from a cancer patient who does not respond to therapy with an immune checkpoint inhibitor, or comparing the abundance to a reference level from a cancer patient who responds to therapy with an immune checkpoint inhibitor; an increase in the abundance of one or more of B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 relative to the reference level is predictive of the subject responding to therapy with an immune checkpoint inhibitor, where the reference level is from a cancer patient who is not responsive to therapy with an immune checkpoint inhibitor; or When the reference level is from a patient who responds to therapy with an immune checkpoint inhibitor, the same, substantially the same, or increased abundance of one or more of B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 predicts that the subject will respond to therapy with an immune checkpoint inhibitor. It also relates to methods.

[0210] An additional step may include identifying subjects who respond to the therapy.

[0211] In particular, the present invention provides a method for predicting response to immune checkpoint inhibitor therapy in a subject with cancer / a method for identifying a subject that will respond to therapy with an immune checkpoint inhibitor, comprising: a) determining the abundance of one or more species of bacteria selected from B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 in Table 1 in a biological sample obtained from the subject; b) comparing the abundance to a reference level derived from a cancer patient or a healthy subject; and c) applying a random forest analysis. In this embodiment, the reference levels are derived from a pool of cancer patients. These may include responders and non-responders.

[0212] Additional steps may include identifying subjects who will respond to therapy or predicting response.

[0213] Thus, the abundance of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 different species of bacteria selected from B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and B15 in Table 1 is determined. Respective sequences characterizing these species are provided as SEQ ID NOS: 1-15. As described elsewhere, SEQ ID NOS: 16-29 can also be used. In some embodiments, the abundance of bacteria selected from at least 9, 10, 11, 12, 13, 14, or 15 different species identified in Table 1 as B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and B15 is determined. Thus, the abundance of bacteria having sequences selected from at least nine of the following SEQ ID NOs: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15, or sequences having at least 97%, 98%, 98.7%, or 99% sequence identity thereto, such as SEQ ID NOs: 16-29, is determined.

[0214] Also provided is a method for predicting recurrence in a patient being or who has been treated for cancer, comprising assessing the presence / relative abundance of one or more bacteria selected from B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, and / or B15 in stool samples obtained from the patient, e.g., at different time points.

[0215] When the abundance is determined, an abundance score is obtained and measured for each of the bacteria, i.e., bacterial species. According to the method, an increase in the abundance, i.e., abundance score, of one or more of the bacteria listed in Table 1 predicts that the subject will respond to therapy with an immune checkpoint inhibitor. The increase refers to an increase in abundance, i.e., abundance score, compared to a reference value. Thus, the method also includes a step of comparing the abundance of one or more of the bacteria listed in Table 1 to one or more reference values. For example, the abundance of one or more of the bacteria listed in Table 1 can be compared to the reference value of one or more of the bacteria listed in Table 1. Alternatively, the average abundance of one or more of the bacteria listed in Table 1 can be compared to a single reference value that is the reference average abundance of one or more of the bacteria listed in Table 1. In one embodiment, the method determines the abundance of at least 9, 10, 11, 12, 13, 14, or 15 different species of bacteria selected from B1 to B15, thereby determining a microbiome biomarker signature, i.e., a microbiome biomarker signature score, based on the composite signature.

[0216] In one embodiment of the above method, the abundance of all of the bacteria listed in Table 1 is determined. In another embodiment, the abundance of a subset of the bacteria listed in Table 1 is determined. For example, the subset includes or consists of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 different bacteria selected from Table 1. In one embodiment, the subset includes or consists of 9 bacteria, e.g., Eisenbergella spp., Butyricicoccus spp., Clostridiales spp., Alistipes obesia, Alistipes indicustus, Gordonibacter urolithinfaciens, Fecalitaria spp., Blautia spp. (B8), and Barnesiella intestinihominis. In one embodiment, the subset includes or consists of 9 or 12 bacteria, e.g., one of the bacterial consortia in Table 3. In one embodiment, the biomarker does not include Alistipes spp.

[0217] The reference values ​​may be predetermined values ​​from a reference sample, for example, the reference values ​​may be the average abundance of each of the bacteria in the reference pool or their composite signature, respectively.

[0218] For example, the reference value is a predetermined value, e.g., a predetermined threshold. Such a value can be predetermined from a reference sample. The predetermined threshold for the abundance of one or more species of bacteria B1 to B15 refers to the abundance of the bacteria in a sample, e.g., a stool sample, as a proportion of the total microbiota in the sample, above or below which the sample is assessed as positive for that signature and therefore responsive to therapy with an immune checkpoint inhibitor. For example, if the abundance score of a test sample is equal to or greater than the predetermined threshold, the sample is considered positive for that signature and the subject is responsive to therapy with an immune checkpoint inhibitor.

[0219] For example, the abundance scores of the test bacterial levels in the sample pool are stored on a computer or computer-readable medium and used as a reference level to which the abundance of the test bacteria from the test samples is compared, if necessary. To establish a reference value and / or to correlate the abundance of bacteria selected from one or more of the bacteria listed in Table 1 in a sample with the subject's responsiveness to treatment with an immune checkpoint inhibitor, machine learning algorithms and / or models commonly used in identifying biomarkers, such as the Cox model, or other models, trained using training data containing information about multiple biomarkers in a set of subjects, can be used.

[0220] As used herein, the term "correlation" is used to determine or calculate responsiveness to a treatment condition based on the adjusted abundance of one or more species of bacteria and should be understood to mean any method of correlation, e.g., an algorithmic method. The methodology described herein uses a mathematical modeling technique known as random forest classification, although other modeling techniques may be used. Thus, in one embodiment, a random forest classification model or a similar model is used to correlate the abundance of bacteria selected from one or more species of bacteria listed in Table 1 in a sample with the subject's responsiveness to treatment with an immune checkpoint inhibitor. Thus, in one embodiment, the method of the present invention may use a computer program to correlate the adjusted abundance of bacteria with immune checkpoint inhibitor treatment response.

[0221] Alternatively, the reference value is not predetermined but is established as part of a single experiment. Thus, the abundance of one or more test bacteria in a test sample may be compared to the abundance of one or more test bacteria in a pool of samples, with the abundance of the test bacteria from the test sample and the abundance of the test bacteria from the pool being determined during the course of a single experiment.

[0222] In various embodiments, the reference sample / sample pool can be a population of cancer patients who have been shown to be responsive or non-responsive to therapy with an immune checkpoint inhibitor, hi other embodiments, the reference sample / sample pool can be a population of cancer patients who have not yet received therapy with a checkpoint inhibitor.

[0223] In one embodiment, the reference sample used to establish the reference value can be from a non-responder to immune checkpoint inhibitor therapy. If the test sample shows an increase in the abundance of one or more bacteria selected from B1 to B15 compared to the reference sample, the test subject is likely to respond to therapy with a checkpoint immune inhibitor. The increase can be at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more than 90%.

[0224] In one embodiment, the reference sample used to establish the reference value may be from a responder to immune checkpoint inhibitor therapy: If the test sample exhibits the same, substantially the same, or an increased abundance of one or more species of bacteria selected from B1 to B15 compared to the reference sample, the test subject is likely to respond to therapy with a checkpoint immune inhibitor.

[0225] As will be understood by one of skill in the art, a reference value or reference gene signature score as used herein refers to a score of a bacterial abundance signature determined to separate a majority of responders from a majority of non-responders in a reference population of subjects.

[0226] As used herein, a "good responder to a treatment," also referred to as a "responder" or "responsive" patient, or in other words, a patient who "benefits from" the treatment, refers to a patient who has cancer and who shows or demonstrates a clinically significant alleviation in cancer after receiving the treatment. Conversely, a "bad responder" or "non-responder" does not show or demonstrate a clinically significant alleviation in cancer after receiving the treatment. Response to a treatment can be assessed according to art-recognized criteria, such as immune-related response criteria (irRC), WHO, or RECIST criteria.

[0227] The signature biomarkers described herein are useful for identifying cancer patients most likely to achieve clinical benefit from treatment with immune checkpoint inhibitors. This utility supports the use of these biomarkers in a variety of research and commercial applications, including, but not limited to, clinical trials of PD-1 antagonists in which patients are selected based on their microbiome gene signature score, diagnostic methods and products for determining a patient's microbiome gene signature score or classifying patients as positive or negative for a microbiome signature biomarker, personalized treatment methods that involve adapting a patient's medication based on their microbiome signature score, and pharmaceutical compositions and formulations comprising PD-1 antagonists for use in treating patients who test positive for a microbiome signature biomarker.

[0228] Those skilled in the art will understand that the usefulness of any of the applications claimed herein does not require that 100% of patients who test positive for a biomarker of the invention achieve an anti-tumor response to an immune checkpoint inhibitor, that a diagnostic method or kit have a particular degree of specificity or sensitivity in determining the presence or absence of a biomarker in every subject, or that a diagnostic method claimed herein be 100% accurate in predicting, for every subject, whether that subject is likely to have a beneficial response to a PD-1 antagonist. Accordingly, the inventors intend that the terms "determine," "determining," and "predicting" herein should not be construed as requiring a definite or specific result; instead, these terms should be understood to mean either that the claimed method provides an accurate result for at least a majority of subjects, or that the result or prediction for any given subject is more likely to be correct than to be wrong. Preferably, the accuracy of the results provided by the diagnostic methods of the invention is one that a skilled artisan or regulatory authority would consider appropriate for the particular application for which the method is used.

