WO2025240901A1 - Méthodes et matériels pour évaluer et traiter des cancers - Google Patents

Méthodes et matériels pour évaluer et traiter des cancers

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Publication number
WO2025240901A1
WO2025240901A1 PCT/US2025/029827 US2025029827W WO2025240901A1 WO 2025240901 A1 WO2025240901 A1 WO 2025240901A1 US 2025029827 W US2025029827 W US 2025029827W WO 2025240901 A1 WO2025240901 A1 WO 2025240901A1
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WIPO (PCT)
Prior art keywords
mammal
cancer
immune checkpoint
polypeptide
antibody
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2025/029827
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English (en)
Inventor
Ruben Albertus Teunis Mars
Purna C. KASHYAP
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mayo Foundation for Medical Education and Research
Mayo Clinic in Florida
Original Assignee
Mayo Foundation for Medical Education and Research
Mayo Clinic in Florida
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Publication of WO2025240901A1 publication Critical patent/WO2025240901A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911—Bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • This document relates to methods and materials involved in assessing and/or treating a mammal (e.g., a human) having cancer.
  • a mammal e.g., a human
  • methods and materials provided herein can be used to identify a cancer as being likely to respond to one or more immune checkpoint inhibitors.
  • 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)).
  • irAEs immune mediated diarrhea or colitis
  • 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).
  • a mammal e.g., a human
  • cancer e., lung cancer such as mesothelioma
  • irAEs e g., IMDC
  • ICI immune checkpoint inhibitors
  • 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).
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • one or more polypeptides e.g., one or more cytokines
  • the presence or absence of one or more microbes in a sample 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).
  • 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.
  • 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.
  • 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.
  • 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 goldstein
  • 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.
  • one or more alternative cancer treatments e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor
  • the presence or absence of one or more 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).
  • a sample e.g., a stool sample
  • a mammal e.g., a human
  • 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.
  • 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
  • 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.
  • one or more alternative cancer treatments e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor
  • the presence or absence of one or more polypeptides 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).
  • a sample e.g., a blood sample
  • a mammal e.g., a human
  • a mammal 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.
  • a blood sample e.g., a plasma sample
  • 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.
  • a mammal e.g., a human
  • 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)
  • a mammal e.g., a human
  • 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.
  • 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
  • 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.
  • a 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.
  • 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.
  • a microbe selected from the group consisting of Blautia sp9000
  • 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.
  • 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 polypeptid
  • 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.
  • this document features methods for selecting a treatment for a mammal having cancer
  • 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.
  • this document features methods for selecting a treatment for a mammal having cancer
  • 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.
  • this document features methods for selecting a treatment for a mammal having cancer
  • 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 C
  • 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.
  • 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.
  • 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.
  • a microbe selected from the group consisting of Blautia sp900066145, Bacteroides intestinalis A, Akker
  • 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.
  • 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.
  • a microbe selected from the group consisting of Blautia sp900066145, Bacteroides intestinalis A, Akkermansia sp0041
  • 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.
  • 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 polypeptid
  • 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 polypeptid
  • 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).
  • FIG. 1 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).
  • 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.
  • Figures 7A-7C Description and data collected on ICI cohort.
  • 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.
  • 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.
  • 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).
  • 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).
  • 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.
  • Orotic acid may be a mechanistic metabolite in ulcerative colitis.
  • Figure 14 Days difference between samples and ICI start and or irAE onset.
  • FIG. 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.
  • LBP intestinal barrier function lipopolysaccharide binding protein
  • 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.
  • FIGS 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.
  • 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).
  • 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).
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • one or more polypeptides e.g., one or more cytokines
  • Any appropriate mammal having cancer can be assessed and/or treated as described herein.
  • 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.
  • a human having cancer can be assessed and/or treated as described herein.
  • a human having cancer that has not received any previous cancer treatment e.g., has not received any previous immune checkpoint inhibitor
  • the cancer can be any type of cancer.
  • a cancer assessed and/or treated as described herein can include one or more solid tumors.
  • a cancer assessed and/or treated as described herein can be a blood cancer.
  • a cancer assessed and/or treated as described herein can be a primary cancer.
  • a cancer assessed and/or treated as described herein can be a metastatic cancer.
  • a cancer assessed and/or treated as described herein can be a refractory cancer.
  • a cancer assessed and/or treated as described herein can be a relapsed cancer.
  • 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.
  • 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.
  • 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).
  • a sample can be a biological sample.
  • a sample can contain one or more microbes (e.g., bacteria, viruses, and fungi).
  • 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).
  • 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).
  • one or more microbes and/or one or more biological molecules can be isolated from a sample.
  • bacteria and/or metabolites can be isolated from a stool sample and can be assessed as described herein.
  • polypeptides can be isolated from a blood sample (e.g., a plasma sample) and can be assessed as described herein.
