Microbiome as a biomarker for prostate cancer

Abstract

Systems and methods are provided for a gut microbiome signature for cancers, including prostate cancer (PCa), using a prostate specific antigen (PSA)-independent, gut microbiome-based PCa biomarker, which can be used for diagnosis, suitability' for screening, treatment decisions and as part of a treatment method.

Description

Dkt. No.92033 / 0040 MICROBIOME AS A BIOMARKER FOR PROSTATE CANCER CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Application No. 63 / 456,086, filed March 31, 2023 and of U.S. Provisional Application No.63 / 546,591, filed October 31, 2023, the contents of each of which are hereby incorporated by reference. FIELD OF INVENTION

[0002] The present invention generally relates to systems and method for using a microbiome score and interpreting the same in relation to cancers. In embodiments, the present invention relates to using artificial intelligence with respect to the same. BACKGROUND OF THE INVENTION

[0003] Throughout this application various publications are referred to, including by number in square brackets. Full citations for these references may be found at the end of the specification. The disclosures of these publications, and all patents, patent application publications and books referred to herein, are hereby incorporated by reference in their entirety into the subject application to more fully describe the art to which the subject invention pertains.

[0004] The microbiome describes the sum of microorganisms and their genetic information that play a role in human microbial ecology [1, 2]. While microbial genetic profiles intuitively can affect the gut, associations with organs outside the gut has drawn attention and criticism [3]. The concept of a gut microbiome-based signature to predict disease outside the intestine is gaining traction, most recently in liver cirrhosis, Alzheimer’s disease, lung cancer, pancreatic cancer, and others [4-7].

[0005] One of the most extreme examples of gut alteration (dysbiosis) linked to prostate cancer is inflammatory bowel diseases (IBD), which has a four-fold (HR: 4.84 (95%CI 3.34-7.02), p<0.001) increased risk of clinically significant PCa among age- and race- matched men (n=1000) [8]. Diet and obesity have associations with prostate cancer and can significantly influence the gut microbiome [9-11]. These studies provide indirect evidence that microbes may be inducing a pro-carcinogenic systemic response [12-15]. Considering the decline in prostate cancer screening with subsequent rise in metastatic disease, a PSA- independent biomarker indicating PCa risk would be advantageous. 4876-8792-2608v.2SUMMARY OF THE INVENTION

[0006] A gut microbiome signature has been developed herein for cancers, including prostate cancer (PCa), using a prostate specific antigen (PSA)-independent, gut microbiome-based PCa biomarker, which can be used for diagnosis, suitability for screening, treatment decisions and as part of a treatment method. Serial sampling using the methods can be employed to track efficacy of treatments or activities, and inform future screening or indicate suitability of different treatments.

[0007] A method is provided of determining whether a gut microbiome sample from a human subject is associated with a clinically significant cancer comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); 4876-8792-2608v.2(d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of clinically significant cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of cancer / negative biopsy.

[0008] A method is provided of determining whether a gut microbiome sample from a human subject is associated with a cancer risk comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; 4876-8792-2608v.2(c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) a cancer risk and (ii) no current risk of cancer, so as to determine if the gut microbiome sample is associated with a cancer risk, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of a cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject not currently at risk of cancer.

[0009] A method is provided of identifying a subject as in need of a cancer screening test, comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; 4876-8792-2608v.2(c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the cutoff indicates the sample comes from a human subject in need of a cancer screening test and a numerical microbiome score less than the cutoff indicates the sample comes from a human subject not in need of a cancer screening test.

[0010] A method is provided of treating a prostate cancer in a subject comprising: (A) obtaining, or having obtained, a diagnosis of the subject as having a clinically significant prostate cancer by a method comprising, on a gut microbiome-containing sample from a human subject, a) performing a genetic analysis on the sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; 4876-8792-2608v.2b) generating a numerical microbiome score from the genetic analysis performed in a), c) comparing the numerical microbiome score generated in b) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of a clinically significant cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of cancer / negative biopsy, (B) treating a subject, having a diagnosis in (A) as having a clinically significant prostate cancer, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or radical prostatectomy (with or without pelvic lymph node dissection) to the subject.

[0011] A kit is provided comprising (a) primer pairs and (b) one or more reagents, wherein in (a) the primer pairs consists essentially of primer pairs for all of: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; 4876-8792-2608v.2f_Bacteroidaceae; and c_Deltaproteobacteria.

[0012] A kit comprising primers or probes specific for the 16s V1V2 variable regions of each of the following, and a human fecal sample: k_Bacteria.p_Bacteroidetes; k_Bacteria.p_Proteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria; k_Bacteria.p_Bacteroidetes.c_Bacteroidia; k_Bacteria.p_Firmicutes.c_Bacilli; k_Bacteria.p_Firmicutes.c_Erysipelotrichi; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales; k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadaceae ; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae; 4876-8792-2608v.2k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Desulf ovibrionaceae; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_Enter obacteriaceae; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae.g_Corynebacterium; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_Bac teroides; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Coproc occus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lachno spira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Oscill ospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Finegoldi ; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Peptonip hilus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Dialister ; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae.g_Eubacterium; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae.g_Campylobacter; and k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Pasteurel laceae.g_Haemophilus.

[0013] In embodiments, the primers or probes comprise pairs for each taxa.

[0014] A method is provided for determining cancer risk using a computer system comprising one or more processors operably connected to one or more memories, the method comprising: 4876-8792-2608v.2(a) accessing, by the one or more processors, from the one or more memories, digitized sequence data associated with a gut microbiome-containing sample; (b) generating, by the one or more processors, 16S rRNA sequence data based on the digitized sequence data; (c) generating, by the one or more processors, a normalized matrix representing (i) presence or not and (ii) abundance of taxa associated with the 16S rRNA sequence data; (d) selecting, by the one or more processors, prevalence data in the normalized matrix which is associated with specific taxonomic features; (e) obtaining, by an artificial intelligence module of the computer system comprising at least one artificial intelligence model, the prevalence data; (f) generating an output, by the artificial intelligence module comprising the at least one artificial intelligence model trained using a training set, based on a query wherein: i) the query comprises the prevalence data; ii) the output comprises a numerical microbiome score; iii) the training set comprises data representing the prevalence of the specific taxonomic features and associated cancer development in patients.

[0015] A method for determining whether a gut microbiome sample from a human subject is associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer comprising: (a) obtaining, or having obtained, a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; 4876-8792-2608v.2g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) risk of future prostate cancer or at risk of a future clinically significant prostate cancer and (ii) low-risk of future prostate cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a risk of future prostate cancer or risk of a future clinically significant prostate cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of future prostate cancer or risk of a future clinically significant prostate cancer, and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of future prostate cancer / negative biopsy.

[0016] A method for reducing a risk of a prostate cancer or risk of a future prostate cancer or clinically significant prostate cancer comprising determining, or receiving a determination that, a human subject is at risk of a future prostate cancer by a method described herein, and then administering to the subject one or more agents effective to modify the numerical microbiome score of the gut microbiome of the subject so as to thereby reduce a risk of a future prostate cancer or risk of a future clinically significant prostate cancer in the subject. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Exemplary embodiments of the present invention will be described with references to the below referenced accompanying figures. 4876-8792-2608v.2

[0018] FIG. 1: CONSORT diagram. On the left we display the biopsy cohort who largely was recruited from the San Antonio Audie Murphy VA as part of a genitourinary tissue bank collection before prostate biopsy. The second group on the right is the men presenting for their follow up within the San Antonio Biomarkers of Risk cohort that undergoes standardized prostate cancer screening. SABOR provides yearly screening and communicates these results to their primary care physician and urologist as part of a community care collaboration. PCa Dx = prostate cancer diagnosis, GG1=grade group 1, GG2+= greater than grade group 2 prostate cancer.

[0019] FIG. 2: Line graph showing biologic gradient of PRIMUS (also referred to herein as the cancer model). We divided the PRIMUS score (also referred to as the microbiome score herein) into deciles with color coded bar graph to demonstrate the representative score. We provide the proportion of any prostate cancer (% cancer) and proportion of GG2+ (% GG2) prostate cancer in the chart. The proportion are also represented in the line graph showing increasing risk of either cancer with rising PRIMUS Score. The final two rows of the table show the raw number of patients the make up the proportions.

[0020] FIGS. 3A-3I: Association with prostate cancer detection and performance. Operational performance of the microbiome score. (3A) Training and validation. The ROC- AUC curve depicts training (2 / 3) and validation (1 / 3) model development with associated histograms for cancer and no cancer, we show that this validation techniques of the score yield significant results. (3B) 10-fold cross-validation. An alternative strategy for model testing involved 10-fold cross-validation during microbiome score development compared to the hold-out validation due to overfitting of 10-fold cross-validations. (3C) Decision analysis curve compared to that of PSA. The decision analysis curve showed that the use of the microbiome score for predicting prostate cancer had the greatest benefit relative to PSA level, biopsy all or biopsy none at thresholds ranging from 10% to 35%. Higher PSA concentrations had a greater net benefit above 35%. (3D) ROC curve analysis of the ability of the microbiome score to predict GG2+ prostate cancer. A microbiome score of 0.25 was used as the threshold to call a positive test and was associated with a sensitivity, specificity, PPV and NPV of 84.8%, 46.9%, 22.2% and 94.5%, respectively. We then used the AUC curve to test the scores performance for the association with clinically significant cancer (GG2+) and noted a similar and significant curve compare the detection of any prostate 4876-8792-2608v.2cancer. (3E) 10-fold cross-validation for GG2+. We again secondarily tested an ROC curve using 10-fold cross validation noting an AUC of 0.712 (95% CI 0.708–0.712) to compliment the hold-out validation. (3F) Decision analysis curve for GG2+ compared to PSA. The decision analysis curve showed that the ability of the microbiome score to predict GG2+ prostate cancer was similar to that of PSA in the lower PSA categories, biopsy all or biopsy none at thresholds with a limit around the 25% threshold. (3G) Correlations between the cross-validation and training / validation models. There was a strong correlation (r=0.79, p<0.001) between the scores. (3H) Association between PPV or NPV and PCa detection or GG2+ PCa detection, by decile. The PRIMUS score was divided equally into 10 groups (deciles). The increasing PPV and decreasing PPV per decile show the biological gradient of the score. (3I) Microbiome score alluvial plot. Alluvial plot showing microbiome score scores in relation to a range of important serum PSA level cutoffs and outcomes used to develop and test the microbial score. (3B). The PRIMUS score cut off of 0.25 will divide the patients into a positive or negative test. These then flow into the “case” (i.e., cancer) or “control” (i.e., no cancer).

[0021] FIGS. 4A-4B: (4A) Time to any PCa diagnosis. A Kaplan‒Meier plot showed decreased prostate cancer-free survival in patients with a baseline PRIMUS score of 0.25 or greater (hazard ratio (HR) 3.97, 95% CI 2.14-7.36, p<0.001). (4B) Time to GG2+ PCa diagnosis. A Kaplan‒Meier plot showed decreased GG2+ prostate cancer-free survival in patients with a baseline microbiome score of 0.25 or greater (HR 11.12, 95% CI 1.42‒87.17; p<0.001).

[0022] FIGS. 5A-5C: External validation with calculation of the PRIMUS score from patients with inflammatory bowel disease noting modifiability in a high-risk cohort. (5A) We obtained 16S rRNA gene DNA sequences from external studies the human microbiome project 2 (HMP2) (top two box and whisker plots) and Crother’s et al. study of FMT in IBD (bottom 3 figures). We calculated the PRIMUS score for PCa and compared the scores of patients with IBD (red) and without IBD (blue). The box and whisker plots in the left subpanel show microbiome score scores of women with and without IBD, which were not significantly different (Student’s t test, p= 0.98). On the right, scores were significantly higher among men with IBD (p=0.0024). (5B) Next, we explored the microbiome score in IBD patients who did or did not undergo fecal microbiota transplantation (FMT). We found lower microbiome scores in donor stool samples than in either the international or control stool samples from patients with IBD (both p<0.001). A comparison of the microbiome 4876-8792-2608v.2score scores 25-35 days post FMT revealed that the intervention group had similar low microbiome scores (p=0.12), while the score of the placebo group was significantly greater than that of the intervention group (p=0.002). (5C) Microbiome score plotted continuously with a smooth line showing separation 20 days post FMT with elevation starting after the end of transplantation treatment on day 35.

[0023] FIGS. 6A-6C: Microbiome diversity profiles. (6A) Alpha diversity according to cancer status (top). Stacked bar plots showing the phylum level of microbes within each sample grouped based on cancer status. GG2+ represents men with grade group 2 or higher (intermediate to high grade) prostate cancer. GG1 represents low-risk prostate cancer. The negative biopsy group included men with a risk high enough to warrant a prostate biopsy; however, the biopsy returned negative. This group may have false negatives. The low-risk group included men who underwent prostate cancer screening at usual risk low enough to not warrant a prostate biopsy. (6B) Alpha diversity: Box and whisker plots comparing alpha diversity by cancer status. Each dot represents an individual microbiome according to the Shannon diversity index (SDI). The box represents the 50% interquartile range (IQR) of the SDI distribution, and the line represents the median SDI. There was no difference between groups (P=0.49). (6C) Beta diversity: A scatter plot based on principal coordinates analysis (PCoA) to represent microbial variation is shown. Each color dot represents a different cancer status. We noted a statistically significant difference among groups (PERMANOVA result for cancer grade status: P < 0.01).

[0024] FIGS. 7A-7B: (7A) Box plots of alpha diversity by race. Box plots for White (Hispanic and non-Hispanic), Black, and Other individuals showed no significant difference in alpha diversity. (7B) Principal component analysis plot for the gut microbiome and race. PCoA plot of microbial variation by race (White, Black, Other) showed significant differences in beta diversity among groups (PERMANOVA: P < 0.01).

[0025] FIGS. 8A-8B: Pipeline showing the development and analysis of the PRIMUS Microbiome Score of Cancer of the Prostate (MiSCaP). 8A The gut microbiome score development schema. The process of analysis included 16S rRNA gene V1-V2 sequencing, cleaning, OTU assignment, and normalization. After, we selected taxonomic features for the machine learning algorithm by forest plot analysis, resulting in an output score ranging from 0 to 1, with higher scores indicating a greater risk of clinically significant prostate cancer. Microbial analysis to select taxonomic features for analysis. We used LEfSe analysis to identify the most differentially abundant taxa. We used LDA score > 2 (x-axis) (left). On 4876-8792-2608v.2the right is the representative cladogram. Machine Learning Models. Using the same 39 bacteria was used machine learning for a 10-fold cross validation and compared this to the scores of a training-validation set. 8B Features list. The features correspond to a particular microbial representative (c=class, o=order, f=family, g=genus)

[0026] FIG. 9: Microbiome scores generated from different bacterial variable regions are significantly correlated. The correlation scatter plot shows 16S rRNA gene V1-V2 sequence scores on the x-axis and the microbiome score generated via the 16S rRNA gene V3-V4 sequence from the same samples on the y-axis (Pearson correlation coefficient = 0.77, p<0.001).

