Methods and materials for assessing and treating cancers

Abstract

This document provides methods and materials involved in assessing and / or treating a mammal (e.g., a human) having cancer. For example, methods and materials that can be used to identify a cancer as being likely to respond to one or more immune checkpoint inhibitors are provided. For example, methods and materials that can be used to identify a mammal having cancer as being likely to develop one or more immune-related adverse events (irAEs) are provided. Methods for treating a mammal (e.g., a human) having cancer (e.g., lung cancer such as mesothelioma) where the cancer treatment is selected based on whether or not the cancer is likely to be responsive to one or more immune checkpoint inhibitors and / or whether or not the mammal is likely to develop one or more irAEs are also provided.

Description

[0001] METHODS AND MATERIALS FOR ASSESSING AND TREATING CANCERS

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Patent Application Serial No. 63 / 649,027, filed on May 17, 2024. The disclosure of the prior application is considered part of, and is incorporated by reference in, the disclosure of this application.

[0004] TECHNICAL FIELD

[0005] This document relates to methods and materials involved in assessing and / or treating a mammal (e.g., a human) having cancer. For example, methods and materials provided herein can be used to identify a cancer as being likely to respond to one or more immune checkpoint inhibitors. In another example, methods and materials provided herein can be used to identify a mammal having cancer as being likely to develop one or more immune- related adverse events (irAEs; e.g., immune mediated diarrhea or colitis (IMDC)). In some cases, methods and materials provided herein can be used to treat a mammal (e.g., a human) having cancer (e g., lung cancer such as mesothelioma) where the cancer treatment is selected based on whether or not the cancer is likely to be responsive to one or more immune checkpoint inhibitors and / or whether or not the mammal is likely to develop one or more irAEs (e g., IMDC).

[0006] BACKGROUND

[0007] Immune activation by immune checkpoint inhibitors (ICI) has shown promise in the treatment of diverse cancer types at the cost of different immune-related adverse events (irAEs). See, e.g., Yin et al., Front Immunol. 14: 1167975 (2023).

[0008] SUMMARY

[0009] This document provides methods and materials for assessing and / or treating a mammal (e.g., a human) having cancer. For example, this document provides methods and materials for identifying a cancer as being likely to respond to one or more immune checkpoint inhibitors. In another example, methods and materials provided herein can be used to identify a mammal having cancer as being likely to develop one or more irAEs (e.g., in response to being administered one or more immune checkpoint inhibitors). In some cases, a gut microbiome of a mammal (e.g., a human) having cancer (e.g., a human having cancer that has not previously been administered any immune checkpoint inhibitor) can be used to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs. For example, a stool sample obtained from a mammal having cancer can be assessed to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs based, at least in part, on the presence or absence of one or more microbes in the sample. For example, a stool sample obtained from a mammal having cancer can be assessed to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs based, at least in part, on the presence or absence of one or more metabolites (e.g., fecal metabolites) in the sample. In some cases, a blood sample obtained from a mammal (e.g., a human) having cancer (e.g., a human having cancer that has not been administered any immune checkpoint inhibitor) can be used to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs. For example, a plasma sample obtained from a mammal having cancer can be assessed to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs based, at least in part, on the presence or absence of one or more polypeptides (e.g., one or more cytokines) in the sample.

[0010] As demonstrated herein, the presence or absence of one or more microbes in a sample (e g., a stool sample) obtained from a mammal (e.g., a human) having cancer can indicate whether or not that mammal is likely to respond to one or more immune checkpoint inhibitors and / or is likely to develop one or more irAEs (e.g., in response to being administered one or more immune checkpoint inhibitors). For example, a mammal (e.g., a human) having cancer and having a gut microbiome including one or more of Lachnospira rogosae A, UBA5416 sp900539175, Collinsella sp900555355, Ruminococcus C sp000433635, Ruminococcus E sp003526955. and Parabacteroides goldsteinii in a stool sample obtained from the mammal is likely to respond to one or more immune checkpoint inhibitors, and can, optionally, be treated by administering one or more immune checkpoint inhibitors to the mammal. In another example, a mammal (e.g., a human) having cancer and having a gut microbiome including one or more of Blautia sp900066145, Bacteroides intestinalis A, Akkermcmsia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, CAG-103 sp900543625, and Alistipes ihumii in a stool sample obtained from the mammal is not likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors, and can, optionally, be treated by administering one or more immune checkpoint inhibitors to the mammal. In yet another example, a mammal (e.g., a human) having cancer and having a gut microbiome including one or more of Muricomes oroticus, Clostridium sp003481775, C. innocuum, Terri sporobacter sp900557165, Lacticaseibacillus rhamnosus, Anaerobutyricum sp900016875, and Blautia spOOl 304935 in a stool sample obtained from the mammal is likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors, and can, optionally, be treated by administering one or more alternative cancer treatments (e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor) to the mammal.

[0011] Also as demonstrated herein, the presence or absence of one or more metabolites (e.g., fecal metabolites) in a sample (e.g., a stool sample) obtained from a mammal (e.g., a human) having cancer can indicate whether or not that mammal is likely to respond to one or more immune checkpoint inhibitors and / or is likely to develop one or more irAEs (e.g., in response to being administered one or more immune checkpoint inhibitors). For example, a mammal (e.g., a human) having cancer and having a gut microbiome including one or more of orotic acid, indolelactic acid, cer(d 18:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil in a stool sample obtained from the mammal is not likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors, and can, optionally, be treated by administering one or more immune checkpoint inhibitors to the mammal. In another example, a mammal (e.g., a human) having cancer and having a gut microbiome including one or more of succinate, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4- hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid in a stool sample obtained from the mammal is likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors, and can, optionally, be treated by administering one or more alternative cancer treatments (e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor) to the mammal.

[0012] Also as demonstrated herein, the presence or absence of one or more polypeptides (e g., one or more cytokines) in a sample (e.g., a blood sample) obtained from a mammal (e.g., a human) having cancer can indicate whether or not that mammal is likely to respond to one or more immune checkpoint inhibitors and / or is likely to develop one or more irAEs (e g., in response to being administered one or more immune checkpoint inhibitors). For example, a mammal (e g., a human) having cancer and having one or more of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide in a blood sample (e.g., a plasma sample) obtained from the mammal is likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors, and can, optionally, be treated by administering one or more alternative cancer treatments (e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor) to the mammal.

[0013] Having the ability to identify a cancer as being likely to respond to one or more immune checkpoint inhibitors as described herein (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from a mammal (e.g., a human) having cancer) allows clinicians to assess cancer patients in a more accurate manner than current protocols. The ability to identify a cancer as being likely to respond to one or more immune checkpoint inhibitors as described herein (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e g., one or more cytokines) in a sample obtained from a mammal (e.g., a human) having cancer) also allows clinicians to provide a personalized approach in selecting cancer treatments, thereby reducing adverse effects (e.g., irAEs) for this identified patient population. In addition, the ability to identify a cancer as being likely to respond to one or more immune checkpoint inhibitors as described herein (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from a mammal (e.g., a human) having cancer can minimize subjecting patients to ineffective treatments.

[0014] In general, one aspect of this document features methods for assessing a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Lachnospira rogosae, a Ruminococcus C sp000433635, Ruminococcus E sp003526955, and Parabacteroides goldsteinii,- and (b) classifying said cancer as being likely to respond to an immune checkpoint inhibitor. The method of claim 1, wherein said mammal is a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0015] In another aspect, this document features methods for assessing a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Blautia sp900066145, Bacteroides intestinalis A, Akkermansia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, CAG-103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(d 18:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil; and (b) classifying said mammal as not being likely to develop an immune-related adverse event in response to an immune checkpoint inhibitor. The mammal can be a human. The immune-related adverse event can be IMDC. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti- PD-Ll antibody, an anti-CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0016] In another aspect, this document features methods for assessing a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting Mur iconics oroticus, Clostridium sp003481775, Clostridium innocuum, Terrisporobacter sp900557165, Lacticaseibacillus rhamnosus, Anaerobutyricum sp900016875, and Blautia sp001304935, or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3 -propionic acid, malonic acid, 4-hydroxybenzoic acid, 4- hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or determining that a plasma sample obtained from said mammal comprises a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide; and (b) classifying said mammal as being likely to develop an immune-related adverse event in response to an immune checkpoint inhibitor. The mammal can be a human. The immune- related adverse event can be IMDC. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti -CTL4 A antibody, or an anti -LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0017] In another aspect, this document features methods for selecting a treatment for a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175 Lachnospira rogosae, a Ruminococcus C sp000433635, Ruminococcus E sp003526955, and Parabacteroides goldsteinir, and (b) selecting an immune checkpoint inhibitor as a treatment for said cancer. The mammal can be a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTL4A antibody, or an anti -LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0018] In another aspect, this document features methods for selecting a treatment for a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Blantia sp900066145, Bacteroides intestinalis A, Akkermansia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, CAG-103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(dl8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil; and (b) selecting an immune checkpoint inhibitor as a treatment for said cancer. The mammal can be a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab. In another aspect, this document features methods for selecting a treatment for a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Muricomes oroticus, Clostridium sp003481775, Clostridium innocuum, Terrisporobacter sp900557I65, Lacticaseibacillus rhamnosus, Anaerobutyricum sp900016875, and Blautia sp001304935, or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3 -propionic acid, malonic acid, 4-hydroxybenzoic acid, 4- hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or determining that a plasma sample obtained from said mammal comprises a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide; and (b) selecting a cancer treatment other than an immune checkpoint inhibitor as a treatment for said cancer. The mammal can be a human. The cancer treatment can include performing surgery. The cancer treatment can include radiation therapy. The cancer treatment can include administering, to said mammal, an anti-cancer agent.

[0019] In another aspect, this document features methods for treating for a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Laclmospira rogosae, a Ruminococcus C sp000433635. Ruminococcus E sp003526955, and Parabacteroides goldsteinir, (b) administering an immune checkpoint inhibitor to said mammal. The mammal can be a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0020] In another aspect, this document features methods for treating cancer where the methods can include, or consist essentially of, administering an immune checkpoint inhibitor to a mammal identified as having a stool sample comprising a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Lachnospira rogosae , a Ruminococcus C sp000433635, Ruminococcus E sp003526955, and Parabacteroides goldsteinii, thereby treating cancer within said mammal. The mammal can be a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0021] In another aspect, this document features methods for treating for a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Blautia sp900066145, Bacteroides intestinalis A, Akkermansia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, C AG- 103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(dl 8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil; (b) administering an immune checkpoint inhibitor to said mammal. The mammal can be a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0022] In another aspect, this document features methods for treating cancer where the methods can include, or consist essentially of, administering an immune checkpoint inhibitor to a mammal identified as having a stool sample comprising (i) a microbe selected from the group consisting of Blautia sp900066145, Bacteroides intestinalis A, Akkermansia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, CAG-103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(dl8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil, thereby treating cancer within said mammal. The mammal can be a human. The immune checkpoint inhibitor can be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTL4A antibody, or an anti-LAG-3 antibody. The immune checkpoint inhibitor can be nivolumab or ipilimumab.

[0023] In another aspect, this document features methods for treating a mammal having cancer where the methods can include, or consist essentially of, (a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Muricomes oroticus, Clostridium sp003481775, Clostridium innocuum, Terrisporobacter sp900557165, Lacticaseibacillus rhamnosus, Anaerobutyricum sp900016875, and Blautia sp001304935, or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3 -propionic acid, malonic acid, 4-hydroxybenzoic acid, 4- hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or determining that a plasma sample obtained from said mammal comprises a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide; and (b) administering a cancer treatment to said mammal, wherein said cancer treatment is not an immune checkpoint inhibitor. The mammal can be a human. The cancer treatment can include performing surgery. The cancer treatment can include radiation therapy. The cancer treatment can include administering, to said mammal, an anti-cancer agent.

[0024] In another aspect, this document features methods for treating cancer where the methods can include, or consist essentially of, administering a cancer treatment that is not an immune checkpoint inhibitor to a mammal identified as having a stool sample comprising (i) a microbe selected from the group consisting of Muricomes oroticus, Clostridium sp003481775 Clostridium innocuum, Terrisporobacter sp900557165, Lacticaseibacilhis rhcimnosus, Anaerobutyricum sp900016875, and Blautia spOO 1304935 , or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or as that a plasma sample comprising a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide. The mammal can be a human. The cancer treatment can include performing surgery. The cancer treatment can include radiation therapy. The cancer treatment can include administering, to said mammal, an anti-cancer agent.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0026] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

[0027] BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figures 1A-1D. Distinct microbial markers of ICI therapy and adverse events. Figures 1A-1C) Clinical and demographic characteristics of patients with (RECIST 4) and without (RECIST 1,2,3) favorable outcome (Figure 1A), with and without colitis (Figure IB), or hepatitis (Figure 1C). Figure ID) Heatmap showing microbial taxa significantly associated with ICI outcomes or irAE (colitis). Stool samples were sequenced using shotgun metagenomics (Illumina, 2 x 150 bp). Taxonomy was mapped using Kraken2 with the GTDB R202 database (median 5.9M reads annotated).

