Optimizing cancer chemotherapy with the gut microbiome

Abstract

The present disclosure provides compositions and methods for preventing and treating fluoropyrimidine toxicity in a subject, including compositions comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

Description

33167 / 70549 / PCOPTIMIZING CANCER CHEMOTHERAPY WITH THE GUT MICROBIOME STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0001] This invention was made with government support under grant R01 CA255116 awarded by The National Institutes of Health. The government has certain rights in the invention.INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ELECTRONICALLY

[0002] This application contains, as a separate part of the disclosure, a Sequence Listing in computer-readable form which is incorporated by reference in its entirety and identified as follows: 70549_SeqListing.xml; Size: 67,604 bytes; Created: December 22, 2025.BACKGROUND

[0003] Dose-limiting toxicities pose a major challenge in drug development (1). Decades of pharmacogenomic research have identified host alleles responsible for variable drug metabolism, yet clinical implementation has lagged due to persistent unexplained variability in toxicity (2, 3). The gut microbiome complements and extends host pathways of drug metabolism, with depletion of hundreds of pharmaceuticals following in vitro incubation with gut strains (4, 5). However, the impact of microbial drug metabolism on toxicity in vivo remains underexplored.

[0004] Despite advances in immunotherapy, cytotoxic chemotherapies such as fluoropyrimidines remain a cornerstone of gastrointestinal cancer treatment (6). A significant obstacle in fluoropyrimidine therapy is high interpatient variability in toxicity, necessitating dose adjustments in 35% of patients and therapy discontinuation in 10% (7). The widely used oral fluoropyrimidine capecitabine (CAP) is a prodrug that is converted to 5-fluorouracil (5-FU), which kills cells through disruption of DNA synthesis and RNA processing (8). Toxic 5-FU is cleared to inactive dihydrofluorouracil (DHFU) by homologous host and microbial enzymes (DPYDand preTA, respectively) (9, 10). While previous work support the hypothesis that bacterial drug inactivation in the gastrointestinal tract could interfere with drug efficacy, the relative impact of this biotransformation on efficacy versus gastrointestinal or other side effects remains unknown (9). While rare patient DPYD sequence variants are associated with severe CAP toxicity (11), host genetic variation and other risk factors cannot explain extensive inter-33167 / 70549 / PCsubject variation in pharmacokinetics and toxicity (12-14). The gut microbiome varies widely between colorectal cancer (CRC) patients (15), offering a potential source of variability in toxicity. However, the impact of microbial preTA on predicting and preventing CAP toxicity remains unknown.SUMMARY

[0005] In some embodiments, the present disclosure provides a method for preventing fluoropyrimidine toxicity in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0006] In some embodiments, the present disclosure provides a method of treating fluoropyrimidine toxicity in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0007] In some embodiments, the present disclosure provides a method for preventing fluoropyrimidine-associated microbiome shifts in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0008] In some embodiments, the present disclosure provides a method of treating fluoropyrimidine-associated microbiome shifts in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0009] In some embodiments, fluoropyrimidine toxicity results from accumulation of 5-fluorouracil (5-FU). In some embodiments, fluoropyrimidine toxicity is associated with capecitabine treatment. In some embodiments, treating fluoropyrimidine toxicity comprises a reduction in severity and / or duration of fluoropyrimidine toxicity symptoms. In some embodiments, fluoropyrimidine toxicity comprises central neurotoxicity, acute cardiac toxicity, or any combination thereof. In some embodiments fluoropyrimidine toxicity comprises vomiting, diarrhea, hand-foot syndrome, or any combination thereof.33167 / 70549 / PC

[0010] In some embodiments, a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof metabolizes 5-Fll to dihydrofluorouracil (DHFU). In some embodiments, a human dihydropyrimidine dehydrogenase enzyme is encoded by a human DPYD gene (SEQ ID NO: 15). In some embodiments, a microbial dihydropyrimidine dehydrogenase enzyme is encoded by a microbial gene or operon with 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% similarity to a nucleotide sequence of any one of SEQ ID NOs: 1-14.

[0011] In some embodiments, a subject is a human subject. In some embodiments, a subject has a cancer. In some embodiments, a cancer comprises a breast cancer, a colon cancer, a gastric cancer, or any combination thereof.

[0012] In some embodiments, a subject is administered a human dihydropyrimidine dehydrogenase enzyme or a microbial dihydropyrimidine dehydrogenase enzyme. In some embodiments, a subject is administered a microorganism that expresses a human dihydropyrimidine dehydrogenase enzyme or a microbial dihydropyrimidine dehydrogenase enzyme.

[0013] In some embodiments, a microorganism comprises a bacteria. In some embodiments, a microorganism is genetically-modified to overexpresses a dihydropyrimidine dehydrogenase enzyme. In some embodiments, a gastrointestinal microbiome of a subject comprises Escherichia coli, Agathobacter rectalis, Anaerosalibacter massiliensis, Anaerostipes caccae, Anaerostipes hadrus Citrobacter braakii, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter pasteurii, Citrobacter werkmanii, Escherichia fergusonii, Eubacterium hallii, Eubacterium siraeum, Kluyvera ascorbate, Lactobacillus oris, Lactobacillus reuteri, Pseudomonas aeruginosa, Pseudomonas extremaustralis, Ruminiclostridium siraeum, Ruminococcus bromii, Salmonella enterica, or any combination thereof. In some embodiments, a microorganism is E. coli.