[0027] FIG. 10: Microbiome score scores correlate with PSA levels. Scatter plot with log(PSA level) on the y-axis and the microbiome score on the x-axis showing good correlation between values (r=0.12, p<0.001).

[0028] FIGS. 11A-11B: Microbiome model using GG2+ as the endpoint. A. Creation and validation of the GG2+ microbiome score. The same methods used to create our microbiome score were implemented for all prostate cancer grades compared to no prostate cancer in order to create a new microbiome score with the endpoint comprising only clinically significant prostate cancer (i.e., GG2+). We again used a trained and validated model and compared it to a 10-fold cross-validation technique. These two scores showed excellent correlation (r=0.8, P<0.001). The operational characteristics at a threshold of 0.158 included a sensitivity, specificity, negative predictive value, and positive predictive value of 72.2%, 58.9%, 24.1%, and 92.2%, respectively. However, the score was not predictive of future risk of GG2+ prostate cancer (HR 1.39, p=0.45; data not shown). The new models focused only on the GG2+ correlation with the original score. Three scatter plots representing a second, separately developed microbiome score focused only on GG2+ detection compared to the original microbiome score. The original microbiome score for detecting all prostate cancers had better performance for predicting GG2+ cancer even though the newer model was built with the endpoint of GG2+ cancer. B. We constructed correlation plots for no cancer, GG1 cancer and GG2+ cancer and noted a significant correlation between scores (Spearman rho, r=0.82, p<0.001). Decision analysis curve: The decision analysis curve showed that the ability of the microbiome score to predict GG2+ prostate cancer was similar to that of PSA in the lower PSA categories, biopsy all or biopsy none at thresholds with a limit around the 25% threshold. 4876-8792-2608v.2

[0029] FIG. 12: Alterations in the microbiome score with previous antibiotic use. Box plots showing the median and 95% confidence intervals (95% CIs) of the microbiome score in patients with and without a previous biopsy (antibiotic exposure) and the resultant endpoint of cancer compared to patients without cancer. There was no difference between patients without cancer and those with cancer with or without previous antibiotic exposure as a result of prostate biopsy.

[0030] FIG.13: Line chart showing the change in the microbiome over time. The x-axis shows the microbiome score, and the y-axis shows the time in days. Fifty-three patients without cancer were recruited from the SABOR cohort at the day 0 time point, and data for 1 or more downstream time points were plotted with colored dots. The line represents change over time. Data for the day 0 timepoint and one or more subsequent timepoints are plotted with colored dots, and the line represents the change over a median of 1.17 years (IQR: 0.99-1.34 years). Overall, the microbiome composition did not significantly change over the time frame measured (paired t test, p=0.833). The gray dots and lines represent scores that started below the threshold of 0.25 (n=25, 47.2%); the yellow dots / lines represent scores that started below the threshold and were identified as above the threshold at a later date without intervention (n=12, 22.6%); the purple dots / lines represent scores that started above the threshold and were identified as below the threshold without intentional intervention (n=7, 13.2%); and the red dots / lines represent scores that started above and remained above the threshold (n=9, 17.0%). None of the subjects developed prostate cancer during the follow-up period, although 6 subjects underwent prostate biopsy. The longitudinal cohort was diverse, with 30% (n=16) Black, 38% (n=20) Latino / Hispanic, and 32% (n=17) White individuals.

[0031] FIG. 14: Predicted metabolic functional analysis between high (red / bottom) and low (blue / top) PRIMUS scores. Differential abundance analysis identified the most differentially abundant pathways between high and low PRIMUS score groups. Comparison of PICRUSt profiles utilized the Mann-Whitney test with FDR P-value adjustment. The bar plot figure describes the top categories related to cancer or metabolism enriched in either high (red) or low (blue) PRIMUS score. All results shown achieved FDR adjusted P-values < 1e-3 with a relative fold change exceeding 1.2. Relative fold change values were capped at 10.

[0032] FIGS. 15A-15B: Polyketide Synthase (PKS) as an added variable to the PRIMUS score. (15A) Time to any PCa diagnosis with PKS as an added variable. A 4876-8792-2608v.2Kaplan‒Meier plot showed the highest increasing incidence of prostate cancer in patients with a baseline PRIMUS score of 0.25 or greater and pks+ however did not show a statistically significant difference compared to the PRIMUShighpks- group (p=0.26). (15B) Time to GG2+ PCa diagnosis with PKS as an added variable. A Kaplan‒Meier plot showed increased incidence of GG2+ prostate cancer in patients with a baseline microbiome score of 0.25 or greater and pks+, however was not a statistically difference when compared to PRIMUShighpks- (p=0.55).

[0033] FIG. 16: Consort Diagram. Sample collection of the gut microbiome was collected from 2016 to 2021 with 500 samples being collected in 2021. Several types of sequencing were used for different; however, samples underwent 16s V1-V2 sequencing and was used as the baseline sequencing method for biomarker development.

[0034] FIG. 17: Alpha diversity of cancer status (top). Standard bar plots are shown noting the phylum level of microbes within each sample grouped based on cancer status. GG2+ represents the men with grade group 2 or high (intermediate to high grade) prostate cancer. GG1 represents low risk prostate cancer. The negative biopsy group are men with a risk high enough to warrant a prostate biopsy; however, the biopsy returned negative. This group may have false negatives. The “low-risk” group are men that underwent prostate cancer screening at usual risk low enough to not warrant a prostate biopsy. Alpha diversity analysis (Bottom left): bar and whisker plots are shown that compare the cancer status groups noting. Each dot represents a person’s microbiome plotted based on the Shannon Diversity Index (SDI). The box represents the 95% confidence interval, and the line represents the median SDI; however there was no difference between groups (P>0.05). Beta Diversity (Bottom Right): a scatter plot is shown based on principle coordinates analysis (PCoA) as the axis to represent microbial variation. Each color dot represents a different cancer status. A statistically significant difference between groups is noted (PERMANOVA result for cancer grade status: P < 0.01).

[0035] FIGS. 18A-18C: Microbiome Score of Cancer the Prostate (MiSCaP) development and analysis. 18A. Gut microbiome score development schema. The process of analysis is shown after 16s V1-2 sequencing, cleaning, OTU assignment, and normalization. After this process taxonomic features were selected for the machine learning algorithm by forest plot analysis. At the end of the analysis the output is a score valued 0 to 1 with higher scores having more risk of clinically significant prostate cancer. 18B Microbial analysis to select taxonomic features for analysis. LEfSe analysis was selected to 4876-8792-2608v.2identify the most differentially abundant taxon. An LDA score > 2 was used shown (x-axis) on the left. On the right is the representative cladogram. Cladogram of significant changes at all taxonomic levels. The root of the cladogram represents the domain bacteria. The size of node represents the abundance of taxa. 18C AUC of Microbiome Score prediction of GG2+ prostate cancer. The upper left of the figure shows the AUC of 69.7 (95%CI 69.4 - 69.7). The threshold used to call a positive test was a microbiome score of 0.158 that led to a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 75.4%, 57%, 24.17% and 92.72%. The top left shows the top 15 microbes and their importance level in the model. The bottom two histograms show the microbiomes scores of those without cancer or grade group 1 prostate cancer on top (green) compared to the distribution of men with grade group 2 or higher prostate cancer on the bottom (orange).

[0036] FIG. 19: Measures of internal microbiome score robustness. Left. Correlation plot comparing the microbiome score generated from different bacterial variable regions. The correlation scatter plot shows V1V216s sequencing generated scores on the x axis and the microbiome score generated via V3V416s sequencing on the same samples. The scores are significantly correlated (Pearson correlation = 0.77, p<0.001). Right. Line chart noted the change in microbiome over time. The x axis shows the microbiome score, and y axis shows time in days. N= 53 subjects without cancer were recruited from the SABOR cohort with day 0 time point and 1 or more downstream time points are plotted with a colored dot and line representing the change over time. The grey dot and lines started below the GG2+ microbiome score of 0.158 (n=13, 24.5%). The yellow dot / line started below the threshold and tracked above the threshold at a later date without intervention (n=8, 15.1%). The purple dot / line started above the threshold and tracked below without intentional intervention (n=5, 9.4%). Lastly, the red dot / line started above the threshold and stayed above the threshold (n=27, 50.9%). Overall, the microbiome was not statistically different over the timeframe compared (paired T test, p=0.78). None of the subject developed prostate cancer during the follow up period although 6 subjects underwent prostate biopsy. A diverse longitudinal cohort of 30% (n=16) Black, 38% (n=20) Latino / Hispanic, and 32% (n=17) White was used.

[0037] FIG.20: Results of the model built for any prostate cancer that includes low risk prostate cancer compared to no cancer. The model required the same base of 39 microbes as the GG2+ model; however, had a different output in machine learning random forest analysis. The features list of microbes is described in the top right of the figure. (Top Left) 4876-8792-2608v.2Using the microbiome score output threshold of 0.256 for the diagnosis of any prostate cancer. Using a training / validation set the microbiome score achieved an AUC of 0.685 (95% CI 0.674 – 0.686, p<0.001). In a second analysis using a 10-fold cross validation approach the microbiome score attained an AUC of 0.732 (95% CI 0.728 – 0.732, p<0.001). The operational characteristics of sensitivity, specificity, positive predictive value, and negative predictive value at this threshold are 84.4%, 53.9%, 41.9%, and 89.8%, respectively. A histogram of each microbiome score shown in blue for negative and red for positive for cancer. Because the two scores have different methods of generation, the outputs were compared on a scatter plot with correlation (Pearson 0.78In univariant analysis, the microbiome score (any prostate cancer vs. no prostate cancer) was statistically significant (p<0.001) and in logistic regression had an odds ratio of 11.7 (95% CI 4.36- 33.2, P<0.001).

[0038] FIGS. 21A-21B: Operational performance of the microbiome signature. (A) Microbiome Score Performance in the detection of prostate cancer. (i) Train-Validate. The ROC curve first is displayed with a training (2 / 3) and validation (1 / 3) model development with associated histograms for cancer and no cancer (AUC 0.686, 95% CI 0681-0.687, p<0.001). (ii) 10-fold cross validation. Second, the AUC is displayed for 10-fold cross validation method for microbiome signature development (AUC 0.717, 95% CI 0.713- 0.717, p<0.001). The sensitivity, specificity, positive predictive value and negative predictive value are 82.7%, 52.1%, 40.2%, and 88.5%, respectively. (iii) Features of the microbiome signature. The top 10 feature characteristics of the microbiome signature with the length of each line demonstrating their relative importance. (iv) Decision analysis curve compared to PSA. The decision analysis curve shows the microbiome signature to predict any prostate cancer has the greatest benefit relative to PSA, biopsy all or biopsy none at thresholds from 10% to 35%. PSA has the greatest net benefit beyond 35%. (v) Time to any PCa. A Kaplan-Meier graph is shown noting less prostate cancer free survival in those with a baseline microbiome signature of 0.25 or greater (HR 3.97, 95% CI 2.14-7.36, p<0.001). (B). AUC of Microbiome Score prediction of GG2+ prostate cancer. (vi) ROC curve using the microbiome signature for detection of GG2+ prostate cancer. The ROC curve displays an AUC of 0.712 (95%CI 0.708 – 0.712). The threshold used to call a positive test was a microbiome score of 0.25 that led to a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 84.8%, 46.9%, 22.2% and 94.5%, respectively. (vii) Time to GG2+. A Kaplan-Meier graph is shown noting less GG2+ 4876-8792-2608v.2prostate cancer free survival in those with a baseline microbiome signature of 0.25 or greater (HR 11.12, 95% CI 1.42-87.17, p<0.001).

[0039] FIG. 22: Microbiome signature alluvial plot. An alluvial plot is shown for the microbiome signature in relation to a range of important PSA cut offs and outcomes used to develop and test the microbial signature.

[0040] FIGS.23A - 23C: Microbiome score is modifiable in a high-risk cohort. (A) The 16s sequences were obtained and calculated the microbiome signature for prostate cancer. The score of those with IBD (red) was compared to those without IBD (blue). On the left the microbiome signatures were compare using box and whisker plot in women and show no difference in the score (Students T, p= 0.98). On the right men with and without IBD are shown noting a significantly higher signature score in men with IBD (p=0.0024). (B) The microbiome signature was then explored in IBD subjects undergoing fecal microbiota transplant or not. The donor stool had a significantly low microbiome score and compared to both groups (international and control) with IBD (both, p<0.001). Then comparing the microbiome signatures after 25-35 days of FMT the intervention group assumed a similar low microbiome score as the donors (p=0.12), while the placebo group was significantly higher than the intervention group (p=0.002). (C) When the microbiome signature is plotted continuously with a smooth line and there is a nic separation after 20 days with elevation starting after stopping the FMT at day 35.

[0041] FIG. 24: Association of genotoxin presence with cancer presence and grade. SABOR and PSA cohorts are pooled (n = 388). DETAILED DESCRIPTION OF THE INVENTION

[0042] The present invention generally relates to a gut microbiome signature for cancers, including prostate cancer (PCa), using a prostate specific antigen (PSA)- independent, gut microbiome-based PCa biomarker, which can be used for diagnosis, suitability for screening, treatment decisions and as part of a treatment method.

[0043] A method is provided of determining whether a gut microbiome sample from a human subject is associated with a clinically significant cancer comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; 4876-8792-2608v.2(b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of clinically significant cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of cancer / negative biopsy.

[0044] A method is provided for detecting the presence or absence of a clinically significant cancer in a human subject, the method comprising: 4876-8792-2608v.2(a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample, so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) detecting: i) the presence of a clinically significant cancer if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between clinically significant cancer and low-risk cancer / negative biopsy; and ii) the absence of a clinically significant cancer, defined as low-risk of cancer / negative biopsy, if the numerical microbiome score generated in (c) is less than the predetermined reference value.

[0045] In embodiments, the numerical microbiome score generated in (c) is generated by: (Y) for each bacterial taxon listed in (b): 4876-8792-2608v.2tabulating the bacterial taxon abundance value according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random forest model, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (Z) summing all classification outputs produced in (Y) from all the bacterial taxa so as to create a numerical microbiome score reflecting the majority vote for the sample.

[0046] In embodiments, the percentage abundance values are provided to a random forest model associated with determining a risk of a clinically significant cancer.

[0047] In embodiments, the percentage abundance values are provided to a random forest associated with determining a risk of a cancer.

[0048] In embodiments, the method further comprises determining whether a gut microbiome sample from a human subject is associated with a risk of a cancer and wherein a second numerical microbiome score is generated by: (W) for each bacterial taxon listed in (b): tabulating the bacterial taxon abundance value according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random model associated with determining a risk of cancer, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (X) summing all classification outputs produced in (W) from all the bacterial taxa so as to create a second numerical microbiome score reflecting the majority vote for the sample, wherein a second numerical microbiome score equal to or greater than a second predetermined reference value which delineates a cutoff between (i) a risk of a cancer and (ii) no current risk of cancer indicates the sample comes from a human subject at risk of a cancer and a second numerical microbiome score less than the second predetermined reference value indicates the sample comes from a human subject not currently at risk of cancer. 4876-8792-2608v.2

[0049] In embodiments, the method further comprises whether a gut microbiome sample from a human subject is associated with a risk of a clinically significant cancer and wherein a numerical second numerical microbiome score is generated by: (U) for each bacterial taxon listed in (b): tabulating the bacterial taxon abundance according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random model associated with determining a clinically significant cancer, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (V) summing all tree classification outputs produced in (U) from all the bacterial taxa so as to create a second numerical microbiome score reflecting the majority vote for the sample, wherein a second numerical microbiome score equal to or greater than a second predetermined reference value which delineates a cutoff between (i) a clinically significant cancer and (ii) low-risk cancer / negative biopsy indicates the sample comes from a human subject at risk of a clinically significant cancer and a numerical microbiome score less than the second predetermined reference value indicates the sample comes from a human subject of low-risk cancer / negative biopsy.