[0029] Figure 2. Interactions among Omics features associated with ICI outcomes and colitis: Select omics features are shown chosen based on prevalence and / or association with ICI colitis (IMDC) or outcomes. Interactions between omics features are from a multiblock(s) PLS model (mixOmics DIABLO) and reveal possible biologically relevant interactions, e.g negative association of TNF plasma cytokine with Blautia spp. and branched chain amino acids valine and isoleucine in pre-treatment stool samples. Quantitative metabolomics was performed (multiple LC-MS based methods, TMIC MEGA, The Metabolomics Innovation Center) on 172 stool samples; 618 metabolites were quantified, of which 202 metabolites were detected in more than 25% of samples. A panel of 96 inflammatory markers was assessed in plasma for 151 samples (Olink inflammation panel).

[0030] Figure 3. Distinct gut microbial species and stool metabolites were associated with Distinct irAEs.

[0031] Figure 4. The pyrimidine pathway was deregulated in patients that develop ICI colitis (IMDC)

[0032] Figure 5. Orotic acid was a central metabolite and was negatively associated to hub of inflammatory cytokines. Figure 6. Distinct gut microbes correlated with ICI efficacy independent of association with adverse events.

[0033] Figures 7A-7C. Description and data collected on ICI cohort. Figure 7 A) The ICI cohort is a subcohort of the real-world Mayo Clinic Cancer Microbiome (MCCM) study with pretreatment stool and blood samples recruited at the 3 sites of Mayo Clinic. MCCM includes patients with varied cancer types at different stages, with inclusion criteria of age over 18 years, any new cancer therapy, and no exclusions based on comorbidities or compliance. Figure 7B) Binary heatmap inspecting overlap between the 3 irAEs and other adverse irAEs plus the started ICI regimen. Rows are columns were ordered using bidirectional hierarchical clustering. Figure 7C) Multi-omics data collected on this cohort, split into stool metagenomics, metabolomics on stool and plasma, and immune protein / cytokine measurements on plasma. N numbers are number of patients, d numbers are number of features. Panel A was made using Biorender.

[0034] Figures 8A-8G. Distinct omics features are associated with distinct irAEs. Figure 8A) Ranking of diversity effect sizes radarplot for the 4 irAE groups based on PERMANOVA. Being further to the outside indicates larger effect size for that group compared to the other groups. Linked to Extended data figure 5 for results of technical variables and significance. Stool species alpha diversity metric is Shannon diversity. Beta diversity metrics are Weighted Unifrac (corrected for batch) for stool species, Manhattan distance for stool metabolome (corrected for water content) and plasma cytokines, and Bray Curtis for the other omics data layers. Figure 8B) Heatmap summarizing results from stool genus level comparisons of patients with the respective irAE group vs. all other patients. Adjusted for Age and Bristol Stool Form Score as these are significantly associated in species level beta diversity analysis (Weighted Unifrac, PERMANOVA p<0.05). Yellow color indicates positive association with the respective irAE and blue indicates negative associations. R2 indicates coefficient effect size from linear model (Zicoseq function). Figure 8C) Heatmap summarizing results from stool species level comparisons of patients with the respective irAE group vs. all other patients. Adjusted as in Figure 8B. Figure 8D) Heatmap summarizing results from group comparisons of quantitative stool metabolomics data.

[0035] Metabolites with p<0.05 (linear models) and absolute log2(FC) >1 are shown. Comparisons were adjusted for stool water content and BMI as these were significantly associated to the stool metabolome (Manhattan distance, PERMANOVA p<0.05). Figure 8E) Boxplots for the 6 metabolites in panel C from comparisons associated with IMDC. Ordered from most positively associated (left) to most negatively associated (right). For orotic acid, indolelactic acid, and alpha-aminobutyric acid 1 subject had a value outside the plotting region and is not shown for visualization purposes only. Figure 8F) Heatmap summarizing results from group comparisons of plasma metabolomics data (Cl 8 negative mode). Comparisons were adjusted for Age as this was significantly associated to plasma metabolome (Bray-Curtis distance, PERMANOVA p<0.05). Figure 8G) Heatmap summarizing results from group comparisons of plasma cytokine data for the irAE groups. Comparisons were adjusted for Age and antibiotics in the last month as these were significantly associated to plasma cytokines (Manhattan distance, PERMANOVA p<0.05).

[0036] Figures 9A-9E. Multi-omics predictions and integration. Figure 9A) AUCs for single omics Random Forest models for the 3 irAEs. Figure 9B) ROC curves and AUCs (inset) for Random Forest models for the most predictive single omics, genus level taxonomy, plasma metabolome, and fcuntional pathways for IMDC, hepatitis, and pneumonitis, respectively. Figure 9C) ROC curves and AUCs (inset) for Random Forest models on all omics data. Included were: genus level taxonomy, functional pathways, stool metabolome, plasma cytokines, and plasma metabolome. Figure 9D) ROC curves and AUCs (inset) for Random Forest models on microbial multi-omics data layers (genus level taxonomy and functional pathways) and Figure 9E) host multi-omics data layers (plasma cytokines and plasma metabolome).

[0037] Figures 10A-10B. Figure 10A) overview of pyrimidine biosynthesis pathway with relevant genes. Figure 10B) Canonical spearman correlation network focused on hypothesis generation by inspecting correlations between microbiome-dominant omics layers and plasma cytokines with orotic acid; stool species (green), functional pathways (grey), cytokines (magenta).

[0038] Figures 11 A-l 1C. Orotic acid may be a mechanistic metabolite in IMDC and ulcerative colitis. Figure 11 A) Representative gating strategy for naive CD4+ T-cell stimulation assays in Thl stimulation conditions. Lymphocytes were gated followed by single cells. FMO controls were used to determine gate positions for all antibodies. Shown is the FoxP3 FMO shown for the FoxP3+ cells. Figure 11B) Representative FoxP3 distributions observed for effect of orotic acid on FoxP3+ expression. Orotic acid at 100 uM or higher results in a shift to higher FoxP3 expression. Figure 11C) Quantification of FoxP3+ cells which may be functional T-regulatory cells. ANOVA with Tukey Honest Significant Difference *q<0.05, **q<0.01, ***q<0.001.

[0039] Figure 12. Orotic acid may be a mechanistic metabolite in ulcerative colitis. Stool metabolomics data on inflammatory bowel disease studies for features annotated as orotic acid in the microbiome-metabolome dataset collection (Muller et al, 2022). iHMP had repeated measures and was corrected for subject using a linear mixed effect model. Statistics for PRISM+validation were assessed using a linear model.

[0040] Figure 13. ICI cohort summary tables for irAEs. ICI cohort summary tables for irAEs. Treatment success at first follow up is coded as follows; PD = progressive disease, SD = stable disease, PR = partial response, CR = complete response.

[0041] Figure 14. Days difference between samples and ICI start and or irAE onset.

[0042] Figure 15. Bristol Stool Form Scale and a metric of barrier function in ICI patients is not associated to irAEs. Exploring whether irAEs might be linked to measures of intestinal barrier function lipopolysaccharide binding protein (LBP) was measured using plasma ELISA measurements. LBP was shown to correlate with other barrier function metrics including the lactulose / mannitol ratio and fecal zonulin levels. No significance was noted.

[0043] Figures 16A-16C. Difference in taxonomic composition of ICI samples with healthy controls is not associated to irAEs. Figure 16A) Bray-Curtis dissimilarity (BCD)-based irregularity (BCDI) was computed by extracting the pairwise dissimilarities between all healthy control (HC) and HC or ICI samples, and the median of these dissimilarities was stored. The 90th percentile of the HC values was used as a cutoff for identifying microbiome samples that were different compared to those of HC. Distributions of ICI BCDI compared the healthy control distribution. Figure 16B) Linear model results from association of the lister variables with BCDI. Pneumonitis, Antibiotic prescriptions in the last month, and IMDC show a trend with more irregular gut microbiome but not significant (p > 0.5). Right panel grey indicates p <0.05. Figure 16C) Scatterplot between BCDI and variable days betw een stool sample and ICI start. There is no trend between BCDI (gut microbiome irregularity) after ICI (positive days value indicate ICI was started before the stool sample).

[0044] Figures 17A-17B. Beta and alpha diversity associated with clinical and demographic variables. Figure 17A) Beta diversity analysis for all omics layers. Figure 17B) Stool species alpha diversity. No significant groupings p>0.05.

[0045] Figures 18A-18D. Distinct omics associate with distinct irAEs. Figure 18A) Hepatitis stool metabolite. Figure 18B) Pneumonitis stool metabolites. Figure 18C) Pathways associated to irAEs. Figure 18D) Plasma metabolomics pathway enrichment positive mode (left) and negative mode (right).

[0046] Figure 19. IrAE classification workflow.

[0047] Figure 20. Baruta features for single omics models.

[0048] Figure 21. Stool metabolite abundance from other studies.

[0049] DETAILED DESCRIPTION

[0050] This document provides methods and materials for assessing and / or treating a mammal (e.g., a human) having cancer. For example, this document provides methods and materials for identifying a cancer as being likely to respond to one or more immune checkpoint inhibitors. In another example, methods and materials provided herein can be used to identify a mammal having cancer as being likely to develop one or more irAEs (e.g., in response to being administered one or more immune checkpoint inhibitors). In some cases, a gut microbiome of a mammal (e.g., a human) having cancer (e.g., a human having cancer that has not been administered any immune checkpoint inhibitor) can be used to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs. For example, a stool sample obtained from a mammal having cancer can be assessed to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs based, at least in part, on the presence or absence of one or more microbes in the sample. For example, a stool sample obtained from a mammal having cancer can be assessed to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs based, at least in part, on the presence or absence of one or more metabolites (e.g., fecal metabolites) in the sample. In some cases, a blood sample obtained from a mammal (e.g., a human) having cancer (e.g., a human having cancer that has not been administered any immune checkpoint inhibitor) can be used to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs. For example, a plasma sample obtained from a mammal having cancer can be assessed to identify the mammal as having a cancer that is likely to respond to one or more immune checkpoint inhibitors and / or as being likely to develop one or more irAEs based, at least in part, on the presence or absence of one or more polypeptides (e.g., one or more cytokines) in the sample.

[0051] Any appropriate mammal having cancer can be assessed and / or treated as described herein. Examples of mammals that can have cancer and can be assessed and / or treated as described herein include, without limitation, humans, non-human primates (e.g., monkeys), dogs, cats, horses, cows, pigs, sheep, mice, and rats. In some cases, a human having cancer can be assessed and / or treated as described herein. For example, a human having cancer that has not received any previous cancer treatment (e.g., has not received any previous immune checkpoint inhibitor) can be assessed and / or treated as described herein.

[0052] When assessing a mammal (e.g., a human) having cancer as described herein and / or treating a mammal (e g., a human) having cancer as described herein, the cancer can be any type of cancer. In some cases, a cancer assessed and / or treated as described herein can include one or more solid tumors. In some cases, a cancer assessed and / or treated as described herein can be a blood cancer. In some cases, a cancer assessed and / or treated as described herein can be a primary cancer. In some cases, a cancer assessed and / or treated as described herein can be a metastatic cancer. In some cases, a cancer assessed and / or treated as described herein can be a refractory cancer. In some cases, a cancer assessed and / or treated as described herein can be a relapsed cancer. Examples of cancers that can be assessed and / or treated as described herein include, without limitation, bladder cancers, breast cancers, colon cancers, head and neck cancers, lung cancers (e.g., non-small lung cancers and small cell lung cancers), melanomas, prostate cancers, renal cell cancers, sarcomas, and squamous cell carcinomas. In some cases, the methods described herein can include identifying a mammal (e.g., a human) as having cancer. Any appropriate method can be used to identify a mammal as having cancer. For example, imaging techniques can be used to identify mammals (e.g., humans) as having cancer.

[0053] Any appropriate sample from a mammal (e.g., a human) having cancer can be assessed as described herein (e.g., to determine whether or not the cancer is likely to respond to one or more immune checkpoint inhibitors and / or whether the mammal is likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in the sample). In some cases, a sample can be a biological sample. In some cases, a sample can contain one or more microbes (e.g., bacteria, viruses, and fungi). In some cases, a sample can contain one or more biological molecules (e.g., nucleic acids such as DNA and RNA, polypeptides, carbohydrates, lipids, hormones, and / or metabolites). Examples of samples that can be assessed as described herein include, without limitation, stool samples, and blood samples (e.g., whole blood samples, plasma samples, and serum samples). In some cases, one or more microbes and / or one or more biological molecules can be isolated from a sample. For example, bacteria and / or metabolites can be isolated from a stool sample and can be assessed as described herein. In another example, polypeptides can be isolated from a blood sample (e.g., a plasma sample) and can be assessed as described herein.

[0054] A sample (e.g., a stool sample) obtained from a mammal (e.g., a human) having cancer can be assessed for the presence or absence of any appropriate number of microbes. For example, a sample obtained from a mammal having cancer can be assessed for the presence or absence of one or more microbes. In some cases, a sample obtained from a mammal having cancer can be assessed for the presence or absence of from about 1 microbe to about 100 microbes.

