Using tumor-associated microbiome to predict melanoma immunotherapy response

Abstract

This disclosure provides methods to determine whether a melanoma patient will be responsive to a therapy comprising a checkpoint inhibitor, as well as selecting a therapy for the melanoma patient.

Description

USING TUMOR-ASSOCIATED MICROBIOME TO PREDICT MELANOMA IMMUNOTHERAPY RESPONSECROSS-REFERENCE TO RELATED APPLICATION[00011 This application claims priority under 35 U.S.C. § 119(e) to U.S. provisional application number U.S. Serial No.: 63 / 637,128, filed April 22, 2024, the contents of which are incorporated herein by reference.BACKGROUND10002 [ Melanomas are characterized by the unregulated growth of melanocytes and yield significantly poorer prognoses compared to basal and squamous cell skin cancers, which are often not fatal [1], Yearly, it is estimated that 97,610 melanoma diagnoses are made in the United States, with 7,990 resulting in death [1],

[0003] For advanced melanomas, immunotherapies have become commonly implemented as adjuvant treatments to surgery. Melanomas are known to make use of innate immune checkpoints to evade the host immune system. As such, the use of specific immune checkpoint inhibitors (ICI) has been effective in reducing the risk of recurrence in these cancers. Programmed death 1 (PD-1) protein inhibitors and cytotoxic T-lymphocyte- associated antigen (CTLA-4) inhibitors are the ICIs most commonly used to treat melanomas [2], However, these therapies yield highly variable response rates in patients [3-5], Understanding the factors that contribute to this variability in treatment success is crucial to improving survival rates for advanced melanomas. Additionally, by precisely identifying which patients should receive immunotherapies, significant side-effects of ICIs in likely nonresponders can be avoided [6],SUMMARY OF THE DISCLOSURE

[0004] In 2023, it is estimated that 97,610 melanoma diagnoses were made in the United States, resulting in 7,990 deaths. For high-risk melanomas, immune checkpoint inhibitors are often administered as an adjuvant to surgery, though they yield variable response rates. The gut microbiome’s association to these treatments’ success has been demonstrated, largely through fecal transplant and probiotic trials. Recently, many genomic factors have beeninvestigated for their potential to influence immunotherapy response, including acquisition of specific mutations, genomic instability, presence of specific gene expression dysregulations, and potential regulation from extracellular vesicles [7-11], External factors, such as the human microbiome, have been demonstrated or hypothesized to affect immunotherapy response, as well [12-14],

[0005] The microbiome is known to be implicated in an array of human diseases, including inflammatory bowel disease, psoriasis, and diabetes [15, 16], Specifically, the gut microbiome is thought to exert immune modulatory effects through the release of microbial metabolites [17, 18], The gut microbiome has been recognized for its implications in colorectal cancers [19-22], though its effects beyond the gastrointestinal system are less understood. Preclinical mouse models have demonstrated the gut microbiome’s importance in immunotherapy response [23, 24], For melanomas, numerous studies have identified specific dysregulations of the gut microbiome in patients who were not responsive to ICIs [12-14], Studies have also investigated the gut microbiome for its ability to predict a patient’s response to ICIs, though they suffer from relatively poor accuracies [25, 26], The importance of the gut microbiome to immunotherapy response has been demonstrated in clinical trials through the use of fecal transplants [27, 28], Nonetheless, attempts to modulate the gut microbiome have shown varying rates of success in improving cancer therapies’ efficacy

[0029] ,

[0006] As shown in this disclosure, Applicant obtained sequencing data of 289 melanoma tumor tissue samples downloaded across three distinct studies. Intratumor microbial species abundance were derived from RNA or DNA sequencing data through direct alignment to microbial reference databases. Gene expression profiling was also performed. Microbial abundance values were used to construct models predicting patients’ responses to check-point blockade immunotherapies, which were then validated on an external dataset.

[0007] Two (2) and 14 microbial species were differentially abundant in anti-PD-1 responders and anti-CTLA-4 responders, respectively. Applicant identified significant correlations of these species to genes of the PD-1 and CTLA-4 signaling pathways. Among others, greater expression / abundance of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii and / or Streptococcus gordonii correlated with decreased expression of PD1and CTLA-4 (i.e., indicating a non-responder for checkpoint inhibitor therapy due to the low expression of checkpoint markers). Conversely, Applicant observed that a patient with low expression / abundance of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, or Streptococcus gordonii in the sample is a responder because they have high checkpoint expression. The correlation of microbial abundance to cytokine expression, immune cell infiltration, and immune pathway activity was also observed. With these taxa, Applicant developed two classifiers to predict a patient’s response to anti-PD-1 and anti- CTLA-4 therapies. When tested externally, these models successfully predicted a majority of responders and non-responders with sensitivities of 80.0% and 60.0% and specificities of 95.2% and 95.8%, respectively.

[0008] Thus, in some aspects, the disclosure provides a method for treating melanoma in a patient in need thereof, the method comprising, or consisting essentially of, or consisting of administering to the patient an effective amount of a therapy including a checkpoint inhibitor therapy, wherein the patient expresses a low expression level of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in a biological sample, e.g., a blood sample isolated from the patient.

[0009] In another aspect, the disclosure provides a method for treating melanoma in a patient in need thereof, the method comprising, or consisting essentially of, or consisting of administering to the patient an effective amount of a therapy including a checkpoint inhibitor therapy, wherein the patient expresses a high expression level of Mesomycoplasma hyopneumoniae in a biological sample, e.g., a blood sample isolated from the patient.

[0010] In some embodiments of the disclosed methods, the checkpoint inhibitor therapy comprises, or consists essentially of, or consists of one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLl therapy, or an anti-PD-L2 therapy. In other aspects, the melanoma is selected from Stage I, Stage II, Stage III or Stage IV melanoma. In a further aspect, the melanoma is advanced melanoma, optionally Stage IV melanoma. In further aspects, the disclosure provides a method, wherein the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy. In a further aspect, the checkpoint inhibitor therapy is administered after surgery or other cytoreductive therapy to remove the cancerous cells.(0011 ] In some aspects, the patient is a mammal, e.g., a rat, a mouse, a simian, a canine, a feline or a human patient.

[0012] This study revealed that checkpoint blockade response for melanomas could be predicted with microbe abundance. Applicant’s analysis demonstrates the potentially promising clinical utility of the intratumor microbiota in cancer treatment. Given their location in the tumor microenvironment and their ability to influence immune processes, intratumor microbes could play a critical role in modulating the therapeutic effects of immune checkpoint blockades.BRIEF DESCRIPTION OF THE FIGURES[00.13] FIGS. 1A - 1C: Cross-Study Normalization and Contamination Correction(FIG. 1A) PCoA plots of samples’ abundance (top) and expression (bottom) profiles before normalization. Points represent samples, graded by study. Farther proximity indicates a greater dissimilarity in the samples’ abundance or expression profiles. Axes are unitless and arbitrary. (FIG. IB) PCoA plots of samples’ abundance (top) and expression (bottom) profiles after normalization. Closer proximity indicates a greater similarity in the samples’ abundance or expression profiles. The studies show a considerably greater extent of overlap after MAD normalization and cumulative sum scaling (see Methods), suggesting a greater comparability for subsequent analyses. (FIG. 1C) Phylogenic tree and bar chart displaying the division of contaminant species by phylum or class. Contamination correction by total abundance was performed (see Methods). The majority of contaminant species identified were of the Proteobacteria phylum.

[0014] FIGS. 2A - 21: Differential Species Abundance by Immunotherapy Responsiveness and Administration Time (FIG. 2A) Volcano plot showing the test statistics and fold-changes in species’ abundances between the anti-PD-1 responsive and nonresponsive cohorts. Points represent species, colored by significance (p < 0.05). 12 species were differentially abundant. (FIG. 2B) Histograms showing the significance distribution (top) and fold-change distribution (bottom) of species between the anti-PD-1 responsive and nonresponsive cohorts. (FIG. 2C) Volcano plot showing the test statistics and fold-changes in species’ abundances between the anti-CTLA-4 responsive and nonresponsive cohorts. 14 species were differentially abundant. (FIG. 2D) Histograms showing thesignificance distribution (top) and fold-change distribution (bottom) of species between the anti-CTLA-4 responsive and nonresponsive cohorts. (FIG. 2E) Volcano plot showing the test statistics and fold-changes in species’ abundances between the anti-PD-1 pre- and posttreatment cohorts. 27 species were differentially abundant. (FIG. 2F) Histograms showing the significance distribution (top) and fold-change distribution (bottom) of species between the anti-PD-1 pre- and post-treatment cohorts. (FIG. 2G) Volcano plot showing the test statistics and fold-changes in species’ abundances between the anti-CTLA-4 pre- and posttreatment cohorts. 15 species were differentially abundant. (FIG. 2H) Histograms showing the significance distribution (top) and fold-change distribution (bottom) of species between the anti-CTLA-4 responsive and nonresponsive cohorts. (FIG. 21) Venn diagram showing the differentially abundant species common to the anti-PD-1 responsive and nonresponsive cohorts, the anti-CTLA-4 responsive and nonresponsive cohorts, the anti-PD-1 pre- and posttreatment cohorts, and the anti-CTLA-4 pre- and post-treatment cohorts. Notable overlap was observed between several of these cohorts.