[0050] In embodiments, the following taxa are the subject of the genetic analysis: (a) k_Bacteria.p_Proteobacteria (b) k_Bacteria.p_Actinobacteria.c_Actinobacteria (c) k_Bacteria.p_Bacteroidetes.c_Bacteroidia (d) k_Bacteria.p_Firmicutes.c_Bacilli (e) k_Bacteria.p_Firmicutes.c_Erysipelotrichi (f) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria (g) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria (h) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria (i) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales (j) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales (k) k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales (l) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales 4876-8792-2608v.2(m) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales (n) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales (o) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales (p) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae (q) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae (r) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacterace ae (s) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellac eae (t) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromona daceae (u) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae (v) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae (w) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae (x) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae (y) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_ Desulfovibrionaceae (z) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae (aa) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_ Enterobacteriaceae (bb) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae.g_Corynebacterium (cc) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae. g_Bacteroides (dd) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_ Coprococcus (ee) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_L achnospira (ff) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_ Oscillospira 4876-8792-2608v.2(gg) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fin egoldia (hh) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pe ptoniphilus (ii) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Di alister (jj) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae.g_Eubacterium (kk) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae.g_Campylobacter, (ll) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Past eurellaceae.g_Haemophilus).

[0051] In embodiments, the cancer is prostate cancer.

[0052] In embodiments, the clinically significant cancer is Grade Group 2 (GG2) or greater and the low-risk cancer / negative biopsy is Grade Group 1 (GG1) or below.

[0053] In embodiments, the cancer is colon cancer or lung cancer.

[0054] In embodiments, the sample is a fecal sample.

[0055] In embodiments, the sample is a fecal sample obtained by a swab.

[0056] In embodiments, the sample has been obtained by the subject.

[0057] In embodiments, the genetic analysis is performed for a variable region of bacterial 16S rRNA genes.

[0058] In embodiments, the genetic analysis comprises one or more of paired end read trimming and merging; primer removal; QIIME; QC filtering; chimera detection; human DNA detection; and non-16S rRNA gene detection, so as to produce a set of 16S rRNA gene sequences for analysis.

[0059] In embodiments, the method further comprises filtering out from the analysis genetic analysis, presence indication, and / or abundance values of the following background taxa prior to calculating the numerical microbiome score: (a) c_Chloroplast (b) c_Alphaproteobacteria (c) f_Propionibacteriaceae (d) o_Oceanospirillales (e) f_Intrasporangiaceae 4876-8792-2608v.2(f) f_Micrococcaceae (g) f_Cytophagaceae (h) o_Hydrogenophilales (i) f_Alcaligenaceae (j) f_Burkholderiaceae (k) f_Burkholderiales_incertae_sedis (l) f_Comamonadaceae (m) f_Oxalobacteraceae.

[0060] In embodiments, the genetic analysis is performed on variable regions 1 and 2 and / or variable regions 3 and 4 of the 16S rRNA gene of each taxon.

[0061] In embodiments, the numerical microbiome score generated in (c) is generated by, for each bacterial taxon listed in (b): i) ascribing a 1 or a 0 if present or not present in the sample, respectively, so as to generate a presence value; ii) ascribing a predetermined weighting value to each bacterial taxon, so as to generate a weighted bacteria taxon value; iii) modifying the abundance value determined by a predetermined weighting for each bacterial taxon, so as to generate a weighted abundance value; and iv) inputting the presence value, weighted abundance value, and weighted bacterial taxon value into a formula and applying the formula so as to generate an output which is the numerical microbiome score.

[0062] In embodiments, the methods further comprise treating a subject, identified as having a clinically significant prostate cancer by the method, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or a radical prostatectomy (with or without pelvic lymph node dissection).

[0063] A method is provided of determining whether a gut microbiome sample from a human subject is associated with a cancer risk comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; 4876-8792-2608v.2(b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) a cancer risk and (ii) no current risk of cancer, so as to determine if the gut microbiome sample is associated with a cancer risk, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of a cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject not currently at risk of cancer.

[0064] A method is provided of determining whether a gut microbiome sample from a human subject is associated with a cancer risk comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; 4876-8792-2608v.2(b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) determining: i) that the gut microbiome sample from the human subject is associated with a current cancer risk if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between a cancer risk and no current risk of cancer; ii) that the gut microbiome sample from the human subject is not associated with a current cancer risk if the numerical microbiome score generated in (c) is less than the predetermined reference value.

[0065] A method is provided of identifying a subject as in need of a cancer screening test, comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; 4876-8792-2608v.2(b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the cutoff indicates the sample comes from a human subject in need of a cancer screening test and a numerical microbiome score less than the cutoff indicates the sample comes from a human subject not in need of a cancer screening test.

[0066] A method is provided of identifying a subject as in need or not in need of a cancer screening test, comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; 4876-8792-2608v.2(b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) identifying the subject as: i) in need of a cancer screening test if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between clinically significant cancer and low-risk cancer / negative biopsy; ii) not in need of a cancer screening test if the numerical microbiome score generated in (c) is less than the predetermined reference value.

[0067] In embodiments, the following taxa are the subject of the genetic analysis: (a) k_Bacteria.p_Proteobacteria (b) k_Bacteria.p_Actinobacteria.c_Actinobacteria (c) k_Bacteria.p_Bacteroidetes.c_Bacteroidia (d) k_Bacteria.p_Firmicutes.c_Bacilli (e) k_Bacteria.p_Firmicutes.c_Erysipelotrichi 4876-8792-2608v.2(f) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria (g) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria (h) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria (i) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales (j) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales (k) k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales (l) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales (m) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales (n) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales (o) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales (p) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae (q) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae (r) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacterace ae (s) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellac eae (t) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromona daceae (u) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae (v) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae (w) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae (x) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae (y) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_ Desulfovibrionaceae (z) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae (aa) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_ Enterobacteriaceae (bb) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae.g_Corynebacterium 4876-8792-2608v.2(cc) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae. g_Bacteroides (dd) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_ Coprococcus (ee) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_L achnospira (ff) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_ Oscillospira (gg) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fin egoldia (hh) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pe ptoniphilus (ii) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Di alister (jj) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae.g_Eubacterium (kk) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae.g_Campylobacter, (ll) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Past eurellaceae.g_Haemophilus.

[0068] In embodiments, the methods further comprise treating a subject, identified as having a clinically significant prostate cancer by the method, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or a radical prostatectomy (with or without pelvic lymph node dissection).

[0069] A method is provided of treating a prostate cancer in a subject comprising: (A) obtaining, or having obtained, a diagnosis of the subject as having a clinically significant prostate cancer by a method comprising, on a gut microbiome-containing sample from a human subject, a) performing a genetic analysis on the sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; 4876-8792-2608v.2g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; b) generating a numerical microbiome score from the genetic analysis performed in a), c) comparing the numerical microbiome score generated in b) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of a clinically significant cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of cancer / negative biopsy, (B) treating a subject, having a diagnosis in (A) as having a clinically significant prostate cancer, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or radical prostatectomy (with or without pelvic lymph node dissection) to the subject.

[0070] A method of treating a prostate cancer in a subject comprising: 4876-8792-2608v.2(A) obtaining, or having obtained, a diagnosis of the subject as having a clinically significant prostate cancer by a method comprising, on a gut microbiome-containing sample from a human subject, a) performing a genetic analysis on the sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; b) generating a numerical microbiome score from the genetic analysis performed in a), c) diagnosing the subject as: (i) having a clinically significant prostate cancer if the numerical microbiome score generated in b) is equal to or greater than a predetermined reference value which delineates a cutoff between clinically significant cancer and low-risk cancer / negative biopsy, (ii) not having a clinically significant prostate cancer, defined as low-risk cancer / negative biopsy, if the numerical microbiome score generated in b) is less than the predetermined reference value, 4876-8792-2608v.2(B) treating the subject, having a diagnosis in (A) as having a clinically significant prostate cancer, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or radical prostatectomy (with or without pelvic lymph node dissection) to the subject.

[0071] In embodiments, the numerical microbiome score generated in b) is generated by: (Y) for each bacterial taxon listed in a): tabulating the bacterial taxon abundance value according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random forest model, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (Z) summing all classification outputs produced in (Y) from all the bacterial taxa so as to create a numerical microbiome score reflecting the majority vote for the sample.

[0072] In embodiments, the percentage abundance values are provided to a random forest model associated with determining a clinically significant cancer.

[0073] In embodiments, the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a clinically significant cancer is 0.158.

[0074] In embodiments, the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a cancer is 0.25 or above.

[0075] In embodiments, the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a cancer is 0.256.

[0076] In embodiments, the predetermined refence value associates with a prostate cancer or prostate cancer risk is determined as set forth below under “Microbiome Signature identification”.

[0077] In embodiments, the following taxa are the subject of the genetic analysis: (a) k_Bacteria.p_Proteobacteria (b) k_Bacteria.p_Actinobacteria.c_Actinobacteria (c) k_Bacteria.p_Bacteroidetes.c_Bacteroidia (d) k_Bacteria.p_Firmicutes.c_Bacilli 4876-8792-2608v.2(e) k_Bacteria.p_Firmicutes.c_Erysipelotrichi (f) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria (g) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria (h) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria (i) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales (j) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales (k) k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales (l) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales (m) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales (n) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales (o) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales (p) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae (q) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae (r) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacterace ae (s) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellac eae (t) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromona daceae (u) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae (v) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae (w) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae (x) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae (y) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_ Desulfovibrionaceae (z) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae (aa) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_ Enterobacteriaceae (bb) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae.g_Corynebacterium 4876-8792-2608v.2(cc) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae. g_Bacteroides (dd) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_ Coprococcus (ee) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_L achnospira (ff) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_ Oscillospira (gg) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fin egoldia (hh) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pe ptoniphilus (ii) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Di alister (jj) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae.g_Eubacterium (kk) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae.g_Campylobacter, (ll) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Past eurellaceae.g_Haemophilus.

[0078] In embodiments, the methods further comprise performing the method on the first individual subject at one or more timepoints subsequent to an initial performance of the method on the first individual subject when the subject has been identified by the method as having risk of clinically significant cancer by the initial performance, and comparing the numerical microbiome score for the initial and subsequent one or more timepoint performances so as to determine if risk of clinically significant cancer identified in the subject in initial performance is (a) worsening, (b) improving, or (c) showing no change.

[0079] In embodiments, the first individual subject is administered a clinical cancer treatment subsequent to the initial performance of the method on the first individual subject and prior to at least one of the one or more timepoints subsequent.

[0080] In embodiments of the methods, the percentage abundance values are provided to a random forest model associated with determining a clinically significant cancer. 4876-8792-2608v.2

[0081] In embodiments, the random forest plot, which may be a machine learning model, may be trained by randomly generating decision trees. Each node in a decision tree of the random forest may be configured to consider a random subset of features, which may be various taxa, to calculate an output of the decision tree. The random forest may then combine the output of individual decision trees to generate a final output. In embodiments, bootstrapping, which is the process of randomly selecting items from a training dataset, may also be employed. In embodiments, bootstrapping may assemble randomized decisions based on several decisions and makes the final decision based on the majority voting.

[0082] In embodiments of the methods, the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a clinically significant cancer is 0.158.

[0083] In embodiments of the methods, the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a cancer is 0.256.

[0084] A kit is provided comprising (a) primer pairs and (b) one or more reagents, wherein in (a) the primer pairs consists essentially of primer pairs for all of: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria.

[0085] A method is provided for determining cancer risk using a computer system comprising one or more processors operably connected to one or more memories, the method comprising: 4876-8792-2608v.2(a) accessing, by the one or more processors, from the one or more memories, digitized sequence data associated with a gut microbiome-containing sample; (b) generating, by the one or more processors, 16S rRNA sequence data based on the digitized sequence data; (c) generating, by the one or more processors, a normalized matrix representing (i) presence or not and (ii) abundance of taxa associated with the 16S rRNA sequence data; (d) selecting, by the one or more processors, prevalence data in the normalized matrix which is associated with specific taxonomic features; (e) (e) obtaining, by an artificial intelligence module of the computer system comprising at least one artificial intelligence model, the prevalence data; (f) generating an output, by the artificial intelligence module comprising the at least one artificial intelligence model trained using a training set, based on a query wherein: i) the query comprises the prevalence data; ii) the output comprises a numerical microbiome score; iii) the training set comprises data representing the prevalence of the specific taxonomic features and associated cancer development in patients;

[0086] In embodiments, the artificial intelligence module may be a machine learning module. In embodiments, the at least one artificial intelligence model may be a machine learning model. For example, the at least one artificial intelligence model may utilize a model including or approximating a random forest model.

[0087] In embodiments, the specific taxonomic features may include: (a) k_Bacteria.p_Proteobacteria (b) k_Bacteria.p_Actinobacteria.c_Actinobacteria (c) k_Bacteria.p_Bacteroidetes.c_Bacteroidia (d) k_Bacteria.p_Firmicutes.c_Bacilli (e) k_Bacteria.p_Firmicutes.c_Erysipelotrichi (f) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria (g) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria (h) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria (i) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales (j) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales 4876-8792-2608v.2(k) k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales (l) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales (m) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales (n) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales (o) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales (p) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae (q) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae (r) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacterace ae (s) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellac eae (t) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromona daceae (u) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae (v) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae (w) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae (x) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae (y) k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_ Desulfovibrionaceae (z) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae (aa) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_ Enterobacteriaceae (bb) k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Coryne bacteriaceae.g_Corynebacterium (cc) k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae. g_Bacteroides (dd) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_ Coprococcus (ee) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_L achnospira 4876-8792-2608v.2(ff) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_ Oscillospira (gg) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fin egoldia (hh) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pe ptoniphilus (ii) k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Di alister (jj) k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotr ichaceae.g_Eubacterium (kk) k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f _Campylobacteraceae.g_Campylobacter, (ll) k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Past eurellaceae.g_Haemophilus.

[0088] In embodiments, the specific taxonomic features are hard coded. In embodiments, the specific taxonomic features are stored in data as a table.

[0089] In embodiments, the generating the 16S rRNA sequence data comprises removing artifacts from the digitized sequence data.