[0229] As used herein, sample refers to a biological sample from the intestine, i.e., containing the intestinal flora. It refers to a sample obtained from the intestine of a subject, such as a fecal sample. Methods for isolating bacteria from fecal samples are known. In some cases, microbiome samples are obtained by mucosal biopsy. Test sample refers to a sample obtained from the subject being evaluated.

[0230] In one embodiment of the method, the abundance is a relative abundance. As used herein, the term "relative abundance" as applied to a bacterium in a sample should be understood to mean the abundance of the bacterium in the sample as a proportion of the total microbiota in the sample or a reference sample. In one embodiment, the relative abundance is the abundance of the bacterium in the sample as a proportion of the total microbiota in the sample.

[0231] In one embodiment, the adjusted abundance is the difference in the relative abundance of the bacteria in a sample compared to the relative abundance in the same sample from a reference subject.

[0232] In one embodiment, to determine the relative abundance of bacteria, measure the abundance of bacteria in sample as a proportion of the total microbiota in sample.Then, in this embodiment, the relative abundance of bacteria in sample is compared with the relative abundance in the same sample from a reference individual (also referred to herein as "reference relative abundance").The difference in the relative abundance of bacteria in sample, for example, the increase compared with reference relative abundance, is the adjusted relative abundance.The detection of adjusted abundance can also be carried out absolutely by comparing sample abundance value with absolute reference value.

[0233] Any suitable method for detecting the presence / abundance of bacteria can be utilized, including, for example, agar plate quantification assays, fluorescent quantitative sample quantification, PCR, 16S rRNA / rDNA gene amplicon sequencing, shotgun metagenomic sequencing, and dye-based metabolite depletion or metabolite production assays. The PCR techniques used can quantitatively measure the starting amount of DNA, cDNA, or RNA. Examples of PCR-based techniques according to the present invention include, but are not limited to, techniques such as quantitative PCR (Q-PCR), reverse transcriptase polymerase chain reaction (RT-PCR), quantitative reverse transcriptase PCR (QRT-PCR), rolling circle amplification (RCA), or digital PCR. These techniques are well known and readily available, and do not require precise description. In certain embodiments, the determination of bacterial gene copy number according to the present invention is performed by quantitative PCR.

[0234] In one embodiment, the sample is analyzed using a nucleic acid amplification reaction. The analysis may include detecting family-, order-, class-, and / or genus-specific 16S rRNA / rDNA or other sequences in the bacterial genome. In one embodiment, full-length 16S rDNA may be detected. In one embodiment, a partial 16S rDNA, such as one of the V regions, may be detected. In one embodiment, the analysis includes hybridizing bacterial nucleic acids in the sample to beads or an array, such as a nucleic acid microarray.

[0235] PCR-based techniques are performed using amplification primers designed to be specific for the sequences to be measured. Therefore, the present invention also relates to a set of primers suitable for performing the above method, i.e., a set of primers comprising a primer pair for amplifying sequences specific to the microbial species to be detected (i.e., at least one or more species selected from those listed in Tables 1, 2, and 3).

[0236] The 16S rDNA sequences of B1-B15 are provided herein and can be used to generate primers for such analyses. In one embodiment, multiple of the above bacteria are detected. In one embodiment, genome sequencing is used to analyze the sample for bacterial nucleic acids.

[0237] In one embodiment, the subject is a cancer patient, such as a melanoma patient. The cancer patient may or may not have received anti-cancer treatment. Thus, the subject may be in need of treatment with an immune checkpoint inhibitor. In one embodiment, the subject is a healthy individual, e.g., a healthy individual with a family history of cancer, such as melanoma.

[0238] In one embodiment, the subject is a cancer patient, and if the subject has been identified as one that will respond to therapy with an immune checkpoint inhibitor, the method may comprise the further step of administering an immune checkpoint inhibitor to said patient.

[0239] In one embodiment, the method comprises the preceding step of obtaining a biological sample comprising gut flora.

[0240] In one embodiment, the method also includes the initial step of identifying a subject in need of treatment with an immune checkpoint inhibitor.

[0241] In one embodiment of the method, if the subject is identified as a responder, for example, if one or more of the bacteria listed in Table 1 are shown to be elevated in abundance in the sample, the subject is administered an anti-cancer therapy comprising an immune checkpoint inhibitor.

[0242] Checkpoint inhibitors are as defined herein. In one method embodiment, the immune checkpoint inhibitor inhibits PD-1 activity, i.e., acts as a PD-1 antagonist. In one method embodiment, the immune checkpoint inhibitor inhibits PD-L1 activity, i.e., acts as a PD-L1 antagonist. In one method embodiment, the immune checkpoint inhibitor inhibits CTLA-4 activity, i.e., acts as a CTLA-4 antagonist. In one method embodiment, the immune checkpoint inhibitor inhibits LAG3, TIGIT, or TIM3 activity.

[0243] In one embodiment, the immune checkpoint inhibitor is an anti-PD-1, PD-L1, or CTLA-4 antibody. In one embodiment, the anti-PD-1 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, or toripalimab, ipilimumab, or tremelimumab.

[0244] In one embodiment, the immune checkpoint inhibitor is an interfering nucleic acid molecule, hi one embodiment, the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule.

[0245] In one embodiment, the immune checkpoint inhibitor is a small molecule or a proteolysis-targeting chimeric molecule (PROTAC) or another immune checkpoint inhibitor as defined above.

[0246] In one embodiment, in a further step of the method, a surgical, radiological and / or chemotherapy cancer intervention is performed or a second anti-cancer therapy is administered to said subject.

[0247] In another aspect, the present invention relates to a method for detecting a risk that a subject will not respond to immune checkpoint inhibitor therapy. The method comprises determining the abundance of one or more species of bacteria listed in Table 1 in a biological sample from the subject comprising intestinal flora, wherein a decreased abundance or an abundance below a reference level of one or more species of bacteria listed in Table 1 predicts that the subject will not respond to immune checkpoint inhibitor therapy. The method also comprises comparing the abundance of one or more species of bacteria listed in Table 1 with one or more reference values. The reference values ​​are as described above. The determined abundances are relative abundances. In a further step, if the subject has been identified as a subject who will not respond to immune checkpoint inhibitor therapy, an alternative anti-cancer treatment is administered. Alternatively, in a further step, if the subject has been identified as a subject who will not respond to immune checkpoint inhibitor therapy, a therapeutic bacterial composition described herein is administered together with checkpoint inhibitor therapy, such as anti-PD-1 therapy.

[0248] In another aspect, the present invention relates to a method for distinguishing between subjects who will respond to immune checkpoint inhibitor therapy and subjects who will not respond to immune checkpoint inhibitor therapy. The method comprises determining the abundance of one or more species of bacteria listed in Table 1 in a biological sample from the subject comprising intestinal flora, wherein a decreased abundance or an abundance below a reference level of one or more species of bacteria listed in Table 1 predicts that the subject will not respond to immune checkpoint inhibitor therapy, and an increased abundance of one or more species of bacteria listed in Table 1 predicts that the subject will respond to immune checkpoint inhibitor therapy. The method may also comprise comparing the abundance of one or more species of bacteria listed in Table 1 with one or more reference values. The reference values ​​are as described above. The determined abundances are relative abundances. In a further step, if the subject is identified as a subject who will not respond to immune checkpoint inhibitor therapy, an alternative anti-cancer treatment is administered.

[0249] In one embodiment of the above biomarker method, the modulated abundance of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 different bacteria selected from Table 1 is predictive of response to treatment. In another embodiment of the above biomarker method, the modulated abundance of at least 9, 10, 11, 12, 13, 14, or 15 different bacteria selected from Table 1 is predictive of response to treatment. Thus, it is a particular embodiment of the method to establish a composite signature comprising the abundance of at least 9, 10, 11, 12, 13, 14, or 15 different bacteria. It is this biomarker signature that provides a bacterial holistic, particularly powerful, discrimination tool.

[0250] In one embodiment of the various methods described above, the abundance of at least nine bacterial species / a population of nine bacterial species selected from Table 1 is assessed, i.e., nine species selected from SEQ ID NOs: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15. In one embodiment, this subset of nine species corresponds to a consortium shown in Table 3, i.e., consortiums 2, 4, 5, 6, or 10. In one embodiment, the nine species include bacteria defined by SEQ ID NO: 1 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO: 2 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO: 3 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO:4 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO:5 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO:6 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO:7 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO:8 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO:9 or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO: 10, or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include bacteria defined by SEQ ID NO: 11, or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto.In one embodiment, the nine species include a bacterium defined by SEQ ID NO: 12, or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include a bacterium defined by SEQ ID NO: 13, or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include a bacterium defined by SEQ ID NO: 14, or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species include a bacterium defined by SEQ ID NO: 15, or a sequence having at least 97%, 98%, 98.7%, or 99% sequence identity thereto. In one embodiment, the nine species do not include Alistipes species.