[0015] FIGS. 3A - 3F: Species Correlations to Immunotherapy Response-Related Genes (FIG. 3A) Heatmap showing the correlation coefficients (R-values) between species’ abundances and immunotherapy response-related genes’ expressions. The species differentially abundant between the anti-PD-1 responsive and nonresponsive cohorts were analyzed for correlation to a panel of 37 immunotherapy response-related genes (see Methods). Numerous species show significant correlation to the expression of these genes, suggesting the species’ potential implications in their regulation. * (0.01 < p < 0.05), ** (0.001 < p < 0.01), *** (p < 0.001). (FIG. 3B) Heatmap showing the correlation coefficients between species’ abundances and immunotherapy response-related genes’ expressions. The species differentially abundant between the anti-CTLA-4 responsive and nonresponsive cohorts were analyzed for correlation to the 37 immunotherapy response-related gene panel. (FIG. 3C) Lollipop plot showing the correlation coefficients of each species-gene pair with respect to the fold-changes in species’ abundances between the anti-PD-1 responsive and nonresponsive cohorts. Positive correlations and negative fold-changes indicate lesser expression in responsive patients (blue), as do negative correlations and positive fold-changes (blue). (FIG. 3D) Boxplots showing the expression of select immunotherapy response-related genes in samples with high and low abundance of the species Corynebacteriumkroppenstedtii. Many genes are significantly overexpressed in samples with low abundance. (FIG. 3E) Lollipop plot showing the correlation coefficients of each species-gene pair with respect to the fold-changes in species’ abundances between the anti-CTLA-4 responsive and nonresponsive cohorts. (FIG. 3F) Boxplots showing the expression of select immunotherapy response-related genes in samples with high and low abundance of the species Streptococcus gordonii.

[0016] FIGS. 4A - 4F: Microbiome-Gene-Based Prediction of anti-PD-1 and anti-CTLA-4 Response (FIG. 4A) ROC curves for the anti-PD-1 internal validation set (top) and external test set (bottom). Plots show the models’ sensitivities at various specificities. Of the internal validation set, an AUC of 1.000 was observed, a metric of the model’s accuracy. This value fell to 0.867 when the model was tested externally. (FIG. 4B) Confusion matrices for the anti-PD-1 validation set (top) and test set (bottom). Plots show the number of true positives, false positives, true negatives, and false negatives predicted by each model. Of the validation set, all patients’ responses were correctly predicted. The responses of two patients (out of 26) were incorrectly predicted when the model was tested externally. (FIG. 4C) ROC curves for the anti-CTLA-4 internal validation set (top) and external test set (bottom). Of the validation set, an AUC of 1.000 was observed. This value fell to 0.835 when the model was tested externally. (FIG. 4D) Confusion matrices for the anti-CTLA-4 validation set (top) and test set (bottom). Of the validation set, all patients’ responses were correctly predicted. The responses of three patients (out of 29) were incorrectly predicted when the model was tested externally. (FIG. 4E) Bar charts showing the anti-PD-1 model’s gain from each of the top features, a measure of the features’ importance to the model. (FIG. 4F) Bar charts showing the anti-CTLA-4 model’s gain from each of the top features.[0017.1 FIGS. 5A - 5D: Gene-Based Prediction of anti-PD-1 and anti-CTLA-4 Response (FIG. 5A) ROC curves for the anti-PD-1 internal validation set (top) and external test set (bottom) using only the expression of the 37 gene panel above. Plots show the models’ sensitivities at various specificities. Of the internal validation set, an AUC of 0.688 was observed, a metric of the model’s accuracy. This value fell to 0.500 when the model was tested externally. (FIG. 5B) Confusion matrices for the anti-PD-1 validation set (top) and test set (bottom). Plots show the number of true positives, false positives, true negatives, and false negatives predicted by each model. Of the validation set, two patients’ responses wereincorrectly predicted. All patients were predicted to be nonresponsive when the model was tested externally. (FIG. 5C) ROC curves for the anti-CTLA-4 internal validation set (top) and external test set (bottom) using only the expression of the 37 gene panel above. Of the internal validation set, an AUC of 0.500 was observed. This value was unchanged when the model was tested externally. (FIG. 5D) Confusion matrices for the anti-CTLA-4 validation set (top) and test set (bottom). Of the validation set, two patients’ responses were incorrectly predicted. All patients were predicted to be nonresponsive when the model was tested externally.[00.18] FIGS. 6A - 6C: Species Correlations to Immune Modulations (FIG. 6A)Heatmaps showing the correlation coefficients (R-values) between species’ abundances and cytokines’ expressions. The species differentially abundant between the anti-PD-1 (left) and anti-CTLA-4 (right) responsive and nonresponsive cohorts were analyzed for correlation to 157 cytokines. Of the anti-PD-1 cohort, numerous species show significant correlation to the expression of these cytokines, suggesting the species’ potential implications in their regulation. * (0.01 < p < 0.05), ** (0.001 < p < 0.01), *** (p < 0.001). (FIG. 6B) Heatmaps showing the correlation coefficients between species’ and immune cells’ abundances. The species differentially abundant between the anti-PD-1 (left) and anti-CTLA-4 (right) responsive and nonresponsive cohorts were analyzed for correlation to 22 immune cell types. Several species of both the anti-PD-1 cohort and the anti-CTLA-4 cohort show significant correlation to the abundance of these immune cells. (FIG. 6C) Bubble plots showing the nominal enrichment scores (NES) of select immune pathways with respect to species’ abundances. Only species differentially abundant between the anti-PD-1 (left) and anti- CTLA-4 (right) responsive and nonresponsive cohorts were analyzed. Bubble size indicates the significance in enrichment.DETAILED DESCRIPTION

[0019] Throughout this disclosure, various publications, patents and published patent specifications are referenced by an identifying citation. The disclosures of these publications, patents and published patent specifications are hereby incorporated by reference into the present disclosure to more fully describe the state of the art to which this disclosure pertains.

[0020] As used herein, certain terms may have the following defined meanings. As used in the specification and claims, the singular form “a,” “an” and “the” include singular and plural references unless the context clearly dictates otherwise. For example, the term “a cell” includes a single cell as well as a plurality of cells, including mixtures thereof.[00211 The practice of the present technology will employ, unless otherwise indicated, conventional techniques of organic chemistry, pharmacology, immunology, molecular biology, microbiology, cell biology and recombinant DNA, which are within the skill of the art. See, e.g., Sambrook, Fritsch and Maniatis, Molecular Cloning: A Laboratory Manual, 2ndedition (1989); Current Protocols In Molecular Biology (F. M. Ausubel, et al. eds., (1987)); the series Methods in Enzymology (Academic Press); PCR 2: A Practical Approach (M. J. MacPherson, B.D. Hames and G.R. Taylor eds., (1995)); Antibodies, a Laboratory Manual, and Animal Cell Culture (R.I. Freshney, ed. (1987)).

[0022] As used herein, the term “comprising” is intended to mean that the compositions and methods include the recited elements, but not excluding others. “Consisting essentially of’ when used to define compositions and methods, shall mean excluding other elements of any essential significance to the composition or method. “Consisting of’ shall mean excluding more than trace elements of other ingredients for claimed compositions and substantial method steps. Embodiments defined by each of these transition terms are within the scope of this disclosure. Accordingly, it is intended that the methods and compositions can include additional steps and components (comprising) or alternatively including steps and compositions of no significance (consisting essentially of) or alternatively, intending only the stated method steps or compositions (consisting of).10023] All numerical designations, e.g., pH, temperature, time, concentration, and molecular weight, including ranges, are approximations which are varied ( + ) or ( - ) by increments of 0.1. It is to be understood, although not always explicitly stated that all numerical designations are preceded by the term “about”. The term “about” also includes the exact value “X” in addition to minor increments of “X” such as “X + 0.1” or “X - 0.1.” It also is to be understood, although not always explicitly stated, that the reagents described herein are merely exemplary and that equivalents of such are known in the art.

[0024] In one aspect, the term “equivalent” or “biological equivalent” of an antibody or therapy means the ability of the antibody to selectively bind its epitope protein or fragment thereof as measured by ELISA or other suitable methods. Biologically equivalent antibodies include, but are not limited to, those antibodies, peptides, antibody fragments, antibody variant, antibody derivative and antibody mimetics that bind to the same epitope as the reference antibody.

[0025] In one aspect, the term “equivalent” of “chemical equivalent” of a chemical means the ability of the chemical to selectively interact with its target protein, DNA, RNA or fragment thereof as measured by the inactivation of the target protein, incorporation of the chemical into the DNA or RNA or other suitable methods. Chemical equivalents include, but are not limited to, those agents with the same or similar biological activity and include, without limitation a pharmaceutically acceptable salt or mixtures thereof that interact with and / or inactivate the same target protein, DNA, or RNA as the reference chemical.

[0026] The term “isolated” as used herein refers to molecules or biological or cellular materials being substantially free from other materials. In one aspect, the term “isolated” refers to nucleic acid, such as DNA or RNA, or protein or polypeptide, or cell or cellular organelle, or tissue or organ, separated from other DNAs or RNAs, or proteins or polypeptides, or cells or cellular organelles, or tissues or organs, respectively, that are present in the natural source. The term “isolated” also refers to a nucleic acid or peptide that is substantially free of cellular material, viral material, or culture medium when produced by recombinant DNA techniques, or chemical precursors or other chemicals when chemically synthesized. Moreover, an “isolated nucleic acid” is meant to include nucleic acid fragments which are not naturally occurring as fragments and would not be found in the natural state. The term “isolated” is also used herein to refer to polypeptides which are isolated from other cellular proteins and is meant to encompass both purified and recombinant polypeptides. The term “isolated” is also used herein to refer to cells or tissues that are isolated from other cells or tissues and is meant to encompass both cultured and engineered cells or tissues.100271 The term “treating” as used herein is intended to encompass curing as well as ameliorating at least one symptom of the condition or disease. In one aspect, the term“treating” excludes curing the disease but rather meeting one of the clinical endpoints relevant for the disease, e.g., see below.