[0014] In some embodiments, a fluoropyrimidine-associated gastrointestinal microbiome shift is measured relative to a reference gastrointestinal microbiome.

[0015] In some embodiments, a method further comprises administering capecitabine to a subject. In some embodiments, a subject has been treated with capecitabine previously. In some embodiments, capecitabine is administered orally.

[0016] In some embodiments, a composition is administered prior to administering capecitabine. In some embodiments, a composition is administered concurrently with33167 / 70549 / PCcapecitabine. In some embodiments, a composition is administered between administrations of capecitabine. In some embodiments, a composition is administered orally. In some embodiments, a composition is formulated for administration as an oral probiotic or fecal transfer.

[0017] In some embodiments, a method further comprises administering an antibiotic to a subject prior to administering a composition. In some embodiments, an antibiotic comprises a penicillin, tetracycline, cephalosporin, quinolones, lincomycins, macrolides, sulfonamids, glycopeptides, aminoglycosides, carbapenems, or any combination thereof.

[0018] In some embodiments, the present disclosure provides a method of treating a cancer in a subject, the method comprising: (a) administering capecitabine to the subject; and (b) administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0019] In some embodiments, the present disclosure provides a method for identifying susceptibility to fluoropyrimidine toxicity in a subject, the method comprising: (a) measuring the level of dihydropyrimidine dehydrogenase expression in the gastrointestinal microbiome of the subject; (b) administering a dosage of capecitabine to the subject, wherein the dosage of capecitabine is adjusted based on the level of dihydropyrimidine dehydrogenase expression; and (c) optionally administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0020] In some embodiments, a level of dihydropyrimidine dehydrogenase expression is measured using sequencing of 16s ribosomal RNA from a gastrointestinal microbiome.BRIEF DESCRIPTION OF THE DRAWING

[0021] Figs. 1 A-F show that oral fluoropyrimidine treatment impacts gut microbiome composition in colorectal cancer patients. Fig. 1 A shows the gut microbiome and oral fluoropyrimidine (GO) study design depicting patients treated with capecitabine (CAP), TAS-102, or combination CAP + immunotherapy (IO). Fig. 1 B shows the change in alpha diversity during the study, normalized to baseline. P- value: mixed-effects model, Shannon ~ Day + 1 jPatient. Solid line with shading represents LOESS interpolation mean±sem. Fig. 1C shows33167 / 70549 / PCthe volcano plot of microbial amplicon sequence variants (ASVs) with respect to treatment time, p-value: mixed-effects model, central log ratio (CLR)-transformed Abundance ~ Day + 1 |Patient. Points represent enriched (blue) and depleted (orange) ASVs after treatment (false discovery rate (FDR) < 0.2). Fig. 1 D shows enriched and depleted ASV Order. Fig. 1 E shows the phylogenetic tree of significantly differentially abundant ASVs, with labels for clades where treatment affected all clade members similarly (enriched (blue) or depleted (orange)). Fig. 1F shows the relationship between the impact of fluoropyrimidines on ASVs in patients and in vitro. p-value: one-sided Mann-Whitney U test.

[0022] Figs. 2A-I show that oral fluoropyrimidine treatment selects for the bacterial drug metabolism gene preTA. Fig. 2A shows the reproducible shifts in gut microbial KEGG ortholog (KO) gene content are observed across study participants with BL and C1 D7 samples (n = 32) on the sixth principal coordinate (PCo6) of Euclidean distances between central log ratio (CLR)-transformed RPKGs. p-value: PERMANOVA with Patient ID as stratum. Fig. 2B shows a volcano plot of KO gene families with respect to treatment time for all n = 40 patients. Points represent significantly enriched (blue) and depleted (orange) gene families (FDR < 0.2). Fig. 2C shows the gene set enrichment analysis of significantly enriched KOs from (Fig. 2B). Gene sets with p< 0.1 are displayed. Pyrimidine metabolism-related pathways are bolded. Fig. 2D shows the fluoropyrimidine metabolism pathway with significantly enriched pyrimidine metabolism genes labeled in blue and 5-FU target thymidylate synthase (TS) labeled in green. Fig. 2E shows the microbes contributing to preTA in patient samples during treatment, with patients ordered by mean preTA abundance. Fig. 2F shows the sum of preTA+ Escherichia, Anaerostipes, Eubacterium, Citrobacter amplicon sequence variant (ASV) relative abundance by 16S sequencing. Solid line with shading represents LOESS interpolation mean±sem. Fig. 2G shows the comparison of preTA+ ASV abundance and preTA gene abundance, p-value:Spearman’s rank correlation. Fig. 2H shows the preTA levels are associated with 5-FU depletion in ex vivo communities incubated with 50 pg / mL 5-FU for 48 hours anaerobically, p-value: Spearman’s rank correlation. Fig. 2I shows the microbes contributing to preTA in ex vivo communities. For Figs. 2G-H, solid lines represent a linear regression best fit with a shaded 95% confidence interval.