[0090] In embodiments, removing artifacts from the digitized sequence data comprises one or more of the steps of: (1) trimming data associated with paired sequences, (2) denoising the data associated with the paired sequences, (3) merging the data associated with the paired sequences (4) removing data representing RNA primers; (5) performing filtering on the digitized sequence data; (6) detecting and filtering data associated with chimeras ; (7) detecting and filtering data associated with non-Human rRNA sequences; (8) detecting and filtering data associated with non-16S rRNA sequences.

[0091] In embodiments, the method further comprises storing the 16S rRNA sequence data in the one or more memories. For example, a function call may be used to save the 16S rRNA sequence data in csv format.

[0092] In embodiments, generating the normalized matrix comprises: (1) generating a taxonomic matrix, by the steps of: 4876-8792-2608v.2i. generating clusters of similar sequences represented in the 16S rRNA sequence data, wherein the clustering is based on a similarity threshold; ii. assigning one or more taxonomies to the clusters; and iii. assigning counts to each of the one or more taxonomies based on the 16S rRNA data. (2) generating a filtered matrix by removing data from the taxonomic matrix which associated with one or more contaminant taxa; (3) conducting rarefaction based on a specified sample size; (4) assigning data to the normalized matrix based on the rarefaction.

[0093] In embodiments, the clusters represent operational taxonomic units.

[0094] In embodiments, the similarity threshold is defined by a user.

[0095] In embodiments, the one or more contaminant taxa are selected from a group consisting of: c_Chloroplast; c_Alphaproteobacteria; f_Propionibacteriaceae; o_Oceanospirillales; f_Intrasporangiaceae; f_Micrococcaceae; f_Cytophagaceae; o_Hydrogenophilales; f_Alcaligenaceae; f_Burkholderiaceae; f_Burkholderiales_incertae_sedis; f_Comamonadaceae; and f_Oxalobacteraceae.

[0096] In embodiments, the one or more contaminant taxa are hard coded. In embodiments, the one or more contaminant taxa are stored in the one or more memories as a table.

[0097] In embodiments, the data are assigned at the genus level.

[0098] In embodiments, the prevalence data is modified to include data related to bacterial abundance and relative importance. 4876-8792-2608v.2

[0099] In embodiments, the at least one artificial intelligence model may be a machine learning model. In embodiments, the at least one artificial intelligence model comprises a random forest model. In embodiments, the at least one artificial intelligence model is trained using actual patient data. In embodiments, the at least one artificial intelligence model is trained using simulated patient data. In embodiments, the at least one artificial intelligence model comprises two artificial intelligence models and wherein the output comprises two distinct numerical microbiome scores. The two distinct numerical microbiome scores may represent scores associated two different grades of cancer. In embodiments, the at least one artificial intelligence model may comprise a greater number of artificial intelligence models than two.

[0100] In embodiments, the random forest plot, which may be a machine learning model, may be trained by randomly generating decision trees. Each node in a decision tree of the random forest may be configured to consider a random subset of features, which may be various taxa, to calculate an output of the decision tree. The random forest may then combine the output of individual decision trees to generate a final output. In embodiments, bootstrapping, which is the process of randomly selecting items from a training dataset, may also be employed. In embodiments, bootstrapping may assemble randomized decisions based on several decisions and makes the final decision based on the majority voting.

[0101] In embodiments, the numerical prevalence score comprises at least one of a cancer score and a Grade Group 2 (GG2) prostate cancer score.

[0102] In embodiments, the method further comprises storing the taxonomic matrix in the one or more memories. In embodiments, the method further comprises storing the filtered matrix in the one or more memories. In embodiments, the method further comprises storing the normalized matrix in the one or more memories.

[0103] In embodiments, the method further comprises displaying, by the one or more processors, elements of the taxonomic matrix in a graphical format. In embodiments, the method further comprises displaying, by the one or more processors, elements of the filtered matrix in a graphical format. In embodiments, the method further comprises displaying, by the one or more processors, elements of the normalized matrix in a graphical format.

[0104] In embodiments, the method further comprises displaying, by the one or more processors, the relative abundance of the specific taxonomic features.

[0105] In embodiments, the one or more processors may be operably connected to a display device (e.g., a monitor) capable of displaying elements of the taxonomic, filtered 4876-8792-2608v.2and / or normalized matrices, in a graphical format and / or the relative abundance of the specific taxonomic features. In embodiments, displaying elements of the taxonomic, filtered and / or normalized matrices and / or the relative abundance of the specific taxonomic features may comprise transmitting to a database to later be displayed upon request by a user.

[0106] In embodiments, the numerical microbiome score is a real number between zero and one and the estimated risk of cancer is calculated by the steps of: i) multiplying the cancer score by 4.476; ii) subtracting 4.421 from the result of the multiplying step; and iii) calculating the exponent of Euler’s Number to the power of the result of the subtracting step.

[0107] In embodiments, the method further comprises calculating a high risk of cancer estimate and a low risk of cancer estimate. In embodiments, the high risk of cancer estimate is half of the estimated risk of cancer. In embodiments, the low risk of cancer estimate is half of the estimated risk of cancer. In embodiments, the high and / or low risk of cancer estimates by the output of the at least one artificial intelligence model.

[0108] Also provided is a method for determining whether a gut microbiome sample from a human subject is associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer comprising: (a) obtaining, or having obtained, a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; 4876-8792-2608v.2c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) risk of future prostate cancer or at risk of a future clinically significant prostate cancer and (ii) low-risk of future prostate cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a risk of future prostate cancer or risk of a future clinically significant prostate cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of future prostate cancer or risk of a future clinically significant prostate cancer, and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of future prostate cancer / negative biopsy.

[0109] Also provided is a method for determining whether bacteria of a gut microbiome sample from a human subject are associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer comprising: (a) obtaining, or having obtained, a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; 4876-8792-2608v.2g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) determining: i) that the bacteria of the gut microbiome sample from the human subject are associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between risk of future prostate cancer or at risk of a future clinically significant prostate cancer and low-risk of future prostate cancer / negative biopsy ii) that the bacteria of the gut microbiome sample from the human subject are not associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer if the numerical microbiome score generated in (c) is less than a predetermined reference value.

[0110] In embodiments, the specific taxonomic features in determining a (i) presence or not and (ii) abundance value of each of the taxa comprise: k_Bacteria.p_Bacteroidetes; k_Bacteria.p_Proteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria; k_Bacteria.p_Bacteroidetes.c_Bacteroidia; k_Bacteria.p_Firmicutes.c_Bacilli; k_Bacteria.p_Firmicutes.c_Erysipelotrichi; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria; 4876-8792-2608v.2k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales; k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebact eriaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellacea e; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadac eae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrich aceae; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Des ulfovibrionaceae; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_ Campylobacteraceae; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_E nterobacteriaceae; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebact eriaceae.g_Corynebacterium; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_ Bacteroides; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Cop rococcus; 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lac hnospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Os cillospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fineg oldia; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pepto niphilus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Diali ster; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrich aceae.g_Eubacterium; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_ Campylobacteraceae.g_Campylobacter; and k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Paste urellaceae.g_Haemophilus.

[0111] In embodiments, the numerical microbiome score is from 0 to 1 and the predetermined reference value for future risk of prostate cancer or future risk of clinically significant prostate cancer is 0.25 or above.

[0112] In embodiments, the methods further comprise determining whether a gut microbiome sample from a human is positive for a colibactin-producing E. coli.

[0113] In embodiments, determination that the gut microbiome sample from a human is positive for a colibactin-producing E. coli and also at risk of a future prostate cancer, indicates that the subject is at a relatively increased risk of a future prostate cancer compared to a subject at risk of a future prostate cancer but negative for colibactin- producing E. coli.

[0114] A method for reducing a risk of a prostate cancer or risk of a future prostate cancer or clinically significant prostate cancer comprising determining, or receiving a determination that, a human subject is at risk of a future prostate cancer by a method described herein, and then administering to the subject one or more agents effective to modify the numerical microbiome score of the gut microbiome of the subject so as to 4876-8792-2608v.2thereby reduce a risk of a future prostate cancer or risk of a future clinically significant prostate cancer in the subject.

[0115] In embodiments, wherein the subject is positive for colibactin-producing E. coli, in addition to one or more agents effective to modify the numerical microbiome score of the gut microbiome the subject is administered an anti-colibactin positive E. coli treatment.

[0116] In embodiments, the one or more agents effective to modify the numerical microbiome score of the gut microbiome of the subject include a Fecal Microbiota Transplant (FMT); a probiotic supplement; a prebiotic; a phage therapy to selectively eliminate one or more gut bacteria that increase the microbiome score; a Microbiota- Accessible Carbohydrates (MAC) to feed one or more beneficial gut bacteria that decrease the microbiome score; a CRISPR-Cas9 editing agent to modify one or more gut bacteria to decrease the microbiome score; a microbiome banking sample previously taken from the subject when they had a healthy microbiome for future therapeutic use; or a nutritional diet that reduce one or more gut bacteria that increase the microbiome score and / or increase one or more beneficial gut bacteria that decrease the microbiome score.

[0117] In embodiments, the anti-colibactin positive E. coli treatment comprises an antibiotic that kills pks-carrying E. coli strain(s), a phage therapy to selectively eliminates pks-carrying E. coli strain(s), a CRISPR-Cas9 editing agent to selectively inhibit colibactin production by pks-carrying E. coli strain(s); a nutritional, FMT, prebiotic or probiotic, that increases non-pks-carrying E. coli strain(s) relative to pks-carrying E. coli strain(s).

[0118] In embodiments, the method comprises reducing the numerical microbiome score of the gut microbiome of the subject below 0.25.

[0119] In embodiments, the method may be executed based on computer code stored in the one or more memories and executed by the one or more processors. In embodiments, the computer system may be local to a user, remote from the user, or both local and remote (e.g., aspects may involve cloud computing).

[0120] In embodiments, the one or more processors may actually perform one or more of the steps of the method and / or cause one or more of the steps of the method to be performed.

[0121] In embodiments, the subject, or subjects, is / are human. In preferred embodiments, all data sets and data subsets are obtained from human subjects.

[0122] In embodiments, samples have been obtained by a medical professional and not the patient subject. In embodiments, samples have been obtained the patient subject. 4876-8792-2608v.2

[0123] In embodiments, the methods can be performed serially. In embodiments, the methods can be performed sequentially on the same subject, or on different subjects.

[0124] A predefined condition is a condition or status decided or determined beforehand.

[0125] In embodiments, where a numerical range is provided herein, it is understood that all numerical subsets of that range, and all the individual integers contained therein, are provided as embodiments of the invention. For example, 30 to 70 includes the subset of 30 to 35, the subset of 40-60, etc. as well as every individual integer value, e.g. 30, 31, 32, 33, and so on.

[0126] “And / or” as used herein, for example with option A and / or option B, encompasses the separate embodiments of (i) option A, (ii) option B, and (iii) option A plus option B.

[0127] All combinations of the various elements described herein are within the scope of the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

[0128] One skilled in the art will readily appreciate that the specific methods and results discussed herein are merely illustrative of embodiments of the invention as described more fully in the claims that follow thereafter. EXAMPLES Study Design and population

[0129] A prospective, observational clinical study design was used collecting fecal samples for gut microbiome analysis and link the microbiome data to the diagnosis of prostate cancer. Two populations were used to obtain fecal samples and prostate cancer status. One group included men presenting for prostate cancer screening as part of our San Antonio Biomarkers of Risk (SABOR) cohort. SABOR is an 18-year prospective 4,174- subject cohort undergoing regular serum prostate-specific antigen (PSA) testing as part of a clinical validation site for the National Cancer Institute’s Early Detection Research Network (EDRN).

[0010] The SABOR cohort represents a usual risk patient population engaged in prostate cancer screening. Men were also enrolled at the point of deciding to proceed to a prostate biopsy, before prophylactic antibiotics are used. The second group of patients are part of a genitourinary repository consisting of men proceeding to a prostate biopsy (IRB#: HSC20050234H), before prophylactic antibiotics are used. 4876-8792-2608v.2Outcomes

[0130] Our primary outcome was defined as clinically significant prostate cancer defined a grade group 2 (Gleason 3+4) prostate cancer or greater. The primary predictor of prostate cancer is the microbiome score. Our secondary outcome was any prostate cancer to include low-risk (i.e., Grade Group 1) prostate cancer. Sample collection and preparation

[0131] All specimens were collected prospectively, especially before antibiotic use in the biopsy cohort. Both rectal swabs and glove tips were used for fecal microbiome collection. We have studied both methods and found them to be highly correlated with results and DNA yield.

[0017] The rectal swab technique involved the urology provider by using sterile Medline E-Z Lubricating Jelly and placing the swab approximately 2 inches into the rectum and turning the swab 360 degrees. Once collection was complete, the rectal swab was removed and examined for stool. The physician reinserted the swab if there was no material collected. The physician placed the swab in a 15mL sterile centrifuge tube containing 1mL of phosphate-buffered saline (PBS). The swab specimen was stored at 4°C during transport to the laboratory within 4 hours of collection. Swab was saved and PBS material at -20°C until and then performed DNA isolation. DNA Isolation and sequencing

[0132] For sample input, we attempted to provide a visible amount of stool on the glove tips and swabs. DNA was isolated from fecal samples using our standard operating procedure.

[0018] A purification was performed of genomic DNA from these respective fecal samples using the QIAamp® Fast DNA Stool Mini Kit according to the kit protocol (Qiagen, Germantown, MD). The DNA concentration was measured using the Thermo Scientific NanoDrop. Genomic DNA was used for amplification of V1-V2 variable region of the 16S rRNA genes with custom-designed primers (F27 / R534) and V3-V4 variable region of the 16S rRNA genes following the Illumina 16S metagenomic library preparation guide.

[0019] Final libraries were quantified, normalized, pooled together, and sequenced by Paired-end sequencing (2 × 300 bp) using Illumina Miseq platform. The average of 264,727 raw pair reads per sample were generated with read length of 300 bp. The sequences were exported as FASTQ files. 4876-8792-2608v.2Statistical analysis and bioinformatics

[0133] Microbial functional imputation of the microbiome signature: PICRUSt was applied to rarefied taxonomic profiles (15,000 sequences per sample) to infer functional categories associated with taxonomic composition. Computational analyses were performed on V1V2 and V3V4 amplicon regions separately. Alpha diversity calculations were performed using QIIME

[0020] . Beta-diversity comparative analysis utilized the vegan R package for PERMANOVA calculations. LEfSe was executed via Galaxy server for comparison of groups of interest [21, 22]. Taxa identified as significant across group comparisons were selected for machine learning model training by random forest using the caret R package requiring 10-fold cross-validation. Filtered results using taxa identified by LEfSe were summarized by PICRUSt analysis as KEGG Level 3 categories and analyzed for differential abundance between patient groups of interest using the nonparametric Mann-Whitney test with False Discovery Rate adjustment of P-values.