[0055] A sample (e g., a stool sample) obtained from a mammal (e.g., a human) having cancer can be assessed for any appropriate one or more microbes. A microbe that can be used to assess a mammal having cancer as described herein can be any type of microbe. In some cases, a microbe that can be used to assess a mammal having cancer as described herein can be a bacterial species. A microbe can be gram-positive or gram-negative. A microbe can be aerobic or anaerobic. A microbe can belong to any appropriate phylum (e.g., Actinobacteria and Bacillota). A microbe can belong to any appropriate genus (e.g., Collinsella and Lachnospira). Examples of microbes that can be present in a gut microbiome and that can be used to assess a mammal having cancer as described herein include, without limitation, Collinsella species (e.g., C. sp900555355), UBA5416 sp900539175, Lachnospira species (e g., L. rogosae), Ruminococcus species (e.g., R. C sp000433635 and R. E sp003526955), Parabacteroides species (e.g., P. goldsteinii), Blautia species (e.g., B. sp900066145 and B. spOO 1304935), Bacteroides species (e.g., B. intestinalis A), Akkermansia species (e.g., A. sp004167605), CAG-83 species (e.g., CAG-83 sp900547745), CAG-103 species (e.g., CAG- 103 sp900543625), Alistipes species (e.g., A. ihuntii), M ricomes species (e.g., M. oroticus), Clostridium species (e.g., C. sp003481775 and C. innocuum), Terri sporobacter species (e.g., T. sp900557165), Lacticaseibacillus species (e.g., L. rhamnosus), and Anaerobutyricum species (e.g., A. sp900016875). In some cases, microbes that can be present in a gut microbiome and that can be used to assess a mammal having cancer as described herein can be as described in Example 1. In some cases, microbes that can be present in a gut microbiome and that can be used to assess a mammal having cancer as described herein can be as shown in Figure 1 and / or Figure 2.

[0056] Any appropriate method can be used to determine the presence, absence, or level of a microbe described herein. Exemplary methods that can be used to determine the presence, absence, or level a microbe described herein in a sample (e.g., a stool sample) include, without limitation, gram stains.

[0057] A sample (e.g., a stool sample) obtained from a mammal (e.g., a human) having cancer can be assessed for the presence or absence of any appropriate number of metabolites (e.g., fecal metabolites). For example, a sample obtained from a mammal having cancer can be assessed for the presence or absence of one or more metabolites. In some cases, a sample obtained from a mammal having cancer can be assessed for the presence or absence of from about 1 metabolite to about 25 metabolites.

[0058] A sample (e.g., a stool sample) obtained from a mammal (e.g., a human) having cancer can be assessed for any appropriate one or more metabolites. A metabolite that can be used to assess a mammal having cancer as described herein can be any type of metabolite. In some cases, a metabolite that can be used to assess a mammal having cancer as described herein can be a fecal metabolite. Examples of metabolites that can be used to assess a mammal having cancer as described herein include, without limitation, succinate, orotic acid, indolelactic acid, cer(d 18:0 / 24:0), 3 -hydroxybutyric acid, xanthine, uracil, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2- methylbutyric acid. In some cases, metabolites that can be used to assess a mammal having cancer as described herein can be as described in Example 1 . In some cases, metabolites that can be used to assess a mammal having cancer as described herein can be as shown in Figure 1 and / or Figure 2.

[0059] In some cases, when a stool sample obtained from a mammal (e.g., a human) has a level of orotic acid that is greater than about 0.1 pM of metabolite per gram stool (pM / g), that mammal can be identified as being unlikely to develop one or more irAEs when administered one or more immune checkpoint inhibitors.

[0060] In some cases, when a stool sample obtained from a mammal (e.g., a human) has a level of orotic acid that is less than about 0.1 pM / g, that mammal can be identified as being likely to develop one or more irAEs when administered one or more immune checkpoint inhibitors.

[0061] In some cases, when a stool sample obtained from a mammal (e.g., a human) has a level of indolelactic acid that is greater than about 0.01 pM / g, that mammal can be identified as being unlikely to develop one or more irAEs when administered one or more immune checkpoint inhibitors.

[0062] In some cases, when a stool sample obtained from a mammal (e.g., a human) has a level of indolelactic acid that is less than about 0.01 pM / g, that mammal can be identified as being likely to develop one or more irAEs when administered one or more immune checkpoint inhibitors.

[0063] Any appropriate method can be used to determine the presence, absence, or level of a metabolite (e.g., a fecal metabolite) described herein. Exemplary methods that can be used to determine the presence, absence, or level a metabolite described herein in a sample (e.g., a stool sample) include, without limitation, chromatography, mass spectrometry, and NMR. A sample (e.g., a blood sample such as a plasma sample) obtained from a mammal (e.g., a human) having cancer can be assessed for the presence or absence of any appropriate number of polypeptides. For example, a sample obtained from a mammal having cancer can be assessed for the presence or absence of one or more polypeptides. In some cases, a sample obtained from a mammal having cancer can be assessed for the presence or absence of from about 1 polypeptide to about 10 polypeptides.

[0064] A sample (e.g., a blood sample such as a plasma sample) obtained from a mammal (e.g., a human) having cancer can be assessed for any appropriate one or more polypeptides. A polypeptide that can be used to assess a mammal having cancer as described herein can be any type of polypeptides. In some cases, a polypeptide that can be used to assess a mammal having cancer as described herein can be a cytokine. Examples of microbes that can be present in a gut microbiome and that can be used to assess a mammal having cancer as described herein include, without limitation, IL-8 polypeptides, TNF polypeptides, CXCL9 polypeptides, IL-12B polypeptides, CD6 polypeptides, CD5 polypeptides, CXCL10 polypeptides, and TNFRSF9 polypeptides. In some cases, polypeptides that can be used to assess a mammal having cancer as described herein can be as described in Example 1. In some cases, polypeptides that can be used to assess a mammal having cancer as described herein can be as shown in Figure 1 and / or Figure 2.

[0065] Any appropriate method can be used to determine the presence, absence, or level of a polypeptide (e.g., a cytokine) described herein. Exemplary methods that can be used to determine the presence, absence, or level a polypeptide described herein in a sample (e.g., a stool sample) include, without limitation, mass spectrometry.

[0066] As described herein, the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from a mammal (e.g., a human) having cancer can be used to identify the cancer as being likely to respond to one or more immune checkpoint inhibitors.

[0067] In some cases, the presence of one or more of a Collinsella species (e.g., C. sp900555355 a UBA5416 species (e.g., UBA5416 sp900539175\ a Lachnospira species (e.g., L. rogosae), a Ruminococcus species (e.g., R. C sp000433635 and R. E sp003526955), and a P ar abacter aides species (e.g., P goldsteinii) can be used to identify the cancer as being likely to respond to one or more immune checkpoint inhibitors.

[0068] Also as described herein, the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from a mammal (e.g., a human) having cancer can be used to identify the mammal as being likely to develop one or more irAEs in response to being administered one or more immune checkpoint inhibitors.

[0069] In some cases, the presence of (a) one or more of a Muricomes species (e.g., M. oroticus), a Clostridium species (e.g., C. sp003481775 and C. innocuum), a Terrisporobacter species (e.g., T. sp900557165), a Lacticaseibacillus species (e.g., L. rhamnosus), a Anaerobutyricum species (e.g., A. sp900016875), and a Blautia species (e.g., B. sp001304935 in a stool sample, (b) one or more of succinate, indole-3 -propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2- methylbutyric acid in a stool sample, and / or (c) one or more of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide in a blood sample (e.g., a plasma sample) can be used to identify a mammal as being likely to develop one or more irAEs in response to being administered one or more immune checkpoint inhibitors.

[0070] In some cases, the presence of (a) one or more of a Blautia species (e.g., B. sp900066145), a Bacteroides species (e.g., B. intestinalis A), a Akkermansia species (e.g., A. sp004167605), a CAG-83 species (e.g., CAG-83 sp900547745), a Parabacteroides species (e.g., P. goldsteinii), a CAG-103 species (e.g., CAG-103 sp900543625), and aAlistipes species (e.g., A. ihumii) in a stool sample, and / or (b) one or more of orotic acid, indolelactic acid, cer(dl 8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil in a stool sample can be used to identify the cancer as not being likely to develop one or more irAEs in response to being administered one or more immune checkpoint inhibitors.

[0071] An irAE can be any appropriate irAE. Examples of irAEs can be colitis (e.g., IMDC, also sometimes referred to as ICI colitis), diarrhea, abdominal pain, abdominal distension, cramping, nausea, and vomiting. In some cases, a mammal (e.g., a human) having a cancer that is identified as being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as being unlikely to develop one or more irAEs (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from the mammal) can be selected to receive one or more (e.g., one, two, three, or more) immune checkpoint inhibitors to treat the cancer. For example, a mammal having cancer and identified as having the presence of one or more of a Collinsella species (e.g., C. sp900555355), a UBA5416 species (e.g., UBA5416 sp900539175)', a Lachnospira species (e.g., L. rogosae), a Ruminococcus species (e.g., R. C sp000433635 and / ?. E sp003526955), and a Parabacteroides species (e.g., P goldsteinii) in a stool sample obtained from the mammal can be selected to receive one or more immune checkpoint inhibitors. For example, a mammal having cancer and identified as having the presence of (a) one or more of a Blautia species (e.g., B. sp900066145 , a Bacteroides species (e.g., B. intestinalis A), a Akkermansia species (e.g., A. sp004167605), a CAG-83 species (e.g., CAG-83 sp900547745), a. Parabacteroides species (e.g., P. goldsteinii), a CAG-103 species (e.g., CAG-103 sp900543625), and aAlistipes species (e.g., A. ihunrii) in a stool sample obtained from the mammal, and / or (b) one or more of orotic acid, indolelactic acid, cer(dl 8:0 / 24:0), 3- hydroxybutyric acid, xanthine, and uracil in a stool sample obtained from the mammal can be selected to receive one or more immune checkpoint inhibitors.

[0072] In some cases, a mammal (e.g., a human) having a cancer that is identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as being unlikely to develop one or more irAEs (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from the mammal) can be selected to receive an alternative cancer treatment (e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor) to treat the cancer. For example, a mammal having cancer and identified as having the presence of (a) one or more of aMuricomes species (e.g., M. oroticus), a Clostridium species (e.g., C. sp003481775 and C. innocuum), a Terri sporobacter species (e.g., T. sp900557165 , a Lacticaseibacillus species (e.g., L. rhamnosus), a Anaerobutyricum species (e.g., A. sp900016875), and & Blaulia species (e.g., B. spOOl 304935} in a stool sample obtained from the mammal, (b) one or more of succinate, indole-3-propionic acid, malonic acid, 4- hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid in a stool sample obtained from the mammal, and / or (c) one or more of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide in a blood sample (e.g., a plasma sample) obtained from the mammal can be selected to receive an alternative cancer treatment (e.g., one or more cancer treatments that do not include any immune checkpoint inhibitors).

[0073] This document also provides methods and materials for treating a mammal (e.g., a human) having cancer. In some cases, a mammal (e.g., a human) having cancer and assessed as described herein (e.g., to determine whether or not the cancer is likely to respond to one or more immune checkpoint inhibitors and / or whether the mammal is likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from the mammal) can be administered or instructed to self-administer one or more (e.g., one, two, three, or more) cancer treatments, where the one or more cancer treatments are effective to treat the cancer within the mammal.

[0074] When treating a mammal (e.g., a human) having a cancer that is identified as being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as not being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors as described herein (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from the mammal), the mammal can be administered or instructed to self-administer one or more immune checkpoint inhibitors. For example, a mammal having cancer and identified as having the presence of one or more of a Collinsella species (e.g., C. sp900555355}, a UBA5416 species (e.g., UBA5416 sp900539175}, a Lachnospira species (e.g., / .. rogosae), a Ruminococcus species (e.g., R. C sp000433635 and R. E spOO3526955), and a Parabacteroides species (e.g., P goldsteinii) in a stool sample obtained from the mammal can be administered or instructed to self-administer one or more (e.g., one, two, three, or more) immune checkpoint inhibitors.

[0075] For example, a mammal having cancer and identified as having the presence of (a) one or more of a Blautia species (e.g., B. sp900066145), a Bacteroides species (e.g., B. intestinalis A), a Akkermansia species (e.g., A. sp004167605), a CAG-83 species (e.g., CAG- 83 sp900547745), a Parabacteroides species (e g., P goldsteinii , a CAG-103 species (e.g., CAG-103 sp900543625), and aAlistipes species (e.g., A. ihumii) in a stool sample obtained from the mammal, and / or (b) one or more of orotic acid, indolelactic acid, cer(dl 8:0 / 24:0), 3- hydroxybutyric acid, xanthine, and uracil in a stool sample obtained from the mammal can be administered or instructed to self-administer one or more (e.g., one, two, three, or more) immune checkpoint inhibitors. In some cases, a mammal having cancer and identified as being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as not being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors as described herein can be administered or instructed to self-administer a single immune checkpoint inhibitor. In some cases, a mammal having cancer and identified as being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as not being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors as described herein can be administered or instructed to self-administer two immune checkpoint inhibitors. In some cases, a mammal having cancer and identified as being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as not being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors as described herein can be administered or instructed to self-administer three immune checkpoint inhibitors.

[0076] An immune checkpoint inhibitor can be any appropriate immune checkpoint inhibitor. An immune checkpoint inhibitor can inhibit one or more polypeptides involved in an immune checkpoint pathway. Examples of immune checkpoint pathways include, without limitation, PD-1 / PD-L1 pathways, PD-1 / PD-L2 pathways, CTLA-4 pathways, and TRAIL pathways. An immune checkpoint inhibitor can inhibit any polypeptide involved in an immune checkpoint pathway. Examples of polypeptides involved in an immune checkpoint pathway that can be inhibited by an immune checkpoint inhibitor as described herein include, without limitation, PD-1 polypeptides, PD-L1 polypeptides, CTLA4 polypeptides, and LAG- 3 polypeptides.