[0023] Figs. 3A-G show a mouse model of capecitabine (CAP) toxicity. (Fig. 3A) shows CAP toxicity mouse model. Mice were gavaged daily with 1500 mg / kg CAP (n = 12) or Vehicle (Veh, n = 8), with half the cohort sacrificed at Day 4. Fig. 3B shows weight loss in CAP and Veh-treated mice, p-value: two-way ANOVA. Figs. 3C-F show the toxicity endpoints at Day 4 and33167 / 70549 / PCDay 12 of CAP treatment: mouse weight (Fig. 3C), colon length (Fig. 3D), lipocalin-2 in colon contents (Fig. 3E), and hind paw withdrawal latency when placed on a 52°C hotplate Fig. 3 (Fig. 3F). p-values: Mann-Whitney U test. Fig. 3G shows the correlation of toxicity endpoints. All colored squares are significant (Spearman’s correlation p-value < 0.05).

[0024] Figs. 4A-H show that microbiota depletion exacerbates capecitabine (CAP) toxicity in a mouse model. Fig. 4A shows AVNM antibiotic depletion CAP toxicity model. Mice were treated with AVNM (n = 12) or Vehicle (Veh, n = 12) for 1 week, then gavaged daily with 1500 mg / kg CAP. Fig. 4B shows CAP-induced weight loss in AVNM and Veh-treated mice. Fig. 4C shows the mouse weight on Day 5. p-value: Mann-Whitney U test. Fig. 4D shows the CAP survival curve in AVNM and Veh treated mice. Fig. 4E shows the CAP toxicity model in conventionally reared (CONV-R, n = 8), germ free (GF, n = 8), and conventionalized (CONV-D, n = 8) mice. Fig. 4F shows the CAP-induced weight loss in CONV-R, GF, and CONV-D mice.Fig. 4G shows the mouse weight on Day 9. p-value: Kruskal-Wallis test. (Fig. 4H) CAP survival curve in CONV-R, GF, and CONV-D mice. (B,F) p-value: two-way ANOVA. (D,H) p-value:Mantel-Cox test.

[0025] Figs. 5A-K show that microbial preTA rescues CAP toxicity. Fig. 5A shows preTA streptomycin (strep) colonization CAP toxicity model. Mice were treated with streptomycin for 1 day, gavaged with E. coli preTA (n= 1Q) or preTA++(n = 16), then gavaged daily with 1500 mg / kg CAP. Fig. 5B shows E. co / / colonization level. Fig. 5C shows CAP-induced weight loss in preTA strep colonization model. Fig. 5D shows mouse weight on Day 8. Fig. 5E shows CAP survival curve in preTA strep colonization model, p-value: Mantel-Cox test. Fig. 5F shows preTA humanization CAP toxicity model. 32 mice were colonized with stool from GO patients for 1 week (n = 8 / donor), then gavaged daily with 1500 mg / kg CAP. Fig. 5G shows the baseline preTA level in humanized mice and group assignments. Fig. 5H shows CAP-induced weight loss in preTA humanization CAP toxicity model. Mouse weight (Fig. 5I-J) and colon length (Fig.5K). (Figs. 5B, D, G, I, K) p-value: Mann-Whitney II Test. (Figs. 5C, H) p-value: two-way ANOVA.

[0026] Figs. 6A-D show that microbial preTA is associated with oral fluoropyrimidine toxicity in patients. Fig. 6A shows the distribution of toxicities in GO patients. HFS: Hand-foot syndrome. Fig. 6B shows the patients experiencing toxicity have lower baseline stool microbial preTA levels (n = 40). p-value: one-sided Mann- Whitney U test. Fig. 6C shows preTA level (above / below mean) vs change in alpha diversity during Cycle 1 (C7D1 -C1 D1 Shannon index, n = 32). Fig. 6D shows preTA level (above / below mean) vs change in beta diversity during Cycle33167 / 70549 / PCI (C7D1 -C1 D1 CLR-Euclidean distance, n = 32). (Fig. 6C, D) p-value: one-sided Wilcoxon signed-rank test.

[0027] Figs. 7A-F show that oral fluoropyrimidine treatment impacts gut microbiome composition in GO study patients. Fig. 7A shows reproducible shifts in gut microbial amplicon sequence variants (ASV) are observed across study participants between BL and C1D7 samples on the second principal coordinate (PCo2) of central log ratio (CLR)-transformed Euclidean distances, p-value: PERMANOVA with Patient ID as stratum, all samples. Figs. 7B-C shows alpha diversity metrics inverse Simpson index (Fig. 7B) and number of ASVs (Fig. 7C) during the study, normalized to baseline, p-values: mixed-effects model, Diversity ~ Day + I I Patient. Fig. 7D shows correlation between taxa changes observed in Cycle 1 and Trough relative to Baseline, p-value: Spearman’s rank correlation. Fig. 7E shows a heatmap of differential ASV abundance in the full cohort and each sub-cohort. Displayed ASVs are those that are significantly altered in the full cohort (Fig. 1C). Shading color indicates treatment effect (i.e. orange is enriched with treatment), with white indicating the ASV was detected in fewer than 3 patients in the cohort. Fig. 7F shows the time course of all significantly altered ASVs from Fig. 1C, with ASVs that increased / decreased during treatment in blue / orange, respectively. Solid lines with shading represent LOESS interpolation mean±sem.