[0134] Statistical analysis Univariant analysis for demographics included chi squared or Fischer’s exact test for categorical variables and Student’s T test for continuous variable. ANOVA was used for multipole comparisons without pos hoc analysis. Logistic regression was used for multivariant comparison specifically to determine if the microbiome score was significant for predicting the dichotomous outcome of clinically significant cancer when controlling for other factors including PSA. A decision curve analysis will be estimated for the microbiome score values using 5-fold cross validation. Bootstrap 95% confidence intervals were constructed for the differences in net benefit. If the predictive model or biomarker has a 95% CI for the difference exceeds 0 relative to another, then this would be preferred. Using logistic regression, the incremental benefit of the microbiome score values will be evaluated. Quality Assurance Sequencing

[0135] the 16s V1V2 variable regions were collected and sequenced using. Later in the study sequencing V3V4 regions was also done in addition to V1V2 to calculate the score using both sequencing techniques for correlation. 4876-8792-2608v.2Validation

[0136] Cross-validation usually performs better than a training and testing set. Therefore, the cohort was divide into a training (2 / 3) and testing (1 / 3) to determine performance. The importance scores of each taxon were then compared for similarity between the two model training versions, finding a significant positive correlation (Pearson, r=0.79; P < 0.001) . Change over time

[0137] Some subjects have had at least one additional collection. The stability of the microbiome signature is compared over time based on these two collections. Microbiome Score performance

[0138] The microbiome score was correlated to standard of care risk measurements to include PSA and the Prostate Cancer Prevention Trial risk calculator. Decision analysis curves

[0139] The clinical benefit of the microbiome predictors was examined for csPCa and any grade prostate cancer using decision curve analysis (DCA) implemented by the R package dcurves

[0023] . The DCA compares a predictor's benefit as a basis for treatment decision relative to treatment for all and treatment for none for a range of probability thresholds. The clinical net benefit is the true positive rate minus the false positive rate scaled by the odds of disease prevalence. Using the validation data set, decision curves were computed for GG2+ and any prostate cancer for microbiome predictors and compared their net benefit relative to PSA. Time to Event Analysis

[0140] Ability of the microbiome score threshold of 0.25 to group men into high risk or low risk categories was tested. Men diagnosed with prostate cancer within 6 months of the microbiome collection were excluded. The Kaplan-Meier curve was then used for visualization and the log-rank statistical test to determine if there was a significant difference in the incidence of prostate cancer and clinically significant prostate cancer. 4876-8792-2608v.2Microbiome Score as a Modifiable Biomarker

[0141] Due to the increased risk of prostate cancer in patients with inflammatory bowel disease (IBD), who have aberrant microbiomes, it was sought to first obtain the 16s RNA publicly available data from the Human Microbiome Project 2 (HMP2) subset with patients with IBD. The data between men and women and people with and without IBD will be investigated. Groups will be compared using the Mann-Whitney test and visualize the microbiome score using box and whisker plots. Subsequently, the publicly available data was obtained from a randomized controlled study of fecal transplant for men with IBD. This data will determine if the microbiome score is modifiable. We will calculate the microbiome score of the donors and both intervention and control groups over the period of the study. The microbiome score will be compared before and after intervention using the Mann-Whitney test. Results

[0142] The results are as set forth in the attached figures and descriptions thereof, and also as additionally described below. Characterization of the study population

[0143] After IRB approvals, a total of 2,028 samples were collected from February 2016 to December 2021. 451 individuals were analyzed from the SABOR screening cohort and 373 individuals from biopsy cohort for a total of 824 (CONSORT diagram). Demographics are shown in Table 1. The groups are broken down into cohorts by prostate biopsy and screening cohorts as well as racial categories.

[0144] Table 1: Cancer Groups (by grade) GG2+ GG1 Negative Biopsy Low Risk P (N=125) (N=106) (N=143) (N=450) value Age68.0 [37.0, 81.0] 66.0 [47.0, 79.0] 66.0 [37.0, 81.0] 68.5 [40.0, 87.0] <0.001Body Mass Index28.8 [19.3, 46.2] 29.0 [18.7, 45.9] 29.1 [18.7, 47.8] 27.6 [14.6, 71.2] 0.00166DRE Normal64 (51.2%) 79 (74.5%) 102 (71.3%) 404 (89.8%) <0.001Abnormal57 (45.6%) 22 (20.8%) 19 (13.3%) 34 (7.6%)Not Performed4 (3.2%) 5 (4.7%) 22 (15.4%) 12 (2.7%)Race White 74 (59.2%) 54 (50.9%) 74 (51.7%) 395 (87.8%) <0.001 Black38 (30.4%) 35 (33.0%) 50 (35.0%) 50 (11.1%)4876-8792-2608v.2Other13 (10.4%) 17 (16.0%) 19 (13.3%) 5 (1.1%)Ethnicity Non-Hispanic89 (71.2%) 83 (78.3%) 108 (75.5%) 309 (68.7%) <0.001Hispanic34 (27.2%) 20 (18.9%) 30 (21.0%) 141 (31.3%)Other2 (1.6%) 3 (2.8%) 5 (3.5%) 0 (0%)Family History of PCa No106 (84.8%) 83 (78.3%) 121 (84.6%) 380 (84.4%) <0.001Yes 3 (2.4%) 5 (4.7%) 4 (2.8%) 69 (15.3%) Not Reported16 (12.8%) 18 (17.0%) 18 (12.6%) 1 (0.2%)Prostate Specific Antigen Level ng / mL 8.06 5.89 6.33 1.40 [0.140, 295] [0.270, 27.6] [0.800, 19.3] (PSA)[0.100, 54.9]<0.001Observed Microbial 62.0 63.0 63.0 63.0 Species [29.0, 85.0] [43.0, 84.0] [34.0, 94.0][38.0, 158]0.0582Shannon Diversity3.563.61 3.54 3.54 [1.21, 4.43][0.952, 4.39] [1.12, 4.39]0.49PRIMUS Microbiome ScoreQuality control

[0145] Amplicon sequenced libraries were submitted for quality trimming, paired-end read merging, chimera removal, host-genome detection removal and other common artifacts associated with 16S rRNA processing (see Methods). Overall, 60 sequenced samples (4.86%) were excluded from consideration due to low coverage after quality control, with statistically similar frequencies of complete subject exclusion between the SABOR and biopsy cohorts (P = 0.99; Fisher’s Exact Test). Microbiome Signature identification

[0146] After sequencing, quality check, and metadata hygiene a random forest plot was used to assess the microbiome in various groups to provide a microbiome score (Figure 17A). The score is a number from 0 to 1 and increasing number is associated with prostate cancer. A cladogram was used to delineate the GG2 microbiome profiles more clearly from GG1 or non-cancer groups. Linear discriminant analysis (LDA) of >2.4 was used as the cut off for valuable microbial profiles. One identified; they were plotted using a circular branching diagram showing the cladistic relationship between several species enriched for GG2 prostate cancer compared to no GG2 prostate cancer. Lastly, an AUC was used and 4876-8792-2608v.2calculated for operational characteristics of the microbiome score. The microbiome score was statically significant whether using either the 10-fold cross validation or training / validation AUC of 69.7 (95%CI 69.4 - 69.7). The threshold used to call a positive test was a microbiome score of 0.158 that led to a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 75.4%, 57%, 24.17% and 92.72%, respectively (Figure 17C). The top left shows the top 15 microbes and their importance level in the model. The bottom two histograms show the microbiomes scores of those without cancer or grade group 1 prostate cancer on top (green) compared to the distribution of men with grade group 2 or higher prostate cancer on the bottom (orange). Alpha and beta diversity

[0147] The overall diversity profiles in the four groups corresponding to Table 1 (GG2+, GG1, negative biopsy, and low risk) was displayed. The alpha diversity between groups was not different (Figure 18A shows the groups and subsequent subgroup analysis of cancer status and race were not statistically different (all p>0.05). However, when comparing beta diversity, differences were noticed between cancer status (Curtis Bray distance, PERMANOVA p<0.01, respectively). Also investigated were populations and race / ethnicity and beta diversity differences noted between groups (both PERMANOVA p<0.01, respectively). Multivariant analysis with a microbiome signature

[0148] There is no difference in number of microbial species across groups when comparing across risk groups. The microbiome score across these groups noted the lowest median score for the low-risk group and the highest median score for the GG2+ group (P<0.001). In Table 2, the results of multivariate analysis with the microbiome score is shown. Controlling for PSA and abnormal prostate exam, the microbiome score was a statistically significant predictor of prostate cancer (p<0.001) controlling for other risk factors.

[0149] Table 2: Multivariable logistic regression for men that underwent prostate biopsy Clinically Significant Clinically Significant An Gr d Pr t t ue4876-8792-2608v.2Age 0.980.95,0.2 0.990.96, 0.5 0.90.96, 1.01 1.0281.000.11 001.00, 1.00, 11685111tcolumn is csPCa (GG2+) compared to no cancer. These two groups should have the most different demographics including the microbiome. The second group compare csPCa to no csPCA which includes both no cancer and GG1 PCa. Some groups are advocating downgrade GG1 and not consider this a cancer, so we provide that analysis to provide a transparency as well as a larger sample size. The right column compares any grade prostate cancer (GG1 and GG2+) compared to no cancer or a negative biopsy to compare the traditional consideration of the PCa pathologic definition. 4876-8792-2608v.2Internal tests of robustness

[0150] We used two methods to check the robustness of the microbiome score to include comparing the score obtained from sequencing different variable regions and consistency of the microbiome score over time without intervention. We investigate the correlation between the microbiome score generated from variable regions 1 and 2 compared to variable regions 3 and 4 to determine the reproducibility across sequencing techniques. In Figure 19A, we display a scatter plot with a fit line showing a positive correlation (Pearson correlation = 0.77, P < 0.001). We also measured the microbiome score for stability of time. Fifty-three subjects had at least two time points to compare. All subjects had V1-V2 variable regions sequenced for consistency. In Figure 19B, we show the individuals baseline microbiome score and connected them with their future score with a line. We compared the baseline and future microbiome scores noting no statistically significant difference (paired t test, p=0.77). However, 25% (13 / 53) subjects changed their microbiome score using a threshold 0.158 (GG2+ microbiome score) without intervention (McNemar, p<0.6). We can therefore assume a variation of 25% of score variation above or below the 0.158 threshold over a median of one year. Microbiome Score correlated with clinically significant prostate cancer risk

[0151] The GG2+ microbiome score is correlated with PSA, overall PCPT cancer risk, and PCPT high-risk prostate cancer (r=0.32, 0.29, and 0.32, all p<0.001). The prostate cancer microbiome score indicating risk of any prostate cancer is correlated with PSA, overall PCPT cancer risk, and PCPT high-risk prostate cancer (r=0.40, 0.37, and 0.39, all p<0.001). Predicted functional analysis

[0152] Using Linear discriminant analysis Effect Size (LEfSe) we note caffeine metabolism and glycan metabolism as important aspects of associated with those without GG2 prostate cancer. We found two very strong association with GG2 prostate cancer are similar to those bacteria also associated colon, small cell lung, and bladder cancer. Analysis for secondary aim of any prostate cancer

[0153] We performed the same analysis on a model where all prostate cancer was compared to no prostate cancer for completeness (Secondary Analysis). The model built for any prostate cancer that includes low risk prostate cancer compared to no cancer. The model 4876-8792-2608v.2required the same base of 39 microbes as the GG2+ model; however, had a different output in machine learning random forest analysis. Using the microbiome score output threshold of 0.256 for the diagnosis of any prostate cancer. Using a training / validation set the microbiome score achieved an AUC of 0.685 (95% CI 0.674 – 0.686, p<0.001). In a second analysis using a 10-fold cross validation approach the microbiome score attained an AUC of 0.732 (95% CI 0.728 – 0.732, p<0.001). The operational characteristics of sensitivity, specificity, positive predictive value, and negative predictive value at this threshold are 84.4%, 53.9%, 41.9%, and 89.8%, respectively. A histogram of each microbiome score shown in blue for negative and red for positive for cancer. Because the two scores have different methods of generation, we compared the outputs on a scatter plot with correlation (Pearson 0.78, p<0.001). In univariant analysis, the microbiome score (any prostate cancer vs. no prostate cancer) was statistically significant (p<0.001) and in logistic regression had an odds ratio of 11.7 (95% CI 4.36- 33.2, P<0.001).). See FIG.20. Microbiome Score correlated with clinically significant prostate cancer risk

[0154] The GG2+ microbiome score is correlated with PSA, overall PCPT cancer risk, and PCPT high-risk prostate cancer (r=0.32, 0.29, and 0.32, all p<0.001). The prostate cancer microbiome score indicating risk of any prostate cancer is correlated with PSA, overall PCPT cancer risk, and PCPT high-risk prostate cancer (r=0.40, 0.37, and 0.39, all p<0.001). Decision Curve Analysis

[0155] For the decision curve using the microbiome score to predict any prostate cancer, we used thresholds from 10% to 35% which are clinically reasonable thresholds for biopsy (treatment) decisions. In this range, the microbiome score has a greatest benefit relative to PSA, biopsy all or biopsy none. PSA has the greatest net benefit beyond 35% near the intersection of PSA and microbiome score curves, at which point the PSA provides a greater than benefit than the microbiome score. (Figure 21, iv). Time to Event Analysis

[0156] We performed two time to event analysis using the same microbiome signature to predict prostate cancer free survival and GG2+ prostate cancer free survival. (i) We display the Kaplan-Meier curves for prostate cancer free survival (PCFS) using the microbiome signature threshold of 0.25 previously discussed that showed a significant 4876-8792-2608v.2reduction in PCFS for those with a high baseline microbiome signature (HR 3.97, 95% CI 2.14-7.36, p<0.001), Figure 21, v). We also display the Kaplan-Meier curves using the same microbiome signature, but for the endpoint of GG2+ prostate cancer free survival also noting a significant reduction in PCFS in men with a high baseline microbiome signature (HR 11.1, 95% CI 1.42-87.17, P<0.001, Figure 21, vii). Neither group reached the 50% median endpoints. Modification of the Microbiome Score

[0157] We initially wanted to inspect the microbiomes signature for prostate cancer in patient with inflammatory bowel disease. We obtained the sequencing from the integrative human microbiome project sub study on IBD.

[0024] . We obtained the publicly available data from the world wide web at hmpdacc.org / ihmp / . They used V4 (MiSeq platform (Illumina) using the 2x250 bp paired end). We analyzed the score comparing IBD vs. non-IBD subjects and divided them by men and women. We showed a significantly higher microbiome signature for prostate cancer in men with IBD compared to controls (p=0.0025), but the scores not different between IBD and non-IBD in women. We then obtained data from Crothers et al. clinical trial using fecal microbiota transplant for patients with inflammatory bowel disease.

[0025] They used 16s V4 sequencing with 2 donors and 12 participants (6 intervention and 6 control). The two donors were selected for high (top quintile, 11.11 mmol / g and 12.67 mmol / g stool) of butyrate production. We show that the donor stool had a significantly low microbiome score and compared to both groups (international and control) with IBD (both, p<0.001). Then comparing the microbiome signatures after 25-35 days of FMT the intervention group assumed a similar low microbiome score as the donors (p=0.12), while the placebo group was significantly higher than the intervention group (p=0.002). Figs.23A-23C. Colibactin exposure is associated with prostate cancer diagnosis

[0158] While pks+ E. coli producing colibactin are associated with colorectal tumors, there remains few investigations into their potential role in cancers at other microbiota niches. To assess the link between pks+ E. coli with tumourigenesis in prostate cancer, a retrospective analysis of two observational cohorts of individuals at elevated risk for prostate cancer was conducted. The two clinical cohorts (PSA and San Antonio Center of Biomarkers of Risk for Prostate Cancer [SABOR]) collectively enrolled 388 individuals for 4876-8792-2608v.2whom we have diagnosis results. Table 1 lists the distribution of demographics and subgroups included in the combined cohort and associated clinical measurements. Colibactin exposure was defined as presence of a pks+ gene island by PCR. Colibactin exposure was significantly positively associated with prostate cancer diagnosis (OR = 1.74, P = 0.036, one-sided Fisher’s Exact Test). There was no significant positive association between either cytolethal distending toxin or cytotoxic necrotizing factor 1 with prostate cancer diagnosis of any grade (Fig.24).