[0077] An immune checkpoint inhibitor can inhibit polypeptide activity of a polypeptide involved in an immune checkpoint pathway or can inhibit polypeptide expression of a polypeptide involved in an immune checkpoint pathway. Examples of compounds that can inhibit polypeptide activity of a polypeptide involved in an immune checkpoint pathway include, without limitation, antibodies (e.g., neutralizing antibodies) that target (e.g., target and bind) to a polypeptide involved in an immune checkpoint pathway and small molecules that target (e.g., target and bind) to a polypeptide involved in an immune checkpoint pathway. Examples of compounds that can inhibit polypeptide expression of a polypeptide involved in an immune checkpoint pathway include, without limitation, nucleic acid molecules designed to induce RNA interference of polypeptide expression of a polypeptide involved in an immune checkpoint pathway (e.g., a siRNA molecule or a shRNA molecule), antisense molecules that can target (e.g., are complementary to) nucleic acid encoding a polypeptide involved in an immune checkpoint pathway, and miRNAs that can target (e.g., are complementary to) nucleic acid encoding a polypeptide involved in an immune checkpoint pathway. Examples of immune checkpoint inhibitors that can be administered to mammal (e.g., a human) having cancer (e.g., a cancer that exhibits little or no response to treatment with immune checkpoint inhibitors) include, without limitation, anti -PD-1 antibodies, anti-PD-Ll antibodies, anti-CTL4A antibodies, and anti -LAG-3 antibodies. In some cases, an immune checkpoint inhibitor that can be administered to mammal (e g., a human) having cancer (e.g., a cancer that exhibits little or no response to treatment with immune checkpoint inhibitors) as described herein can be as shown in Table 1.

[0078] Table 1. Exemplary immune checkpoint inhibitors. In some cases, an immune checkpoint inhibitor can be as described elsewhere (see, e.g., Smith et al., Am. J. Transl. Res., 11(2):529-541 (2019) at, for example, Table 1; and Terranova-Barberio et al., Immunotherapy, 8(6):705-719 (2016) at, for example, Table 1).

[0079] When treating a mammal (e.g., a human) having a cancer that is identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein and / or as being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors (e.g., based, at least in part, on the presence or absence of (a) one or more microbes, (b) one or more metabolites (e.g., fecal metabolites), and / or (c) one or more polypeptides (e.g., one or more cytokines) in a sample obtained from the mammal), the mammal can be administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) alternative cancer treatments (e.g., one or more cancer treatments that do not include any immune checkpoint inhibitor). For example, a mammal having cancer and identified as having the presence of (a) one or more of aMuricomes species (e.g., M. oroticus), a Clostridium species (e.g., C. sp003481775 and C. innocuum), a Terrisporobacter species (e.g., T. sp900557165), a Lacticaseibacillus species (e.g., L. rhamnosus), a Anaerobutyricum species (e.g., A. sp900016875), and a Blautia species (e.g., B. sp001304935) in a stool sample obtained from the mammal, (b) one or more of succinate, indole-3 -propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid in a stool sample obtained from the mammal, (c) one or more of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide in a blood sample (e.g., a plasma sample) obtained from the mammal can be administered or instructed to self-administer one or more alternative cancer treatments (e.g., one or more cancer treatments that do not include any immune checkpoint inhibitor).

[0080] In some cases, one or more (e.g., one, two, three, four, five, or more) alternative cancer treatments (e g., one or more cancer treatments that do not include any immune checkpoint inhibitor) can include administering to a mammal (e.g., a human) having cancer one or more (e.g., one, two, three, or more) alternative anti-cancer agents used to treat cancer and / or performing one or more (e.g., one, two, three, or more) therapies used to treat cancer. For example, an alternative anti-cancer agent that can be used to treat a mammal having cancer and identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein can be a chemotherapeutic agent. For example, an alternative anti-cancer agent that can be used to treat a mammal having cancer and identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein can be a cytotoxic agent. For example, an alternative anti -cancer agent that can be used to treat a mammal having cancer and identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein can be an angiogenesis inhibitor. Examples of anticancer agents that can be administered to a mammal having cancer and identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein to treat the mammal include, without limitation, sorafenib, regorafenib, ramucirumab, and any combinations thereof Examples of therapies that can be used to treat a mammal having cancer and identified as not being likely to respond to one or more immune checkpoint inhibitors as described herein include, without limitation, radiation therapies, and / or surgeries.

[0081] In some cases, when treating a mammal (e.g., a human) having cancer as described herein, the treatment can be effective to treat the cancer. For example, the number of cancer cells present within a mammal can be reduced using the methods and materials described herein. In another example, the size (e.g., volume) of one or more tumors present within a mammal can be reduced using the methods and materials described herein. In some cases, the methods and materials described herein can be used to reduce the size of one or more tumors present within a mammal having cancer by, for example, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or more percent. In some cases, the methods and materials described herein can be used to treat cancer in a manner such that the size (e.g., volume) of one or more tumors present within a mammal does not increase.

[0082] In some cases, when treating a mammal (e.g., a human) having cancer as described herein, the treatment can be effective to improve survival of the mammal. For example, the methods and materials described herein can be used to improve disease-free survival (e.g., relapse-free survival). For example, the methods and materials described herein can be used to improve progression-free survival. For example, the methods and materials described herein can be used to improve the survival of a mammal having cancer by, for example, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or more percent. For example, the methods and materials described herein can be used to improve the survival of a mammal having cancer by, for example, at least 6 months (e.g., about 6 months, about 8 months, about 10 months, about 1 year, about 1.5 years, about 2 years, about 2.5 years, or about 3 years).

[0083] One or more immune checkpoint inhibitors can be administered to a mammal (e.g., a human) having cancer in any appropriate amount (e.g., any appropriate dose). In some cases, an effective dose of one or more immune checkpoint inhibitors can be a flat dose. In some cases, as effective dose of one or more immune checkpoint inhibitors can be based on the body of a mammal to be treated as described herein. An effective amount of one or more immune checkpoint inhibitors can be any amount that can treat a mammal having cancer without producing significant toxicity to the mammal. The effective amount of one or more immune checkpoint inhibitors can remain constant or can be adjusted as a sliding scale or variable dose depending on the mammal’s response to treatment. Various factors can influence the actual effective amount used for a particular application. For example, the frequency of administration, duration of treatment, use of multiple treatment agents, route of administration, and / or severity of the cancer in the mammal being treated may require an increase or decrease in the actual effective amount administered.

[0084] One or more immune checkpoint inhibitors can be administered to a mammal (e.g., a human) having cancer at any appropriate frequency. The frequency of administration can be any frequency that can treat a mammal having cancer without producing significant toxicity to the mammal. For example, the frequency of administration can be from about twice a day to about one every other day, from about once a day to about once a week, from about once a day to about once a month, from about once a week to about once a month, or from about twice a month to about once a month. The frequency of administration can remain constant or can be variable during the duration of treatment. As with the effective amount, various factors can influence the actual frequency of administration used for a particular application. For example, the effective amount, duration of treatment, use of multiple treatment agents, and / or route of administration may require an increase or decrease in administration frequency. One or more immune checkpoint inhibitors can be administered to a mammal (e.g., a human) having cancer for any appropriate duration. An effective duration can be any duration that can treat a mammal having cancer without producing significant toxicity to the mammal. For example, the effective duration can vary from several weeks to several months, from several months to several years, or from several years to a lifetime. Multiple factors can influence the actual effective duration used for a particular treatment. For example, an effective duration can vary with the frequency of administration, effective amount, use of multiple treatment agents, and / or route of administration.

[0085] The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

[0086] EXAMPLES

[0087] Example 1: Microbial Pathways Linked to 1C1 Efficacy and irAEs

[0088] This Example describes the identification of common pre-treatment microbiome- driven pathways that contribute to both ICI efficacy and irAEs.

[0089] Methods

[0090] Stool and plasma samples were prospectively collected from patients with varied cancers prior to starting ICI therapy (including both CTLA4 and PD1 / PDL1 blockers) and submitted for metagenomics, metabolomics and immune protein analysis as outlined in figure legends. Prospectively collected clinical data was used to assess outcomes (RECIST) and adverse events.

[0091] Pretreatment plasma and stool samples were collected from a multi-site cohort of 183 patients treated with ICIs. A panel of 96 inflammatory markers was assessed in plasma for 151 samples (Olink inflammation panel). Stool samples were sequenced using shotgun metagenomics and taxonomy was mapped using Kraken2 with the GTDB R202 database. Quantitative metabolomics was performed (TMIC MEGA method) on 161 stool samples; 618 metabolites were quantified, of which 305 metabolites were detected in more than 50% of samples. Taxonomic comparisons with a set of healthy controls revealed a distinct shift in the gut microbiome of the ICI cohort, probably due to previous treatments and other confounders related to disease. However, an outlying gut microbiome was not predictive of colitis. Pairwise comparisons were performed between ICI patients that did not develop colitis with those that did for all combined data layers.

[0092] Gunifrac analysis revealed a significant effect of sequencing batch, age, Bristol Stool Form Score, and Elixhauser comorbidity score on the gut microbiome. For the fecal metabolome PERMANOVA analysis on Bray Curtis dissimilarity matrix was performed and only the water content of the sample was identified as significant. These confounders were corrected for in downstream group comparisons.

[0093] Results

[0094] Among the 183 patients, 57 patients had complete response. The most common indication was melanoma, while the most prevalent irAE’s were colitis (n=19) and hepatitis (n=24). There were no significant differences in the clinical / demographic factors among patients with or without favorable outcomes, colitis or hepatitis (Figures 1A-1C).

[0095] A significant difference in microbial community structure (GUniFrac, p<0.05) was seen when comparing the cancer cohort (n=183) with a pre-defined cohort of healthy controls (n=200), however, this difference was not associated with specific treatment outcomes or irAEs. The development of an irAE was not associated to outcomes of ICI therapy (equality of proportions, p=0.7). There was also no overlap among patients with different irAEs like colitis and hepatitis. This was also reflected in the microbiome where distinct microbial taxa were associated with the irAE’s and outcomes (Figure ID) suggesting different microbes might drive immune processes underlying these phenomena. Disregarding irAEs, Collinsella sp900555355 was associated with a favorable treatment outcome. Meanwhile, Lachnospira rogosae A and UBA5416 sp900539175 were linked to a positive outcome in the absence of irAEs.

[0096] Additional omics layers were integrated and 6 immune markers (e.g. IL8, TNF), eight species, and two metabolites (e.g. succinate) were found to be positively associated with ICI- colitis, while 1 immune marker and 31 metabolites (e.g. orotic and indolelactic acid) were found to be negatively associated with ICI-colitis (Figure 2) with orotic acid exhibiting the strongest effect size. Integration of omics data using a partial least squares regression framework identified potential metabolites and immune markers driven by specific microbes (e g., negative correlation of Blautia spp and TNF; Figure 2).

[0097] Together, these results demonstrate identify distinct pre-treatment microbial pathways can be used to identify cancer patients that are likely to respond to one or more ICIs and / or are likely to develop one or more irAEs. As such, these microbial pathways can be leveraged to stratify available treatments as well as develop microbiome-based therapies to optimize treatment outcomes and reduce irAEs.

[0098] Example 2: Distinct multi-omics pathways underlie organ-specific immune-related adverse events of checkpoint blockade therapy across cancer types

[0099] The results in this Example re-present and expand on at least some of the results provided in other Examples.

[0100] Methods

[0101] Patient recruitment and clinical data extraction

[0102] Cancer patient samples were obtained. Healthy controls were from a study on personalized diets and insulin resistance. Blood samples for Peripheral Blood Mononuclear Cells (PBMC) isolation were from healthy male donors aged 19 to 60 years. These samples were obtained as buffy coats from apheresis cones.

[0103] Exclusion criteria were minimal - inclusion criteria were cancer diagnosis age >18 years and starting a new cancer treatment. The employed stool kit included 3 tubes with scoop (Sarstedt, product number 80.9924.014) without preservatives, cooled immediately, and express shipped using FedEx on ice packs. Stool was stored at -80°C.

[0104] Basic clinical data, cancer diagnosis and treatment plan were recorded and verified by comparing with the electronic health record (EHR). Clinical data was extracted from clinical notes, medication registry, and imaging results from the EHR. Any grade irAEs were considered relevant as patients received proactive management of irAEs mostly through prescription of corticosteroids. Definitions and grading for diarrhea, colitis, hepatitis, and pneumonitis was based on the Common Terminology Criteria for Adverse Events. In brief, diarrhea or colitis (combined into IMDC) was assessed based on frequency and consistency of bowel movements as well as abdominal pain. Liver toxicity (hepatitis) was assessed using clinical serum measurements of aminotransferase (ALT) or aspartate aminotransferase (AST) measurements. Pneumonitis was diagnosed based on focal or diffuse inflammation of the lung parenchyma identified through imaging with or without symptoms like shortness of breath and chest pain. Other adverse events were recorded when they were mentioned in the clinical notes.