[0028] When the disease is cancer, the following clinical endpoints are non-limiting examples of treatment: (1) elimination of a cancer in a subject or in a tissue / organ of the subject or in a cancer loci; (2) reduction in tumor burden (such as number of cancer cells, number of cancer foci, number of cancer cells in a foci, size of a solid cancer, concentrate of a liquid cancer in the body fluid, and / or amount of cancer in the body); (3) stabilizing or delay or slowing or inhibition of cancer growth and / or development, including but not limited to, cancer cell growth and / or division, size growth of a solid tumor or a cancer loci, cancer progression, and / or metastasis (such as time to form a new metastasis, number of total metastases, size of a metastasis, as well as variety of the tissues / organs to house metastatic cells); (4) less risk of having a cancer growth and / or development; (5) inducing an immune response of the patient to the cancer, such as higher number of tumor-infiltrating immune cell, higher number of activated immune cells, or higher number cancer cell expressing an immunotherapy target, or higher level of expression of an immunotherapy target in a cancer cell; (6) higher probability of survival and / or increased duration of survival, such as increased overall survival (OS, which may be shown as 1-year, 2-year, 5-year, 10-year, or 20-year survival rate), increased progression free survival (PFS), increased disease free survival (DFS), increased time to tumor recurrence (TTR) and increased time to tumor progression (TTP). In some embodiments, the subject after treatment experiences one or more endpoints selected from tumor response, reduction in tumor size, reduction in tumor burden, increase in overall survival, increase in progression free survival, inhibiting metastasis, improvement of quality of life, minimization of drug-related toxicity, and avoidance of side-effects e.g., decreased treatment emergent adverse events). In some embodiments, improvement of quality of life includes resolution or improvement of cancer-specific symptoms, such as but not limited to fatigue, pain, nausea / vomiting, lack of appetite, and constipation; improvement or maintenance of psychological well-being (e.g., degree of irritability, depression, memory loss, tension, and anxiety); improvement or maintenance of social well-being (e.g., decreased requirement for assistance with eating, dressing, or using the restroom; improvement or maintenance of ability to perform normal leisure activities, hobbies, or social activities; improvement or maintenance of relationships with family). In some embodiments, improvedpatient quality of life that is measured qualitatively through patient narratives or quantitatively using validated quality of life tools known to those skilled in the art, or a combination thereof. Additional non-limiting examples of endpoints include reduced hospital admissions, reduced drug use to treat side effects, longer periods off-treatment, and earlier return to work or caring responsibilities. In one aspect, prevention or prophylaxis is excluded from treatment.

[0029] Additionally or alternatively, a cancer may refer to a local cancer (which is an invasive malignant cancer confined entirely to the organ or tissue where the cancer began), a metastatic cancer (referring to a cancer that spreads from its site of origin to another part of the body), a non-metastatic cancer, a primary cancer (a term used describing an initial cancer a subject experiences), a secondary cancer (referring to a metastasis from primary cancer or second cancer unrelated to the original cancer), an advanced cancer, an unresectable cancer, or a recurrent cancer. As used herein, an advanced cancer refers to a cancer that had progressed after receiving one or more of: the first line therapy, the second line therapy, the third line therapy, or the fourth line therapy. An advanced cancer is a Stage III or Stage IV cancer.

[0030] A “composition” as used herein, refers to an active agent, such as a compound as disclosed herein and a carrier, inert or active. The carrier can be, without limitation, solid such as a bead or resin, or liquid, such as phosphate buffered saline.

[0031] Administration or treatment in “combination” refers to administering two agents such that their pharmacological effects are manifest at the same time. Combination does not require administration at the same time or substantially the same time, although combination can include such administrations.

[0032] As used herein, the term “administration” and “administering” are used to mean introducing an agent into a subject. Routes of administration include, but are not limited to, oral (such as a tablet, capsule or suspension), topical, transdermal, intranasal, vaginal, rectal, subcutaneous intravenous, intravenous, intraarterial, intramuscular, intraosseous, intraperitoneal, intraocular, subconjunctival, sub-Tenon’s, intravitreal, retrobulbar, intracameral, intratumoral, epidural and intrathecal. The preferred route of administration will vary with the checkpoint inhibitor therapy and the patient or subject being treated.

[0033] “An effective amount” intends to indicate the amount of a compound or agent administered or delivered to the patient which is most likely to result in the desired response to treatment. The amount is empirically determined by the patient’s clinical parameters including, but not limited to the Stage of disease, age, gender, histology, and likelihood for tumor recurrence.

[0034] A “subject” or “patient” as used herein intends an animal, a mammal or yet further a human patient. For the purpose of illustration only, a mammal includes but is not limited to a simian, a murine, a bovine, an equine, a canine, a feline, a human, a porcine or ovine.100351 As used herein, the term “biological sample” refers to a sample obtained from a biological subject (e.g., a cancer patient, or a subject suspected of having cancer), including sample of biological tissue or fluid origin obtained in vivo or in vitro. Such samples can be, but are not limited to, body fluid (e.g., blood, blood plasma, serum, or urine), biopsy samples, tumor samples, organs, tissues, fractions, cells isolated from mammals including humans, and cell organelles. Biological samples also may include sections of the biological sample including tissues (e.g., sectional portions of an organ or tissue). Biological samples may also include extracts from a biological sample. Biological samples may comprise proteins, carbohydrates or nucleic acids (e.g., cfDNAs, miRNAs, etc.). In some embodiments, the biological sample is selected from a plasma sample, a blood sample, a saliva sample, a cystic fluid sample, a spinal fluid sample, a brain fluid sample, a urine sample, a sweat sample, or a tear sample. In some embodiments, the biological sample is a blood sample. In addition, the sample may be a primary sample (isolated from a patient, preserved or freshly isolated) or a cell line or organoid.

[0036] Checkpoint inhibitor therapy is a form of cancer immunotherapy. The therapy targets immune checkpoints, key regulators of the immune system that when stimulated can dampen the immune response to an immunologic stimulus. Some cancers can protect themselves from attack by stimulating immune checkpoint targets.

[0037] As used herein the term “PD-1” refers to a specific protein fragment associated with this name and any other molecules that have analogous biological function that share at least 70%, or alternatively at least 80% amino acid sequence identity, or alternatively 90% sequence identity, or alternatively at least 95% sequence identity with the PD-1 sequence asshown herein and / or a suitable binding partner of PD-L1. Non-limiting example sequences of PD-1 are provided herein, such as but not limited to those under the following reference numbers - GCID:GC02M241849; HGNC: 8760; Entrez Gene: 5133; Ensembl: ENSG00000188389; OMIM: 600244; and UniProtKB: QI 5116 - and the sequence: MQIPQAPWPVVWAVLQLGWRPGWFLDSPDRPWNPPTFSPALLVVTEGDNATFTCSF SNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVR ARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLV VGVVGGLLGSLVLLVWVLAVICSRAARGTIGARRTGQPLKEDPSAVPVFSVDYGEL DFQWREKTPEPPVPCVPEQTEYATIVFPSGMGTSSPARRGSADGPRSAQPLRPEDGHC SWPL (SEQ ID NO: 1), and equivalents thereof. Non-limiting examples of commercially available antibodies thereto include pembrolizumab (Merck), nivolumab (Bristol-Myers Squibb), pidilizumab (Cure Tech), AMP-224 (GSK), AMP-514 (GSK), PDR001 (Novartis), and cemiplimab (Regeneron and Sanofi).

[0038] As used herein the term “PD-L1” refers to a specific protein fragment associated with this name and any other molecules that have analogous biological function that share at least 70%, or alternatively at least 80% amino acid sequence identity, or alternatively 90% sequence identity, or alternatively at least 95% sequence identity with the PD-L1 sequence as shown herein and / or an suitable binding partner of PD-1. Non-limiting example sequences of PD-L1 are provided herein, such as but not limited to those under the following reference numbers - GCID: GC09P005450; HGNC: 17635; Entrez Gene: 29126; Ensembl: ENSG00000120217; OMIM: 605402; and UniProtKB: Q9NZQ7 - and the sequence:|0039] MRIFAVFIFMTYWHLLNAFTVTVPKDLYVVEYGSNMTIECKFPVEKQLDLAA LIVYWEMEDKNIIQFVHGEEDLKVQHSSYRQRARLLKDQLSLGNAALQITDVKLQD AGVYRCMISYGGADYKRITVKVNAPYNKINQRILVVDPVTSEHELTCQAEGYPKAE VIWTSSDHQVLSGKTTTTNSKREEKLFNVTSTLRINTTTNEIFYCTFRRLDPEENHTAE LVIPELPLAHPPNERTHLVILGAILLCLGVALTFIFRLRKGRMMDVKKCGIQDTNSKK QSDTHLEET (SEQ ID NO: 2), and equivalents thereof. Non-limiting examples of commercially available antibodies thereto include atezolizumab (Roche Genentech), avelumab (Merck Soreno and Pfizer), durvalumab (AstraZeneca), BMS-936559 (Bristol- Myers Suibb), and CK-301 (Chekpoint Therapeutics).

[0040] Cytotoxic T-lymphocyte associated protein 4 (CTLA-4) is also known as CD 152 (cluster of differentiation 152), is a protein receptor that functions as an immune checkpoint and downregulates immune responses. CTLA-4 is constitutively expressed in regulatory T cells but only upregulated in conventional T cells after activation, a phenomenon which is particularly notable in cancers. It is encoded by the gene CTLA4 in humans.[0041 [ Additional non-limiting examples of checkpoint inhibitor therapies include antibodies such as those recognizing and binding to CTLA-4 [for example Yervoy™ (ipilimumab)], or one recognizing and binding to PD-1 [for example Keytruda™ (pembrolizumab) and Opdivo™ (nivolumab)], or one recognizing and binding to PD-L1 (for example, Tecentriq™ (atezolizumab)] or the combination therapy of ipilimumab and nivolumab. Therapies and combination therapy for the treatment of advanced melanoma (Stage III / IV) are discussed in https: / / pmc.ncbi.nlm.nih.gov / articles / PMC5883082 / , incorporated herein by reference and last accessed on April 20, 2025.