[0028] Figs. 8A-F show that oral fluoropyrimidine treatment selects for pyrimidine metabolism genes. Fig. 8A shows microbial community distance to Cycle 1 Day 1 location calculated per-patient (central log ratio (CLR)-Euclidean ordination), p-value: mixed-effects model, Distance ~ Day + 11 Patient. Fig. 8B show the time course of significantly altered KEGG Ortholog gene families from pyrimidine metabolism, pantothenate and CoA biosynthesis, and p-alanine metabolism pathways. Fig. 8C shows a volcano plot of UniRef90 gene families detected in at least 50% of samples with respect to treatment time. Points represent significantly enriched (blue) and depleted (orange) gene families (FDR < 0.2). Fig. 8D shows the gene set enrichment analysis of significantly enriched UniRef90 gene families from (Fig. 8C). UniRef90 gene sets with p < 0.1 are displayed. Pyrimidine metabolism-related pathways are bolded. Fig. 8E shows volcano plot of Enzyme Classes (EC) detected in at least 50% of samples with respect to treatment time. Points represent significantly enriched (blue) and depleted (orange) ECs (FDR < 0.2). Fig. 8F shows time course of ECs corresponding to genes from (Fig. 8B). (Figs. 8A, 8B, and 8F) Solid line with shading represents LOESS interpolation mean±sem.

[0029] Figs. 9A-D show that capecitabine (CAP) alters murine gut microbiota composition. Fig. 9A shows the shannon index during the experiment, p-value: Mann-Whitney U test. Fig. 9B33167 / 70549 / PCshows shifts in gut microbial genera are observed between Day 0 and 4 in CAP-treated mice using principal coordinate analysis (PCoA) of central log ratio (CLR)-transformed Euclidean distances, p-value: PERMANOVA with Mouse ID as stratum. Fig. 9C shows a volcano plot of microbial genera with respect to treatment time, p-value: mixed- effects model, CLR-transformed Abundance ~ Day + 11 Mouse. Points represent enriched (blue) and depleted (orange) genera after treatment (false discovery rate (FDR) < 0.2). Fig. 9D shows order membership of enriched and depleted genera from (Fig. 9C).

[0030] Figs. 10A-C show microbial preTA predicts CAP toxicity. Univariate receiver operating characteristic (ROC) curves for classification of > 5% weight loss in humanized mice (Fig. 10A), any documented toxicity in GO patients (Fig. 10B), and dose reductions in GO patients (Fig. 10C), all using preTA level. True positive rate (TPR), false positive rate (FPR), and area under the receiver operating characteristic curve (AUROC) are reported.DETAILED DESCRIPTION

[0031] Dose-limiting toxicities remain a major barrier to drug development and therapy, revealing the limited predictive power of human genetics. In some embodiments of the present disclosure, the utility of a more comprehensive approach to studying drug toxicity is shown through a longitudinal study of the human gut microbiome during colorectal cancer (CRC) treatment (NCT04054908) coupled to cell culture and mouse experiments. 16S rRNA gene and metagenomic sequencing revealed significant shifts in gut microbial community structure during treatment with oral fluoropyrimidines, which was validated in an independent cohort. Gene abundance was also markedly changed by oral fluoropyrimidines, including an enrichment for the preTA operon, which is sufficient for the inactivation of active metabolite 5-fluorouracil (5-FU). Higher levels of preTA led to increased 5- FU depletion by the gut microbiota grown ex vivo. Germ-free and antibiotic-treated mice had increased fluoropyrimidine toxicity, which were rescued by colonization with the mouse gut microbiota, preTA+ E. coli, or CRC patient stool with high preTA levels. preTA abundance was negatively associated with patient toxicities. In some embodiments, these data together support a causal, clinically relevant interaction between a human gut bacterial operon and the dose-limiting side effects of cancer treatment. In some embodiments, a method is generalizable to other drugs, including cancer immunotherapies, and provides valuable insights into host-microbiome interactions in the context of disease.

[0032] In one embodiment, a method for preventing fluoropyrimidine toxicity in a subject comprises administering a composition, the composition comprising (i) a dihydropyrimidine33167 / 70549 / PCdehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0033] In one embodiment, a method of treating fluoropyrimidine toxicity in a subject comprises administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.Fluoropyrimidine toxicity

[0034] Since the 1950s, 5-fluorouracil (5-FU) has been one of the most commonly prescribed anticancer drugs for the treatment of various solid tumors. Belonging to the antimetabolite fluoropyrimidine (FL) class, 5-FU and its oral prodrug capecitabine act as false high-affinity substrates for thymidylate synthase, thereby inhibiting pyrimidine biosynthesis in cells displaying high proliferation rates. Metabolites of 5-FU exhibit potent cytotoxic activity due to the biosynthetic depletion of endogenous thymidine, along with direct damage to DNA and RNA, leading to cell death and tumor growth suppression. While the pharmacological efficacy of FLs is well-established, the narrow therapeutic index is a major issue in the management of chemotherapy. Although most patients can be safely treated with FLs, 20-30% will develop severe or even life-threatening untoward toxicity during the course of chemotherapy, resulting in treatment delays and patient discomfort.

[0035] Inter-individual differences in the pharmacokinetics and pharmacodynamics pathways of FLs could play a role in the observed variability in therapeutic outcome. The metabolic pathway of FLs includes a number of proteins such as ATP-binding cassette (ABC), solute carrier transporters (SLC), nuclear receptors, and enzymes (e.g., thymidine phosphorylase, uridine monophosphate synthase, cytidine deaminase), that have been previously reported to have genetic polymorphisms which could affect drug bioavailability and exposure to some extent. Moreover, thymidylate synthase, which is the drug target, and 5,10-methylenetetrahydrofolate reductase, which is involved in the pharmacodynamic effect of the drug, have been reported to play a relevant pharmacogenetic role in the toxicity and efficacy of FLs.