[0105] Metagenomics sequencing and bioinformatics

[0106] DNA was extracted using Qiagen's DNeasy 96 PowerSoil Pro QIAcube HT Kit following the manufacturer's instructions. PowerBead Pro plates were combined with a TissueLyser II step for sample homogenization and then loaded onto the QIAcube HT for DNA extraction. Samples were sequenced using Illumina NovaSeq 6000 targeting 8 million reads per sample (2 x 150 bp).

[0107] Shotgun metagenomics data processing started with the use of BBDuk v38.69 to remove adapters and low-complexity sequences from the dataset. Subsequently, BioBloomTools v2.3.2 was used to remove human (hg38) sequences. Quality control was further refined by SHI7 to trim reads, eliminate low-quality bases, and discard reads smaller than 80 bases, ensuring the remaining reads have high average quality scores above 35. For taxonomic classification, Kraken2 (v2.1.1) was used to annotate the cleaned reads against the GTDB v202 database, and Bayesian Reestimation of Abundance after Classification with KrakEN (Bracken) then computed the relative abundance estimates for each microbial taxon identified in the samples. HUMAnN 3.0 using the GTDB v202 database was used to profile microbial metabolic pathways which were normalized by copies per million (CPMs). HUMAaN output contained 3 levels, Metacyc pathways, Uniref KEGG Ontology terms, and Enzyme commission (EC) numbers, as well as their mapping to specific genomes.

[0108] Samples were sequenced together with the full cohort across 8 batches. To inspect batch effects, stool from 4 samples was extracted and sequenced in every batch. Quantitative stool metabolomics

[0109] A targeted quantitative metabolomics approach was employed using multiple reaction monitoring (MRM) to quantify up to 600 metabolites (TMIC MEGA method; Wishart lab University of Alberta, Canada). To increase coverage 2 metabolite extraction methods were employed. Authentic isotopically labeled standards were included for quantification. Samples were measured on an AB Sciex QTrap 5500 coupled to Agilent 1290 series UHPLC system. Data tables with quantified metabolites were provided by the service lab.

[0110] Plasma metabolomics

[0111] Untargeted LC-MS plasma metabolomics measurements (TMIC untargeted) were performed as follows. Plasma was aliquoted in the Biospecimen Accessioning and Processing Core and shipped on dry ice. To extract metabolites, 10-50 pL of plasma sample was added to 40-200 pL Acetonitrile (MeCN) (maintaining the 1 :4 v:v plasma:MeCN ratio; 80% MeCN final) containing 1 pM caffeine-d9 and 1 pM Adipic acid-13C6 as internal standards. Samples were then vortexed and centrifuged at 21000 x g for one minute at room temperature (23°C). Supernatants were transferred to the Eppendorf tubes and stored at - 80°C until subsequent analysis. Pooled samples for each group were made by combining 10 pL of each sample extract of the first 100 samples. Before measurement, samples were thawed at room temperature for 30 minutes and 100 pL aliquots were transferred to autosampler vials with glass inserts. 10 pL extract was injected and leftover samples were stored at -80°C. LC analyses were performed on a Vanquish UPLC system (Thermo Fisher Scientific) employing a 10 minute 0-100% gradient from 0.1% formic acid (FA) to 0.1%FA / MeCN at a 200 pL / min flow rate through a Waters Acquity UPLC HSS T3 column with 1.8 pm particle size and dimensions of 2.1x100 mm (RP-LC-MS). Mass spectra were acquired using a Thermo Orbitrap QE+ spectrometer in positive and negative modes in separate runs. Pooled samples were additionally injected to obtain MS2 spectra using default data-dependent acquisition settings.

[0112] Untargeted metabolomics data were processed using MZmine version 4.2.0, an open- source software for mass spectrometry (MS) data analysis. Thermo Fisher Orbitrap raw data were converted to the .mzML format and imported into MZmine’s MZWizard workflow optimized for UHPLC-Orbitrap-DDA data. This processing pipeline included the following steps and modules; 1) Mass Detection: MSI scans were analyzed using the "Factor of Lowest Signal" mass detector algorithm, 2) Chromatogram Building: The Modular ADAP Chromatogram Builder was employed and the tolerances set to account for mass accuracy typical of Orbitrap instruments, 3) Smoothing: Chromatograms were smoothed using the Savitzky-Golay algorithm, 4) Feature Resolution: The Local Minimum Search Feature Resolver was applied to ensure robust peak detection, 5) RT Correction module: Retention time alignment was performed to correct for RT shifts across samples, 6) Isotope Grouping: The Isotope Grouper module filtered for13C isotopes with a maximum charge of 3, selecting the most intense isotope as the representative feature, 7) Join Alignment module: Features across samples were aligned using the Join Aligner module, 8) Filtering: The Rows Filter module retained features present in at least 10% of aligned samples to eliminate sporadic or irreproducible signals, 9) Multi Thread Peak Finder module: Gap-filling used the Multi Thread Peak Finder module to recover missing features based on m / z and RT from aligned samples. The resulting feature table, including m / z, RT, and peak intensities, were exported for subsequent metabolite annotation.

[0113] Metabolite features were annotated based on accurate mass and retention times using Metab o Annotation from the MetaboAnnotation package (version 1.6.1) in R (version 4.3.2). The annotation workflow uses the matchValues function to compare feature m / z values from the MZmine output (column "mz") against a reference database compiled from the Human Metabolome Database (HMDB, version 5.0) using the createCompDb function from the R CompoundDb package (version 1.6.0). The Mass2MzParam object was configured to match m / z values with a mass tolerance of 0.05 Da and a parts-per-million (ppm) tolerance of 10. Acceptable adduct were [M+H]+, [M+Na]+, [M+K]+, [M-HSTL] , [M+CH OH+H] , [M+2H]2+, and [M+2Na]2+for positive ionization mode and [M-H] , [M+Cl] ", [2M-H]", [M+CHO2]" for negative ionization mode. The tables with putative annotations were processed further before statistical analysis. To do this, the annotation with the smallest ppm difference was identified for every annotated feature, the corresponding chemical formula linked to that feature identified, and only rows with that formula were kept. Zero values were imputed with the half min of the feature values (+ or - 1 standard deviation).

[0114] Cytokine measurements

[0115] Plasma cytokines from the Olink Target 96 Inflammation (v.3024) panel were quantified on an Olink Signature instrument according to the manufacturer’s instructions by Psomagen, Inc. (Rockville, MD).

[0116] Statistical analysis

[0117] Alpha diversity was calculated using Shannon diversity index (diversity from the R vegan package) and beta diversity using weighted UniFrac (WUniFrac) from the R GUniFrac package were calculated on rarefied species-level data. Manhattan distance was used for stool metabolome and plasma cytokines, and Bray Curtis for the other omics data layers. The effect sizes of the adjusted beta diversity analysis were ranked for visualization purposes in Figure 8 A.

[0118] Group comparisons were adjusted for variables nominally significant (p<0.05) in these beta diversity analyses. ZicoSeq from the R GUniFrac package was used to test the taxonomic associations between taxa and variable of interest while correcting for other variables. Multiple testing correction was performed using the permutation-based false discovery rate control implemented in ZicoSeq (q-value). Taxa comparisons were adjusted for Age and imputed (with floor of median) Bristol Stool Form Scale (i.e. Bristol score). Group comparisons of functional microbiome at the pathway level (adjusted for Age and Elixhauser comorbidity score), olink plasma cytokines (adjusted for Age and antibiotic in last month), and plasma metabolome (adjusted for Age) were also compared using Zicoseq using setting feature. dat.type = "other". Stool metabolomics data was analyzed using linear models (Im function in R) adjusting for imputed Bristol score and Age. Taxa or pathways below 20% prevalence or a maximum proportion of 0.2% were excluded from the differential abundance analysis. Before fitting linear models on stool metabolomics data it was transformed by filtering by prevalence > 70% and average intensity > le-5, imputation of 0’s with the half minimum, normalization using Probabilistic Quotient Normalization (PQN), and log2 transformation. Log2 fold changes were calculated by subtraction of group means of this processed data. Plasma metabolome data was filtered and imputed as described above.

[0119] Bray-Curtis dissimilarity (BCD)-based irregularity (BCDI) scores were computed based on rarified species abundance between the healthy control cohort and ICI cohort samples.

[0120] For statistical analysis of the flow cytometry data, data were exported from FlowJo (BD Biosciences). Per experiment outliers were tested for using Grubbs test (grubbs.test function from the R outliers package). One outlier was removed in the fecal water experiment (Grubbs test p=7.302e-08). Data from the naive CD4+ stimulation assay using fecal water was analyzed using Imer from the R lme4 package using formula percentage_foxP3 ~ IMDC / nonIMDC + (1 (aspirate donor).

[0121] All statistical analyses were performed in R version >4.2.2 (R Development Core Teams).

[0122] Comparison with published cohorts

[0123] Two IBD cohorts with available stool metabolomics data were queried; iHMP and PRISM, both curated and accessible through the microbiome-metabolome dataset collection. iHMP is a longitudinal study with multiple samples per subject and was therefore adjusted for subject using a linear mixed effect model. PRISM+validation is cross-sectional and significance was tested using a linear model. Metabolite feature annotations were identified from the mtb.tsv files of the respective study by querying the column names resulting in orotic acid annotation HILn_QI93 orotate for iHMP and HILIC.neg_Cluster_0203..orotate for PRISM. The only other matching metabolome feature annotation was HILn_QI5 _ 2- hydroxy-3 -methylbutyrate from iHMP.

[0124] Multi-omics prediction ofirAEs

[0125] Data was preprocessed as follows: Taxonomic abundance data (664 species, 261 genera, 68 families, 36 orders, 14 phyla, 177 samples): filter - prevalence > 20%, mean abundance > 0.01%; normalization - total sum scaling (TSS). Functional abundance data (306 pathways, 942 Uniref KEGG Ontology terms, 1173 Enzyme commission (EC) numbers, 177 samples): filter - prevalence > 20%, mean abundance > 10 per million reads; normalization - TSS. Olink proteomics data (72 proteins in 145 samples), no further preprocessing. Stool metabolomics data (251 metabolites, 167 samples): filter - prevalence > 70%, average intensity > le-5; imputation - half minimum; normalization - Probabilistic Quotient Normalization (PQN); transformation - logarithmic. Plasma metabolomics data (4168 features negative mode, 5516 features positive mode, 145 samples): filter - prevalence > 70%, one sample excluded due to > 50% missing; imputation - half minimum; normalization - Probabilistic Quotient Normalization (PQN); transformation - logarithmic. After preprocessing correlations of PCI and Mantel test was performed to inspect expected correlation structure. Before Random Forest, dimensions of the plasma metabolomics data were reduced to 1,000 clusters using hierarchical clustering (average link) after combining data from both positive and negative measurement modes. The first principal component of the cluster was used as a feature for machine learning.

[0126] Random Forest machine learning was performed using the R caret package (version 7.0-1) with the R ranger implementation of Random Forest (version 0.17.0). Default parameters were used without further tuning. To address class imbalance in the dataset, up- sampling was employed as implemented in the R caret package (sampling = ‘up’ in trainControl function). Ten-fold cross-validation was used to evaluate predictive performance, where the data were randomly divided into 10 folds with 9 folds used for training and the remaining fold for testing. This process was repeated 10 times. No feature selection was performed in the training dataset. ROC curves were then constructed based on the predicted probabilities on the test folds and AUC (area under the ROC curve) was calculated along with 95% confidence interval using R pROC package (version 1.18.5). To identify features driving the prediction accuracy, Boruta feature selection (R Boruta package version 8.0.0) was applied to the full dataset to identify the most predictive features.

[0127] Correlation networks

[0128] Canonical correlation networks were constructed for all patients with complete data using Spearman correlation on the following omics data layers: species taxonomy, functional pathways, stool metabolomics, olink plasma cytokines. Before correlations the data were processed by prevalence filtering and scaling. Rarified species abundance and pathway abundance was clr transformed using the mclr function from the R compositions package. Pairwise omics correlations were adjusted for multiple testing using the p.adjust(“fdr”) function (q-values).

[0129] Because of the focus on orotic acid the correlation results were first filtered to only retain correlations between orotic acid and any cytokines (p<0.05; orotic acid cytokines) / species (q<0.1) / and pathways (q<0.1). Subsequently correlations were also included between the orotic acid and cytokines and species and pathways (both p<0.01), and between species and pathways present meeting the above cutoffs (q<0.01).

[0130] The resulting correlations were visualized using the R igraph package after filtering to only include nodes with a degree of 2 or more.

[0131] In vitro experiments and flow cytometry

[0132] Peripheral blood mononuclear cells (PBMCs) were isolated using density gradient centrifugation. Whole blood samples were diluted 1 :8 with phosphate-buffered saline (PBS) and carefully layered over Ficoll-Paque Premium (Cytiva) at a 3: 1 ratio. The samples were centrifuged at 1500 rpm for 30 minutes at room temperature with the brake off. After centrifugation, the PBMC layer was carefully collected from the interface between the plasma and Ficoll layers using a sterile pipette. The isolated PBMCs were treated with RBC lysis buffer for 5 minutes to remove any remaining red blood cells. The cells were then plated on petri dishes and incubated at 37°C for 30 minutes. Finally, the non-adherent cells were collected from the plates for further isolation.