[0251] In one embodiment, the biomarker method described above may include the further step of determining another biomarker that predicts tumor response to therapy with an immune checkpoint inhibitor, e.g., a PD-1, PD-L1, or CTLA-4 antagonist. For example, the biomarker may be a programmed cell death-ligand 1 (PD-L1) or a programmed cell death-ligand 2 (PD-L2) gene signature. Thus, the method may include obtaining a sample from a test tumor, measuring the RNA expression level of one or more genes in the PD-1 and / or PD-L1 gene signature in the tumor sample, and comparing the RNA expression level to a reference level. Expression may be measured by any suitable method, including immunohistochemistry.

[0252] In another aspect, the invention relates to one or more of the bacteria listed in Table 1 for use as a predictive biomarker in determining the efficacy of a therapeutic intervention with a checkpoint inhibitor, e.g., PD-1 therapy. As used herein, the term predictive biomarker describes a biomarker that provides information regarding the efficacy of a therapeutic intervention, i.e., responsiveness to treatment with an immune checkpoint inhibitor. Accordingly, the invention relates to the use of one or more species of bacteria, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 different species of bacteria selected from one or more of the bacterial species listed in Table 1, in identifying patients who will respond to therapy with an immune checkpoint inhibitor.

[0253] The present invention also relates to a biomarker signature that is a consortium of one or more of the bacteria listed in Table 1, e.g., in Table 3, that can be used to predict the effectiveness of therapeutic intervention with a checkpoint inhibitor therapy, e.g., with a PD-1 inhibitor or another checkpoint inhibitor therapy.

[0254] System and computer-readable medium

[0010] Embodiments of the present invention also provide systems (and computer-readable media for providing such systems) that implement the methods for determining responsiveness in a subject to treatment with an immune checkpoint inhibitor. In another aspect, a computer-implemented method is provided for indicating the likelihood that a subject will respond to treatment with an immune checkpoint inhibitor. The method includes collecting computer biomarker information for an individual, the biomarker information including biomarker values ​​each corresponding to the abundance of one or more bacteria selected from the group of bacteria set forth in Table 1, performing a classification of each of the biomarker values ​​on a computer, and indicating the likelihood that the subject will respond to treatment with an immune checkpoint inhibitor based on the plurality of classifications.

[0255] In another aspect, a computer program product for indicating the likelihood that a subject will respond to treatment with an immune checkpoint inhibitor is provided. The computer program product includes a computer-readable medium having embedded thereon program code executable by a processor of a computing device or system, the program code including code for collecting data resulting from a biological sample from an individual, the data including biomarker values ​​each corresponding to the abundance of one or more bacteria selected from the group of bacteria set forth in Table 1, and code for executing a classification method for indicating the likelihood that the individual will respond to treatment with the immune checkpoint inhibitor as a function of the biomarker values.

[0256] In one embodiment, the comparison module reads stored reference data, e.g., the relative abundance of specific bacteria in the reference sample described herein, and compares the data. The "comparison module" can compare the bacterial abundance information data determined by the determination system with the reference sample and / or stored reference data, e.g., a predetermined threshold value, using various software programs and formats available for comparison operations. In one embodiment, the comparison module is configured to use pattern recognition techniques to compare information from one or more inputs with one or more reference data patterns. The comparison module may be configured using existing commercially available or freely available software for pattern comparison, and may be optimized for the specific data comparison being performed. The comparison module provides computer-readable information related to response-related bacteria.

[0257] The comparison module provides computer-readable comparison results that can be processed in computer-readable form according to predefined or user-defined criteria, and provides content based in part on the comparison results that can be stored and output.

[0258] Thus, the methods described herein provide systems (and computer readable media for providing the computer systems) that perform the methods for determining responsiveness in a subject to treatment with an immune checkpoint inhibitor.

[0259] FMT The transplantation or administration of human microbiota into the intestines of diseased patients is called fecal microbiota transplantation (FMT), also commonly known as fecal bacteriotherapy. FMT is thought to repopulate the intestine with a diverse population of microorganisms that provide missing beneficial functions or microbiota to resident gut bacteria, replace harmful microbiota, or control important pathogens by creating unfavorable ecological environments.

[0260] In another aspect, the present invention relates to a method for screening / identifying a fecal donor, comprising: evaluating a fecal sample from a subject for the presence of one or more bacteria associated with cancer response (e.g., cancer response when the patient is treated with an immune checkpoint inhibitor); and identifying the fecal donor based on the presence and / or abundance of the one or more bacteria.

[0261] For example, in such methods, one or more bacteria selected from Table 1 and the fecal donor are identified based on the presence and / or abundance of one or more bacteria selected from Table 1.

[0262] In another aspect, the present invention relates to a method for screening / identifying a fecal donor, the method comprising: evaluating a subject's fecal sample for the presence of one or more bacteria selected from Table 1; and identifying a fecal donor based on the presence and / or abundance of one or more bacteria selected from Table 1. The method may also include obtaining a fecal sample from the donor. Evaluating the subject's fecal sample for the presence of one or more bacteria can be performed by methods known in the art, such as by sequence analysis of bacterial genomes, using a shotgun sequencing approach. For example, if one or more of the bacteria are present above a predetermined threshold, the donor is selected as a donor for bacteriotherapy purposes. The predetermined threshold can be based on the average abundance of one or more bacteria in fecal samples obtained from a donor population. A higher-than-average abundance indicates that the feces is suitable for FMT therapy.

[0263] The present invention also relates to the use of one or more bacteria selected from Table 1 in a method for identifying a donor for FMT therapy.

[0264] The present invention relates to a method for treating a fecal transplant prior to administration to a subject, the method comprising supplementing the fecal transplant with one or more bacterial isolates selected from Table 1 or with a fecal sample obtained from a donor by the method described above.

[0265] According to another aspect of the invention, an individual in need of treatment with immune checkpoint inhibitor therapy is treated by FMT using fecal microbiota from a healthy individual shown to have an elevated abundance of one or more of the species in table 1, and / or fecal microbiota from one or several individuals treated with immune checkpoint inhibitor therapy and proven to respond to said therapy, and / or fecal microbiota from one or several individuals or responsive patients displaying a gut microbiota profile that identifies the individual as likely to respond to the envisaged treatment.

[0266] In the above embodiments, the FMT therapy is for the treatment of a disease referred to herein (eg, cancer, eg, melanoma).

[0267] Compositions and methods for increasing bacterial abundance in a host In another aspect of the present invention, a subject's microbiome can be altered to increase the abundance of the bacteria listed in Table 1 or a subset thereof. Glycan metabolism has been shown to influence the human gut microbiota, and prebiotics can enrich bacterial taxa that promote antitumor immunity (Koropatkin et al., Nature Reviews Microbiology, Vol. 10, No. 323-335 (2012); Li et al., Cell Reports, Vol. 30, No. 6, pp. 1753-1766.e6, February 11, 2020). Accordingly, methods are provided for increasing the abundance of the bacteria listed in Table 1 or a subset thereof in a subject by administering a composition comprising oligosaccharides (e.g., glycans). Compositions comprising oligosaccharides (e.g., glycans) for use in such methods are also contemplated.

[0268] kit In a further aspect, the present invention relates to a kit. The kit comprises a composition described herein and optionally comprises an anti-cancer treatment comprising an immune checkpoint inhibitor described herein. In an example, the kit can include materials for transporting the collected material without damaging the sample (e.g., packaged in lyophilized form or in an aqueous medium). The kit can include the processed material or treatment in a sterile container, such as a nasogastric (NG) tube, a vial (e.g., for use with a retention enema), a gastric acid-resistant capsule (e.g., acid-bioresistant and having a sterile exterior for reaching the intestinal tract), etc. The kit can also include instructions for use.

[0269] In an alternative embodiment, the kit comprises: a sealable container configured to receive a biological sample (e.g., a fecal sample); polynucleotide primers for amplifying a 16S rDNA polynucleotide sequence from at least one gut-associated bacterium to form an amplified 16S rDNA polynucleotide sequence, wherein the amplified 16S rDNA sequence has at least 97%, 98%, 98.7%, or 99% homology to a polynucleotide sequence selected from SEQ ID NO:1-SEQ ID NO:15 (e.g., SEQ ID NOs:16-29); detection reagents for detecting the amplified 16S rDNA sequences; · Instructions for use and; Includes.

[0270] The present invention also relates to a kit for use in a method for increasing the abundance of bacteria listed in Table 1 or a subset thereof in a subject by administering a composition comprising an oligosaccharide (e.g., a glycan) to the kit.