  • 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.
  • a sample obtained from a mammal having cancer can be assessed for the presence or absence of one or more microbes.
  • 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.
  • 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.
  • 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).
  • 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.
  • A. sp004167605 Akkermansia species
  • CAG-83 species e.g., CAG-83 sp900547745
  • CAG-103 species e.g., CAG- 103 sp900543625
  • Alistipes species e
  • 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.
  • 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.
  • 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 include, without limitation, gram stains.
  • 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).
  • a sample obtained from a mammal having cancer can be assessed for the presence or absence of one or more metabolites.
  • 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.
  • 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.
  • 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.
  • metabolites that can be used to assess a mammal having cancer as described herein can be as described in Example 1 .
  • 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.
  • a stool sample obtained from a mammal e.g., a human
  • a level of orotic acid that is greater than about 0.1 pM of metabolite per gram stool (pM/g)
  • pM/g 0.1 pM of metabolite per gram stool
  • a stool sample obtained from a mammal e.g., a human
  • 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.
  • a stool sample obtained from a mammal e.g., a human
  • 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.
  • a stool sample obtained from a mammal e.g., a human
  • 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.
  • 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 include, without limitation, chromatography, mass spectrometry, and NMR.
  • a sample e.g., a blood sample such as a plasma sample
  • a mammal e.g., a human
  • a sample obtained from a mammal having cancer can be assessed for the presence or absence of any appropriate number of polypeptides.
  • a sample obtained from a mammal having cancer can be assessed for the presence or absence of one or more polypeptides.
  • 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.
  • a sample e.g., a blood sample such as a plasma sample
  • a mammal e.g., a human
  • a polypeptide that can be used to assess a mammal having cancer as described herein can be any type of polypeptides.
  • a polypeptide that can be used to assess a mammal having cancer as described herein can be a cytokine.
  • polypeptides that can be used to assess a mammal having cancer as described herein can be as described in Example 1.
  • 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.
  • 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 include, without limitation, mass spectrometry.
  • 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.
  • a mammal e.g., a human
  • 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.
  • 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
  • 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.
  • 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
  • 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.
  • a blood sample e.g., a plasma sample
  • a Blautia species e.g., B. sp900066145
  • Bacteroides species e.g., B. intestinalis A
  • Akkermansia species e.g., A. sp004167605
  • CAG-83 species e.g., CAG-83 sp900547745
  • Parabacteroides species e.g., P. goldsteinii
  • CAG-103 species e.g., CAG-103 sp900543625
  • 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.
  • An irAE can be any appropriate irAE.
  • irAEs can be colitis (e.g., IMDC, also sometimes referred to as ICI colitis), diarrhea, abdominal pain, abdominal distension, cramping, nausea, and vomiting.
  • 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.
  • colitis e.g.,
  • 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
  • a Parabacteroides species e.g., P goldsteinii
  • a stool sample obtained from the mammal can be selected to receive one or more immune checkpoint inhibitors.
  • 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.
  • a Blautia species e.g., B. sp900066145
  • Bacteroides species e.g., B. intestinalis A
  • a Akkermansia species e.g., A. sp004167605
  • CAG-83 species
  • 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.
  • 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.
  • an alternative cancer treatment e.g., one or more cancer treatments that do not include an immune checkpoint inhibitor
  • 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.
  • aMuricomes species e.g., M. oroticus
  • Clostridium species e.g., C. sp003481775 and C. innocuum
  • a Terri sporobacter species e.g., T. sp900557165
  • a Lacticaseibacillus species
  • 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).
  • an alternative cancer treatment e.g., one or more cancer treatments that do not include any immune checkpoint inhibitors.
  • a mammal e.g., a human having cancer
  • 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.
  • one or more cancer treatments are effective to treat the cancer within the mammal.
  • a mammal e.g., a human
  • a mammal 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.
  • a mammal e.g., a human
  • the mammal can be administered or instructed to self-administer one or more immune checkpoint inhibitors.
  • 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.
  • a Collinsella species e.g., C. sp900555355 ⁇
  • UBA5416 species e.g., UBA5416 sp900539175 ⁇
  • Lachnospira species e.g., /.. rogosae
  • a Ruminococcus species e.g., R. C sp000433635 and R.
  • E spOO3526955 E spOO3526955
  • a Parabacteroides species e.g., P goldsteinii
  • 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.
  • 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.
  • a Blautia species e.g., B. sp900066145
  • Bacteroides species e.g., B. intestinalis A
  • a Akkermansia species e.g., A. sp004167605
  • CAG-83 species
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • miRNAs that can target (e.g., are
  • 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.
  • 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
  • Table 1 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.
  • 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).
  • a mammal e.g., a human
  • a mammal 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).
  • one or more e.g., one, two, three, four, five, or more
  • alternative cancer treatments
  • 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.
  • 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
  • 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).
  • a blood sample e.g., a plasma sample
  • one or more (e.g., one, two, three, four, five, or more) alternative cancer treatments 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.
  • a mammal e.g., a human
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • the treatment when treating a mammal (e.g., a human) having cancer as described herein, the treatment can be effective to treat the cancer.
  • the number of cancer cells present within a mammal can be reduced using the methods and materials described herein.
  • the size (e.g., volume) of one or more tumors present within a mammal can be reduced using the methods and materials described herein.
  • 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.
  • 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.
  • the treatment when treating a mammal (e.g., a human) having cancer as described herein, the treatment can be effective to improve survival of the mammal.
  • the methods and materials described herein can be used to improve disease-free survival (e.g., relapse-free survival).