[0133] CD4+ T cells were isolated from human peripheral blood mononuclear cells using the CD4+ T-cell isolation kit from Miltenyi Biotec (Catalogue No. 130-096-533), following the manufacturer's protocol. Subsequently, naive CD4+ T cells were isolated using CD45RA microbeads from Miltenyi Biotec (Catalogue No. 130-045-901), again following the manufacturer's instructions. The isolated human naive CD4+ T cells were cultured under standard conditions in RPMI 1640 medium supplemented with 10% fetal calf serum (FCS), 10 mM HEPES (pH 7.4), 1 mM sodium pyruvate, 2 mM L-glutamine, 1% nonessential amino acids, and 50 pM P-mercaptoethanol. Antibiotics (penicillin and streptomycin) were also added to the culture medium. To polarize naive CD4+ T cells into Thl subsets, the cells were cultured for 6 days. First, 48-well flat-bottom plates were coated with anti-human CD3 (2 pg / mL) in PBS and incubated at 37°C for 2 hours. The plates were then washed twice with PBS. Next, 0.3 million naive CD4+ T cells per well were added along with anti-human CD28 (2 pg / mL). On day 2, the media was refreshed with anti-human CD28 (2 pg / mL) and human IL- 12 (20 ng / mL), with or without addition of fecal water or three different concentrations of orotic acid (25 pM, 100 pM, and 400 pM from a 5 mM stock solution in water). Fecal water was prepared using a 1 :5 dilution of 100 mg / mL feces which was homogenized in PBS and sterilized using a 22 pm filter - 20pL of the diluted fecal homogenized water was added to 500 pL of media.

[0134] On day 6, the differentiated T cells were harvested and resuspended in fresh media containing anti-human (2 pg / mL) CD28 and plated on freshly prepared CD3 coated plates and incubated until staining.

[0135] Cells were treated with brefeldin A (BioLegend, catalogue no. 420601) to enhance intracellular cytokine staining for flow cytometry analysis. For fixation and permeabilization, the FOXP3 Fix / Perm Buffer Set (BioLegend, catalogue no. 421403) was used according to the manufacturer's instructions. The process involved fixing cells with Foxp3 Fix buffer for 45 minutes at 4°C, followed by permeabilization with Foxp3 Perm / Wash buffer. To block non-specific binding, cells were incubated with Fc Receptor Binding Inhibitor Antibody (catalogue no. 14-9161-73) for 10 minutes at 4°C. Subsequently, the cells were stained with fluorochrome-conjugated primary antibodies: anti-human FOXP3 (Clone 259D, BioLegend) and anti-human CD25 (Clone BC96, BioLegend). After staining, the cells were washed twice with Perm / Wash buffer and analyzed using flow cytometry. Flow cytometry data was analyzed using FloJo. Fluorescence Minus One (FMO) controls were included for all antibodies to guide setting of gates in downstream analysis and account for fluorescence spread from multiple fluorochromes. Results

[0136] Cohort description and demographics

[0137] The study included 207 patients scheduled to start ICI-containing regimens as part of a real-world study with varied cancer types and stages (Figure 7A). Patients were asked to provide stool and blood specimen prior to starting the ICI therapy but samples were accepted if they were returned within the first treatment cycle. Prospective clinical follow up was conducted through manual review of the electronic health record (EHR), capturing immune- related adverse events (irAEs) and treatment outcome after the first cycle of ICI therapy initiation (objective response assessments per Response Evaluation Criteria in Solid Tumors; RECIST 1.1). Demographic and baseline clinical information - including antibiotic and proton pump inhibitor (PPI) use, prior therapies and Elixhauser comorbidity scores at the time of specimen collection - were retrieved from the EHR. A total of 177 patients were considered for further analysis, after excluding patients who did not provide a sample, lacked accurate follow-up data (including records from outside Mayo Clinic), or experienced IMDC, hepatitis, or pneumonitis symptoms during a prior ICI treatment or before sample collection (Table 2).

[0138] Table 2. ICI cohort summary table split by RECIST criteria treatment outcome at the time of first follow up. There are no significant differences in clinical or demographic factors among patients with or without favorable treatment outcomes, and irAEs. The development of an irAE was not associated to outcomes of ICI therapy at first follow up. PD = progressive disease, SD = stable disease, PR = partial response, 4 = complete response.

[0139] Among the 177 patients, 97 (54.8%) experienced one or more irAEs, consistent with previously reported incidence rates (Figure 7B) (Yin et al., Front. Immunol., 14: 1167975 (2023)). The irAEs which most frequently lead to treatment discontinuation and hospitalization were focused on: immune-mediated diarrhea and colitis (IMDC), hepatitis, and pneumonitis, which collectively occurred in 40 patients (22.6%) (Figure 7B, Table 2, Figure 13 for cohort tables of these 3 irAEs). Additional irAEs were combined into an “other irAE” group. These included - sorted by decreasing prevalence - thyroid dysfunction, cutaneous manifestations, musculoskeletal involvement, adrenal dysfunction, cardiovascular events, pancreatic abnormalities, and renal events. All grades of IMDC, hepatitis, and pneumonitis were included as binary in the analysis. Time between sample collection and first infusion of ICIs as well as time to event plots for irAE onset are shown in Figure 14. In brief, IMDC (n=16, 9.0%) was assessed based on frequency and consistency of bowel movements as well as abdominal pain, hepatitis (n=16, 9.0%) using serum measurements of aminotransferase (ALT) or aspartate aminotransferase (AST), and pneumonitis (n=9, 5.1%) as lung inflammation with or without symptoms like shortness of breath and chest pain.

[0140] Most patients experienced a single irAE, and irAEs were not linked to treatment outcomes

[0141] Upon inspection of the clinical data, it was found that patients typically have a single irAE (Figure 7C). There was no overlap between patients with hepatitis and IMDC or hepatitis and pneumonitis, while one patient had both pneumonitis and IMDC (Figure 7B). The occurrence of irAEs was not associated with age, body mass index (BMI), sex, Elixhauser comorbidity score, or cancer type (Table 2). Occurrence of irAEs was not associated with receiving chemotherapy in addition to ICI. IMDC was moderately associated to receiving a combination regimen of oc-CLTA-4 with ot-PD(L)-l (16.7% of patients on combination therapy vs. 10.6% on oc-PD(L)-l regimens, 2-sample equality of proportions test, p=0.062). Hepatitis and pneumonitis were not associated with a specific ICI regimen in this cohort (p>0.49). Additionally, the development of irAEs was not associated with malignancy outcomes of ICI therapy at the time of first follow up scan (Table 2). Finally, stool consistency at the time of stool collection (Bristol Stool Form Scale; BSFS) and a measure of intestinal barrier function (plasma lipopolysaccharide binding protein) was not associated with the later occurrence of irAEs or malignancy treatment outcomes (Figure 15A-15B).

[0142] To better understand whether different irAEs were driven by shared or distinct mechanisms, multi-omics data were generated on this ICI cohort. This included metagenomic species-level microbiome data, metagenomic functional microbiome data, quantitative stool metabolomics, untargeted plasma metabolomics (Cl 8 negative and positive mode), and a panel of plasma immune markers (Figure 7C). These diverse data layers quantify output from both microbial and host functions which may underlie irAEs. Cancer-related microbiome changes do not correlate with irAEs

[0143] Before identifying omics features associated with irAE among cancer patients, it was explored whether differences in the microbiome associated with cancer in general were also associated with irAEs. For this stool samples from 281 healthy subjects collected as part of a previous study on patients without chronic medical conditions were used metagenomic sequencing was performed along with the larger cohort of cancer patients.

[0144] The Bray-Curtis dissimilarity index (BCDI) was calculated for each ICI cohort cancer sample in comparison to the healthy controls. Samples were classified as irregular if they fell beyond the 90th percentile of the healthy control samples. Using this cutoff, 70 out of 177 (39.5%) of the gut microbiomes were considered irregular. Direct comparison of BCDI scores revealed a significant difference between the gut microbiomes of healthy controls and those with cancer (Welch t-test p<2.2e-16), indicating that the gut microbiome composition in cancer patients was distinct from that of healthy individuals (Figure 16A). However, within the ICI cancer cohort, BCDI was not significantly associated with any irAE (Figure 16B). This suggests that overall gut microbiome differences observed in cancer are not the primary drivers of irAEs.

[0145] As samples were still included when returned within the first cycle of treatment (but before irAE onset), whether BCDI score was associated with receiving stool samples after treatment was also inspected. There was no relation detected between BCDI score and obtaining a sample after treatment (Figure 16C). This suggests that - at least early in the treatment course and in absence of irAEs - the gut microbiome does not change markedly during ICI treatment.

[0146] Distinct omics signatures are associated with specific irAEs

[0147] Which omics data layers were associated with irAEs independent of demographic variables was next investigated. To do this, Permutational Multivariate Analysis of Variance (PERMANOVA) analyses based on beta diversity matrices from the six distinct omics layers generated on the ICI cohort wereconducted, quantifying the percentage of variation in each data layer that can be attributed to a specific variable (Figure 17A). The analysis was then repeated focused on irAEs, adjusting for the demographic variables that had significant (p<0.05) effects on that particular omics layer; age, Elixhauser comorbidity score, BSFS, and antibiotic use in the past month were all associated with a significant proportion of variation in some of the omics layers (Figure 17A). The resulting effect sizes were ranked and it was found that different omics layers were linked to distinct irAEs (Figure 8A). For example, differences in stool taxonomic features and alpha diversity were strongly associated with the development of pneumonitis, while stool metabolome and plasma cytokines were more linked to the development of IMDC (Figure 8A, alpha diversity shown in Figure 16B). Pairwise comparisons were next performed on the irAE groups (comparisons of groups of patients with a specific irAE vs. all other ICI patients) for the omics layers individually, while adjusting for demographic variables with significant effects on the specific omics layer.

[0148] Comparisons of genus and species abundances revealed completely non-overlapping trends between the three main irAEs. In other words, no genus or species was associated with more than 1 of the 3 main irAEs Figures 8B-8C). Pneumonitis patients had lower levels of the Bacteroides genus and Bacteroides species (4 species, all p<0.01 ) but elevated Streptococcus genus and species (4 species, all p<0.01 ), as well as Collinsella species (6 species, all p<0.01) (Figures 8B-8C). Species comparisons for the development of hepatitis showed strong significance for Dorea A longicatena B (p<0.001) and Dorea B phocaeensis (p<0.01), as well as their corresponding genera. Relative abundance of the lactic acid bacterium Lacticaseibacillus paracasei (p<0.005) was also elevated (Figure 8C). IMDC was positively associated with the genus Lacticaseibacillus but a different species compared to hepatitis, Lacticaseibacillus rhanmosus (p<0.001) (Figures 8B-8C). At the species level, IMDC patients also had elevated levels of the Lachiospiraceae Muricomes oroticus, Clostridium AQ sp003481775 and Adlercreutzia equolifaciens (Figure 8C).

[0149] The findings for hepatitis are particularly intriguing as increased levels of Lactobacillus (the previous genus name of Limosilactobacillus) and Dorea species have been reported in patients with non-alcoholic fatty liver disease (NAFLD) (Raman et al., Clin. Gastroenterol. Hepatol., 11(7) : 868-75 (2013)) and the Dorea genus was positively associated with the progression of NAFLD to nonalcoholic steatohepatitis (NASH) (Del Chierico et al., Hepatology, 65(2):451-464 (2017)). This overlap in the identification of Lactobacillus and Dorea between previous studies and these findings suggested a potential mechanistic role for these bacteria in liver-related side effects associated with ICI therapy. Like taxonomic associations, the functional metagenomics analysis also revealed distinct metabolic pathways linked to specific irAEs, with the highest number of pathway associations observed with pneumonitis (Figure 16).

[0150] The association of features within quantitative stool metabolome with the later development of the specific irAEs was next focused on. Nominally significant metabolite associations were identified for all irAE groups (p<0.05, Figure 8D) which again displayed distinct patterns with very little overlap. The tyrosine breakdown product 4-hydroxybenzoic acid and pyrimidine metabolite orotic acid were the only exception as they were elevated and reduced, respectively, in both the stool metabolome from patients that later developed pneumonitis as well as IMDC (Figure 8D). Besides orotic acid, IMDC was associated to lower levels of 4 more metabolites; the tryptophan derivative indolelactic acid (ILA), the purine base xanthine, the pyrimidine nucleotide base cytosine, and the non-proteinogenic amino acid alpha-aminobutyric acid (also known as homoalanine) (Figures 8D-8E). Patients that later developed hepatitis had lower levels of serotonin (Figure 8D, Figure 18 A). Besides 4-hydroxybenzoic acid, pneumonitis patients showed elevated levels of creatine, the creatine derivative guanidinopropionic acid, the amino acid derivative methylhistidine, and the polyamine derivative methoxytyramine (Figure 8D, Figure 18B). These changes likely reflect the marked differences in species composition observed for pneumonitis.