[0042] “Cytoreductive therapy,” as used herein, includes but is not limited to surgery, chemotherapy, cryotherapy, and radiation therapy. Agents that act to reduce cellular proliferation are known in the art and widely used. Chemotherapy drugs that kill cancer cells only when they are dividing are termed cell-cycle specific. These drugs include agents that act in S-phase, including topoisomerase inhibitors and anti-metabolites. Cryotherapy includes, but is not limited to, therapies involving decreasing the temperature, for example, hypothermic therapy. Radiation therapy includes, but is not limited to, exposure to radiation, e.g., ionizing radiation, UV radiation, as known in the art. Exemplary dosages include, but are not limited to, a dose of ionizing radiation at a range from at least about 2 Gy to not more than about 10 Gy and / or a dose of ultraviolet radiation at a range from at least about 5 J / m2to not more than about 50 J / m2, usually about 10 J / m2.

[0043] Melanoma is a kind of skin cancer that starts in the melanocytes. Melanocytes are cells that make the pigment that gives skin its color. The pigment is called melanin. Melanoma typically starts on skin that's often exposed to the sun. This includes the skin on the arms, back, face and legs. Melanoma also can form in the eyes. Rarely, it can happen inside the body, such as in the nose or throat. The exact cause of all melanomas isn't clear. Most melanomas are caused by exposure to ultraviolet light. Ultraviolet light, also called UVlight, comes from sunlight or tanning lamps and beds. Limiting exposure to UV light can help reduce the risk of melanoma. See, https: / / , www.mayoclinic. org / diseases- conditions / melanoma / symptoms-causes / syc-20374884, last accessed on April, 20, 2025. Treatment of melanoma will vary with the stage, see e.g., https: / / www.cancer.org / cancer / types / melanoma-skin-cancer / treating / by-stage.html, last accessed on April, 20, 2025. Available checkpoint inhibitors for advanced melanoma include: pembrolizumab (Keytruda™) or nivolumab (Opdivo™); nivolumbab combined with retalimab (Opdualag™); and nivolumab or pembrolizumab plus ipilimubab (Yervoy™), see, https: / / www.cancer.org / cancer / types / melanoma-skin-cancer / treating / by-stage.html, last accessed on April, 20, 2025.

[0044] Advanced melanoma intends Stage III or Stage IV. In one aspect, advanced melanoma is limited to Stage IV melanoma.

[0045] Low expression level intends a level of expression, before or after normalization that is a value lower than an expression level in a patient or patient population that is determined to exhibit or separately not exhibit responsiveness to the checkpoint inhibitor therapy.

[0046] High expression level intends a level of expression, before or after normalization that is a value higher than an expression level in a patient or patient population that is determined to exhibit or separately not exhibit responsiveness to the checkpoint inhibitor therapy.100471 In some embodiments, “high” or “low” expression of a given bacterium species is based on the relation of a measured expression level of the bacterium to a predetermined value (e.g., a median abundance of the bacterial species). In some embodiments, a high expression level correlates with high abundance of the bacteria. In some embodiments, a low expression level correlates with low abundance of the bacteria.

[0048] Normalization intends preprocessing step in data analysis and machine learning. It involves adjusting values measured on different scales to a common scale, often prior to averaging or other statistical operations. This process ensures that each feature contributes equally to the analysis, preventing features with larger magnitudes from dominating the results. Median absolute deviation (MAD) normalization is a measure of the variability of the univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample.(0049] Lactiplantibacillus plantarum was previously known as Lactobacillus plantarum strains are one of the lactic acid bacteria (LAB) commonly used in fermentation and their probiotic and functional properties along with their health-promoting roles come to the fore. Food-derived L. plantarum strains have shown good resistance and adhesion in the gastrointestinal tract (GI) and excellent antioxidant and antimicrobial properties. See, https: / / www.sciencedirect.com / science / article / pii / S0944501322003299, last accessed on April 20, 2025. Research grade is commercial available at https : / / live-biotherapeutic. creativebiolabs. com / lactobacillus-plantarum-powder-145.htm, last accessed on April 20, 2025.

[0050] Corynebacterium kroppenstedtii is a genus of Gram-positive bacteria and most are aerobic. They are bacilli (rod-shaped), and in some phases of life they are, more specifically, club -shaped, which inspired the genus name.(0051 j Streptococcus gordonii is a Gram-positive bacterium. The organism has a high- affinity for molecules in the salivary pellicle and can rapidly colonize clean tooth surfaces. It has been reported that the whole genome of S. gordonii CCUG 33482 type strain was deposited GenBank in 2016 under the accession number LQWV00000000, last accessed on April 20, 2025.

[0052] Mesomycoplasma hyopneumoniae is a species of bacteria known to cause the disease porcine enzootic pneumonia, a highly contagious and chronic disease affecting pigs. As with other mollicutes, M. hyopneumoniae is small in size (400-1200 nm), has a small genome (893-920 kilo-base pairs (kb)) and lacks a cell wall.

[0053] When a microbial marker is used as a basis for identifying or selecting a patient for a treatment described herein, the marker can be measured before and / or during treatment, and the values obtained are used by a clinician in assessing any of the following: (a) probable or likely suitability of an individual to initially receive treatment(s); (b) probable or likely unsuitability of an individual to initially receive treatment(s); (c) responsiveness to treatment; (d) probable or likely suitability of an individual to continue to receive treatment(s); (e) probable or likely unsuitability of an individual to continue to receive treatment(s); (f) adjusting dosage; (g) predicting likelihood of clinical benefits; or (h) toxicity. As would be well understood by one in the art, measurement of the marker in a clinical setting is a clearindication that this parameter was used as a basis for initiating, continuing, adjusting and / or ceasing administration of the treatments described herein.MODES FOR CARRYING OUT THE DISCLOSURE

[0054] The disclosure further provides diagnostic, prognostic and therapeutic methods, which are based, at least in part, on determination of the identity and amount (level) of microorganisms in a subject’s biological sample, e.g., a blood sample.]0055[ For example, information obtained using the diagnostic assays described herein is useful for determining if a subject is suitable for checkpoint inhibitor therapy. Based on the prognostic information, a doctor can recommend a therapeutic protocol for treating melanoma.

[0056] It is to be understood that information obtained using the diagnostic assays described herein may be used alone or in combination with other information, such as, but not limited to, genotypes or expression levels of other genes, clinical chemical parameters, histopathological parameters, or age, gender and weight of the subject. When used alone, the information obtained using the diagnostic assays described herein is useful in determining or identifying the clinical outcome of a treatment, selecting a patient for a treatment, or treating a patient, etc. When used in combination with other information, on the other hand, the information obtained using the diagnostic assays described herein is useful in aiding in the determination or identification of clinical outcome of a treatment, aiding in the selection of a patient for a treatment, or aiding in the treatment of a patient and etc. In a particular aspect, capture molecules that specifically recognize and bind molecular markers and / or expression levels of one or more markers as disclosed herein are used in a panel, each of which contributes to the final diagnosis, prognosis or treatment.

[0057] The methods are useful in the assistance of an animal, a mammal or yet further a human patient. For the purpose of illustration only, a mammal includes but is not limited to a human, a simian, a murine, a bovine, an equine, a porcine, a feline, a canine, or an ovine.

[0058] Kits]0059] In a further aspect, this disclosure provides a kit for detecting and determining the expression level of an organism of interest in a biological sample, e.g., a blood sampleisolated from the patient. These kits include reagents to detect the presence of the organism, for example detecting and determining the level of the organisms’ DNA in the biological sample, e.g., a blood sample, wherein the organism is one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii and Mesomycoplasma hyopneumoniae .

[0060] Diagnostic and Prognostic Methods

[0061] Also provided is a method for selecting a therapy for a melanoma patient, comprising, or consisting essentially of, or yet further consisting of detecting the level of one or more microorganisms selected from Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii and Mesomycoplasma hyopneumoniae in a biological sample, e.g., a blood sample, isolated from a subject, wherein low expression level of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in a biological sample, e.g., a blood sample, isolated from the patient selects the patient for a therapy comprising checkpoint inhibitor therapy, and a high level of expression selects the patient for a therapy that excludes a checkpoint inhibitor therapy. In one aspect, the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti- CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy. The melanoma is selected from Stage I, Stage II, Stage III or Stage IV melanoma and the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy. In a further aspect, the melanoma is advanced melanoma, optionally Stage IV melanoma and the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy. The method can be performed before or after cytoreductive therapy.

[0062] Also provided is a method for selecting a therapy for a melanoma patient, comprising, or consisting essentially of, or yet further consisting of detecting the level of Mesomycoplasma hyopneumoniae in a biological sample, e.g., a blood sample, isolated from a subject, wherein high expression level of Mesomycoplasma hyopneumoniae in a biological sample, e.g., a blood sample isolated from the patient selects the patient for a therapy comprising the checkpoint inhibitor, and a low level of expression selects the patient for a therapy that excludes a checkpoint inhibitor. In one aspect, the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLltherapy, and an anti-PD-L2 therapy. The melanoma is selected from Stage I, Stage II, Stage III or Stage IV melanoma and the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy, n a further aspect, the melanoma is advanced melanoma, optionally Stage IV melanoma and the therapy is selected from first-line, second-line, third- line, fourth line or fifth-line therapy. The method can be performed before or after cytoreductive therapy.

[0063] To determine the level of expression, the sample is compared to a reference level in patients that are responsive and non-responsive to a therapy comprising, consisting essentially of, or consisting of checkpoint inhibitor therapy. In one aspect, the method further comprises, or consists essentially of, or yet further consists of detecting the level of 1, 2, or 3 of the organisms.

[0064] One of skill in the art can detect and quantify the microorganisms by preparing the biological sample, e.g., a blood sample for analysis using methods known in the art and then detecting using nucleic acid amplification techniques such as any one or more polymerase chain reaction (PCR) methods, or RNA or DNA sequencing or assays that bind unique cell wall proteins or enzymes using antibody technology. Other available methods include, whole genome sequencing, and methods described in https: / / besjoumals.onlinelibrary.wiley.eom / doi / full / 10.l 111 / j.1365-2435.2009.01592.x; https: / / www.eppendorf.com / us-en / lab-academy / life-science / microbiology / how-to-quantify- bacterial-cultures / ; and https: / / www.sciencedirect.com / science / article / pii / S0732889323001840.