[0036] One more specific cause of FL-related toxicity is inefficient catabolism of the drug, which is mainly mediated by the detoxification enzyme dihydropyrimidine dehydrogenase (DPD).33167 / 70549 / PC

[0037] In some embodiments, fluoropyrimidine toxicity results from accumulation of 5-fluorouracil (5-FU). In some embodiments, fluoropyrimidine toxicity is associated with capecitabine treatment. In some embodiments, treating fluoropyrimidine toxicity comprises a reduction in the severity and / or duration of fluoropyrimidine toxicity symptoms. In some embodiments, fluoropyrimidine toxicity and / or fluoropyrimidine toxicity symptoms comprise central neurotoxicity, acute cardiac toxicity, or any combination thereof. In some embodiments, fluoropyrimidine toxicity comprises vomiting, diarrhea, hand-foot syndrome, or any combination thereof.

[0038] In some embodiments, a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or the microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof metabolizes 5-FU to dihydrofluorouracil (DHFU).

[0039] Human Dihydropyrimidine dehydrogenase enzymes Many efforts have been made to better characterize the genetic basis of the metabolic defect. Currently, only four singlenucleotide polymorphisms in the dihydropyrimidine dehydrogenase gene (human DPYD, SEQ ID NO: 15) are classified as clinically relevant and listed in international pharmacogenetics guidelines for drug dose recommendations — such as those provided by the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Royal Dutch Association for the Advancement of Pharmacy (DPWG). The following variants are known for their role in impairing DPD activity: DPYD*2A (rs3918290) and DPYD*13 (rs55886062), which are associated with nearly complete protein deficiency in homozygotes, and c.2846A>T (rs67376798) and c.1129-5923C>G (rs75017182, tagging HapB3), which are associated with moderate loss of protein function. On May 2020, this compelling evidence prompted the European regulatory agency to publish its own pharmacogenetic recommendations to improve appropriate FL use. EMA now recommends using a reduced initial dose of FLs in patients with DPD deficiency, as determined either by phenotyping (i.e., measuring plasma uracil concentration) or by genotyping patients with the four-variant DPYD panel.

[0040] In some embodiments, a human dihydropyrimidine dehydrogenase enzyme is encoded by a human DPYD gene (SEQ ID NO: 15).

[0041] Bacterial Dihydropyrimidine dehydrogenase enzymes sequences, % homology While rare patient DPYD sequence variants are associated with severe CAP toxicity host genetic variation and other risk factors cannot explain extensive inter-subject variation in pharmacokinetics and toxicity. The gut microbiome varies widely between colorectal cancer (CRC) patients, offering a potential source of variability in toxicity. However, low levels of33167 / 70549 / PCmicrobial preTA levels were predictive of increased adverse effects in humanized mice and patients, providing a proof-of-concept for the use of microbial “genotyping” as a tool for predicting toxicity.

[0042] In some embodiments, a microbial dihydropyrimidine dehydrogenase enzyme is encoded by a microbial gene or operon with 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% similarity to the nucleotide sequence of the E. coli preTA operon (SEQ ID NO: 1), S. enterica PreTA operon (SEQ ID NO: 2), L. reuteri preTA operon (SEQ ID NO: 3), O. formigenes PreTA operon (SEQ ID NO: 4), the A. hadrus PreT and PreA genes (SEQ ID Nos: 5 and 6), or the A. caccae PreT and PreA genes . In some embodiments, a microbial dihydropyrimidine dehydrogenase enzyme is encoded by E. coli hydA gene (SEQ ID NO: 9), A. hydrus hydA gene (SEQ ID NO: 10), E. coli pdp gene (SEQ ID NO: 11), E. coli upp gene (SEQ ID NO: 12), A. hydrus upp gene (SEQ ID NO:13), and / or B. subtilus comEB gene (SSEQ ID NO: 7).Microbiome

[0043] In one embodiment, a method for preventing fluoropyrimidine-associated microbiome shifts in a subject comprises administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0044] In one embodiment, a method of treating fluoropyrimidine-associated microbiome shifts in a subject comprises administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0045] As used herein, the term “microbiome” refers to the community of organisms and genetic material of all microbes - archaea, bacteria, microscopic eukaryotes, and viruses - that live on and inside the human body. The bacteria in the microbiome help digest our food, regulate our immune system, protect against other bacteria that cause disease, and produce vitamins, including B vitamins B12, thiamine and riboflavin, and vitamin K, which is needed for blood coagulation. As used herein, then, the term “gut microbiome” refers to the microorganisms and genetic material therein that live in the digestive organs which include the gut, intestines and digestive track.33167 / 70549 / PC

[0046] The microbiome is essential for human development, immunity and nutrition. Each individual is provided with a unique gut microbiota profile that plays many specific functions in host nutrient metabolism, maintenance of structural integrity of the gut mucosal barrier, immunomodulation, and protection against pathogens. Gut microbiota are composed of different bacteria species taxonomically classified by genus, family, order, and phyla. Each human’s gut microbiota is shaped in early life as their composition depends on infant transitions (birth gestational date, type of delivery, methods of milk feeding, weaning period) and external factors such as antibiotic use. These personal and healthy core native microbiota differ between individuals due to dietary intake, pharmaceutical use, sex, age, body mass index (BMI), exercise, and other lifestyle and cultural habits. Accordingly, there is not a unique optimal gut microbiota composition since it is different for each individual. Understanding the cause or consequence of these gut microbiota balances in health and disease and how to maintain or restore a healthy gut microbiota composition should be useful in developing promising therapeutic interventions.