[0271] The present invention also relates to the use of a composition of the present invention, i.e., a composition comprising or consisting of one or more bacterial isolates shown in Table 1 with respect to the SEQ ID NOs shown in Table 1, in increasing the efficacy of anti-cancer treatment with immune checkpoint inhibitors. The present invention also relates to the use of a composition of the present invention, i.e., a composition comprising or consisting of one or more bacterial isolates shown in Table 1 with respect to the SEQ ID NOs shown in Table 1, in enhancing immune checkpoint blockade. The present invention also relates to a method for increasing the efficacy of anti-cancer treatment with immune checkpoint inhibitors, the method comprising administering to a subject a composition of the present invention, i.e., a composition comprising or consisting of one or more bacterial isolates shown in Table 1 with respect to the SEQ ID NOs shown in Table 1. The present invention also relates to a method for enhancing immune checkpoint blockade, the method comprising administering to a subject a composition of the present invention, i.e., a composition comprising or consisting of one or more bacterial isolates shown in Table 1 with respect to the SEQ ID NOs shown in Table 1.

[0272] The present invention also relates to the use of the compositions of the present invention in providing an immunostimulatory effect.

[0273] The present invention also relates to a method for determining whether a cancer patient requires a bacterial composition of the present invention, i.e., a bacterial composition comprising or consisting of one or more bacterial isolates shown in Table 1 with respect to the SEQ ID NOs shown in Table 1, prior to administration of an immune checkpoint inhibitor, the method comprising assessing a stool sample from the patient for the presence or absence of one or more bacterial isolates selected from the species in Table 1.

[0274] Aspects The present invention is further described in the following aspects. 1. A composition comprising isolated bacteria selected from at least two species, wherein the bacteria from a first species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from a second species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:2. 2. The composition of aspect 1, further comprising isolated bacteria from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 different species, wherein the bacteria comprise a 16S rDNA sequence selected from the sequences having at least 98.7% sequence identity to the nucleic acid sequences set forth in SEQ ID NOs: 3-15. 3. The composition of embodiment 1, further comprising isolated bacteria from at least four different species, wherein the bacteria comprise a 16S rDNA sequence selected from the sequences having at least 98.7% sequence identity to the nucleic acid sequences set forth in SEQ ID NOs: 3-15. 4. The composition of embodiment 1, further comprising isolated bacteria from at least seven different species, wherein the bacteria comprise a 16S rDNA sequence selected from the sequences having at least 98.7% sequence identity to the nucleic acid sequences set forth in SEQ ID NOs: 3-15. 5. The composition of embodiment 1, further comprising a bacterial isolate comprising a 16S rDNA sequence selected from the sequences having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:7. 6. The composition of embodiment 1, comprising a consortium selected from consortia 1 to 4 or 6 to 10 shown in Table 3. 7. The composition of any one of aspects 1 to 6, which is formulated for oral or rectal administration. 8. The composition of embodiment 7, which is in the form of a capsule, tablet, gel, or liquid. 9. The composition of embodiment 8, wherein the composition is encapsulated in an enteric coating. 10. The composition of any one of aspects 1 to 9, comprising live, attenuated, or killed bacteria. 11. The composition of any one of aspects 1 to 10, comprising a bacterial spore. 12. The composition of any one of aspects 1 to 11, wherein the composition is free of bacterial spores. 13. The composition of any one of aspects 1 to 12, comprising bacterial strains derived from one or more human donors. 14. The composition of any one of aspects 1 to 13, wherein the bacteria is freeze-dried. 15. At least about 1 x 10 3 ~1×10 13 15. The composition of any one of embodiments 1 to 14, comprising CFU of bacteria. 16. The composition of any one of aspects 1 to 15, wherein administration of the composition induces an immune response in a subject and / or increases the efficacy of an anti-cancer therapy comprising an immune checkpoint inhibitor. 17. A pharmaceutical composition comprising the composition of any one of aspects 1 to 16 and a pharmaceutical carrier. 18. The pharmaceutical composition of aspect 17, further comprising an effective amount of an immune checkpoint inhibitor or vaccine. 19. The pharmaceutical composition of aspect 18, wherein the immune checkpoint inhibitor inhibits the activity of PD-1, PDL-1, CTLA-4, LAG3, or TIM-3. 20. The pharmaceutical composition of aspect 19, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody, anti-PDL-1 antibody, or anti-CTLA-4 antibody, or a fragment thereof. 21. The pharmaceutical composition of aspect 20, wherein the anti-PD-1 antibody, anti-PDL-1 antibody, or anti-CTLA-4 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, toripalimab, ipilimumab, or tremelimumab. 22. The pharmaceutical composition of aspect 18, wherein the immune checkpoint inhibitor is an interfering nucleic acid molecule, a small molecule, or a proteolysis-targeting chimeric molecule (PROTAC), an alternative protein scaffold, or another immune checkpoint inhibitor. 23. The pharmaceutical composition of aspect 22, wherein the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule. 24. A composition according to any one of aspects 1 to 16 or a pharmaceutical composition according to any one of aspects 17 to 23 for use in the treatment of a disease. 25. A composition according to any one of aspects 1 to 16, or a pharmaceutical composition according to any one of aspects 17 to 23, for use in the treatment of cancer or an infectious disease, or for use as a vaccine adjuvant, or for increasing the efficacy of cancer treatment. 26. A method for treating cancer or an infectious disease in a subject in need thereof, comprising administering to the subject a composition according to any one of aspects 1 to 16 or a pharmaceutical composition according to aspect 17. 27. The method of aspect 26, wherein the subject is undergoing, has already undergone, or will undergo therapy with an immune checkpoint inhibitor to treat cancer or an infectious disease. 28. A method for treating cancer in a subject in need thereof, comprising administering to the subject the composition of aspect 18. 29. The method of aspect 26 or 27, wherein administration of the composition enhances an immune response by the subject, and / or inhibits immune evasion by the cancer, and / or increases the efficacy of anti-cancer treatment with an immune checkpoint inhibitor. 30. The cancer is melanoma, e.g., Harding-Passey melanoma, juvenile melanoma, lentigo maligna melanoma, malignant melanoma, acral lentiginous melanoma, amelanotic melanoma, benign juvenile melanoma, Cloudman melanoma, S91 melanoma, nodular melanoma, subungual melanoma, cutaneous melanoma, uveal / intraocular melanoma and superficial spreading melanoma or bone cancer, pancreatic cancer, skin cancer, head and neck cancer, cutaneous malignant melanoma or intraocular malignant melanoma, uterine cancer, ovarian cancer, rectal cancer, cancer of the anal region, stomach cancer, testicular cancer, breast cancer, brain cancer, egg 29. The composition for use according to aspect 25, or the method according to any one of aspects 26 to 29, wherein the cancer is selected from cancer of the esophagus, small intestine, endocrine system, thyroid cancer, parathyroid cancer, adrenal gland cancer, kidney cancer, soft tissue sarcoma, urethral cancer, bladder cancer, rectal cancer, lung cancer, non-small cell lung cancer, thymoma, urothelial carcinoma, leukemia, prostate cancer, mesothelioma, adrenocortical carcinoma, lymphoma, e.g., Hodgkin's disease, non-Hodgkin's lymphoma, gastric cancer, and multiple myeloma. 31. The composition for use according to aspect 25 or the method according to any one of aspects 26 to 29, wherein the composition or pharmaceutical composition is administered orally or rectally. 32. The composition for use of aspect 25 or the method of any one of aspects 26 to 29, wherein the subject has already undergone prior anti-cancer therapy with an immune checkpoint inhibitor. 33. The composition for use according to aspect 25 or the method according to any one of aspects 26 to 29, further comprising the step of administering an anti-cancer therapy using an immune checkpoint inhibitor. 34. The composition or method for use of aspect 33, wherein the immune checkpoint inhibitor is administered before, after, or simultaneously with the bacterial preparation. 35. A composition or method for use according to aspect 33 or 34, wherein the immune checkpoint inhibitor is administered by injection. 36. The composition or method for use of any one of aspects 33 to 35, wherein the injection is intravenous, intramuscular, intratumoral, or subcutaneous injection. 37. The composition or method for use of any one of aspects 33 to 36, wherein the immune checkpoint inhibitor inhibits the activity of PD-1, PDL-1, or CTLA-4. 38. The composition or method for use according to aspect 37, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody, an anti-PDL-1 antibody, or an anti-CTLA-4 antibody. 39. The composition or method for use of aspect 38, wherein the anti-PD-1 antibody, anti-PDL-1 antibody, or anti-CTLA-4 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, toripalimab, ipilimumab, or tremelimumab. 40. The composition or method for use of any one of aspects 33 to 37, wherein the immune checkpoint inhibitor is an interfering nucleic acid molecule, a small molecule, or a proteolysis-targeting chimeric molecule (PROTAC) or other immune checkpoint inhibitor. 41. The composition or method for use according to aspect 40, wherein the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule, or a small molecule or peptide. 42. The composition for use of any one of aspects 25 or 30 to 41, or the method of any one of aspects 26 to 41, further comprising surgical, radiological, and / or chemotherapeutic cancer intervention or administration of a second anti-cancer therapeutic. 43. The composition for use according to any one of aspects 25 or 30 to 42, or the method according to any one of aspects 26 to 42, further comprising administering to the subject an antibiotic. 44. The composition for use according to any one of aspects 25 or 30 to 43, or the method according to any one of aspects 26 to 43, wherein the subject has been identified as being at risk of developing cancer. 45. A kit comprising the composition of any one of embodiments 1 to 17, and optionally comprising an anti-cancer treatment comprising an immune checkpoint inhibitor. 46. ​​A food or vaccine adjuvant comprising the composition of any one of aspects 1 to 17. 47. A method for treating a fecal transplant prior to administration to a subject, comprising supplementing the fecal transplant with isolated bacteria selected from at least two species, wherein the bacteria from a first species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from a second species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:2. 48. Use of a composition according to any one of aspects 1 to 17 or a pharmaceutical composition according to aspect 18 in increasing the efficacy of anti-cancer treatment with immune checkpoint inhibitors. 49. Use of a composition according to any one of aspects 1 to 17 or a pharmaceutical composition according to aspect 18 in enhancing immune checkpoint blockade. 50. A method for enhancing immune checkpoint blockade, comprising administering a composition according to any one of aspects 1 to 17 or a pharmaceutical composition according to aspect 18. 51. A composition comprising a bacterium selected from one or more bacteria selected from Table 1. 52. A method for treating or preventing cancer, comprising modulating the level of one or more bacteria selected from the bacteria in Table 1 in a subject.