[0065] Therapeutic Methods

[0066] Also provided is a method for treating melanoma to a patient in need thereof, comprising, or consisting essentially of, or yet further consisting of administering to the patient an effective amount of a therapy comprising a checkpoint inhibitor, wherein the patient expresses a low expression level of one or more of l ctiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in a biological sample, e.g., a blood sample isolated from the patient. In another aspect, the method further comprises, or consists essentially of, or yet further consists of detecting the level of 1, 2 or all 3 of the organisms. Methods to detect such organisms are discussed herein. In one aspect, thecheckpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti- CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy. The melanoma is selected from Stage I, Stage II, Stage III or Stage IV melanoma and the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy, n a further aspect, the melanoma is advanced melanoma, optionally Stage IV melanoma and the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy. The method can be performed before or after cytoreductive therapy.

[0067] To determine the level of expression, the sample is compared to a reference level in patients that are responsive and non-responsive to a therapy comprising, consisting essentially of, or consisting of checkpoint inhibitor therapy. In one aspect, the method further comprises, or consists essentially of, or yet further consists of detecting the level of 1, 2, or 3 of the organisms.

[0068] Further provided is a method for treating melanoma to a patient in need thereof, comprising, or consisting essentially of, or yet further consisting of administering to the patient an effective amount of a therapy comprising a checkpoint inhibitor, wherein the patient expresses a high expression level of Mesomycoplasma hyopneumoniae in a biological sample, e.g., a blood sample isolated from the patient.

[0069] Methods to detect such organisms are discussed herein. In one aspect, the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy. The melanoma is selected from Stage I, Stage II, Stage III or Stage IV melanoma and the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy, n a further aspect, the melanoma is advanced melanoma, optionally Stage IV melanoma and the therapy is selected from first- line, second-line, third-line, fourth line or fifth-line therapy. The method can be performed before or after cytoreductive therapy.(0070] To determine the level of expression, the sample is compared to a reference level in patients that are responsive and non-responsive to a therapy comprising, consisting essentially of, or consisting of checkpoint inhibitor therapy.

[0071] In some embodiments, the checkpoint inhibitor comprises, consists essentially of, or consists of one or more selected from an anti-PD-1 agent, an anti-PD-Ll agent, an anti-CTLA-4 agent, and an anti-PD-L2 therapy. In some embodiments, the anti-PD-1 agent, the anti-PD-Ll agent, the anti-CTLA-4 agent, the anti-PD-L2 agent is an antagonist. In some embodiments, the anti-PD-1 agent, the anti-PD-Ll agent, the anti-CTLA-4 agent, or the anti- PD-L2 agent is an agonist. In some embodiments, the anti-PD-1 agent, the anti-PD-Ll agent, the anti-CTLA-4 agent, or the anti-PD-L2 agent is an inhibitor.

[0072] In some embodiments, the anti-PDl agent comprises, consists essentially of, or consists of an anti-PDl antibody or an antigen binding fragment thereof. In some embodiments, the anti-PDl antibody comprises, consists essentially of, or consists of nivolumab, pembrolizumab, cemiplimab, spartalizumab, camrelizumab, sintilimab, tislelizumab, toripalimab, AMF 514 (MEDI0680), balstilimab, or a combination of two or more thereof.

[0073] In some embodiments, the anti-PD-Ll agent comprises, consists essentially of, or consists of an anti-PD-Ll antibody or an antigen binding fragment thereof. In some embodiments, the anti-PD-Ll antibody comprises, consists essentially of, or consists of avelumab, durvalumab, atezolizumab, envafolimab, or a combination of two or more thereof.

[0074] In some embodiments, the checkpoint inhibitor comprises, consists essentially of, or consists of an anti-CTLA-4 agent. In some embodiments, the anti-CTLA-4 agent comprises, consists essentially of, or consists of an anti-CTLA-4 antibody or an antigen binding fragment thereof. In some embodiments, the anti-CTLA-4 antibody comprises, consists essentially of, or consists of ipilimumab, tremelimumab, zalifrelimab, or AGEN1181, or a combination thereof.

[0075] In some embodiments, the anti-PD-L2 therapy is an anti-PD-L2 antibody.

[0076] Experimental Methods

[0077] Data Acquisition

[0078] RNA-Seq data of melanoma tumor tissues were downloaded from the NCBI BioProject Database (https: / / www.ncbi.nlm.nih.gov / gds) from PRJNA693857 (anti-CTLA-4, pre-treatment n = 56, post-treatment n = 93) and PRJNA356761 (anti-PD-1 and anti-CTLA- 4, pre-treatment n = 51, post-treatment n = 58). Whole exome sequencing (WXS) data ofmelanoma tumor tissues were downloaded from PRJNA397813 (anti-PD-1 and anti-CTLA-4, pre-treatment n = 19, post-treatment n = 32).

[0079] Bacterial Read Mapping

[0080] Sequences were mapped to bacterial species using the Pathoscope 2.0 software

[0033] , with reference sequences sourced from the NCBI Nucleotide Database (https: / / www.ncbi.nlm.nih.gov / nucleotide / , last accessed on April 20, 2025). Bacterial reads were targeted for quantification, and human reads were removed prior to microbial read quantification. Standard parameters were chosen.

[0081] Applicant cautions that the species mapped are likely not present within these tumors. Rather, Applicant presumes that genetic remnants of the gut microbiome may circulate to these tissues. It is these remnants that Applicant has mapped to bacterial sequences, which are analyzed for their utility in the prediction of immunotherapy response.

[0082] Gene Expression Quantification

[0083] Sequences were mapped to the hg38 reference genome and mRNA read counts were profiled using the STAR software

[0034] , with reference sequences sourced from the NCBI Nucleotide Database (https: / / www.ncbi.nlm.nih.gov / nucleotide / , last accessed on April 20, 2025). Standard parameters were chosen.

[0084] Cross Study Normalization

[0085] Median absolute deviation (MAD) normalization was performed to account for differences in sequencing techniques and procedures, increasing the validity of cross-study comparisons. In this technique, the median expression of a gene is subtracted from the expression of that gene in each individual sample. These values are then divided by the median absolute deviation of the gene to yield relative expression values for each sample. This technique was similarly performed on species abundance values. MAD normalization assumes an equal median and distribution across samples and is resilient to outliers. This technique has been shown to be of great robustness in gene expression experiments compared to other standard batch normalization methods

[0035] ,

[0086] Species and genes with MADs of 0 were normalized using cumulative sum scaling. In this technique, the expression of a gene in each individual sample is divided by the sum ofthat gene’s expression in all samples to yield relative expression values for each sample. This technique was similarly performed on species abundance values.

[0087] Principle coordinate analyses (PCoA) were used to further demonstrate the effectiveness of these techniques. Sample dissimilarities were calculated by a Euclidean distance.

[0088] Contamination Correction

[0089] Across all samples, noncontaminant species are expected to be of greater abundance in samples of a greater total abundance of taxa. Contaminant species are likely introduced in a fixed amount and will not display this behavior. Spearmen’s correlations were computed between each species and the total abundance of all species in a sample. Species that did not show a significant correlation to the total abundance of taxa were deemed contaminants and excluded from subsequent analyses.

[0090] Differential Abundance Analysis

[0091] The Kruskal-Wallis test was used to identify differentially abundant species (p < 0.05). The samples of patients treated with anti-PD-1 therapies were assessed independently from those of patients treated with anti-CTLA-4 therapies. Differential abundance was assessed between responsive and nonresponsive cohorts, as well as pre- and post-treatment cohorts.

[0092] Immunotherapy Response-Related Gene Expression Correlations

[0093] A panel of 37 genes with expressions implicated in immunotherapy response was collected from literature [8, 9], Among others, this includes the PD-1 receptor, CTLA-4 receptor, their specific ligands, related receptor homologues, and related ligand homologues.

[0094] Spearmen’s correlations were used to assess the relation of individual species’ abundances to these genes’ expressions. Only the species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed.

[0095] The abundance values of each species were further simplified to binary “high” or “low” classifications based on their relation to the median abundance of that species. The Kolmogorov-Smirnov test was used to determine whether the expressions of these genes were significantly altered between samples of high and low abundance of each species (p <0.05). Only the species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed.

[0096] Immunotherapy Response Predictive Models

[0097] The LightGBM R package was used to construct independent machine learning models for the prediction of a patient’s response to anti-PD-1 and anti-CTLA-4 therapies. A 90: 10 train-test split was applied to samples from PRJNA356761 during the models’ validation phases. Samples from the external cohort PRJNA397813 were then used to test models’ external performance and accuracies.

[0098] The abundances of all species common to both studies were considered in the training of these models, as were the expressions of the immunotherapy response-related genes in the above panel.{0099] In the validation and testing of these models, responsive patients were denoted by a positive label. The areas under the curves (AUC) were calculated of the produced receiver operating characteristic (ROC) curves to assess each model’s sensitivity and specificity.10100] Both models were tuned using 3 leaves and 5 bins maximally. The unbalanced parameter and different weightings were applied to avoid biases resulting from the unequal distribution of responsive and nonresponsive patients. For the prediction of anti-PD-1 response, 3 iterations and a learning rate of 0.4 yielded the greatest accuracy. For the prediction of anti-CTLA-4 response, 5 iterations and a learning rate of 1.6 yielded the greatest accuracy.

[0101] Cytokine Expression Correlations

[0102] Spearmen’s correlations were used to assess the relation of individual species’ abundances to the expression of 157 cytokines. Only the species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed.

[0103] Immune Infdtration Correlations

[0104] CIBERSORTx

[0036] was used to impute the cell counts of 22 types of immune cells.