[0151] While it remains to be determined whether these metabolites contribute to the development of irAEs after treatment, contextualizing them provides some relevant insights for hepatitis and IMDC. The only metabolite associated with hepatitis, serotonin, is important for liver regeneration after injury. For IMDC, the bacterial metabolite ILA mediated activation of the aryl hydrocarbon receptor (AhR) has been shown to reduce intestinal inflammation and prevent colitis in rodent models. In addition, elevated fecal levels of xanthine may suggest elevated activity of xanthine oxidase enzyme which may be important in some patients with IBD. Finally, alpha-aminobutyric acid was reported to constrain macrophage-associated colitis in a rodent model. Shifting the focus from microbiome-driven omics layers to those primarily influenced by the host, it was observed that omics features from both the plasma metabolome (Figure 8F) and plasma cytokines (Figure 8G) were also irAE-specific. Fifteen plasma metabolome features were associated with IMDC (negative mode plasma, q<0.2). As none of these metabolome features have a highly likely annotation based on retention time and accurate mass, all putative annotations were considered for metabolome features nominally associated with irAE groups (p<0.01) and pathway enrichment was performed to get a more global view of plasma metabolome associations (Figure 16D). This showed plasma fatty acids and conjugates and bilirubin pathways associated with IMDC, glycerophosphocholines and piperazines pathways associated to hepatitis, and bile acids, alcohols and derivatives and indolines pathways associated with pneumonitis. Again, these enriched pathways were nonoverlapping between the irAEs.

[0152] Four plasma cytokines, transforming growth factor (TGF)-alpha, Latency-associated peptide TGF beta-1 (LAP TGF-beta-1), CCL19, and VEGFA were elevated in the samples of patients that later developed hepatitis, all of which are implicated in the pathophysiology of liver disorders. Circulating TGF-alpha levels correlate with liver regeneration and LAP regulates TGF-beta-1 bioavailability which is important since TGF-beta-1 is responsible for regulating a range of physiological processes in the liver. In addition, CCL19 is involved in recruiting immune cells to the liver and circulating levels of the angiogenesis cytokine VEGFA is a marker of liver fibrosis.

[0153] Nine plasma cytokines, tumor necrosis factor (TNF-alpha), CXCL9, CXCL10, CD5, and interleukin 8 (IL8), IL-12B, CD6, CCL3, and CCL19 were elevated in the samples of patients that later developed IMDC. While TNF is the hallmark IBD cytokine with key therapeutics targeting this (and IL-12 / 23) pathways, IL8 is responsible for attracting neutrophils to sites of infection or injury and can influence IBD pathogenesis. Notably, TNF and IL8 were strongly correlated in this dataset (Spearman rho 0.44, p<0.0001). Also acting as chemokines, CXCL9 / CXCL10 play a role in recruiting T cells to sites of inflammation and CD5 is a co-stimulatory molecule modulating T cell activation and survival. These associations are striking given their relevance in inflammatory bowel disease (IBD) and the fact that these patients did not have symptoms indicative of diarrhea or colitis at the time of sample collection.

[0154] The identification of distinct, non-overlapping pretreatment omics features associated with specific irAEs suggests that these irAEs have unique etiologies and should be regarded as distinct pathologies. This distinction underscores the limitation of combining irAEs in broader analyses, as this will dilute signals unique to each condition. The elevated levels of organ-specific cytokines in patients who develop hepatitis or IMDC suggest a potential immune predisposition to specific side effects. This predisposition may be influenced by the gut microbiome, which, upon immune checkpoint inhibitor (ICI) therapy, could trigger the expansion of specific autoreactive immune cells, ultimately leading to the development of specific irAEs. The significance and specificity of the identified omics features also raise the possibility of predicting which patients may develop a particular irAE based on pretreatment measurements.

[0155] The promise of multi-omics data to predict the occurrence of main adverse events

[0156] Given the specificity of the pretreatment multi omics features associated with irAEs, the predictive power of the multi-omics data in identifying patients likely to develop specific irAE was explored. To achieve this, a series of Random Forest models was developed to classify patients into four categories based on single omics layers: those likely to develop immune-mediated diarrhea and colitis (IMDC), hepatitis, pneumonitis, or no irAE (Figure 19, Figure 9A). Gut microbiome-related omics layers (pathways and taxonomy) were surprisingly predictive of pneumonitis, achieving high area under the curve (AUCs) with functional pathways (AUC 0.836, Figures 9A-9B) and even using phylum-level abundance data (AUC 0.824, Figure 9A). Plasma metabolome (combined positive and negative mode) and family level taxonomy showed the largest power to predict hepatitis (AUC 0.725 and 0.653, respectively), while genus level taxonomy and plasma cytokines showed the largest predictive power for IMDC (AUC 0.688 and 0.605, respectively) (Figure 9A). For these single-omics models the most predictive features were inspected using Boruta feature selection and compared them with omics features associated with the irAEs from conventional statistical analyses (Figure 20). The most informative omics features broadly fit with observations above where pneumonitis patients were seen to have the most dramatic difference in their gut microbiome (e.g. members of the Bacteroides and Streptococcus genus overlaps with conventional statistics and Baruta features), hepatitis had a strong association with species from specific genera (Dorea), and IMDC is associated with omics features from diverse omics layers (overlap between conventional statistics and Baruta features particularly seen for plasma cytokines) (Figure 8).

[0157] It was asked whether combining omics layers would increase classification accuracy since there may be non-linear combinations of omics features that are predictive of irAEs. Before combining omics layers, the number of features was further reduced but still included 1888 combined features from data layers genus level taxonomy, functional pathways, stool metabolome, plasma cytokines, and plasma metabolome. As more omics data features are included, more relevant and irrelevant features enter the model. In case signal density is low, the benefit of including more relevant features may be outweighed by the harm by including even more irrelevant features (which represent noise). Thus, when the sample size is limited and the signal density is low, the multi-omics model may not perform better than the most predictive single-omics model. Indeed, this is what was observed since the AUCs of the full multi-omics model were drastically reduced compared to the single omics classifiers (AUCs 0.587, 0.563, 0.687 for IMDC, hepatitis, and pneumonitis, respectively, Figure 9C).

[0158] To balance the number of included features while still employing a multi-omics predictor either the most predictive microbial data layers (genus level taxonomy and functional pathways), or the most predictive host data layers (plasma cytokines and plasma metabolome) was selected and included these in multi-omics predictive models (Figures 9D- 9E). Splitting microbial and host data layers revealed that IMDC and pneumonitis were reasonably well predicted just from microbial omics data (AUC 0.741 and 0.853, respectively) while hepatitis was not (AUC 0.585) (Figure 9D). In contrast, compared to IMDC and hepatitis, pneumonitis had the worst predictive performance using host plasma omics data layers (AUC 0.624, 0.603, 0.571, respectively) (Figure 9E).

[0159] Overall, despite the limited number of cases, these results demonstrate the value of multi-omics modeling as a strategy to identify the most relevant omics data layers or features. This approach allows for the refinement of predictive models by narrowing down to a minimal collection of highly predictive data. Such a method could enhance clinical applicability by reducing the need for comprehensive multi-omics profiling in all patients while maximizing predictive power.

[0160] Microbial pathways associated to orotic acid levels

[0161] If proven to be mechanistically relevant, metabolites present in stool at lower levels represent potential therapeutic targets as increasing their levels may be achieved through supplementation. It was intriguing that the stool metabolite orotic acid was the only one present at lower levels in both IMDC and pneumonitis. Orotic acid was also the stool metabolite with the largest fold-change association with IMDC. In addition, despite a long history of study orotic acid had not been previously implicated in signaling or immune mechanisms in the gut. It as therefore decided to focus on orotic acid as a potential novel therapeutic target to prevent occurrence of IMDC and / or pneumonitis. Orotic acid, which may be derived from dietary sources, serves as an intermediate in the mitochondrial de novo pyrimidine biosynthesis pathway and contributes to pyrimidine salvage pathways. Before investigating the potential biological effects of orotic acid, the different microbial omics layers (taxonomy, functional metagenomics, and metabolomics) were examined to determine whether changes in stool orotic acid levels were supported by converging signals across different omics.

[0162] To do this, functional pathways nominally associated with IMDC were inspected. Notably, among the 13 nominally associated pathways (p<0.05) 3 were related to pyrimidine deoxyribonucleotide biosynthesis (PWY-6545, PWY-7184, PWY-7210). One of these 3 pyrimidine pathways was among the two most predictive in the Random Forest to classify IMDC (Baruta feature selection, Figure 20). the abundance of the species associated with IMDC (Figure 8B) with these pathways was contextualized. It was found that Lacticaseibacillus rhamnosus, a lactic acid bacterium that is elevated in IMDC patients (Figure 8B) was responsible for higher levels of 2 of pyrimidine these 3 pathways in some patients (PWY-7184 and PWY-7210); thus linking the species-level taxonomy with the functional metagenomics data. When exploring the functional metagenomics at the level of individual enzymes related to pyrimidine metabolism using enzyme commission (EC)-number level annotations (Figure 9A), it was observed that the abundance of two of these pyrimidine metabolism genes in stool, orotate phosphoribosyltransferase (OPRT) (EC 2.4.2.10) and dihydroorotase (DHOase, EC 3.5.2.3) were also explained by species that were elevated in IMDC. Specifically, OPRT abundance from the species Terri sporobacter sp900557165, Adlercreutzia equolifaciens, and Faecalibacterium sp900539945. and the DHOase gene from Lb. rhamnosus are elevated between no IMDC and IMDC (all p<0.02).

[0163] Stool metabolomics data were next incorporated to investigate whether abundance of these pyrimidine metabolism genes in stool was correlated to stool orotic acid concentrations. Doing this, it was found that the OPRT and DHOase from species Clostridia AQ sp003481775, which is again elevated in IMDC, was negatively correlated to orotic acid (Spearman rho -0.23 and -0.25, p<0.01). This suggested that some of the specific taxonomic alterations seen in the pre-irAE gut microbiome of patients who later develop IMDC were linked to an enhanced capacity to metabolize orotic acid.

[0164] Correlation based multi-omics network integration focused on orotic acid

[0165] To investigate the potential biological effects of orotic acid in prevention of IMDC and or pneumonitis, canonical correlation analyses were performed across all microbial and host omics features, filtering for associations with orotic acid. As this was hypothesisgenerating, the correlation matrices were refined to retain an interpretable number of associations for each omics layer and plasma metabolome data was excluded from this analysis.

[0166] This led to an orotic acid-centered network with 5 plasma immune proteins, 5 pathways, and 19 species (Figure 10B). First focusing on plasma immune proteins, orotic acid was negatively correlated with TRANCE (TNF -Related Activation-Induced Cytokine also known as RANKL), MCP-2 (CCL8), SIRT2, STAMBP, and IL-12B. Circulatory levels of TRANCE, MCP-2, IL-12B are elevated in patients with IBD, and STAMBP and Sirt2 have preclinical evidence as being involved in inflammasome regulation and colitis, respectively. TRANCE is particularly interesting since it is involved in regulation of CD4+Foxp3+ regulatory T cells which are important in IBD and ICI colitis. Of these, IL-12B is also elevated patients that later develop IMDC (Figure 10G). One functional pathway, PWYO-1261 : anhydromuropeptides recycling I, was negatively associated to orotic acid as well as IL-12B. This is interesting as reduced recycling of 1,6-anhydro-N-acetylmuramyl peptides from bacterial cell walls leads to the accumulation of these fragments, which in turn stimulates IL- 12 production via the TLR2 / 4 and MyD88 pathways. Two pathways related to thiamine metabolism are positively associated to orotic acid. Interestingly, correlation coefficients between orotic acid and species are mostly positive and are with species considered beneficial to the host such as Anaerobutyricum hallii, Faecalibacterium sp900772565, Dysosmobacter sp001916835, Ruminococcus E sp003526955, and Agathobacter faecis. The associations between orotic acid and inflammation-related cytokines suggest that orotic acid may play a direct role in modulating immune pathways, potentially influencing the development of IMDC.

[0167] Orotic acid stimulates regulatory T cells development

[0168] Given the above a role for immune regulation relevant for IMDC was assessed. Amongst different pathways studied, a recent study in wild rodents demonstrated that IMDC development was driven by the depletion of peripherally induced regulatory T (Treg) cells (Lo et al., Science, 383(6678):62-70 (2024)). It was hypothesized that orotic acid could promote Treg development necessary for restraining inflammatory immune responses. To test this, in vitro studies were performed using human naive CD4+ T cells isolated from peripheral blood mononuclear cell (PBMC) donors. In the presence of T cell receptor (TCR) and CD28 ligation under Thl polarizing conditions, these naive CD4+ T cells were exposed to physiologically relevant concentrations of orotic acid (25-400 pM) to assess Treg induction capacity via fluorescent-activated cell sorting (FACS). The protein expression of FOXP3, the lineage-defining transcription factor critical driving Treg development and transcriptional program, and cluster of differentiation 25 (CD25), a distinct marker of immunosuppressive Tregs, were measured. At low concentrations (25 pM), close to those found in patients who later developed IMDC, FOXP3 expression and FOXP3 expression within CD25+ T cells was significantly lower compared to 100 pM orotic acid, which reflects levels observed in patients who did not develop IMDC (Figures 1 IB-11C). Increasing the concentration to 400 pM did not further enhance Treg induction, suggesting a dose-response effect with plateau. This suggested that orotic acid identified through pretreatment stool metabolomics and affected by gut microbiome composition may contribute to the induction of Tregs following ICI treatment thereby reducing the likelihood of developing IMDC.

[0169] Reduced levels of fecal orotic acid are linked to ulcerative colitis in independent studies

[0170] Since IMDC closely resembles the ulcerative colitis (UC) form of IBD, including its response to drug therapy, it was next investigated whether stool metabolites associated with IMDC development might also play a role in UC. To explore this, two large IBD cohorts with available metabolomics data were analyzed: the iHMP and PRISM studies, both curated and accessible through the microbiome-metabolome dataset collection. These studies include untargeted metabolomics profiling of stool samples from non-IBD controls, Crohn’s disease (CD), and ulcerative colitis (UC) patients.