[0159] Table 3: Demographics of the two study population cohorts. Biopsy Cohort SABOR Cohort P value (n=373) (n=451) Age 67.0 [37.0, 81.0] 69.0 [40.0, 87.0] <0.001Digital rectal exam <0.001 Normal 244 (65.4%) 405 (89.8%) Abnormal 98 (26.3%) 34 (7.5%) Not performed 31 (8.3%) 12 (2.7%) Race <0.001 White 202 (54.2%) 395 (87.6%) Black 122 (32.7%) 51 (11.3%) Other 49 (13.1%) 5 (1.1%) Ethnicity <0.001 Non-Hispanic 279 (74.8%) 310 (68.7%) Hispanic 84 (22.5%) 141 (31.3%) Other 10 (2.7%) 0 (0%) Family History of Prostate Cancer <0.001 No 309 (82.8%) 381 (84.5%) Yes 12 (3.2%) 69 (15.3%) Not Reported 52 (13.9%) 1 (0.2%) Prostate Specific Antigen (PSA) 6.79 [0.140, 295] 1.40 [0.100, 54.9] <0.001 Cancer <0.001 No 143 (38.3%) 450 (99.8%) Yes 230 (61.7%) 1 (0.2%) Grade Group <0.001 GG1 (Gleason 3+3) 105 (28.2%) 1 (0.2%) GG2 (Gleason 3+4) 59 (15.8%) 0 (0%) GG3 (Gleason 4+3) 26 (7.0%) 0 (0%) GG4 (Gleason 4+4) 24 (6.4%) 0 (0%) GG5 (Gleason 4+5, 5+4, or 5+5) 16 (4.3%) 0 (0%) Negative 143 (38.3%) 450 (99.8%) 4876-8792-2608v.2Observed microbial species 63.0 [29.0, 94.0] 63.0 [38.0, 158] 0.0115 Shannon diversity 3.57 [0.952, 4.43] 3.54 [1.90, 4.59] 0.625 Microbiome Score 0.370 [0.050, 0.702] 0.216 [0.0240, 0.696] <0.001 4876-8792-2608v.2Discussion

[0160] Our findings have identified a PSA-independent, gut microbiome-based signature associated with clinically significant prostate cancer. We also discovered that a baseline microbiome signature assessment may have increased risk of prostate cancer over the next 4-6 years, which coincided with minimal change on repeat testing up to 1 year apart. Lastly, the microbiome signature is potentially modifiable, opening a new area of clinical investigation to determine if microbiome modification could be a biomarker in prostate cancer prevention clinical trials.

[0161] While other studies have identified a few associated bacteria, our study is the first to attribute a predictive microbial signature. Matsushita and colleagues published an 18 microbe score termed the Fecal Microbiome prostate index (n=152) in a Japanese cohort noted AUC of 0.85 for high risk and 0.70 for any cancer.

[0026] It may be difficult to extrapolate these findings due to the uniqueness of the Japanese microbiome.

[0027] Our signature reached a similar AUC of 0.71 with good sensitivity (83%) and negative predictive value (89%). While current pre-biopsy biomarkers have better performance metrics, the unique ability of a microbiome signature is the ability to individual lifestyle recommendations. However, we do not know the exact mechanism of the gut microbiome’s influence on prostate cancer nor that manipulating the microbiome reduces prostate cancer risk. Using several animal models our working hypothesis is that there is a systemic component that could be related to metabolites or immune alterations or both.

[0028]

[0162] Reichard et al. published that several gut microbe-produced metabolites are associated with lethal prostate cancer.

[0029] Our group previously published on predicted metabolites associated with prostate cancer.

[0016] However, new research has investigated microbial interactions with the immune system that may increase the risk of cancer. For instance, the microbiome may interact closely with colonic cells to influence Treg cell differentiation and Tregs have been implicated in prostate cancer formation. [30,31] We used the example of IBD as an extreme example of dysbiosis and the increased risk of prostate cancer. The mechanism of increased cancer in this group has been suspected to be increased inflammation leading to elevated levels of TNF.[32-34]

[0163] We acknowledge limitations of our study including the enrollment of two major groups of patients noted differences in demographics. We took this into account in the 4876-8792-2608v.2quality section of the manuscript. We also used different collection techniques between stool, rectal swab, and glove tip techniques, which has previously been published to not significantly effect microbiome analysis

[0017] . Due to prostate needle traversing the colon to obtain a prostate biopsy in most cases, we did not compare gut microbes to prostate cancer tissue as there may DNA contamination. Other groups have focused on the gut microbiome and treatment outcomes

[0035] ; however, the current study remains the largest and most comprehensive study on gut microbiome, and its association with prostate cancer and prostate cancer risk.

[0164] In conclusion, we have developed a PSA independent, gut-microbiome based prostate cancer biomarker. We anticipate such a biomarker may be used to explore the interaction of the gut microbiome with current and future therapeutic strategies as a potential targetable biomarker. Currently there are no prevention strategies for those a genetic or regular risk to follow and reduce their prostate cancer. While this study does not provide causality, our future investigations plan to develop clinical trials to answer these new questions.

[0165] Now that embodiments of the present invention have been shown and described in detail, various modifications and improvements thereon can become readily apparent to those skilled in the art. Accordingly, the exemplary embodiments of the present invention, as set forth above, are intended to be illustrative, not limiting. The spirit and scope of the present invention is to be construed broadly. Those of ordinary skill in the art will recognize that the method and apparatus of the present invention described herein and others implied have many applications; therefore, the present invention which is the subject of this application is not limited by or to the representative examples and / or methods disclosed herein, nor limited by or to the preferred embodiments described herein. Moreover, various other embodiments and modifications to these exemplary embodiments may be made by those skilled in the relevant art without departing from the scope or spirit of these inventions. Accordingly, the inventions are not to be limited by the foregoing specification, except as by the appended claims.

[0166] References

[0167] 1 Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer statistics, 2023. CA Cancer J Clin 73, 17-48 (2023). https: / / doi.org / 10.3322 / caac.21763 4876-8792-2608v.2

[0168] 2 Force, U. S. P. S. T. et al. Screening for Prostate Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 319, 1901-1913 (2018). https: / / doi.org / 10.1001 / jama.2018.3710

[0169] 3 Miller, K. D. et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin 72, 409-436 (2022). https: / / doi.org / 10.3322 / caac.21731

[0170] 4 Carlsson, S. V. & Kattan, M. W. Prostate cancer: Personalized risk - stratified screening or abandoning it altogether? Nat Rev Clin Oncol 13, 140-142 (2016). https: / / doi.org / 10.1038 / nrclinonc.2016.11

[0171] 5 Kachuri, L. et al. Genetically adjusted PSA levels for prostate cancer screening. Nat Med 29, 1412-1423 (2023). https: / / doi.org / 10.1038 / s41591-023-02277-9

[0172] 6 McBride, C. M. et al. Understanding the role of cancer worry in creating a "teachable moment" for multiple risk factor reduction. Soc Sci Med 66, 790-800 (2008). https: / / doi.org / 10.1016 / j.socscimed.2007.10.014

[0173] 7 Lippman, S. M. et al. Effect of selenium and vitamin E on risk of prostate cancer and other cancers: the Selenium and Vitamin E Cancer Prevention Trial (SELECT). JAMA 301, 39-51 (2009). https: / / doi.org / 10.1001 / jama.2008.864

[0174] 8 Thompson, I. M. et al. The influence of finasteride on the development of prostate cancer. N Engl J Med 349, 215-224 (2003). https: / / doi.org / 10.1056 / NEJMoa030660

[0175] 9 Ziglioli, F. et al. Impact of modifiable lifestyle risk factors for prostate cancer prevention: a review of the literature. Front Oncol 13, 1203791 (2023). https: / / doi.org / 10.3389 / fonc.2023.1203791

[0176] 10 Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59-65 (2010). https: / / doi.org / 10.1038 / nature08821

[0177] 11 Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804-810 (2007). https: / / doi.org / 10.1038 / nature06244

[0178] 12 Sepich-Poore, G. D. et al. The microbiome and human cancer. Science 371 (2021). https: / / doi.org / 10.1126 / science.abc4552

[0179] 13 Markowski, M. C. et al. The Microbiome and Genitourinary Cancer: A Collaborative Review. Eur Urol 75, 637-646 (2019). https: / / doi.org / 10.1016 / j.eururo.2018.12.043 4876-8792-2608v.2

[0180] 14 Liss, M. A. et al. Metabolic Biosynthesis Pathways Identified from Fecal Microbiome Associated with Prostate Cancer. Eur Urol 74, 575-582 (2018). https: / / doi.org / 10.1016 / j.eururo.2018.06.033

[0181] 15 Matsushita, M. et al. The gut microbiota associated with high-Gleason prostate cancer. Cancer Sci 112, 3125-3135 (2021). https: / / doi.org / 10.1111 / cas.14998

[0182] 16 Nishijima, S. et al. The gut microbiome of healthy Japanese and its microbial and functional uniqueness. DNA Res 23, 125-133 (2016). https: / / doi.org / 10.1093 / dnares / dsw002

[0183] 17 Kustrimovic, N., Bombelli, R., Baci, D. & Mortara, L. Microbiome and Prostate Cancer: A Novel Target for Prevention and Treatment. Int J Mol Sci 24 (2023). https: / / doi.org / 10.3390 / ijms24021511

[0184] 18 Biragyn, A. & Ferrucci, L. Gut dysbiosis: a potential link between increased cancer risk in ageing and inflammaging. Lancet Oncol 19, e295-e304 (2018). https: / / doi.org / 10.1016 / S1470-2045(18)30095-0

[0185] 19 Wheeler, K. M. & Liss, M. A. The Microbiome and Prostate Cancer Risk. Curr Urol Rep 20, 66 (2019). https: / / doi.org / 10.1007 / s11934-019-0922-4

[0186] 20 Liss, M. A. et al. Higher baseline dietary fat and fatty acid intake is associated with increased risk of incident prostate cancer in the SABOR study. Prostate Cancer Prostatic Dis 22, 244-251 (2019). https: / / doi.org / 10.1038 / s41391-018-0105-2

[0187] 21 Liss, M. A. et al. A diagnosis of prostate cancer and pursuit of active surveillance is not followed by weight loss: potential for a teachable moment. Prostate Cancer Prostatic Dis 19, 390-394 (2016). https: / / doi.org / 10.1038 / pcan.2016.28

[0188] 22 Francescone, R., Hou, V. & Grivennikov, S. I. Microbiome, inflammation, and cancer. Cancer J 20, 181-189 (2014). https: / / doi.org / 10.1097 / PPO.0000000000000048

[0189] 23 Porter, C. M., Shrestha, E., Peiffer, L. B. & Sfanos, K. S. The microbiome in prostate inflammation and prostate cancer. Prostate Cancer Prostatic Dis 21, 345-354 (2018). https: / / doi.org / 10.1038 / s41391-018-0041-1

[0190] 24 Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65-74 (2016). https: / / doi.org / 10.1038 / nature18847

[0191] 25 Chen, J., Pitmon, E. & Wang, K. Microbiome, inflammation and colorectal cancer. Semin Immunol 32, 43-53 (2017). https: / / doi.org / 10.1016 / j.smim.2017.09.006 4876-8792-2608v.2

[0192] 26 Ge, Y., Shi, Q., Yao, W., Cheng, Y. & Ma, G. The association between inflammatory bowel disease and prostate cancer risk: a meta-analysis. Prostate Cancer Prostatic Dis 23, 53-58 (2020). https: / / doi.org / 10.1038 / s41391-019-0177-7

[0193] 27 Burns, J. A. et al. Inflammatory Bowel Disease and the Risk of Prostate Cancer. Eur Urol 75, 846-852 (2019). https: / / doi.org / 10.1016 / j.eururo.2018.11.039

[0194] 28 Matsushita, M., Fujita, K. & Nonomura, N. Influence of Diet and Nutrition on Prostate Cancer. Int J Mol Sci 21 (2020). https: / / doi.org / 10.3390 / ijms21041447

[0195] 29 Short, M. I. et al. Comparison of rectal swab, glove tip, and participant- collected stool techniques for gut microbiome sampling. BMC Microbiol 21, 26 (2021). https: / / doi.org / 10.1186 / s12866-020-02080-3

[0196] 30 Besasie, B. D., Shah, D. P., Leach, R. J. & Liss, M. A. Comparison of in Clinic-Based Fecal Microbiome Collection Techniques for Increase in Study Participation and Utilization of Microbiome Analysis. Open Journal of Urology 9, 51 (2019).

[0197] 31 Youssef, N. et al. Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys. Appl Environ Microbiol 75, 5227-5236 (2009). https: / / doi.org / 10.1128 / aem.00592-09

[0198] 32 Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 (2014). https: / / doi.org / 10.1093 / bioinformatics / btu170

[0199] 33 Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335-336 (2010). https: / / doi.org / 10.1038 / nmeth.f.303

[0200] 34 Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957-2963 (2011). https: / / doi.org / 10.1093 / bioinformatics / btr507

[0201] 35 Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357-359 (2012). https: / / doi.org / 10.1038 / nmeth.1923

[0202] 36 Kuczynski, J. et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Bioinformatics Chapter 10, Unit 10 17 (2011). https: / / doi.org / 10.1002 / 0471250953.bi1007s36

[0203] 37 Oksanen, J. Vegan : community ecology package. http: / / vegan.r-forge.r- project.org / (2010). 4876-8792-2608v.2

[0204] 38 Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31, 814-821 (2013). https: / / doi.org / 10.1038 / nbt.2676

[0205] 39 Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125, 279-284 (2001).