[0105] Spearmen’s correlations were used to assess the relation of individual species’ abundances to these cells’ abundances. Only the species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed.|0106| Immune Gene Set Enrichment Analysis101071 The clusterProfile R package was used to assess immune pathway enrichment with respect to the abundance of individual species. Gene sets were sourced from the KEGG PATHWAY Database (https: / / www.genome.jp / kegg / pathway.html). Only the species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed.

[0108] Experimental Results

[0109] Cross-Study Normalization and Contamination Correction

[0110] The imputed species abundance and gene expression values were first normalized in order to account for variations in sequencing techniques and procedures across the chosen studies. MAD normalization was initially performed, which is known to be a robust normalization tool in gene expression experiments

[0035] , Cumulative sum scaling was performed on species and genes with MADs of 0. PCoAs were conducted to visualize samples’ abundance and expression profiles before and after normalization (FIG. 1A, FIG. IB) A considerably greater overlap was observed between studies after normalization was performed, indicating that the samples’ abundance and expression profiles are less studydependent. This served to confirm the effectiveness of normalization and to demonstrate the comparability of samples for subsequent analyses.

[0011] Taxa may occasionally be introduced during the extraction, processing, or sequencing of tissue samples, and are not reflective of the microbial remnants truly present within a tumor. Applicant leveraged the expected behavior of non-contaminant and contaminant species to correct for contamination. Samples with a greater total abundance of taxa are expected to contain a greater abundance of each individual taxa. Contaminant species are likely introduced in a fixed amount, and will not display this behavior. Spearmen’s correlations were computed between the abundance of each species and the total abundance of all species in each sample. Species that did not show a significant correlation to the totalabundance of taxa were deemed contaminants and excluded from subsequent analyses. In total, 115 species were considered contaminants, 32.0% of all of the species mapped (FIG. IB)

[0112] Differentially Abundant Species

[0113] The samples of patients treated with anti-PD-1 therapies were analyzed independently from those of patients treated with anti-CTLA-4 therapies. Of the 244 noncontaminant species common to all studies, 12 were differentially abundant between patients responsive and nonresponsive to anti-PD-1 therapy (FIG. 2A, FIG. 2B), and 14 were differentially abundant between those responsive and nonresponsive to anti-CTLA-4 therapy (FIG. 2C, FIG. 2D) Differential abundance of Haemophilus influenzae, Neisseria meningitidis, Corrynebacterium kroppenstedtii, Streptococcuss gordonii, and Azobacteriodes pseudotrichonympha was common to both cohorts. 27 species were differentially abundant between samples extracted before and after anti-PD-1 therapy (FIG. 2A, FIG. 2B), and 15 were differentially abundant between samples extracted before and after anti-CTLA-4 therapy (FIG. 2C, FIG. 2D). This might suggest that the microbiome and these therapies mutually influence one another. Applicant observed a notable overlap in differentially abundant species between the anti-PD-1 and anti-CTLA-4 treatment groups regarding both the responsive and nonresponsive cohorts and the pre- and post-treatment cohorts (FIG. 2E).

[0114] Species Correlations to Immunotherapy Response-Related Genes

[0115] Applicant collected a panel of 37 genes which have been previously implicated in immunotherapy response [8, 9], Among others, this includes the PD-1 receptor, CTLA-4 receptor, their specific ligands, related receptor homologues, and related ligand homologues. For both the anti-PD-1 and anti-CTLA-4 treatment groups, Applicant assessed the correlation of each species’ abundance to these genes’ expressions. Only species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed. Applicant observed a remarkable number of positive correlations in the anti-PD-1 treatment group, especially of the species Mesomycoplasma hyopneumoniae among others (FIG. 3A). This significance was less pronounced in the anti-CTLA-4 treatment group, though the species Streptococcus gordonii showed consistent negative correlations to the expression of these genes (FIG. 3B). The computed correlation coefficients of each species-gene pair wereplotted against the fold-changes in species abundance between the responsive and nonresponsive cohorts (FIG. 3C, FIG. 3E). Numerous species of greater abundance in responsive patients correlated significantly negatively to the expressions of these genes. Among others, a greater abundance of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii correlated to decreased expression of PD1 (PDCD1), CTLA-4 (CTLA4), PD-L1 (CD274), and PD-L2 (PDCD1LG2). Likewise, several species of lesser abundance in responsive patients correlated positively to these genes’ expressions, including Mesomycoplasma hyopneumoniae .101.16] The abundance values of each species were further simplified to binary “high” or “low” classifications based on their relation to the median abundance of that species. Differences in these genes’ expressions were analyzed between samples of high and low abundance of each species. As expected, Applicant observed significant differences in expression for many species-gene pairs. Applicant visualized these differences for several genes with respect to the species Corynebacterium kroppenstedtii and Streptococcus gordonii (FIG. 3D, FIG. 3F)

[0117] Machine-Learning Classifier to Predict Immunotherapy Response

[0118] The studies PRJNA356761 and PRJNA397813A report immunotherapy response data for both anti-PD-1 and anti-CTLA-4 therapies. The abundance values of all species common to both studies were considered in the training of these models, as were the expression values of the immunotherapy response-related genes above.

[0119] 10% of the total samples from PRJNA356761 were withheld during training for the model validation. ROC curves were plotted to visualize the models’ initial sensitivities and specificities (FIG. 4A, FIG. 4C). When tested on the internal validation sets, the AUCs for anti-PD-1 and anti-CTLA-4 therapies were 1.000 and 1.000, respectively, describing the models’ predictive performance. Both models correctly predicted the response of all patients (FIG. 4B, FIG. 4D)

[0120] Samples from PRJNA397813 were then used during the models’ testing phases to assess their accuracies when tested externally. ROC curves were again created to visualize the models’ sensitivities and specificities (FIG. 4A, FIG. 4C). The AUCs for anti-PD-1 and anti-CTLA-4 therapies were 0.867 and 0.835, respectively. Both models correctly predictedthe response of a majority of patients, with sensitivity of 80.0% and 60.0% and specificities of 95.2% and 95.8%, (FIG. 4B, FIG. 4D). The microbial and genetic features of the greatest importance to the models were plotted (FIG. 4E, FIG. 4F). The models rely heavily on microbial abundance, largely of the species Verminephrobacter eiseniae, Chelativorans sp., and Brachybacterium faecium. CD244, VTCN1, and NT5E expression contributed notably, as well.

[0121] To demonstrate the great utility of the microbiome in the prediction of immunotherapy response, Applicant performed an ablation study by training separate models using only the expression of the 37 gene panel above. The results highlight the extent by which the microbiome was necessary to achieve the above accuracies. A 90: 10 trainvalidation split was again applied to samples from PRJNA356761. ROC curves were created to visualize the models’ sensitivities and specificities (FIG. 5A, FIG. 5C). When tested on the internal validation sets, the AUCs for anti-PD-1 and anti-CTLA-4 therapies were 0.688 and 0.500, respectively. Both models showed a drastic reduction in their predictive power (FIG. 5B, FIG. 5D)

[0122] Samples from PRJNA397813 were then used during the models’ testing phases. ROC curves were again created to visualize the models’ sensitivities and specificities (FIG. 5A, FIG. 5C). The AUCs for anti-PD-1 and anti-CTLA-4 therapies were 0.500 and 0.500, respectively, equating to random guessing. Both models predicted that all patients were nonresponsive (FIG. 5B, FIG. 5D) Without being bound by theory, it is Applicant’s belief that the microbiome provide prediction of immunotherapy response.

[0123] Microbial-Associated Immune Modulations

[0124] Applicant further assessed these species for correlations to cytokine expression. Only species found to be differentially abundant between the responsive and nonresponsive cohorts were analyzed for correlation to 157 cytokines. A considerable number of significant correlations were observed in the anti-PD-1 treatment group, consistently of the species Mesomycoplasma hyopneumoniae, Corynebacterium kroppenstedtii, and Lactiplantibacillus plantarum. Less were observed in the anti-CTLA-4 treatment group with the exception of Streptococcuss gordonii (FIG. 6A). These species were further assessed for correlation to the infiltration of 22 immune cell types. Among others, the species Neisseria meningitidis wassignificantly related to the abundance of several immune cells, largely T Cells and eosinophils (FIG. 6B). Applicant then identified specific enrichment of 21 immune- associated pathways with respect to the abundance of these species (FIG. 6C). IL- 17 and complement and coagulation signaling were notably enriched with several species in the anti- CTLA-4 cohort. Greater abundance of the species Neisseria meningitidis, Streptococcus thermophilus, Corynebacterium diphtheriae, Haemophilus influenzae, and Azobacteroides pseudotrichonymphae consistently correlated to negative enrichment of these immune pathways.

[0125] Discussion

[0126] Applicant’s results show that the intratumor microbiome is highly predictive of a patient’s response to both anti-PD-1 and anti-CTLA-4 therapies for melanoma skin cancers. Applicant identified 12 species that were differentially abundant between samples of anti-PD- 1 responsive and nonresponsive patients, and 14 species between those of anti-CTLA-4 responsive and nonresponsive patients. Differential abundance of Haemophilus influenzae, Neisseria meningitidis, Corrynebacterium kroppenstedtii, Streptococcuss gordonii, and Azobacteriodes pseudotrichonympha was common to both the anti-PD-1 and anti-CTLA-4 responsive and nonresponsive cohorts. Applicant further observed longitudinal microbiome dysregulations, with 27 species differentially abundant between samples extracted before and after anti-PD-1 administration, and 15 species between those extracted before and after anti- CTLA-4 administration. This might suggest that the microbiome and checkpoint blockade therapies mutually influence one another.