  • the methods and materials described herein can be used to improve progression-free survival.
  • 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.
  • 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).
  • 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.
  • 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).
  • an effective dose of one or more immune checkpoint inhibitors can be a flat dose.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • an effective duration can vary with the frequency of administration, effective amount, use of multiple treatment agents, and/or route of administration.
  • Example 1 Microbial Pathways Linked to 1C1 Efficacy and irAEs
  • This Example describes the identification of common pre-treatment microbiome- driven pathways that contribute to both ICI efficacy and irAEs.
  • 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.
  • 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.
  • 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.
  • 6 immune markers e.g. IL8, TNF
  • two metabolites e.g. succinate
  • 1 immune marker and 31 metabolites e.g. orotic and indolelactic acid
  • Example 2 Distinct multi-omics pathways underlie organ-specific immune-related adverse events of checkpoint blockade therapy across cancer types
  • PBMC Peripheral Blood Mononuclear Cells
  • 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.
  • EHR electronic health record
  • 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.
  • ALT aminotransferase
  • AST aspartate aminotransferase
  • 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).
  • 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.
  • Kraken2 v2.1.1
  • Bayesian Reestimation of Abundance after Classification with KrakEN Bracken
  • 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.
  • 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.
  • MRM multiple reaction monitoring
  • Untargeted LC-MS plasma metabolomics measurements 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- 13 C6 as internal standards. Samples were then vortexed and centrifuged at 21000 x g for one minute at room temperature (23°C).
  • MeCN Acetonitrile
  • 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.
  • 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 for 13 C 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)
  • 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).
  • 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).
  • 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.
  • BCD Bray-Curtis dissimilarity
  • BCDI Bray-Curtis dissimilarity
  • 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.
  • 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.
  • 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.
  • 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).
  • Boruta feature selection (R Boruta package version 8.0.0) was applied to the full dataset to identify the most predictive features.
  • 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).
  • the resulting correlations were visualized using the R igraph package after filtering to only include nodes with a degree of 2 or more.
  • PBMCs Peripheral blood mononuclear cells
  • 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.
  • FCS fetal calf serum
  • HEPES pH 7.4
  • Antibiotics penicillin and streptomycin
  • 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.
  • 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.
  • 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
  • EHR electronic health record
  • irAEs immune- related adverse events
  • treatment outcome after the first cycle of ICI therapy initiation objective response assessments per Response Evaluation Criteria in Solid Tumors; RECIST 1.1.
  • PPI proton pump inhibitor
  • 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.
  • IMDC IMDC
  • ALT aminotransferase
  • AST aspartate aminotransferase
  • 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
  • BCDI Bray-Curtis dissimilarity index
  • Distinct omics signatures are associated with specific irAEs
  • IMDC 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).
  • IAA tryptophan derivative indolelactic acid
  • purine base xanthine purine base xanthine
  • pyrimidine nucleotide base cytosine also known as homoalanine
  • Figures 8D-8E non-proteinogenic amino acid alpha-aminobutyric acid
  • TGF-alpha transforming growth factor
  • LAP TGF-beta-1 Latency-associated peptide TGF beta-1
  • CCL19 CCL19
  • VEGFA VEGFA
  • TNF-alpha tumor necrosis factor
  • CXCL9 tumor necrosis factor
  • CXCL10 CXCL10
  • CD5 interleukin 8
  • IL8 interleukin 8
  • CD6, CCL3, and CCL19 were elevated in the samples of patients that later developed IMDC.
  • TNF tumor necrosis factor
  • IL8 is responsible for attracting neutrophils to sites of infection or injury and can influence IBD pathogenesis.
  • TNF and IL8 were strongly correlated in this dataset (Spearman rho 0.44, p ⁇ 0.0001).
  • 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.
  • IBD inflammatory bowel disease
  • 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.
  • metabolites present in stool at lower levels represent potential therapeutic targets as increasing their levels may be achieved through supplementation. It was interesting 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.
  • 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.
  • TRANCE TNF -Related Activation-Induced Cytokine also known as RANKL
  • MCP-2 CCL8
  • SIRT2 SIRT2
  • STAMBP STAMBP
  • 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.
  • IL-12B is also elevated patients that later develop IMDC ( Figure 10G).
  • PWYO-1261 anhydromuropeptides recycling I
  • PWYO-1261 anhydromuropeptides recycling I
  • Two pathways related to thiamine metabolism are positively associated to orotic acid.
  • Orotic acid stimulates regulatory T cells development
  • Example 3 Assessing cancer for responsiveness to treatment with one or more ICIs
  • 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).
  • 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.
  • a Collinsella species e.g., C. sp900555355
  • UBA5416 species e.g., UBA5416 sp900539175
  • a Lachnospira species e.g., L. rogosae
  • a Ruminococcus species e.g., R. C sp000433635 and /?
  • 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.
  • a Blautia species e.g., B. sp900066145
  • Bacteroides species e.g., B. intestinalis A
  • aAkkermansia species e.g., A. sp004167603
  • CAG-83 species e.g., CAG-83
  • 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.
  • 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.
  • 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
  • the mammal 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.
  • 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).
  • 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.
  • a Collinsella species e.g., C. sp900555355
  • UBA5416 species e.g., UBA5416 sp900539175
  • a Lachnospira species e.g., L. rogosae
  • 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.
  • aBlautia species e.g., B. sp900066145
  • Bacteroides species e.g., B. intestinalis A
  • a Akkermansia species e.g., A. sp004167605
  • CAG-83 species e.g., CAG-83 s
  • 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.
  • one or more e.g., one, two, three, four, five, or more
  • the one or more immune checkpoint inhibitors can reduce the number of cancer cells present in the human.
  • 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).
  • 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.
  • 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. r
  • 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).
  • alternative cancer treatments e.g., one or more cancer treatments that do not include any immune checkpoint inhibitors.
  • the alternative cancer treatment can reduce the number of cancer cells present in the human.