[0275] The present invention is also further described in the following additional aspects. 1. A method for identifying a subject who will respond to therapy with an immune checkpoint inhibitor, comprising determining the abundance of bacteria from at least nine different species in a biological sample containing gut flora from the subject, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15, and the abundance is indicative of the subject's response to therapy with an immune checkpoint inhibitor. 2. A method for identifying a patient who will respond to therapy with an immune checkpoint inhibitor according to embodiment 1, comprising: a) determining the abundance of bacteria in a biological sample obtained from the subject; and b) comparing the abundance to a reference level from a cancer patient who is unresponsive to therapy with an immune checkpoint inhibitor or a cancer patient who is responsive to therapy with an immune checkpoint inhibitor; Including, If the baseline level is from a patient who is not responsive to therapy with an immune checkpoint inhibitor, an increase in the abundance of each of the bacteria compared to the baseline level indicates that the subject will respond to therapy with an immune checkpoint inhibitor; or When the reference level is from a patient who responds to therapy with an immune checkpoint inhibitor, the same abundance or substantially the same abundance or an increase in abundance of each of the bacteria indicates that the subject will respond to therapy with an immune checkpoint inhibitor. 3. a) determining the abundance of bacteria in a biological sample obtained from the subject; b) comparing the abundance to a reference level from a cancer patient; and c) applying a random forest analysis; 2. A method for identifying a patient who will respond to therapy with an immune checkpoint inhibitor according to embodiment 1, comprising: 4. The method of any one of aspects 1 to 3, wherein the bacterial species comprises a 16S rDNA sequence selected from SEQ ID NO: 1 or 2, or comprises a 16S rDNA sequence having at least 98.7% sequence identity to the 16S rDNA sequence. 5. The method of any one of aspects 1 to 4, comprising determining the abundance of bacteria from species 10, 11, 12, 13, 14, or 15, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence with at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15. 6. The method of any one of aspects 1 to 5, wherein the subject is a cancer patient. 7. The method of aspect 6, wherein the cancer is selected from melanoma, bone cancer, pancreatic cancer, head and neck cancer, cutaneous or intraocular melanoma, uterine cancer, ovarian cancer, rectal cancer, cancer of the anal region, stomach cancer, testicular cancer, breast cancer, brain cancer, fallopian tube cancer, endometrial cancer, cervical cancer, vaginal cancer, vulvar cancer, esophageal cancer, small intestine cancer, cancer of the endocrine system, thyroid cancer, parathyroid cancer, adrenal gland cancer, kidney cancer, soft tissue sarcoma, urethral cancer, bladder cancer, rectal cancer, lung cancer, non-small cell lung cancer, thymoma, urothelial carcinoma, leukemia, prostate cancer, mesothelioma, adrenocortical carcinoma, lymphoma, e.g., Hodgkin's disease, non-Hodgkin's lymphoma, gastric cancer, and multiple myeloma. 8. The method of aspect 7, wherein the melanoma is selected from Harding-Passey melanoma, juvenile melanoma, lentigo maligna melanoma, malignant melanoma, acral lentiginous melanoma, amelanotic melanoma, benign juvenile melanoma, Cloudman melanoma, S91 melanoma, nodular melanoma, subungual melanoma, cutaneous melanoma, uveal / intraocular melanoma, and superficial spreading melanoma. 9. The method of any one of aspects 1 to 8, further comprising identifying a subject in need of treatment with an immune checkpoint inhibitor. 10. The method of any one of aspects 1 to 9, further comprising administering to said subject an immune checkpoint inhibitor. 11. The method of any one of aspects 1 to 10, wherein the immune checkpoint inhibitor inhibits the activity of PD-1, PD-L1, or CTLA-4. 12. The method of aspect 11, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody, an anti-PDL-1 antibody, or an anti-CTLA-4 antibody. 13. The method of aspect 12, wherein the anti-PD-1 antibody, anti-PDL-1 antibody, or anti-CTLA-4 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, toripalimab, ipilimumab, or tremelimumab. 14. The method of any one of aspects 1 to 11, wherein the immune checkpoint inhibitor is an interfering nucleic acid molecule, a small molecule, or a proteolysis-targeting chimeric molecule (PROTAC) or other immune checkpoint inhibitor. 15. The method of embodiment 14, wherein the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule, or a small molecule or peptide. 16. The method of any one of aspects 1 to 15, wherein the abundance is the abundance of bacteria in the sample as a proportion of the total microbiota in the sample. 17. The method of any one of aspects 1 to 16, further comprising obtaining a biological sample comprising intestinal flora from the subject. 18. The method of any one of aspects 1 to 17, wherein the sample is a fecal sample. 19. Use of a bacterium selected from at least nine different bacterial species in identifying patients who will respond to therapy with an immune checkpoint inhibitor, wherein the bacterium comprises a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15. 20. A sealable container configured to receive a biological sample; polynucleotide primers for amplifying 16S rDNA polynucleotide sequences from at least nine different bacterial species, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15; a detection reagent for detecting the amplified 16S rDNA sequence; Instructions for use and Includes a kit. 21. A method for identifying a fecal donor, comprising: evaluating a fecal sample from a subject for the presence of bacteria from at least nine different bacterial species, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15; and identifying the fecal donor based on the presence and / or abundance of the bacteria. 22. Use of bacteria from at least nine different bacterial species in a method for identifying donors for FMT therapy, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15. 23. A method for determining whether a cancer patient requires bacterial compensation prior to administration of an immune checkpoint inhibitor, comprising assessing the presence or absence of bacteria from at least nine different bacterial species in a fecal sample from the patient, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15. 24. A method for predicting response to immune checkpoint inhibitor therapy in a subject with cancer, comprising determining the abundance of bacteria from at least nine different species in a biological sample containing gut flora from the subject, wherein the bacteria comprise a 16S rDNA sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15, and the abundance is indicative of the subject's response or non-response to therapy with an immune checkpoint inhibitor. 25. A method for predicting response to immune checkpoint inhibitor therapy in a subject with cancer according to aspect 24, comprising: a) determining the abundance of bacteria in a biological sample obtained from the subject; b) comparing the abundance to a reference level from a patient who does not respond to immune checkpoint inhibitor therapy or a patient who responds to immune checkpoint inhibitor therapy; Including, If the baseline levels are from a patient who is not responsive to immune checkpoint inhibitor therapy, an increase in the abundance of each of the bacteria compared to the baseline levels indicates that the subject will respond to therapy with an immune checkpoint inhibitor; or When the reference level is from a patient who responds to immune checkpoint inhibitor therapy, the same abundance or substantially the same abundance or increased abundance of each of the bacteria indicates that the subject will respond to therapy with an immune checkpoint inhibitor. 26. a) determining the abundance of bacteria in a biological sample obtained from a subject; and b) comparing the abundance to a reference level from a cancer patient or a healthy subject; and c) applying a random forest analysis; 25. The method of embodiment 24, comprising: 27. The method of any one of aspects 24 to 26, comprising predicting the response. 28. The method of any one of aspects 24 to 27, wherein if the subject is predicted to be a non-responder, an anti-cancer therapy that is not an immune checkpoint inhibitor is administered. 29. The method of any one of aspects 24-28, wherein if the subject is predicted to be a non-responder, a composition comprising isolated bacteria from one or more species is administered, wherein the bacteria comprise a sequence selected from SEQ ID NOs: 1-15 or a sequence having at least 98.7% sequence identity to a nucleic acid sequence selected from SEQ ID NOs: 1-15. 30. The method of embodiment 29, wherein if the subject is predicted to be a non-responder, a composition comprising isolated bacteria selected from at least two species is administered, wherein the bacteria from the first species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from the second species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:2. 31. The method of any one of aspects 24-31, wherein the composition further comprises isolated bacteria from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 different species, wherein the bacteria comprise a 16S rDNA sequence selected from a sequence having at least 98.7% sequence identity to a nucleic acid sequence set forth in SEQ ID NOs: 3-15. 32. The method of any one of aspects 24-27, wherein if the subject is predicted to be a responder, immune checkpoint inhibitor therapy is administered.