[0047] As discussed in Rinninella et al., (Rinninella, E., et al., Microorganisms, 7(1):14 (2019); incorporated by reference in its entirety herein) gut microbiota are composed of several species of microorganisms, including archea, bacteria, microscopic eukaryotes, and viruses. Taxonomically, bacteria are classified according to phyla, classes, orders, families, genera, and species. Typically 9 to 10 phyla are represented. The dominant gut microbial phyla are Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia, with the two phyla Firmicutes and Bacteroidetes typically being highly represented in the gut microbiota. The Firmicutes phylum is composed of more than 200 different genera such as Lactobacillus, Bacillus, Clostridium, Enterococcus, and Ruminicoccus. Clostridium genera represent 95% of the Firmicutes phyla. Bacteroidetes consists of predominant genera such as Bacteroides and Prevotella. The Actinobacteria phylum is proportionally less abundant and mainly represented by the Bifidobacterium genus.

[0048] In some embodiments, a microorganism comprises a bacteria.

[0049] In some embodiments, a microorganism comprises a genetically modified version of a microorganism naturally occurring the gastrointestinal microbiome.

[0050] In some embodiments, a fluoropyrimidine-associated gastrointestinal microbiome shift is measured relative to a reference gastrointestinal microbiome.33167 / 70549 / PC

[0051] In general, any bacterial cell that resides in the gut microbiota of a subject are contemplated herein. In some embodiments, a gastrointestinal microbiome of a subject comprises Escherichia coli, Agathobacter rectalis, Anaerosalibacter massiliensis, Anaerostipes caccae, Anaerostipes hadrus Citrobacter braakii, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter pasteurii, Citrobacter werkmanii, Escherichia fergusonii, Eubacterium hallii, Eubacterium siraeum, Kluyvera ascorbate, Lactobacillus oris, Lactobacillus reuteri, Pseudomonas aeruginosa, Pseudomonas extremaustralis, Ruminiclostridium siraeum, Ruminococcus bromii, Salmonella enterica, or any combination thereof.

[0052] In some embodiments, a microorganism is E. coli.

[0053] In some embodiments, a method includes preventing microbial growth of diverse gut bacterial strains.Methods of Administration

[0054] Enzymes Enzymes are essential in biochemical processes. They catalyze hundreds of stepwise metabolism reactions, preserving and transforming chemical energy and generating biological macromolecules from precursors. Their catalytic activity depends on the integrity of their native protein conformation. In this regard, the activity of one or more enzymes is impaired in many diseases due to mutations. Because of the necessity of the correct performance of the enzymes, many drugs have been developed with the aim to target dysfunctional enzymes.

[0055] An alternative approach is to use enzymes directly as therapeutic drugs. They were used firstly at the end of the 19th century, when enzymes such as pepsin were used to treat dyspepsia.

[0056] To support the growing demand for these enzymatic treatments, major efforts are being invested in their industrial production, using recombinant expression of these molecules in plants, mammalian systems and microbial systems (microscopic eukaryotes or bacteria).However, some enzyme drugs are taken directly from nature, for instance, snake venom.

[0057] Together with economic growth, an increase in the number of publications concerning enzyme therapy has been observed, highlighting the growing interest and potential of this field. The observed increase in research publications and patents to date highlights the efforts invested in this field mainly because of the promising therapeutic potential of enzymes.Presently, enzymes are not only being used and investigated for the treatment of metabolic deficiencies but also for many different pathologies such as cancer and cardiovascular diseases.33167 / 70549 / PC

[0058] The potential of enzyme-based drugs can be improved in regard to specific factors. First, the in vivo half-life of the molecules should be improved; second, the targeted action is not always accurate; and third, valid methods are necessary to control the patient’s immune system response during treatments based on enzymes. In this context, novel approaches to monitor the immune response, such as microarrays, are of ongoing interest for personalized medicine. Moreover, newer approaches based on enzymes are being studied to treat infections such as SARS-CoV-2 and its associated pathology, COVID-19, highlighting the potential benefit of enzyme therapy.

[0059] In some embodiments, a subject is administered a human dihydropyrimidine dehydrogenase enzyme or a microbial dihydropyrimidine dehydrogenase enzyme.

[0060] Engineered bacteria Broad interest in the influence that the gut microbiome has on host health and disease has inevitably led to the development of strategies with which to manipulate the structure and function of host-associated microbial communities. Established communities in the gut microbiota are difficult to manipulate in vivo.