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Abstract

Ce document concerne des méthodes et des matériels impliqués dans l'évaluation et/ou le traitement d'un mammifère (par exemple, un être humain) atteint d'un cancer. Par exemple, l'invention concerne des méthodes et des matériels qui peuvent être utilisés pour identifier un cancer comme étant susceptibles de répondre à un ou plusieurs inhibiteurs de points de contrôle immunitaires. Par exemple, l'invention concerne des méthodes et des matériels qui peuvent être utilisés pour identifier un mammifère atteint d'un cancer comme étant susceptible de développer un ou plusieurs événements indésirables liés à l'immunité (EILI). L'invention concerne également des méthodes de traitement d'un mammifère (par exemple, un être humain) atteint d'un cancer (par exemple, un cancer du poumon tel que le mésothéliome) dans lesquelles le traitement anticancéreux est sélectionné sur la base du fait que le cancer est susceptible de réagir ou non à un ou plusieurs inhibiteurs de point de contrôle immunitaire et/ou dans lesquelles le mammifère est susceptible de développer un ou plusieurs EILI.
PCT/US2025/029827 2024-05-17 2025-05-16 Méthodes et matériels pour évaluer et traiter des cancers Pending WO2025240901A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210346438A1 (en) * 2016-12-22 2021-11-11 Institut Gustave Roussy Microbiota composition, as a marker of responsiveness to anti-pd1/pd-l1/pd-l2 antibodies and use of microbial modulators for improving the efficacy of an anti-pd1/pd-l1/pd-l2 ab-based treatment
US20240148803A1 (en) * 2021-02-17 2024-05-09 Seres Therapeutics, Inc. Use of immunotherapy and microbiome modulation to treat cancer

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210346438A1 (en) * 2016-12-22 2021-11-11 Institut Gustave Roussy Microbiota composition, as a marker of responsiveness to anti-pd1/pd-l1/pd-l2 antibodies and use of microbial modulators for improving the efficacy of an anti-pd1/pd-l1/pd-l2 ab-based treatment
US20240148803A1 (en) * 2021-02-17 2024-05-09 Seres Therapeutics, Inc. Use of immunotherapy and microbiome modulation to treat cancer

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Title
DATABASE GTDB 29 January 2019 (2019-01-29), ANONYMOUS: "Collinsella genome assembly UMGS1816 - NCBI - NLM", XP093373760, Database accession no. GCA_900555355.1 *

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