[0276] Further aspects and embodiments of the present invention will be apparent to those skilled in the art from consideration of this disclosure, including the following experimental examples.

[0277] Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have the meanings commonly understood by those skilled in the art. The above disclosure provides a general description of the subject matter encompassed within the scope of the present invention, including how to make and use the invention and the best mode thereof, while the following examples are provided to further enable those skilled in the art to practice the invention and to provide a complete description of the invention. However, those skilled in the art will understand that the details of these examples should not be read as limiting the invention, and that the scope of the invention should be understood from the claims appended to this disclosure and their equivalents. Various further aspects and embodiments of the present invention will be apparent to those skilled in the art in light of the present disclosure.

[0278] All documents referred to herein are incorporated by reference in their entirety, including any references to gene accession numbers and references to patent publications.

[0279] "And / or," as used herein, should be taken as a detailed disclosure of each of the two specified features or components, with or without the other. For example, "A and / or B" should be taken as a detailed disclosure of (i) A, (ii) B, and (iii) each of A and B, as if each were individually set forth herein. Unless the context indicates otherwise, the descriptions and definitions of features set forth above are not limited to any particular aspect or embodiment of the invention, but apply equally to all aspects and embodiments described.

[0280] The present invention is further illustrated in the following non-limiting examples. [Example]

[0281] Identification of gut bacteria and isolates that drive responses to immunotherapy In the MELRESIST study, we analyzed how the microbiome of melanoma patients influences their response to immune checkpoint inhibitor therapy. The study was conducted at Cambridge University Hospitals and used state-of-the-art sample collection and processing standards. The study included 69 patients, many of whom underwent longitudinal fecal sampling. We analyzed the relative abundance of gut bacteria in baseline MELRESIST fecal samples by shotgun metagenomic sequencing. Metagenomic sequencing was analyzed using a comprehensive and highly curated reference genome database built primarily on reference-quality genomes derived from culture isolates. This reference-based metagenomic analysis provides sensitive and accurate bacterial identification (Forster et al. Nat Biotechnol. 2019: 37: 186). To support this analysis, we reanalyzed three additional shotgun metagenomic datasets from melanoma patients undergoing immune checkpoint inhibitor therapy using the same analytical platform.

[0282] To select specific bacterial species that most strongly predict response to immune checkpoint inhibitor therapy, we examined the microbiome using a machine learning approach. For the first time in the field, we identified a consistent microbiome signature that is associated with and strongly predicts response across multiple studies. The size and quality of the MELRESIST dataset, the comprehensive and accurate identification of bacteria through reference-based metagenomic analysis, and machine learning analysis all contributed to the discovery of this cross-study microbiome signature. This signature further confirms the central importance of the gut microbiome as a key driver of immune checkpoint inhibitor response. This provides a basis for both predictive biomarkers and live-bacterial therapeutic combination therapy to increase the proportion of patients who respond to checkpoint inhibitors. Using a feature reduction step, we reduced this microbiome signature to a small bacterial consortium containing species with high abundance in patients who respond to immunotherapy. These reduced consortia can predict response across multiple studies and therefore act as biomarkers. Additionally, these consortia could form live bacterial therapeutics for co-administration with immune checkpoint inhibitors in the treatment of cancer.

[0283] This analysis allowed the identification of isolates representing 13 species in the consortium. Dendritic cells stimulated with these strains, individually or as consortia of up to nine species, potently activated cytotoxic T lymphocytes. When two consortia of nine species were tested in syngeneic mouse models of cancer, both consortia demonstrated tumor growth inhibition. These results validate bacteria as drivers of antitumor responses.

[0284] 1.1 MELRESIST Clinical Study-Based Findings MELRESIST was a study conducted at Cambridge University Hospitals, during which 69 patients with advanced melanoma provided fecal samples before and / or after treatment with anti-PD1-based immunotherapy. Complete clinical metadata, including response to therapy, antibiotic use, and toxicity, was also recorded. A strict sample collection protocol was used to ensure the highest possible standards. DNA was extracted in a single batch at Microbiotica and subjected to shotgun metagenomics. Shotgun metagenomics sequencing is well known in the art and is described, for example, in Quince, C. et al., "Shotgun metagenomics, from sampling to analysis." Nat Biotechnol 35, 833-844 (2017).

[0285] To provide higher sensitivity and specificity for bacterial identification, reference-based metagenomics was used to analyze the sequences of baseline fecal samples. Accuracy was further improved by bioinformatics tools that mask mobile elements, thereby reducing spurious signals caused by horizontal gene transfer. Suitable methods are also described in WO2020065347, which is incorporated by reference. Additional classification filtering removes misassigned reads caused by contaminants and gene duplications. This platform can accurately classify over 95% of metagenomic reads, resulting in accurate mapping of the abundance of almost all bacteria in a sample.

[0286] To support and validate the analysis, Microbiotica's high-precision platform was used to reanalyze three additional datasets from melanoma patients undergoing immune checkpoint inhibitor therapy. These were: ·Frankel Neoplasia (2017) 19:848, advanced melanoma, 39 patients ·Gopalakrishnan et al. and Wargo Science (2018) 359:97, metastatic melanoma, 25 patients (listed as Wargo in the figure) Matson et al. and Gajewski Science (2018) 359:104, metastatic melanoma, 39 patients (listed as Gajewski in the figure)

[0287] 1.2 Bioinformatics analysis to obtain microbiome signatures predictive of response to immunotherapy Using baseline samples from MELRESIST, a response signature was defined by linking the relative abundance of each bacterium in the sample to clinical outcome data. In the primary analysis, stable disease, partial response, and complete response at 6 months were all determined to be responses, while progressive disease was considered a non-response. A machine learning approach, including a random forest model, was used to select species with the highest power to predict response as part of the signature.

[0288] Random forest classification is an algorithm based on the results of multiple decision trees. In a single decision tree, the feature that best separates samples into responder and non-responder categories is iteratively selected until all features are utilized. In the case of prevalence data, these features could be the presence or absence of a given species, in which case the presence of a single species may be preferentially associated with responder samples, and vice versa. Alternatively, the relative abundance of a given species could be predictive of response, with that species being over- or under-abundant in responder samples. Because a single decision tree typically overfits the data and does not produce robust results, random forests are often used instead. Random forest classification is based on multiple different decision trees, each of which only uses a subset of the available data, e.g., randomly omitting 20% ​​of the observed species for each tree. In some cases, a subset of samples is used to train the random forest. Thus, random forest classification learns which signal is strongest across all possible features and samples. For all random forest models, out-of-bag error was used to prevent overoptimism and improve generalizability.

[0289] We expanded our analysis by including additional melanoma datasets to identify bacteria linked to response across multiple studies. First, we standardized the data from the different studies, e.g., changing response criteria, if necessary, to be consistent with the MELRESIST study. We then used a machine learning process to generate a signature for the combined dataset of all four melanoma datasets. This signature's ability to function as a biomarker was then tested on the combined dataset, predicting whether a patient would respond to therapy with 91% accuracy (Figure 1A). The receiver operating characteristic (ROC) curve for this analysis had an area under the curve (AUC) of 0.98 (Figure 1C), thereby confirming how strongly predictive this signature was. Importantly, when tested against the studies individually, the signature was 83–100% accurate (Figure 1B), and the ROC curve had an AUC of 0.96–1 (Figure 1D). This is the first demonstration of a microbiome-based predictive biomarker that accurately predicts response across multiple studies.

[0290] To advance the biomarker signature and select bacteria for inclusion in live bacterial treatments, we identified the bacteria most robustly associated with response. Species whose abundance consistently increased in responding patients across all three or four studies were selected for advancement. A filtering step was then applied to select bacteria with the cleanest signal by excluding species for which metagenomic read data did not provide broad and even genome coverage.

[0291] The entire analysis was repeated from the beginning, except that, where possible, patients with stable disease were excluded to focus on bacteria associated with better clinical response. This reanalysis, which had a significant degree of overlap with the initial therapy tested, was used to refine the final list of species. These analyses generated a list of 15 bacterial species, a consortium, all of which subsequently increased in abundance in melanoma patients who responded to immune checkpoint inhibitor therapy across multiple studies (see Tables 1 and 3). Repeated testing demonstrated the robustness of this miniaturized signature as a biomarker in the combined dataset. It predicted whether a patient would respond to therapy with 77% accuracy (Figure 2A). The receiver operating characteristic (ROC) curve for this analysis had an area under the curve (AUC) of 0.8 (Figure 2C), thereby confirming how strongly predictive this signature is. Importantly, when tested individually across studies, the signatures were 67–84% accurate (Figure 2B), with receiver operating characteristics (ROC) ranging from 0.73 to 0.88 (Figure 2D). Six additional consortia, consisting of 9 or 12 of the 15 signatures listed above (Table 3), were also tested as biomarkers and successfully predicted response in both the combined dataset and individual studies (Figures 3–7 and 17).

[0292] Therefore, the results indicated that the identified bacteria could be used as a predictive biomarker for response to anti-PD1 therapy in melanoma patients, and also as a bacterial combination therapy to increase the proportion of patients who respond to checkpoint inhibitors.