[0127] Species of the genus Streptococcus were consistently enriched in responsive patients. Similar findings have been reported in fecal samples of melanoma patients responsive to immunotherapies

[0013] , Applicant observed enrichment of the genus Bacteroides in responsive patients, also consistent with these findings

[0013] , The presence of this genus has been shown to be necessary for effective CTLA-4 blockade in mouse models

[0024] ,

[0128] Moreover, Applicant identified exact correlations between the abundance of these species and the expression of several genes implicated in immunotherapy response [8, 9], These largely include the PD-1 receptor, CTLA-4 receptor, their specific ligands, related receptor homologues, and related ligand homologues. Notably, a greater abundance ofLactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in responders correlated to decreased expression of PD1, CTLA-4, PD-L1, and PD-L2. Lesser abundance of Mesomycoplasma hyopneumoniae, also observed in responders, correlated to decreased PD1, CTLA-4, PD-L1, and PD-L2 expression. Decreased expression of these genes may account in part for the ability of responders to effectively create a PD-1 or CTLA-4 blockade. The microbiome is thought to act through means of immune modulation through the release of metabolites [17, 18], which are then absorbed into the circulatory and lymphatic systems. Certain metabolites are known to cause DNA damage and cell cycle escape, ultimately regulating oncogenic and tumor suppressive pathways [37, 38], Through interacting with host receptors, these metabolites are also capable of triggering a diverse array of immune responses [39, 40], In this way, Applicant propose that these species might influence the expression of the immunotherapy response-related genes above.

[0129] To investigate this mechanistic link, Applicant explored specific immune-related genes’ expression in association with these differentially abundant species. Cytokine expression was significantly enriched in samples with a greater abundance of Mesomycoplasma hyopneumoniae, and significantly diminished with a greater abundance of Corynebacterium kroppenstedtii, Lactiplantibacillus plantarum, and Streptococcuss gordonii . As expected, several of these species also correlated to greater infiltration of certain immune cells, largely T cells and eosinophils. The microbiome is known to be influential in regulating innate immunity through metabolite interactions, ultimately yielding differential recruitment of immune cells and enrichment of immune pathways [17, 18, 41], With significant ties to the immune landscape, the microbiome may influence the regulation of the PD-1 and CTLA-4 signaling pathways, ultimately rendering blockades less effective in certain patients.

[0130] Indeed, fecal transplants have been effective in improving the responses of high-risk melanoma patients to immunotherapies [27, 28], In mouse models, probiotic administration has shown similar effects in improving the PD-1 blockade

[0023] , Other trials have attempted to improve cancer therapy responses through fecal transplants, probiotic administration, and dietary considerations, though they yield variable rates of success

[0029] , Nonetheless, these studies demonstrate that the microbiome is likely functionally linked to checkpoint blockade response.

[0131] Applicant developed two classifier models to predict a patient’s response to anti-PD-1 and anti-CTLA-4 therapies, respectively. These models incorporate the abundance of 244 microbial features with a panel of 37 genes previously implicated in immunotherapy response. When tested on external datasets, these models function with accuracies of 86.7% and 83.5%, respectively. Of 26 patients, the response to anti-PD-1 therapy was correctly predicted for all but 2. This yielded a sensitivity of 80% and a specificity of 95.2%. Of 29 patients, the response to anti-CTLA-4 therapy was correctly predicted for all but 3. This yielded a sensitivity of 60%, though a specificity of 95.8%. Other studies have developed similar algorithms that make use of the gut microbiome to predict a patient’s response to ICIs [25, 26], These models are relatively inaccurate, however, with ROCs consistently in the range of 0.5-0.7. Applicant’s improved accuracies are likely the result of a few considerations, the first of which being that a greater number of samples was used to train the models. Applicant also believes that analyzing tumor tissue samples may have provided a more faithful representation of the microbiome’s local effects than fecal samples would offer. The analysis of stool alone fails to consider the immense quantity of microbial species that reside on the skin

[0042] , Moreover, tumor tissue samples can provide information on the gutskin-axis, which has been notably implicated in an array of skin disorders

[0043] , rather than the gut alone. Thirdly, Applicant believes that it was necessary to consider the patients administered different immunotherapies independently of one another. Anti-PD-1, anti- CTLA-4, and other immunotherapies commonly used for melanomas all act by distinct molecular mechanisms [8], The microbiome and its metabolites might influence these therapies in different ways

[0039] , As such, this disclosure and the improved accuracy may also be a result of discerning anti-PD-1 and anti-CTLA-4 therapies.

[0132] Applicant envisions that such models would be critical to improving cancer treatment outcomes by directing patients to the optimal treatment regimen without costly delays by ineffective therapies. With immunotherapies most often administered as an adjuvant treatment to surgery [2], tumoral tissue is readily available of these patients to be tested with these models. For the patients predicted to be nonresponsive, clinicians may be more informed in electing chemotherapy or radiation therapy as an adjuvant treatment. Moreover, patients would not have to unnecessarily suffer from the potentially debilitating side-effects of checkpoint blockade therapies [6], Alternatively, these models may be used in tandem withthe above trials to confirm the effectivity of gut microbiome modulation from fecal transplants or probiotic administration [23, 27, 28],

[0133] Ultimately, understanding the factors that influence immunotherapy response may significantly improve the outcomes of patients with advanced melanomas. By harnessing the intratumor microbiome for its predictive capacity, this information is useful for predicting immunotherapy response that is reliable, accurate, and straightforward to utilize in a clinical setting.

[0134] Equivalents

[0135] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this technology belongs.{0136] The present technology illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms “comprising,” “including,” “containing,” etc. shall be read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the present technology claimed.10.1.37] Thus, it should be understood that the materials, methods, and examples provided here are representative of preferred aspects, are exemplary, and are not intended as limitations on the scope of the present technology.

[0138] The present technology has been described broadly and generically herein. Each of the narrower species and sub-generic groupings falling within the generic disclosure also form part of the present technology. This includes the generic description of the present technology with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

[0019] In addition, where features or aspects of the present technology are described in terms of Markush groups, those skilled in the art will recognize that the present technology is alsothereby described in terms of any individual member or subgroup of members of the Markush group.

[0140] All publications, patent applications, patents, and other references mentioned herein are expressly incorporated by reference in their entirety, to the same extent as if each were incorporated by reference individually. Some technical publications are referenced by an Arabic number, the full bibliographic citation for each can be found immediately preceding the claims.

[0141] Clauses[0.142] Clause 1. A method for treating melanoma to a patient in need thereof, comprising administering to the patient an effective amount of a therapy comprising a checkpoint inhibitor therapy, wherein the patient expresses a low expression level of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in a biological sample isolated from the patient.

[0143] Clause 2. A method for treating melanoma to a patient in need thereof, comprising administering to the patient an effective amount of a therapy comprising a checkpoint inhibitor therapy, wherein the patient expresses a high expression level of Mesomycoplasma hyopneumoniae in the biological sample, e.g., a blood sample isolated from the patient.

[0144] Clause 3. The method of clause 1 or 2, wherein the melanoma is an advanced melanoma, optionally Stage III or Stage IV, further optionally Stage IV.

[0145] Clause 4. The method of any one of clauses 1-3, wherein the level of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, o Mesomycoplasma hyopneumoniae is normalized prior to determining the expression level, optionally comprising median absolute deviation (MAD) normalization.

[0146] Clause 5. The method of any one of clauses 1-4, wherein the expression level is determined by detecting and quantifying genetic expression of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, or Mesomycoplasma hyopneumoniae in the biological sample isolated from the patient.

[0147] Clause 6. The method of any one of clauses 1-5 or 2, wherein the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy.10148] Clause 7. The method of any one of clauses 1-6, wherein the checkpoint inhibitor therapy comprises an anti-PD-1 therapy selected from nivolumab or pembrolizumab.

[0149] Clause 8. The method of any one of clauses 1-6, wherein the checkpoint inhibitor therapy comprises an anti-CTLA-4 therapy selected from the group of ipilimumab, tremelimumab, or nirovlamb, and ipilnivolumab in combination.

[0150] Clause 9. The method of any one of clauses 1-4, wherein the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy.

[0151] Clause 10. The method of any one of clauses 1-5, wherein the patient is a human patient.

[0152] Clause 11 : The method of any one of clauses 1-10, wherein the biological sample is selected from a plasma sample, a blood sample, a saliva sample, a cystic fluid sample, a spinal fluid sample, a brain fluid sample, a urine sample, a sweat sample, or a tear sample.101531 Clause 12. A method for selecting a therapy for a melanoma patient, comprising of detecting the level of one or more microorganisms selected from Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii and Mesomycoplasma hyopneumoniae in a blood sample isolated from a subject, wherein low expression level of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in a biological sample isolated from the patient selects the patient for a therapy comprising checkpoint inhibitor therapy, and a high level of expression selects the patient for a therapy that excludes a checkpoint inhibitor therapy.(0154] Clause 13. The method of clause 12, wherein the melanoma is an advanced melanoma, optionally Stage III or Stage IV, further optionally Stage IV.|O155| Clause 14. The method of clause 12 or 13, wherein the level of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, or Mesomycoplasma hyopneumoniae is normalized prior to determining the expression level optionally comprising median absolute deviation (MAD) normalization.

[0156] Clause 15. The method of any one of clauses 12-14, wherein the expression level is determined by detecting and quantifying genetic expression of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, or Mesomycoplasma hyopneumoniae in the biological sample isolated from the patient.

[0157] Clause 16. The method of any one of clauses 12-15, wherein the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy.

[0158] Clause 17. The method of any one of clauses 12-16, wherein the checkpoint inhibitor therapy comprises an anti-PD-1 therapy selected from nivolumab or pembrolizumab.101591 Clause 18. The method of any one of clauses 12-16, wherein the checkpoint inhibitor therapy comprises an anti-CTLA-4 therapy selected from the group of ipilimumab, tremelimumab, or nirovlamb, and ipilnivolumab in combination.

[0160] Clause 19. The method of any one of clauses 12-18, wherein the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy.

[0061] Clause 20. The method of any one of clauses 12-19, wherein the patient is a human patient.