[0061] Various approaches for microbiome modification have recently been described, including engrafting bacterial strains in a naive host by providing exclusive nutrient sources in the diet (Shepherd, E. S., et al., Nature 557, 434-438 (2018); Kearney, S. M., et al., Cell Rep.24, 1842-1851 (2018)) or treating with antibiotics (Staley, C. et al., Microbiome 5, 87 (2017), and Thompson, J. A., et al., Cell Rep. 10, 1861-1871 (2015)); introducing transient bacteria as a live therapeutic to complement an absent host metabolic activity (Isabella, V. M. et al., Nat.Biotechnol. 36, 857-864 (2018), and Kurtz, C. B. et al., Sci. Transl. Med. 11 (2019)); and chemically inhibiting microbial pathways active in host disease states (Zhu, W. et al., Nature 553, 208-211 (2018)). Bacteria have also been engineered to respond in vivo - that is, within the gut - to dietary compounds and synthetic inducers (Mimee, M., et al., Cell Systems 1 , 62-71 (2015), and Lim, B., et al., Cell 169, 547-558. e15 (2017)), as well as to deliver genetic payloads to the gut microbiota in a conjugation-based strategy, opening the door to the simultaneous editing of diverse members of a bacterial consortium through delivery of a single donor organism (Ronda, C., et al., Nat. Methods 1 (2019)). Current strategies for microbiome editing, however, either lack species- or strain-level precision or require the introduction of an exogenous bacterium into the host.

[0062] In some embodiments, a method of selectively engineering at least one bacterial strain among a mixed population of bacterial strains in the gut of a subject comprising administering at least one bacteriophage comprising at least one nucleic acid, wherein said33167 / 70549 / PCbacteriophage selectively infects the at least one bacterial strain under conditions that allow expression of said at least one nucleic acid is provided.

[0063] As used herein, the term “selectively” refers to the ability to specifically infect a desired bacterial strain, species, genus, etc.

[0064] The term “engineering,” “engineer” or “engineered” as used herein means modifying a bacterial cell, strain, species or genus, etc., by, for example, electroporation or introducing a bacteriophage comprising a nucleic acid. Those of skill in the art will understand that this modification includes, for example, introduction of exogenous nucleic acid sequences into a bacterial genome or expression of an exogenous gene product (e.g., structural / functional nucleic acid or protein).

[0065] The phrase “at least one” with respect to bacterial strain or species or genus or family or bacteriophage or nucleic acid includes 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10 or more members of the specified class.

[0066] “Nucleic acids” that are carried by or otherwise transferred to a bacteria according to the disclosure can be, for example, a plasmid. Additionally, the nucleic acid can be a fragment of DNA that can be integrated in to the genome of a bacterium.

[0067] In some embodiments, a subject is administered a microorganism that expresses a human dihydropyrimidine dehydrogenase enzyme or a microbial dihydropyrimidine dehydrogenase enzyme.

[0068] In some embodiments, a microorganism is genetically modified to overexpresses the dihydropyrimidine dehydrogenase enzyme.

[0069] Routes and Formulations In some embodiments, a composition is administered orally. In some embodiments, a composition is administered rectally. In some embodiments a composition is formulated for administration as an oral probiotic or fecal transfer.

[0070] The compositions described herein may be formulated as pharmaceutical compositions. The term “pharmaceutically acceptable” as used herein refers to a pharmaceutically acceptable material, composition, or vehicle, such as a liquid or solid filler, diluent, excipient, solvent, or encapsulating material. Each component must be “pharmaceutically acceptable” in the sense of being compatible with the other ingredients of a pharmaceutical formulation. It must also be suitable for use in contact with tissue, organs or other human component without excessive toxicity, irritation, allergic response, immunogenicity,33167 / 70549 / PCor other problems or complications, commensurate with a reasonable benefit / risk ratio. (See Remington: The Science and Practice of Pharmacy, 21st Edition; Lippincott Williams & Wilkins: Philadelphia, PA, 2005; Handbook of Pharmaceutical Excipients, 5th Edition; Rowe et al., Eds., The Pharmaceutical Press and the American Pharmaceutical Association: 2005; and Handbook of Pharmaceutical Additives, 3rd Edition; Ash and Ash Eds., Gower Publishing Company: 2007; Pharmaceutical Preformulation and Formulation, Gibson Ed., ORO Press LLC: Boca Raton, FL, 2004)).

[0071] Embodiments of the pharmaceutical composition of the disclosure is formulated to be compatible with its intended route of administration (i.e. , oral, topical, and / or rectal administration). Solutions or suspensions used for parenteral, intradermal, or subcutaneous application can include the following components: a sterile diluent such as water, saline solution, fixed oils, polyethylene glycols, glycerine, propylene glycol or other synthetic solvents; dimethyl sulfoxide (DMSO); antibacterial agents such as benzyl alcohol or methyl parabens; antioxidants such as ascorbic acid or sodium bisulfite; chelating agents such as ethylenediaminetetraacetic acid (EDTA); buffers such as acetates, citrates or phosphates, and agents for the adjustment of tonicity such as sodium chloride or dextrose. The pH can be adjusted with acids or bases, such as hydrochloric acid or sodium hydroxide. The parenteral preparation can be enclosed in ampoules, disposable syringes, or one or more vials comprising glass or polymer (e.g., polypropylene). The term “vial” as used herein means any kind of vessel, container, tube, bottle, or the like that is adapted to store embodiments of the vaccine composition as described herein.

[0072] In some embodiments, the composition further comprises a pharmaceutically acceptable carrier. The term “carrier” as used herein encompasses diluents, excipients, adjuvants, and combinations thereof. Pharmaceutically acceptable carriers are well known in the art (See Remington: The Science and Practice of Pharmacy, 21st Edition). Exemplary “diluents” include sterile liquids such as sterile water, saline solutions, and buffers (e.g., phosphate, tris, borate, succinate, or histidine). Exemplary “excipients” are inert substances that may enhance vaccine stability and include but are not limited to polymers (e.g., polyethylene glycol), carbohydrates (e.g., starch, glucose, lactose, sucrose, or cellulose), and alcohols (e.g., glycerol, sorbitol, or xylitol).