[0293] To understand whether these bacteria may have utility in other cancer indications where checkpoint inhibitors are used, we analyzed the predictive value of the complete signature in a cohort of non-small cell lung cancer (NSCLC) patients (Routy et al., 2018, Science 359:91-97). In this study, patient stool samples were collected prior to anti-PD1-based therapy and subjected to shotgun metagenomic sequencing. This was then reanalyzed using Microbiotica's high-precision platform. Fifteen species in Consortium 1 were predictive of whether NSCLC patients would respond to anti-PD1 therapy (ROC AUC = 0.722; Figure 8). Therefore, the bacteria described herein found in melanoma patients are also linked to response in NSCLC. This indicates that the bacteria described herein can be used as predictive biomarkers in other cancer indications. This also suggests that the bacteria described herein can be used as bacterial combination therapies for other cancer indications.

[0294] 1.3 Selection of bacterial isolates Reference-based metagenomic analysis using genomes obtained from culture isolates allows for relinking identified bacteria to isolates of closely related strains in specific strains and / or associated culture collections. To select strains with desirable developability and safety profiles, in silico characterization was performed on all available strains representing the species in Table 1. Primary selection criteria consisted of antimicrobial resistance, bacteriophage production, and sporulation. Strains with favorable profiles were selected for further testing. These were propagated, cell banks were generated, and growth was characterized to enable testing in in vitro assays and in vivo models. Additionally, each strain underwent complete developability and safety testing via laboratory testing and in silico analysis. For each genome assembly, the 16S rDNA region was identified in two ways. First, sequences of the desired length (1200-1800 bp) were identified using barrnap (https: / / github.com / tseemann / barrnap) and second, by in silico extracting DNA matching the 7F (5'-AGAGTTTGATYMTGGCTCAG-3') (SEQ ID NO: 30) and 1510R (5'-ACGGYTACCTTGTTACGACTT-3') (SEQ ID NO: 31) universal 16S primers. Multiple overlapping 16S sequences were extracted from the assembly, and the longest one was retained.

[0295] 1.4 Host interactions Lead bacteria were selected based on their strong association with clinical responses across multiple studies and are therefore considered suitable candidates for inclusion in live bacterial therapeutics. To understand their mechanism of action, the bacteria were individually profiled in several in vitro assays using human cells, both as a full consortium and as subconsortia. Cytotoxic T lymphocytes (CTLs) are key effector cells in the antitumor immune response by directly lysing tumor cells via granzyme B and perforin release and production of cytokines such as IFNγ. CTLs can express costimulatory and co-inhibitory receptors. Immune checkpoint inhibitor therapy blocks the suppression of CTL activity by blocking the interaction between co-inhibitory receptors (e.g., PD-1) and their ligands (e.g., PD-L1). The latter can be expressed by tumor cells as a mechanism of action to escape immune-mediated deficiency, which is reversed by checkpoint inhibitor therapy. CTLs are activated and educated by dendritic cells. Dendritic cells are sentinel innate immune cells with many receptors that sense and respond to bacteria. Therefore, identified bacteria (individually or as consortia) were tested for their ability to stimulate dendritic cells (DCs), which then activate CTLs.

[0296] Bacterial strains representing 13 of the 15 species were identified and grown in bacterial culture medium. They were washed and added to and co-cultured with human monocyte-derived DCs in anaerobic conditions. Antibiotics were then added, and the DCs were cultured aerobic. DC activation was measured by upregulation of the maturation markers CD86 (costimulatory ligand) and CD83. Eleven of the 13 species robustly induced the expression of both markers, while Gordonibacter urolithinfaciens and Alistipes indistinctus were poor at activating DC maturation (Figures 9A and 9B). Indeed, many induced levels of CD86 and CD83 expression similar to those of the positive controls lipopolysaccharide (LPS), poly(I:C), and Salmonella typhimurium, all of which are known to be highly potent activators of DCs. Two consortia consisting of nine species (Consortia 5 and 6) and subconsortia of Consortium 6 containing six, three, and two species all induced DC maturation, as measured by upregulation of CD86 and CD83 (Figures 9B–9E). These bacteria also induced cytokine release from DCs. IL-12 is a cytokine critical for priming CTL responses, while IL-10 is associated with suppression of T cell responses. Therefore, the ratio of IL-10 to IL-12 was used to determine whether DCs could be potent inducers of positive CTL responses. Of the 10 species tested, consortia 5 and 6 induced higher levels of IL-12 than IL-10, even when compared with strong inflammatory stimuli such as LPS and poly I:C (Figure 9G). Data for Gordonibacter urolithinfaciens are not shown because the levels of released cytokines were too low to form meaningful ratios. These data indicate that bacteria identified as associated with response to immune checkpoint inhibitor therapy increase potent activators of DC maturation and release cytokines that can direct enhanced T cell responses.

[0297] To understand how effective these DCs were at priming CTLs, mature DCs were cocultured with allogeneic CD8-expressing T cells (CTLs) for 6 days. CTL activation was quantified by upregulation of granzyme B, perforin, and IFNγ. Thirteen of the bacterial species listed above were tested, and all were shown to induce DCs capable of potentially activating CTLs (Figure 10). The level of activation was comparable to or even greater than that observed with potent inflammatory stimuli such as LPS, poly I:C, and Salmonella typhimurium. Interestingly, even Gordonibacter urolithinfaciens and Alistipes indistinctus were shown to result in robust CTL activation, despite being poor stimulators of CD86 and CD83 expression by DCs. All consortia tested (5, 6, 7, 8, and 9) also induced robust CTL activation (Figures 11 and 12). These data demonstrate that bacteria identified as associated with responses to immune checkpoint inhibitor therapy are potent activators of CTL responses via DC stimulation. This applies to individual species and to consortia of 2-9 species. This induction of CTL activation may be a key mechanism by which elevated abundance of these bacteria leads to enhanced antitumor immunity in the presence of anti-PD1. This may also indicate that therapeutic compositions containing these bacteria can enhance vaccine responses and / or antiviral immunity.

[0298] Bacteria induce potent CTL activation. Therefore, we next tested their ability to kill tumor cells. In this assay, CTLs activated with bacterially stimulated DCs were cocultured with the tumor cell line SKOV-3 cells. All 10 species tested induced potent CTL-mediated cytolysis of tumor cells, as measured by a reduction in electrical impedance. The level of tumor cell killing exceeded that of other known potent innate stimulators. The consortia tested (5, 6, 7, 8, and 9) also induced high levels of tumor cell killing (Figures 13 and 14). These data demonstrate that the bacteria identified as associated with response to immune checkpoint inhibitor therapy are potent activators of immune-mediated tumor cell killing. This was true for both the individual species tested and the consortia of species 2 to 9 tested.

[0299] Overall, the data above demonstrate that the bacterial species identified as associated with anti-PD-1 responses can stimulate DCs to trigger CTL activation and tumor cell killing. This mechanism of action likely explains, at least in part, why these bacteria are associated with responses to anti-PD-1-based therapy in melanoma. This mechanism of action is associated with the efficacy of immune checkpoint inhibitors in multiple tumors, indicating that the bacteria described herein could be used as a bacterial combination therapy for other cancer indications. Indeed, the bacteria described herein are likely to be effective combination therapies with any immunotherapy that enhances CTL responses, for example, adoptive T cell transfer therapy and CAR-T cell therapy. Interestingly, two of the strains tested (Gordonibacter urolithinfaciens and Alistipes indistinctus) did not induce classical markers of DC activation (CD86 and CD83), yet DCs still induced CTL activation.

[0300] Type 1 interferons (IFNs), IFNα and IFNβ, are potent inducers of CTL immunity and may have direct antitumor effects. Plasmacytoid dendritic cells (PD-1) can produce very high levels of IFNα and IFNβ. To test whether isolated bacteria associated with anti-PD-1 responses induced IFNα release, we stimulated plasmacytoid dendritic cells with strains representing nine of the above species. Plasmacytoid dendritic cells did not tolerate anaerobic conditions. Therefore, heat-killed bacteria were used in an aerobic environment. Seven of the nine strains induced IFNα release from plasmacytoid DCs (Figure 15). This may be another potential mechanism by which these bacteria enhance antitumor immune responses. Interestingly, sustained type 1 interferon signals from the microbiome have been associated with enhanced antiviral responses in the lung (Bradley et al., 2019, Cell Reports 28:245-256). Therefore, the identified species may also be potential drivers of antiviral responses and / or antiviral vaccine efficacy.

[0301] In addition to the mechanistic assays described above, we tested two selected consortia for efficacy in syngeneic cancer models. SPF mice were treated with antibiotics prior to inoculation with a melanoma patient's microbiome one day before implantation with MCA205 tumor cells. Administration of consortia 5 or 6 by oral gavage induced tumor growth inhibition, albeit not to the same extent as the positive control (anti-PD1) (Figure 16). This demonstrates the antitumor potential of these consortia and validates their selection by association with improved clinical outcomes. MCA-205 is a fibrosarcoma cell line. This further demonstrates that the bacteria described herein have potential in cancer indications beyond melanoma. Together, the data presented here demonstrate that we have identified bacterial species that predict response to checkpoint inhibitor therapy in multiple melanoma studies and NSCLC. These species are capable of priming DCs, leading to CTL activation and tumor cell killing. Two of these consortia will be further validated in in vivo cancer models.