[0162] Clause 21 : The method of any one of clauses 12-20, wherein the biological sample is selected from a plasma sample, a blood sample, a saliva sample, a cystic fluid sample, a spinal fluid sample, a brain fluid sample, a urine sample, a sweat sample, or a tear sample.REFERENCES1. SEER Cancer Stat Facts: Melanoma of the Skin Facts & Figures 2023. National Cancer Institute American Cancer Society.2. Practice Guidelines in Oncology: Cutaneous Melanoma. 2019, National Comprehensive Cancer Network (NCCN).3. S chadendorf, D . , et al . , Pooled Analysis of Long- Term Survival Data From Phase II and Phase III Trials of Ipilimumab in Unresectable or Metastatic Melanoma. J Clin Oncol, 2015. 33(17): p. 1889-94.4. Robert, C., et al., Pembrolizumab versus Ipilimumab in Advanced Melanoma. N Engl J Med, 2015. 372(26): p. 2521-32.5. Robert, C., et al., Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med, 2015. 372(4): p. 320-30.6. Kichloo, A., et al., Systemic adverse effects and toxicities associated with immunotherapy: A review. World J Clin Oncol, 2021. 12(3): p. 150-163.7. Patel, S.J., et al., Identification of essential genes for cancer immunotherapy. Nature, 2017. 548(7669): p. 537-542.8. Pardoll, D.M., The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer, 2012. 12(4): p. 252-64.9. Jamieson, N.B. and A. V. Maker, Gene-expression profiling to predict responsiveness to immunotherapy. Cancer Gene Ther, 2017. 24(3): p. 134-140.10. Huang, R., et al., A novel immune -related genes prognosis biomarker for melanoma: associated with tumor microenvironment. Aging (Albany NY), 2020. 12(8): p. 6966- 6980.11. Zhou, Q., et al., Tumor-derived extracellular vesicles in melanoma immune response and immunotherapy. Biomed Pharmacother, 2022. 156: p. 113790.12. Gopalakrishnan, V, et al., Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science, 2018. 359(6371): p. 97-103.13. Frankel, A.E., et al., Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients. Neoplasia, 2017. 19(10): p. 848-855.14. Chaput, N., et al., Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann Oncol, 2017. 28(6): p. 1368-1379.15. Lynch, S.V. and O. Pedersen, The Human Intestinal Microbiome in Health and Disease. N Engl J Med, 2016. 375(24): p. 2369-2379.16. Manos, J., The human microbiome in disease and pathology. Apmis, 2022. 130(12): p. 690-705.17. Rooks, M.G. and W.S. Garrett, Gut microbiota, metabolites and host immunity. Nat Rev Immunol, 2016. 16(6): p. 341-52.18. Kayama, H., R. Okumura, and K. Takeda, Interaction Between the Microbiota, Epithelia, and Immune Cells in the Intestine. Annu Rev Immunol, 2020. 38: p. 23-48.19. Sepich-Poore, G.D., et al., The microbiome and human cancer. Science, 2021. 371(6536).20. Cullin, N., et al., Microbiome and cancer. Cancer Cell, 2021. 39(10): p. 1317-1341.Song, M., A.T. Chan, and J. Sun, Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer. Gastroenterology, 2020. 158(2): p. 322- 340. Rebersek, M., Gut microbiome and its role in colorectal cancer. BMC Cancer, 2021. 21(1): p. 1325. Sivan, A., et al., Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-Ll efficacy. Science, 2015. 350(6264): p. 1084-9. Vetizou, M., et al., Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science, 2015. 350(6264): p. 1079-84. Lee, K.A., et al., Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat Med, 2022. 28(3): p. 535-544. Liang, H., et al., Predicting cancer immunotherapy response from gut microbiomes using machine learning models. Oncotarget, 2022. 13: p. 876-889. Baruch, E.N., et al., Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science, 2021. 371(6529): p. 602-609. Davar, D., et al., Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science, 2021. 371(6529): p. 595-602. Gopalakrishnan, V, et al., The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy. Cancer Cell, 2018. 33(4): p. 570-580. Riaz, N., et al., Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell, 2017. 171(4): p. 934-949.el6. Roh, W., et al., Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med, 2017. 9(379). Zappasodi, R., et al., CTLA-4 blockade drives loss ofT(reg) stability in glycolysis-low tumours. Nature, 2021. 591(7851): p. 652-658. Hong, C., et al., PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples. Microbiome, 2014. 2: p. 33. Dobin, A., et al., STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 2013. 29(1): p. 15-21. Fundel, K., et al., Normalization strategies for mRNA expression data in cartilage research. Osteoarthritis Cartilage, 2008. 16(8): p. 947-55. Newman, A.M., et al., Robust enumeration of cell subsets from tissue expression profiles. Nat Methods, 2015. 12(5): p. 453-7. Liou, G.Y. and P. Storz, Reactive oxygen species in cancer. Free Radic Res, 2010. 44(5): p. 479-96. Jakubczyk, K., et al., Reactive oxygen species - sources, functions, oxidative damage. Pol Merkur Lekarski, 2020. 48(284): p. 124-127. Kim, C.H., Immune regulation by microbiome metabolites. Immunology, 2018. 154(2): p. 220-229. Yang, W. and Y. Cong, Gut microbiota-derived metabolites in the regulation of host immune responses and immune-related inflammatory diseases. Cell Mol Immunol, 2021. 18(4): p. 866-877. Thaiss, C.A., et al., The microbiome and innate immunity. Nature, 2016. 535(7610): p. 65-74.Williams, R.E., Benefit and mischief from commensal bacteria. J Clin Pathol, 1973. 26(11): p. 811-8. De Pessemier, B., et al., Gut-Skin Axis: Current Knowledge of the Interrelationship between Microbial Dysbiosis and Skin Conditions. Microorganisms, 2021. 9(2).

Claims

WHAT IS CLAIMED IS:

1. A method for treating melanoma to a patient in need thereof, comprising administering to the patient an effective amount of a therapy comprising a checkpoint inhibitor therapy, wherein the patient expresses a low expression level of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, or Streptococcus gordonii in a biological sample isolated from the patient.

2. A method for treating melanoma to a patient in need thereof, comprising administering to the patient an effective amount of a therapy comprising a checkpoint inhibitor therapy, wherein the patient expresses a high expression level of Mesomycoplasma hyopneumoniae in the biological sample isolated from the patient.

3. The method of claim 1 or 2, wherein the melanoma is an advanced melanoma, optionally Stage III or Stage IV, further optionally Stage IV.

4. The method of any one of claims 1-3, wherein the level of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, or Mesomycoplasma hyopneumoniae is normalized prior to determining the expression level, optionally comprising median absolute deviation (MAD) normalization.

5. The method of any one of claims 1-4, wherein the expression level is determined by detecting and quantifying genetic expression of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, ox Mesomycoplasma hyopneumoniae in the biological sample isolated from the patient.

6. The method of any one of claims 1-5 or 2, wherein the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy.

7. The method of any one of claims 1-6, wherein the checkpoint inhibitor therapy comprises an anti-PD-1 therapy selected from nivolumab or pembrolizumab.

8. The method of any one of claims 1-6, wherein the checkpoint inhibitor therapy comprises an anti-CTLA-4 therapy selected from the group of ipilimumab, tremelimumab, or nirovlamb, and ipilnivolumab in combination.

9. The method of any one of claims 1-4, wherein the therapy is selected from first-line, second-line, third-line, fourth line or fifth-line therapy.

10. The method of any one of claims 1-9, wherein the patient is a human patient.

11. The method of any one of claims 1-10, wherein the biological sample is selected from a plasma sample, a blood sample, a saliva sample, a cystic fluid sample, a spinal fluid sample, a brain fluid sample, a urine sample, a sweat sample, or a tear sample.

12. A method for selecting a therapy for a melanoma patient, comprising of detecting the level of one or more microorganisms selected from Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii o Mesomycoplasma hyopneumoniae in a biological sample isolated from a subject, wherein low expression level of one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, and Streptococcus gordonii in a biological sample isolated from the patient selects the patient for a therapy comprising checkpoint inhibitor therapy, and a high level of expression selects the patient for a therapy that excludes a checkpoint inhibitor therapy.

13. The method of claim 12, wherein the melanoma is an advanced melanoma, optionally Stage III or Stage IV, further optionally Stage IV.

14. The method of claim 12 or 13, wherein the level of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, or Mesomycoplasma hyopneumoniae is normalized prior to determining the expression level optionally comprising median absolute deviation (MAD) normalization.

15. The method of any one of claims 12-14, wherein the expression level is determined by detecting and quantifying genetic expression of the one or more of Lactiplantibacillus plantarum, Corynebacterium kroppenstedtii, Streptococcus gordonii, ox Mesomycoplasma hyopneumoniae in the biological sample isolated from the patient.

16. The method of any one of claims 12-15, wherein the checkpoint inhibitor therapy is selected from one or more of an anti-PDl therapy, an anti-CTLA-4 therapy, an anti-PDLl therapy, and an anti-PD-L2 therapy.

17. The method of any one of claims 12-16, wherein the checkpoint inhibitor therapy comprises an anti-PD-1 therapy selected from nivolumab or pembrolizumab.

18. The method of any one of claims 12-16, wherein the checkpoint inhibitor therapy comprises an anti-CTLA-4 therapy selected from the group of ipilimumab, tremelimumab, or nirovlamb, and ipilnivolumab in combination.

19. The method of any one of claims 12-18, wherein the therapy is selected from first- line, second-line, third-line, fourth line or fifth-line therapy.

20. The method of any one of claims 12-19, wherein the patient is a human patient.

21. The method of any one of claims 12-20, wherein the biological sample is selected from a plasma sample, a blood sample, a saliva sample, a cystic fluid sample, a spinal fluid sample, a brain fluid sample, a urine sample, a sweat sample, or a tear sample.

Images