[0171] The five stool metabolites present at lower levels in patients that later developed IMDC; orotic acid, cytosine, xanthine, indolelactic acid, and alpha-aminobutyric acid were focused on (Figures 8C-8D). Whether metabolite features from the iHMP and PRISM datasets contained annotations for these metabolites was inspected. Both studies contained annotations for orotic acid, xanthine, cytosine, and aminobutyric acid but neither study contained annotations for indolelactic acid. Cytosine and aminobutyric were not significantly reduced in patients with IBD. In contrast, xanthine had lower levels in both studies with significance for both IBD subtypes in the PRISM dataset but muted significance only for UC in the iHMP dataset (Figure 21). Orotic acid was highly significantly decreased (p<0.0001) in UC compared to non-IBD controls in both datasets (Figure 12). This supported its potential protective role in the development of IMDC.

[0172] Example 3: Assessing cancer for responsiveness to treatment with one or more ICIs

[0173] A stool sample and a plasma sample are obtained from a human having cancer. The obtained stool sample is examined for the presence or absence of (a) one or more microbes and / or (b) one or more metabolites (e.g., fecal metabolites). The obtained plasma sample is examined for the present or absence of (c) one or more polypeptides (e.g., one or more cytokines).

[0174] If the stool sample includes one or more of a Collinsella species (e.g., C. sp900555355), a UBA5416 species (e.g., UBA5416 sp900539175), a Lachnospira species (e.g., L. rogosae), a Ruminococcus species (e.g., R. C sp000433635 and / ?. E sp003526955), and a Parabacteroides species (e.g., P goldsteinii) then the cancer is classified as being responsive to one or more immune checkpoint inhibitors.

[0175] If the stool sample includes (a) one or more of a Blautia species (e.g., B. sp900066145), a Bacteroides species (e.g., B. intestinalis A), aAkkermansia species (e.g., A. sp004167603), a CAG-83 species (e.g., CAG-83 sp900547745), Parabacteroides species (e.g., P. goldsteinii)', a CAG-103 species (e.g., CAG-103 sp900543625), and ^Alistipes species (e.g., A. ihumii), and / or (b) one or more of orotic acid, indolelactic acid, cer(dl 8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil, then the mammal is classified as not being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors.

[0176] If the stool sample includes (a) one or more of Muricomes species (e.g., M. oroticus), a Clostridium species (e.g., C. sp003481775 and C. innocuurri), a Terrisporobacter species (e.g., T. sp900557165), a Lacticaseibacillus species (e.g., L. rhamnosus , a Anaerobutyricum species (e.g., A. sp900016875), and a Blautia species (e g., B. sp001304935) in a stool sample obtained from the mammal, and / or (b) one or more of succinate, indole-3 -propionic acid, malonic acid, 4-hydroxybenzoic acid, 4- hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid, and / or the plasma sample includes (c) one or more of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide, then the mammal is classified as being likely to develop one or more irAEs in response to one or more immune checkpoint inhibitors.

[0177] Example 4: Treating cancer

[0178] A stool sample and a plasma sample are obtained from a human having cancer. The obtained stool sample is examined for the presence or absence of (a) one or more microbes and / or (b) one or more metabolites (e.g., fecal metabolites). The obtained plasma sample is examined for the present or absence of (c) one or more polypeptides (e.g., one or more cytokines).

[0179] If the stool sample includes one or more of a Collinsella species (e.g., C. sp900555355), a UBA5416 species (e.g., UBA5416 sp900539175), a Lachnospira species (e.g., L. rogosae), a Ruminococcus species (e.g., R. C sp000433635 and / ?. E sp003526955), and a Parabacteroides species (e.g., P goldsteinii), then the human is administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) immune checkpoint inhibitors.

[0180] If the stool sample includes (a) one or more of aBlautia species (e.g., B. sp900066145), a Bacteroides species (e.g., B. intestinalis A), a Akkermansia species (e.g., A. sp004167605), a CAG-83 species (e.g., CAG-83 sp900547745), a Parabacteroides species (e.g., P. goldsteinii), a CAG-103 species (e.g., CAG-103 sp900543625), and aAlistipes species (e.g., A. ihumii), and / or (b) one or more of orotic acid, indolelactic acid, cer(dl 8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil, then the human is administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) immune checkpoint inhibitors.

[0181] Once administered to the human, the one or more immune checkpoint inhibitors can reduce the number of cancer cells present in the human.

[0182] Example 5: Treating cancer

[0183] A stool sample and a plasma sample are obtained from a human having cancer. The obtained stool sample is examined for the presence or absence of (a) one or more microbes and / or (b) one or more metabolites (e.g., fecal metabolites). The obtained plasma sample is examined for the present or absence of (c) one or more polypeptides (e.g., one or more cytokines).

[0184] If the stool sample includes (a) one or more of aMuricomes species (e.g., M. oroticus), a Clostridium species (e.g., C. sp003481775 and C. innocuum), a Terri sporobacter species (e.g., T. sp900557165), a Lacticaseibacillus species (e.g., T. rhamnosus), a Anaerobutyricum species (e.g., A. sp900016875), and a Blautia species (e.g., B. sp001304935 and / or (b) one or more of succinate, indole-3 -propionic acid, malonic acid, 4- hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid, and / or the plasma sample includes (c) one or more of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide, then the human is administered one or more (e.g., one, two, three, four, five, or more) alternative cancer treatments (e.g., one or more cancer treatments that do not include any immune checkpoint inhibitors).

[0185] The alternative cancer treatment can reduce the number of cancer cells present in the human.

[0186] OTHER EMBODIMENTS

[0187] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method for assessing a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Lachnospira rogosa , a Ruminococcus C sp000433635, Ruminococcus E sp003526955, and Parabacteroides goldsteinii,' and(b) classifying said cancer as being likely to respond to an immune checkpoint inhibitor.

2. The method of claim 1, wherein said mammal is a human.

3. The method of any one of claims 1-2, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

4. The method of any one of claims 1-2 wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

5. A method for assessing a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting o Blautia sp900066145, Bacteroides intestinalis A, Akkermansia sp004167605 , CAG-83 sp900547745, Parabacteroides goldsteinii, CAG- 103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(d 18:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil; and(b) classifying said mammal as not being likely to develop an immune-related adverse event in response to an immune checkpoint inhibitor.

6. The method of claim 5, wherein said mammal is a human.

7. The method of any one of claims 5-6, wherein said immune-related adverse event is immune mediated diarrhea or colitis (IMDC).

8. The method of any one of claims 5-7, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

9. The method of any one of claims 5-7 wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

10. A method for assessing a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Muricomes oroticus, Clostridium sp003481775 Clostridium innocuum, Terrisporobacler sp900557165, Laclicaseibacillus rhamnosus, Anaerobutyricum sp900016875, and Blautia spOO 1304935 , or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or determining that a plasma sample obtained from said mammal comprises a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide; and(b) classifying said mammal as being likely to develop an immune-related adverse event in response to an immune checkpoint inhibitor.

11. The method of claim 10, wherein said mammal is a human.

12. The method of any one of claims 10-11, wherein said immune-related adverse event is IMDC.

13. The method of any one of claims 10-12, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

14. The method of any one of claims 10-12, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

15. A method for selecting a treatment for a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Lachnospira rogosae , a Ruminococcus C sp000433635 Ruminococcus E sp003526955, and Parabacteroides goldsteinir, and(b) selecting an immune checkpoint inhibitor as a treatment for said cancer.

16. The method of claim 15, wherein said mammal is a human.

17. The method of any one of claims 15-16, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

18. The method of any one of claims 15-16, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

19. A method for selecting a treatment for a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting o Blautia sp900066145 Bacteroides intestinalis A, Akkermansia sp004167605 , CAG-83 sp900547745, Parabacteroides goldsteinii, CAG- 103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the groupconsisting of orotic acid, indolelactic acid, cer(d 18:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil; and(b) selecting an immune checkpoint inhibitor as a treatment for said cancer.

20. The method of claim 19, wherein said mammal is a human.

21. The method of any one of claims 19-20, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

22. The method of any one of claims 19-20, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

23. A method for selecting a treatment for a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Mur iconics oroticus, Clostridium sp003481775, Clostridium innocuum, Terrisporobacter sp900557165, Lacticaseibacillus rhamnosus, Anaerobutyricum sp900016875 , and Blautia spOO 1304935 , or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or determining that a plasma sample obtained from said mammal comprises a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a LL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide; and(b) selecting a cancer treatment other than an immune checkpoint inhibitor as a treatment for said cancer.

24. The method of claim 23, wherein said mammal is a human.

25. The method of any one of claims 23-24, wherein said cancer treatment comprises performing surgery.

26. The method of any one of claims 23-24, wherein said cancer treatment comprises radiation therapy.

27. The method of any one of claims 23-24, wherein said cancer treatment comprises administering, to said mammal, an anti-cancer agent.

28. A method for treating for a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Lachnospira rogosae , a Ruminococcus C sp000433635, Ruminococcus E sp003526955, and P ar abacter aides goldsteinir,(b) administering an immune checkpoint inhibitor to said mammal.

29. The method of claim 28, wherein said mammal is a human.

30. The method of any one of claims 28-29, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

31. The method of any one of claims 28-29, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

32. A method for treating cancer, wherein said method comprises administering an immune checkpoint inhibitor to a mammal identified as having a stool sample comprising a microbe selected from the group consisting of Collinsella sp900555355, UBA5416 sp900539175, Lachnospira rogosae, a Ruminococcus C sp000433635, RuminococcusE sp003526955, and Parabacteroides goldsteimi, thereby treating cancer within said mammal.

33. The method of claim 32, wherein said mammal is a human.

34. The method of any one of claims 32-33, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti -LAG-3 antibody.

35. The method of any one of claims 32-33, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

36. A method for treating for a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting oiBlautia sp900066145, Bacteroides intestinalis A, Akkermansia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, CAG- 103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(d 18:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil;(b) administering an immune checkpoint inhibitor to said mammal.

37. The method of claim 36, wherein said mammal is a human.

38. The method of any one of claims 36-37, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

39. The method of any one of claims 36-37, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

40. A method for treating cancer, wherein said method comprises administering an immune checkpoint inhibitor to a mammal identified as having a stool sample comprising (i) a microbe selected from the group consisting o Blautia sp900066145, Bacteroides intestinalis A, Akkermcmsia sp004167605, CAG-83 sp900547745, Parabacteroides goldsteinii, CAG-103 sp900543625, and Alistipes ihumii, or (ii) a fecal metabolite selected from the group consisting of orotic acid, indolelactic acid, cer(dl8:0 / 24:0), 3 -hydroxybutyric acid, xanthine, and uracil, thereby treating cancer within said mammal.

41. The method of claim 40, wherein said mammal is a human.

42. The method of any one of claims 40-41, wherein said immune checkpoint inhibitor is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti- CTL4A antibody, and an anti-LAG-3 antibody.

43. The method of any one of claims 40-41, wherein said immune checkpoint inhibitor is nivolumab or ipilimumab.

44. A method for treating a mammal having cancer, wherein said method comprises:(a) determining that a stool sample obtained from said mammal comprises (i) a microbe selected from the group consisting of Muricomes oroticus, Clostridium sp003481775, Clostridium innocuum. Terrisporobacler sp900557165, Lacticaseibacillus rhamnosus. Anaerobutyricum sp900016875 , and Blautia spOOl 304935, or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2-methylbutyric acid; or determining that a plasma sample obtained from said mammal comprises a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide; and(b) administering a cancer treatment to said mammal, wherein said cancer treatment is not an immune checkpoint inhibitor.

45. The method of claim 44, wherein said mammal is a human.

46. The method of any one of claims 44-45, wherein said cancer treatment comprises performing surgery.

47. The method of any one of claims 44-45, wherein said cancer treatment comprises radiation therapy.

48. The method of any one of claims 44-45, wherein said cancer treatment comprises administering, to said mammal, an anti-cancer agent.

49. A method for treating cancer, wherein said method comprises administering a cancer treatment that is not an immune checkpoint inhibitor to a mammal identified as having a stool sample comprising (i) a microbe selected from the group consisting of Muricomes oroticus, Clostridium sp003481775, Clostridium innocuum, Terrisporobacter sp900557165, Lacticaseibacillus rhamnosus, Anaerobutyricum sp900016875, and Blautia sp001304935, or (ii) a fecal metabolite selected from the group consisting of succinate, indole-3-propionic acid, malonic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylacetic acid, and 2-hydroxy-2- methylbutyric acid; or as that a plasma sample comprising a cytokine selected from the group consisting of a TNF polypeptide, a CXCL9 polypeptide, a IL-12B polypeptide, a CD6 polypeptide, a IL8 polypeptide, a CD5 polypeptide, a CXCL10 polypeptide, and a TNFRSF9 polypeptide.

50. The method of claim 49, wherein said mammal is a human.

51. The method of any one of claims 49-50, wherein said cancer treatment comprises performing surgery.

52. The method of any one of claims 49-50, wherein said cancer treatment comprises radiation therapy.

53. The method of any one of claims 49-50, wherein said cancer treatment comprises administering, to said mammal, an anti-cancer agent.

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