[0206] 40 Jeng, L. L., Moore, R. M., Jr., Kaczmarek, R. G., Placek, P. J. & Bright, R. A. How frequently are home pregnancy tests used? Results from the 1988 National Maternal and Infant Health Survey. Birth 18, 11-13 (1991). https: / / doi.org / 10.1111 / j.1523- 536x.1991.tb00046.x

[0207] 41 Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol 12, R60 (2011). https: / / doi.org / 10.1186 / gb-2011-12-6-r60

[0208] 42 Kuhn, M. Building Predictive Models in R Using the caret Package. Journal of Statistical Software 28, 1 - 26 (2008). https: / / doi.org / 10.18637 / jss.v028.i05

[0209] 43 Vickers, A. J., Van Calster, B. & Steyerberg, E. W. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. Bmj 352, i6 (2016). https: / / doi.org / 10.1136 / bmj.i6

[0210] 44 Gelfond, J. A. et al. Prediction of future risk of any and higher-grade prostate cancer based on the PLCO and SELECT trials. BMC Urol 22, 45 (2022). https: / / doi.org / 10.1186 / s12894-022-00986-w

[0211] 45 The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276-289 (2014). https: / / doi.org / 10.1016 / j.chom.2014.08.014

[0212] 46 Crothers, J. W. et al. Daily, oral FMT for long-term maintenance therapy in ulcerative colitis: results of a single-center, prospective, randomized pilot study. BMC Gastroenterol 21, 281 (2021). https: / / doi.org / 10.1186 / s12876-021-01856-9

[0213] 47 Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26, 565-574 (2006). https: / / doi.org / 10.1177 / 0272989X06295361

[0214] 48 Ankerst, D. P. et al. Evaluating the Prostate Cancer Prevention Trial High Grade Prostate Cancer Risk Calculator in 10 international biopsy cohorts: results from the Prostate Biopsy Collaborative Group. World J Urol 32, 185-191 (2014). https: / / doi.org / 10.1007 / s00345-012-0869-2 4876-8792-2608v.2

[0215] 49 Bryant, A. K. et al. Association of Prostate-Specific Antigen Screening Rates With Subsequent Metastatic Prostate Cancer Incidence at US Veterans Health Administration Facilities. JAMA Oncology 8, 1747-1755 (2022). https: / / doi.org / 10.1001 / jamaoncol.2022.4319

[0216] 50 Prevention, C. f. D. C. a. National Health Interview Survey, 2005-2021., <https: / / progressreport.cancer.gov / detection / prostate_cancer#:~:text=In%202021%2C%203 7.1%25%20of%20men,test%20within%20the%20past%20year.> (2023).

[0217] 51 Golombos, D. M. et al. The Role of Gut Microbiome in the Pathogenesis of Prostate Cancer: A Prospective, Pilot Study. Urology 111, 122-128 (2018). https: / / doi.org / 10.1016 / j.urology.2017.08.039

[0218] 52 Bultman, S. J. The microbiome and its potential as a cancer preventive intervention. Semin Oncol 43, 97-106 (2016). https: / / doi.org / 10.1053 / j.seminoncol.2015.09.001

[0219] 53 Liu, Y., Yang, C., Zhang, Z. & Jiang, H. Gut Microbiota Dysbiosis Accelerates Prostate Cancer Progression Through Increased LPCAT1 Expression and Enhanced DNA Repair Pathways. Front Oncol 11, 679712 (2021). https: / / doi.org / 10.3389 / fonc.2021.679712

[0220] 54 Xue, X. et al. The role of the symbiotic microecosystem in cancer: gut microbiota, metabolome, and host immunome. Front Immunol 14, 1235827 (2023). https: / / doi.org / 10.3389 / fimmu.2023.1235827

[0221] 55 Pernigoni, N. et al. Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis. Science 374, 216-224 (2021). https: / / doi.org / 10.1126 / science.abf8403 4876-8792-2608v.2

Claims

CLAIMS What is claimed is:

1. A method of determining whether a gut microbiome sample from a human subject is associated with a clinically significant cancer comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, 4876-8792-2608v.2wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of clinically significant cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of cancer / negative biopsy.

2. A method of detecting the presence or absence of a clinically significant cancer in a human subject, the method comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample, so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) detecting: (i) the presence of a clinically significant cancer if the numerical microbiome score generated in (c) is equal to or greater than a 4876-8792-2608v.2predetermined reference value which delineates a cutoff between clinically significant cancer and low-risk cancer / negative biopsy; and (ii) the absence of a clinically significant cancer, defined as low-risk of cancer / negative biopsy, if the numerical microbiome score generated in (c) is less than the predetermined reference value.

3. A method of determining whether a gut microbiome sample from a human subject is associated with a cancer risk comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) a cancer 4876-8792-2608v.2risk and (ii) no current risk of cancer, so as to determine if the gut microbiome sample is associated with a cancer risk, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of a cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject not currently at risk of cancer.

4. A method of determining whether a gut microbiome sample from a human subject is associated with a cancer risk comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) determining: 4876-8792-2608v.2(i) that the gut microbiome sample from the human subject is associated with a current cancer risk if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between a cancer risk and no current risk of cancer; (ii) that the gut microbiome sample from the human subject is not associated with a current cancer risk if the numerical microbiome score generated in (c) is less than the predetermined reference value.

5. The method of any one of claims 1-4, wherein the numerical microbiome score generated in (c) is generated by: (Y) for each bacterial taxon listed in (b): tabulating the bacterial taxon abundance value according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random forest model, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (Z) summing all classification outputs produced in (Y) from all the bacterial taxa so as to create a numerical microbiome score reflecting the majority vote for the sample.

6. The method of claim 5, wherein the percentage abundance values are provided to a random forest model associated with determining a risk of a clinically significant cancer.

7. The method of claim 5, wherein the percentage abundance values are provided to a random forest associated with determining a risk of a cancer.

8. The method of any one of claims 1-2, or 5-6, wherein the method further comprises determining whether a gut microbiome sample from a human subject is associated with a risk of a cancer and wherein a second numerical microbiome score is generated by: (W) for each bacterial taxon listed in (b): 4876-8792-2608v.2tabulating the bacterial taxon abundance value according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random model associated with determining a risk of cancer, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (X) summing all classification outputs produced in (W) from all the bacterial taxa so as to create a second numerical microbiome score reflecting the majority vote for the sample, wherein a second numerical microbiome score equal to or greater than a second predetermined reference value which delineates a cutoff between (i) a risk of a cancer and (ii) no current risk of cancer indicates the sample comes from a human subject at risk of a cancer and a second numerical microbiome score less than the second predetermined reference value indicates the sample comes from a human subject not currently at risk of cancer.

9. The method of any one of claims 3-5, or 7, wherein the method further comprises whether a gut microbiome sample from a human subject is associated with a risk of a clinically significant cancer and wherein a second numerical microbiome score is generated by: (U) for each bacterial taxon listed in (b): tabulating the bacterial taxon abundance according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random model associated with determining a clinically significant cancer, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (V) summing all tree classification outputs produced in (U) from all the bacterial taxa so as to create a second numerical microbiome score reflecting the majority vote for the sample, 4876-8792-2608v.2wherein a second numerical microbiome score equal to or greater than a second predetermined reference value which delineates a cutoff between (i) a clinically significant cancer and (ii) low-risk cancer / negative biopsy indicates the sample comes from a human subject at risk of a clinically significant cancer and a numerical microbiome score less than the second predetermined reference value indicates the sample comes from a human subject of low-risk cancer / negative biopsy.

10. The method of any one of claims 1 to 9, wherein the following taxa are the subject of the genetic analysis: k_Bacteria.p_Proteobacteria k_Bacteria.p_Actinobacteria.c_Actinobacteria k_Bacteria.p_Bacteroidetes.c_Bacteroidia k_Bacteria.p_Firmicutes.c_Bacilli k_Bacteria.p_Firmicutes.c_Erysipelotrichi k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Desulf ovibrionaceae k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_Enter obacteriaceae k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae.g_Corynebacterium k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_Bac teroides k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Coproc occus k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lachno spira k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Oscill ospira k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Finegoldi a k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Peptonip hilus k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Dialister k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae.g_Eubacterium k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae.g_Campylobacter, k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Pasteurel laceae.g_Haemophilus).

11. The method of any one of claims 1 to 10, wherein the cancer is prostate cancer. 4876-8792-2608v.

212. The method of any one of claims 1, 2, 5, 6, 9-11, wherein the clinically significant cancer is Grade Group 2 (GG2) or greater and the low-risk cancer / negative biopsy is Grade Group 1 (GG1) or below.

13. The method of any of Claims 1 to 10, wherein the cancer is colon cancer or lung cancer.

14. The method of any of Claims 1 to 13, wherein the sample is a fecal sample.

15. The method of any of Claims 1-14, wherein the sample is a fecal sample obtained by a swab.

16. The method of any of Claims 1-15, wherein the sample has been obtained by the subject.

17. The method of any of Claims 1-16, wherein the genetic analysis is performed for a variable region of bacterial 16S rRNA genes.

18. The method of any of Claims 1-17, wherein the genetic analysis comprises one or more of paired end read trimming and merging; primer removal; QIIME; QC filtering; chimera detection; human DNA detection; and non-16S rRNA gene detection, so as to produce a set of 16S rRNA gene sequences for analysis.

19. The method of any of Claims 1-18, further comprising filtering out from the analysis genetic analysis, presence indication, and / or abundance values of the following background taxa prior to calculating the numerical microbiome score: c_Chloroplast c_Alphaproteobacteria f_Propionibacteriaceae o_Oceanospirillales f_Intrasporangiaceae f_Micrococcaceae f_Cytophagaceae o_Hydrogenophilales f_Alcaligenaceae f_Burkholderiaceae 4876-8792-2608v.2f_Burkholderiales_incertae_sedis f_Comamonadaceae f_Oxalobacteraceae.

20. The method of any of Claims 1-19, wherein the genetic analysis is performed on variable regions 1 and 2 and / or variable regions 3 and 4 of the 16S rRNA gene of each taxon.

21. The method of any one of claims 1-4, wherein the numerical microbiome score generated in (c) is generated by, for each bacterial taxon listed in (b): (i) ascribing a 1 or a 0 if present or not present in the sample, respectively, so as to generate a presence value; (ii) ascribing a predetermined weighting value to each bacterial taxon, so as to generate a weighted bacteria taxon value; (iii) modifying the abundance value determined by a predetermined weighting for each bacterial taxon, so as to generate a weighted abundance value; and (iv) inputting the presence value, weighted abundance value, and weighted bacterial taxon value into a formula and applying the formula so as to generate an output which is the numerical microbiome score.

22. A method of identifying a subject as in need of a cancer screening test, comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; 4876-8792-2608v.2g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the cutoff indicates the sample comes from a human subject in need of a cancer screening test and a numerical microbiome score less than the cutoff indicates the sample comes from a human subject not in need of a cancer screening test.

23. A method of identifying a subject as in need or not in need of a cancer screening test, comprising: (a) obtaining or having obtained a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; 4876-8792-2608v.2f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) identifying the subject as: (i) in need of a cancer screening test if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between clinically significant cancer and low-risk cancer / negative biopsy; (ii) not in need of a cancer screening test if the numerical microbiome score generated in (c) is less than the predetermined reference value.

24. The method of claim 22 or 23, wherein the following taxa are the subject of the genetic analysis: k_Bacteria.p_Proteobacteria k_Bacteria.p_Actinobacteria.c_Actinobacteria k_Bacteria.p_Bacteroidetes.c_Bacteroidia k_Bacteria.p_Firmicutes.c_Bacilli k_Bacteria.p_Firmicutes.c_Erysipelotrichi k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Desulf ovibrionaceae k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_Enter obacteriaceae k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae.g_Corynebacterium k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_Bac teroides k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Coproc occus k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lachno spira k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Oscill ospira k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Finegoldi a 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Peptonip hilus k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Dialister k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae.g_Eubacterium k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae.g_Campylobacter, k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Pasteurel laceae.g_Haemophilus).

25. The method of any of Claims 1-24, further comprising treating a subject, identified as having a clinically significant prostate cancer by the method, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or a radical prostatectomy (with or without pelvic lymph node dissection).

26. A method of treating a prostate cancer in a subject comprising: (A) obtaining, or having obtained, a diagnosis of the subject as having a clinically significant prostate cancer by a method comprising, on a gut microbiome-containing sample from a human subject, a) performing a genetic analysis on the sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; 4876-8792-2608v.2g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; b) generating a numerical microbiome score from the genetic analysis performed in a), c) comparing the numerical microbiome score generated in b) with a predetermined reference value which delineates a cutoff between (i) clinically significant cancer and (ii) low-risk cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a clinically significant cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of a clinically significant cancer and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with low-risk of cancer / negative biopsy, (B) treating a subject, having a diagnosis in (A) as having a clinically significant prostate cancer, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or radical prostatectomy (with or without pelvic lymph node dissection) to the subject.

27. A method of treating a prostate cancer in a subject comprising: (A) obtaining, or having obtained, a diagnosis of the subject as having a clinically significant prostate cancer by a method comprising, on a gut microbiome-containing sample from a human subject, a) performing a genetic analysis on the sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; 4876-8792-2608v.2o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi;f_Bacteroidaceae; and c_Deltaproteobacteria; b) generating a numerical microbiome score from the genetic analysis performed in a), c) diagnosing the subject as: (i) having a clinically significant prostate cancer if the numerical microbiome score generated in b) is equal to or greater than a predetermined reference value which delineates a cutoff between clinically significant cancer and low-risk cancer / negative biopsy, (ii) not having a clinically significant prostate cancer, defined as low-risk cancer / negative biopsy, if the numerical microbiome score generated in b) is less than the predetermined reference value, (B) treating the subject, having a diagnosis in (A) as having a clinically significant prostate cancer, comprising administering an androgen suppression therapy, abiraterone, external beam radiation (with or without brachytherapy), and / or radical prostatectomy (with or without pelvic lymph node dissection) to the subject.

28. The method of claim 26 or 27, wherein the numerical microbiome score generated in b) is generated by: (Y) for each bacterial taxon listed in a): 4876-8792-2608v.2tabulating the bacterial taxon abundance value according to its read count and converting to a percentage abundance value; and providing the percentage abundance value to a random forest model, wherein each tree in the random forest model applies its own threshold(s) to produce a classification output; and (Z) summing all classification outputs produced in (Y) from all the bacterial taxa so as to create a numerical microbiome score reflecting the majority vote for the sample.

29. The method of Claim 28, wherein the percentage abundance values are provided to a random forest model associated with determining a clinically significant cancer.

30. The method of any of claims 1-29, wherein the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a clinically significant cancer is 0.

158.

31. The method of any of claims 1-29, wherein the numerical microbiome score is from 0 to 1 and the predetermined reference value for risk of a cancer is 0.

256.

32. The method of any of Claims 26 to 31, wherein the following taxa are the subject of the genetic analysis: k_Bacteria.p_Proteobacteria k_Bacteria.p_Actinobacteria.c_Actinobacteria k_Bacteria.p_Bacteroidetes.c_Bacteroidia k_Bacteria.p_Firmicutes.c_Bacilli k_Bacteria.p_Firmicutes.c_Erysipelotrichi k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales 4876-8792-2608v.2k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadaceae k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Desulf ovibrionaceae k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_Enter obacteriaceae k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae.g_Corynebacterium k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_Bac teroides k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Coproc occus k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lachno spira k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Oscill ospira k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Finegoldi a k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Peptonip hilus 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Dialister k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae.g_Eubacterium k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae.g_Campylobacter, k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Pasteurel laceae.g_Haemophilus).

33. The method of any one of claims 26 to 32, wherein the clinically significant prostate cancer is Grade Group 2 (GG2) or greater, and the and the low-risk cancer / negative biopsy is Grade Group 1 (GG1) or below.