[0302] Table 2A

[0303] Table 2B

[0304] Table 2C

[0305] Table 2D

[0306] Table 2E

[0307] Table 2F

[0308] Table 2G

[0309]

Table 2H

[0310]

Table 2I

[0311] Table 2J

[0312] Table 2K

[0313] Table 2L

[0314] Table 2M

[0315]

Table 2N

[0316]

Table 2O

[0317]

Table 2P

[0318]

Table 2Q

[0319]

Table 2R

[0320]

Table 2S

[0321]

Table 2T

[0322]

Table 3

Claims

1. 1. A composition comprising isolated bacteria selected from at least two species, wherein the bacteria from a first species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from a second species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:

2.

2. The composition described in claim 1, further comprising isolated bacteria from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13 different species, wherein the bacteria comprise a 16S rDNA sequence selected from sequences having at least 98.7% sequence identity to the nucleic acid sequences set forth in SEQ ID NOs: 3 to 15.

3. 10. The composition of claim 1, further comprising isolated bacteria from at least four different species, wherein the bacteria comprise a 16S rDNA sequence selected from sequences having at least 98.7% sequence identity to a nucleic acid sequence set forth in SEQ ID NOs: 3-15.

4. 10. The composition of claim 1, further comprising isolated bacteria from at least seven different species, wherein the bacteria comprise a 16S rDNA sequence selected from sequences having at least 98.7% sequence identity to the nucleic acid sequences set forth in SEQ ID NOs: 3-15.

5. 10. The composition of claim 1, further comprising a bacterial isolate comprising a 16S rDNA sequence selected from sequences having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:

7.

6. The composition of any one of claims 1 to 5, formulated for oral or rectal administration.

7. The composition of claim 6 in the form of a capsule, tablet, gel or liquid.

8. The composition of claim 7, encapsulated in an enteric coating.

9. 9. The composition of any one of claims 1 to 8, comprising live, attenuated or killed bacteria.

10. 10. The composition of any one of claims 1 to 9, comprising bacterial spores.

11. 10. The composition of any one of claims 1 to 9, which is free of bacterial spores.

12. 12. The composition of any one of claims 1 to 11, comprising bacterial strains derived from one or more human donors.

13. The composition of any one of claims 1 to 12, wherein the bacteria is freeze-dried.

14. At least 1 x 10 3 ~1×10 13 14. The composition of any one of claims 1 to 13, comprising CFU of bacteria.

15. 15. The composition of any one of claims 1 to 14, wherein administration of the composition induces an immune response in a subject and / or increases the efficacy of an anti-cancer therapy comprising an immune checkpoint inhibitor.

16. A pharmaceutical composition comprising the composition of any one of claims 1 to 15 and a pharmaceutical carrier.

17. The pharmaceutical composition described in claim 16, further comprising an effective amount of an immune checkpoint inhibitor or vaccine.

18. The pharmaceutical composition described in claim 17, wherein the immune checkpoint inhibitor inhibits the activity of PD-1, PDL-1, CTLA-4, LAG3 or TIM-3.

19. The pharmaceutical composition described in claim 18, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody, anti-PDL-1 antibody, or anti-CTLA-4 antibody, or a fragment thereof.

20. The pharmaceutical composition described in claim 19, wherein the anti-PD-1 antibody, anti-PDL-1 antibody or anti-CTLA-4 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, toripalimab, ipilimumab or tremelimumab.

21. 18. The pharmaceutical composition of claim 17, wherein the immune checkpoint inhibitor is an interfering nucleic acid molecule, a small molecule or a proteolysis-targeting chimeric molecule (PROTAC), an alternative protein scaffold or other immune checkpoint inhibitor.

22. The pharmaceutical composition of claim 21, wherein the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule.

23. A composition according to any one of claims 1 to 15 or a pharmaceutical composition according to any one of claims 16 to 22 for use in the treatment of a disease.

24. 24. A composition according to any one of claims 1 to 15 or a pharmaceutical composition according to any one of claims 16 to 23 for use in the treatment of cancer or an infectious disease or for use as a vaccine adjuvant or for increasing the effectiveness of cancer treatment in a subject in need thereof.

25. The cancer is melanoma, Harding-Passey melanoma, juvenile melanoma, lentigo maligna melanoma, malignant melanoma, acral lentigo melanoma, amelanotic melanoma, benign juvenile melanoma, Cloudman melanoma, S91 melanoma, nodular melanoma, subungual melanoma, cutaneous melanoma, uveal / intraocular melanoma and superficial spreading melanoma or bone cancer, pancreatic cancer, skin cancer, head and neck cancer, cutaneous malignant melanoma or intraocular malignant melanoma, uterine cancer, ovarian cancer, rectal cancer, cancer of the anal region, stomach cancer, 25. The composition of claim 24, wherein the cancer is selected from testicular cancer, breast cancer, brain cancer, fallopian tube cancer, endometrial cancer, cervical cancer, vaginal cancer, vulvar cancer, esophageal cancer, small intestine cancer, cancer of the endocrine system, thyroid cancer, parathyroid cancer, adrenal cancer, kidney cancer, soft tissue sarcoma, urethral cancer, bladder cancer, rectal cancer, lung cancer, non-small cell lung cancer, thymoma, urothelial carcinoma, leukemia, prostate cancer, mesothelioma, adrenocortical carcinoma, lymphoma, Hodgkin's disease, non-Hodgkin's lymphoma, gastric cancer, and multiple myeloma.

26. 26. The composition of claim 24 or 25, wherein the subject is undergoing, has already undergone, or will undergo therapy with an immune checkpoint inhibitor to treat cancer or an infectious disease.

27. 27. The composition of any one of claims 24 to 26, wherein administration of the composition enhances an immune response by a subject, and / or inhibits immune evasion by cancer, and / or increases the efficacy of anti-cancer treatment with immune checkpoint inhibitors.

28. The composition of any one of claims 24 to 27, wherein the composition is administered orally or rectally.

29. 29. The composition of any one of claims 24 to 28, wherein the subject has already received prior anti-cancer therapy with an immune checkpoint inhibitor.

30. The composition of any one of claims 24 to 28, wherein the treatment further comprises administering an anti-cancer therapy using an immune checkpoint inhibitor.

31. The composition described in claim 30, wherein the immune checkpoint inhibitor is administered before, after, or simultaneously with the bacterial preparation.

32. 32. The composition of claim 30 or 31, wherein the immune checkpoint inhibitor is administered by injection.

33. A composition described in any one of claims 30 to 32, wherein the injection is intravenous injection, intramuscular injection, intratumoral injection or subcutaneous injection.

34. 32. The composition of claim 30 or 31, wherein the immune checkpoint inhibitor inhibits the activity of PD-1, PDL-1, or CTLA-4.

35. The composition described in claim 34, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody, an anti-PDL-1 antibody, or an anti-CTLA-4 antibody.

36. The composition described in claim 35, wherein the anti-PD-1 antibody, anti-PDL-1 antibody or anti-CTLA-4 antibody is selected from nivolumab, pembrolizumab, cemiplimab, avelumab, durvalumab, atezolizumab, spartalizumab, camrelizumab, sintilimab, tislelizumab, pidilizumab, toripalimab, ipilimumab or tremelimumab.

37. 35. The composition of any one of claims 30 to 34, wherein the immune checkpoint inhibitor is an interfering nucleic acid molecule, a small molecule or a proteolysis-targeting chimeric molecule (PROTAC), a peptide inhibitor or other immune checkpoint inhibitor.

38. The composition of claim 37, wherein the interfering nucleic acid molecule is an siRNA molecule, an shRNA molecule, or an antisense RNA molecule.

39. 39. The composition of any one of claims 24 to 38, wherein the treatment further comprises surgical, radiological, and / or chemotherapeutic cancer intervention or administration of a second anti-cancer therapeutic agent.

40. 40. The composition of any one of claims 24 to 39, wherein the treatment further comprises administering to the subject an antibiotic.

41. 41. The composition of any one of claims 24 to 40, wherein the subject has been identified as being at risk for developing cancer.

42. A kit comprising the composition of any one of claims 1 to 16.

43. A kit containing the composition described in claim 42, comprising an anti-cancer treatment including an immune checkpoint inhibitor.

44. A food or vaccine adjuvant comprising the composition of any one of claims 1 to 16.

45. 1. A method for treating a fecal transplant prior to administration to a subject, comprising supplementing the fecal transplant with isolated bacteria selected from at least two species, wherein the bacteria from a first species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:1, and the bacteria from a second species comprises a 16S rDNA sequence having at least 98.7% sequence identity to the nucleic acid sequence set forth in SEQ ID NO:

2.

46. A composition according to any one of claims 1 to 15 or a pharmaceutical composition according to claim 16 for increasing the efficacy of anti-cancer treatment with immune checkpoint inhibitors.

47. A composition according to any one of claims 1 to 15 or a pharmaceutical composition according to claim 16 for enhancing immune checkpoint blockade.

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