34. The method of any of Claims 1 to 33, further comprising performing the method on the first individual subject at a one or more timepoints subsequent to an initial performance of the method on the first individual subject when the subject has been identified by the method as having risk of clinically significant cancer by the initial performance, and comparing the numerical microbiome score for the initial and subsequent one or more timepoint performances so as to determine if risk of clinically significant cancer identified in the subject in initial performance is (a) worsening, (b) improving, or (c) showing no change.

35. The method of Claim 34, wherein the first individual subject is administered a clinical cancer treatment subsequent to the initial performance of the method on the first individual subject and prior to at least one of the one or more timepoints subsequent.

36. A kit comprising (a) primer pairs and (b) one or more reagents, wherein in (a) the primer pairs consists essentially of primer pairs for all of: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; 4876-8792-2608v.2g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria.

37. A method for determining cancer risk using a computer system comprising one or more processors operably connected to one or more memories, the method comprising: (a) accessing, by the one or more processors, from the one or more memories, digitized sequence data associated with a gut microbiome-containing sample; (b) generating, by the one or more processors, 16S rRNA sequence data based on the digitized sequence data; (c) generating, by the one or more processors, a normalized matrix representing (i) presence or not and (ii) abundance of taxa associated with the 16S rRNA sequence data; (d) selecting, by the one or more processors, prevalence data in the normalized matrix which is associated with specific taxonomic features; (e) obtaining, by an artificial intelligence module of the computer system comprising at least one artificial intelligence model, the prevalence data; (f) generating an output, by the artificial intelligence module comprising the at least one artificial intelligence model trained using a training set, based on a query wherein: (i) the query comprises the prevalence data; (ii) the output comprises a numerical microbiome score; (iii) the training set comprises data representing the prevalence of the specific taxonomic features and associated cancer development in patients.

38. The method of claim 37, wherein generating the 16S rRNA sequence data comprises removing artifacts from the digitized sequence data. 4876-8792-2608v.

239. The method of claim 38, wherein removing artifacts from the digitized sequence data comprises one or more of the steps of: (1) trimming data associated with paired sequences, (2) denoising the data associated with the paired sequences, (3) merging the data associated with the paired sequences (4) removing data representing RNA primers; (5) performing filtering on the digitized sequence data; (6) detecting and filtering data associated with chimeras ; (7) detecting and filtering data associated with non-Human rRNA sequences; (8) detecting and filtering data associated with non-16S rRNA sequences.

40. The method of claim 37, wherein the method further comprises storing the 16S rRNA sequence data in the one or more memories.

41. The method of claim 37, wherein generating the normalized matrix comprises: (1) generating a taxonomic matrix, by the steps of: i. generating clusters of similar sequences represented in the 16S rRNA sequence data, wherein the clustering is based on a similarity threshold; ii. assigning one or more taxonomies to the clusters; and iii. assigning counts to each of the one or more taxonomies based on the 16S rRNA data. (2) generating a filtered matrix by removing data from the taxonomic matrix which associated with one or more contaminant taxa; (3) conducting rarefaction based on a specified sample size; (4) assigning data to the normalized matrix based on the rarefaction.

42. The method of claim 41, wherein the clusters represent operational taxonomic units.

43. The method of claim 41, wherein the similarity threshold is defined by a user.

44. The method of claim 41, wherein the one or more contaminant taxa are selected from a group consisting of: c_Chloroplast; c_Alphaproteobacteria; f_Propionibacteriaceae; 4876-8792-2608v.2o_Oceanospirillales; f_Intrasporangiaceae; f_Micrococcaceae; f_Cytophagaceae; o_Hydrogenophilales; f_Alcaligenaceae; f_Burkholderiaceae; f_Burkholderiales_incertae_sedis; f_Comamonadaceae; and f_Oxalobacteraceae.

45. The method of claim 41, wherein the one or more contaminant taxa are hard coded.

46. The method of claim 41, wherein the one or more contaminant taxa are stored in the one or more memories as a table.

47. The method of claim 41, wherein the data is assigned at the genus level.

48. The method of claim 37, wherein the specific taxonomic features comprise: k_Bacteria.p_Bacteroidetes; k_Bacteria.p_Proteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria; k_Bacteria.p_Bacteroidetes.c_Bacteroidia; k_Bacteria.p_Firmicutes.c_Bacilli; k_Bacteria.p_Firmicutes.c_Erysipelotrichi; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales; k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales; 4876-8792-2608v.2k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebact eriaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellacea e; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadac eae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrich aceae; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Des ulfovibrionaceae; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_ Campylobacteraceae; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_E nterobacteriaceae; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebact eriaceae.g_Corynebacterium; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_ Bacteroides; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Cop rococcus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lac hnospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Os cillospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fineg oldia; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pepto niphilus; 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Diali ster; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrich aceae.g_Eubacterium; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_ Campylobacteraceae.g_Campylobacter; and k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Paste urellaceae.g_Haemophilus.

49. The method of claim 37, wherein the specific taxonomic features are hard coded.

50. The method of claim 37, wherein the specific taxonomic features are stored in the one or more memories as a table.

51. The method of claim 37, wherein the prevalence data is modified to include data related to bacterial abundance and relative importance.

52. The method of claim 37, wherein the at least one artificial intelligence model comprises a random forest model.

53. The method of claim 37, wherein the at least one artificial intelligence model is trained using actual patient data.

54. The method of claim 37, wherein the at least one artificial intelligence model is trained using simulated patient data.

55. The method of claim 37, wherein the at least one artificial intelligence model comprises two artificial intelligence models and wherein the output comprises two distinct numerical microbiome scores.

56. The method of claim 37, wherein the numerical prevalence score comprises at least one of a cancer score and a Grade Group 2 (GG2) prostate cancer score.

57. The method of claim 37, wherein the method further comprises storing the taxonomic matrix in the one or more memories. 4876-8792-2608v.

258. The method of claim 37, wherein the method further comprises storing the filtered matrix in the one or more memories.

59. The method of claim 37, wherein the method further comprises storing the normalized matrix in the one or more memories.

60. The method of claim 37, wherein the method further comprises displaying, by the one or more processors, elements of the taxonomic matrix in a graphical format.

61. The method of claim 37, wherein the method further comprises displaying, by the one or more processors, elements of the filtered matrix in a graphical format.

62. The method of claim 37, wherein the method further comprises displaying, by the one or more processors, elements of the normalized matrix in a graphical format.

63. The method of claim 37, wherein the method further comprises displaying, by the one or more processors, the relative abundance of the specific taxonomic features.

64. The method of claim 37, wherein the method further comprises calculating, by the one or more processors, an estimated risk of cancer based on at least one of the numerical microbiome score and the prevalence data.

65. The method of claim 64, wherein the numerical microbiome score is a real number between zero and one and the estimated risk of cancer is calculated by the steps of: i) multiplying the cancer score by 4.476; ii) subtracting 4.421 from the result of the multiplying step; and iii) calculating the exponent of Euler’s Number to the power of the result of the subtracting step.

66. The method of claim 64, wherein the method further comprises calculating a high risk of cancer estimate and a low risk of cancer estimate.

67. The method of claim 66, wherein the high risk of cancer estimate is half of the estimated risk of cancer.

68. The method of claim 66, wherein the low risk of cancer estimate is half of the estimated risk of cancer. 4876-8792-2608v.

269. A method for determining whether bacteria of a gut microbiome sample from a human subject are associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer comprising: (a) obtaining, or having obtained, a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) comparing the numerical microbiome score generated in (c) with a predetermined reference value which delineates a cutoff between (i) risk of future prostate cancer or at risk of a future clinically significant prostate cancer and (ii) low-risk of future prostate cancer / negative biopsy, so as to determine if the gut microbiome sample is associated with a risk of future prostate cancer or risk of a future clinically significant prostate cancer, wherein a numerical microbiome score equal to or greater than the predetermined reference value indicates the sample comes from a human subject at risk of future 4876-8792-2608v.2prostate cancer or at risk of a future clinically significant prostate cancer, and a numerical microbiome score less than the predetermined reference value indicates the sample comes from a human subject with a low-risk of future prostate cancer / negative biopsy.

70. A method for determining whether bacteria of a gut microbiome sample from a human subject are associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer comprising: (a) obtaining, or having obtained, a gut microbiome-containing sample from a human subject; (b) performing a genetic analysis on the gut microbiome-containing sample so as to determine a (i) presence or not and (ii) abundance value of each of the following taxa: g_Oscillospora; g_Lachnospira; f_Corynebacteriaceae; g_Corynebacterium; o_Actinomycetales; f_Paraprevotellaceae; g_Eubacterium; g_Peptoniphilus; o_Enterobacteriales; c_Erysipelotrichi; c_Gammaproteobacteria; g_Finegoldia; p_Proteobacteria; f_Bacteroidaceae; and c_Deltaproteobacteria; (c) generating a numerical microbiome score from the genetic analysis performed in (b); (d) determining: (i) that the bacteria of the gut microbiome sample from the human subject are associated with a risk of a future prostate cancer or risk of 4876-8792-2608v.2a future clinically significant prostate cancer if the numerical microbiome score generated in (c) is equal to or greater than a predetermined reference value which delineates a cutoff between risk of future prostate cancer or at risk of a future clinically significant prostate cancer and low-risk of future prostate cancer / negative biopsy (ii) that the bacteria of the gut microbiome sample from the human subject are not associated with a risk of a future prostate cancer or risk of a future clinically significant prostate cancer if the numerical microbiome score generated in (c) is less than a predetermined reference value.

71. The method of claim 69 or 70, wherein the specific taxonomic features in determining a (i) presence or not and (ii) abundance value of each of the taxa comprise: k_Bacteria.p_Bacteroidetes; k_Bacteria.p_Proteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria; k_Bacteria.p_Bacteroidetes.c_Bacteroidia; k_Bacteria.p_Firmicutes.c_Bacilli; k_Bacteria.p_Firmicutes.c_Erysipelotrichi; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales; k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebact eriaceae; 4876-8792-2608v.2k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellacea e; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadac eae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrich aceae; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Des ulfovibrionaceae; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_ Campylobacteraceae; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_E nterobacteriaceae; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebact eriaceae.g_Corynebacterium; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_ Bacteroides; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Cop rococcus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lac hnospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Os cillospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Fineg oldia; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Pepto niphilus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Diali ster; 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrich aceae.g_Eubacterium; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_ Campylobacteraceae.g_Campylobacter; and k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Paste urellaceae.g_Haemophilus.

72. The method of any one of claims 69 to 71, wherein the numerical microbiome score is from 0 to 1 and the predetermined reference value for future risk of prostate cancer or future risk of clinically significant prostate cancer is 0.25 or above.

73. The method of any one of claims 69 to 72, wherein the clinically significant prostate cancer is Grade Group 2 (GG2) or greater.

74. The method of any of Claims 1-73, further comprising determining whether a gut microbiome sample from a human is positive for a colibactin-producing E. coli.

75. The method of Claim 74, wherein determination that the gut microbiome sample from a human is positive for a colibactin-producing E. coli and also at risk of a future prostate cancer, indicates that the subject is at a relatively increased risk of a future prostate cancer compared to a subject at risk of a future prostate cancer but negative for colibactin-producing E. coli.

76. A method for reducing a risk of a prostate cancer or risk of a future prostate cancer or clinically significant prostate cancer comprising determining, or receiving a determination that, a human subject is at risk of a future prostate cancer by the method of any of claims 1-75, and then administering to the subject one or more agents effective to modify the numerical microbiome score of the gut microbiome of the subject so as to thereby reduce a risk of a future prostate cancer or risk of a future clinically significant prostate cancer in the subject.

77. The method of Claim 76, wherein the subject is positive for colibactin-producing E. coli and in addition to one or more agents effective to modify the numerical microbiome score of the gut microbiome the subject is administered an anti- colibactin positive E. coli treatment. 4876-8792-2608v.

278. The method of Claim 76 or 77, wherein one or more agents effective to modify the numerical microbiome score of the gut microbiome of the subject include a Fecal Microbiota Transplant (FMT); a probiotic supplement; a prebiotic; a phage therapy to selectively eliminate one or more gut bacteria that increase the microbiome score; a Microbiota-Accessible Carbohydrates (MAC) to feed one or more beneficial gut bacteria that decrease the microbiome score; a CRISPR-Cas9 editing agent to modify gut one or more bacteria to decrease the microbiome score; a microbiome banking sample previously taken from the subject when they had a healthy microbiome for future therapeutic use; or a nutritional diet that reduce one or more gut bacteria that increase the microbiome score and / or increase one or more beneficial gut bacteria that decrease the microbiome score.

79. The method of Claim 77 or 78, wherein the an anti-colibactin positive E. coli treatment comprises an antibiotic that kills pks-carrying E. coli strain(s), a phage therapy to selectively eliminates pks-carrying E. coli strain(s), a CRISPR-Cas9 editing agent to selectively inhibit colibactin production by pks-carrying E. coli strain(s); or a nutritional, FMT, prebiotic or probiotic, that increases non-pks- carrying E. coli strain(s) relative to pks-carrying E. coli strain(s).

80. The method of any of Claims 76 to 79, comprising reducing the numerical microbiome score of the gut microbiome of the subject below 0.

25.

81. A kit comprising primers or probes specific for the 16s V1V2 variable regions of each of the following, and a human fecal sample: k_Bacteria.p_Bacteroidetes; k_Bacteria.p_Proteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria; k_Bacteria.p_Bacteroidetes.c_Bacteroidia; k_Bacteria.p_Firmicutes.c_Bacilli; k_Bacteria.p_Firmicutes.c_Erysipelotrichi; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales; 4876-8792-2608v.2k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales; k_Bacteria.p_Firmicutes.c_Bacilli.o_Lactobacillales; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Odoribacteraceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Paraprevotellaceae; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Porphyromonadaceae ; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Rikenellaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae; k_Bacteria.p_Proteobacteria.c_Deltaproteobacteria.o_Desulfovibrionales.f_Desulf ovibrionaceae; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae; k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Enterobacteriales.f_Enter obacteriaceae; k_Bacteria.p_Actinobacteria.c_Actinobacteria.o_Actinomycetales.f_Corynebacteria ceae.g_Corynebacterium; k_Bacteria.p_Bacteroidetes.c_Bacteroidia.o_Bacteroidales.f_Bacteroidaceae.g_Bac teroides; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Coproc occus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Lachnospiraceae.g_Lachno spira; 4876-8792-2608v.2k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Ruminococcaceae.g_Oscill ospira; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Finegoldi ; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Tissierellaceae.g_Peptonip hilus; k_Bacteria.p_Firmicutes.c_Clostridia.o_Clostridiales.f_Veillonellaceae.g_Dialister ; k_Bacteria.p_Firmicutes.c_Erysipelotrichi.o_Erysipelotrichales.f_Erysipelotrichace ae.g_Eubacterium; k_Bacteria.p_Proteobacteria.c_Epsilonproteobacteria.o_Campylobacterales.f_Cam pylobacteraceae.g_Campylobacter; and k_Bacteria.p_Proteobacteria.c_Gammaproteobacteria.o_Pasteurellales.f_Pasteurel laceae.g_Haemophilus.

82. The kit of Claim 81, further comprising one or more polymerase chain reaction reagents. 4876-8792-2608v.2

Images