[0073] In some embodiments, a composition is formulated as a probiotic. In some embodiments, a composition is formulated for fecal transplant.33167 / 70549 / PCTreatment of cancer

[0074] Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. Possible signs and symptoms include a lump, abnormal bleeding, prolonged cough, unexplained weight loss, and a change in bowel movements. While these symptoms may indicate cancer, they can also have other causes. Over 100 types of cancers affect humans.

[0075] In some embodiments, a subject is a human subject. In some embodiments, a subject has a cancer.

[0076] Cancers are classified by the type of cell that the tumor cells resemble and is therefore presumed to be the origin of the tumor.

[0077] In some embodiments, a cancer is a carcinoma. Carcinomas are cancers derived from epithelial cells. This group includes many of the most common cancers and include nearly all those in the breast, prostate, lung, pancreas and colon. Most of these are of the adenocarcinoma type, which means that the cancer has gland-like differentiation.

[0078] In some embodiments, a cancer is a sarcoma. Sarcomas are cancers arising from connective tissue (i.e. bone, cartilage, fat, nerve), each of which develops from cells originating in mesenchymal cells outside the bone marrow.

[0079] In some embodiments, a cancer is a lymphoma or leukemia. Lymphomas and leukemias arise from hematopoietic (blood-forming) cells that leave the marrow and tend to mature in the lymph nodes and blood, respectively.

[0080] In some embodiments, a cancer is a germ cell tumor. Germ cell tumors are cancers derived from pluripotent cells, most often presenting in the testicle or the ovary (seminoma and dysgerminoma, respectively).

[0081] In some embodiments, a cancer is a blastoma. Blastomas are cancers derived from immature "precursor" cells or embryonic tissue.

[0082] In some embodiments, a cancer comprises a carcinoma. In some embodiments, a carcinoma comprises a carcinoma that originates from breast, colon, or gastric tissue. In some embodiments, a cancer comprises breast cancer, colon cancer, gastric cancer, or any combination thereof.

[0083] Many treatment options for cancer exist. The primary ones include surgery, chemotherapy, radiation therapy, hormonal therapy, targeted therapy and palliative care. Which33167 / 70549 / PCtreatments are used depends on the type, location and grade of the cancer as well as the patient's health and preferences.

[0084] In some embodiment, a method of treating a cancer in a subject comprises: (a) administering capecitabine to the subject; and (b) administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0085] Combination therapies Capecitabine is a type of chemotherapy drug referred to as an antimetabolite. In the body, capecitabine gets broken down into substances that interfere with the production of DNA, RNA, and proteins. This stops or slows the growth of cancer cells and other rapidly growing cells and causes them to die.

[0086] In some embodiments, capecitabine is used at a dosage of 625-1250 mg / m2 once or twice daily.

[0087] Capecitabine is approved to be used alone or with other drugs to treat a cancer.

[0088] In some embodiments, capecitabine is used to treat a breast cancer that has spread. In some embodiments, capecitabine is used alone in patients whose cancer cannot be treated with an anthracycline ortaxane chemotherapy drug. In some embodiments, capecitabine is used with docetaxel in patients whose cancer was treated with an anthracycline chemotherapy drug but it is no longer working.

[0089] In some embodiments, capecitabine is used to treat colorectal cancer. In some embodients, it is used alone or with other chemotherapy in patients who have had surgery to remove stage III colon cancer, to help keep the cancer from coming back after surgery. In some embodiments, capecitabine is used around the time of surgery with radiation therapy and other chemotherapy in adults whose rectal cancer has spread to nearby tissues. In some embodiments, capecitabine is used alone or with other chemotherapy in patients whose colorectal cancer cannot be removed by surgery or has spread to other parts of the body.

[0090] In some embodiments, capecitabine is used to treat pancreatic adenocarcinoma. In some embodiments, capecitabine is used with other chemotherapy drugs in adults who have had surgery to remove the cancer, to help keep the cancer from coming back after surgery.

[0091] In some embodiments, capecitabine is used to treat stomach (gastric) cancer, esophageal cancer, or gastroesophageal junction cancer in adults. In some embodiments,33167 / 70549 / PCcapecitabine is used with other chemotherapy drugs when the cancer cannot be removed by surgery or has spread to other parts of the body. In some embodiments, capecitabine is used when the cancer is HER2 positive, has spread to other parts of the body, and has not been treated with a chemotherapy regimen that included capecitabine.

[0092] Capecitabine is also being studied in the treatment of other types of cancer.

[0093] In some embodiments, a subject has been treated with capecitabine previously. In some embodiments, capecitabine is administered orally. In some embodiments, a method further comprises administering capecitabine to the subject.

[0094] In some embodiments, a composition is administered prior to administering capecitabine. In some embodiments, a composition is administered concurrently with capecitabine. In some embodiments, a composition is administered between administrations of capecitabine.

[0095] Treatment with antibiotics is known to deplete commensal and mutualistic microbes. This can be detrimental to patient health in situations such as extended courses of antibiotics to treat Clostridium difficile infection. However, antibiotic depletion prior to fecal transfer can improve colonization following fecal transplant.

[0096] In some embodiments, a method further comprises administering an antibiotic to the subject prior to administering the composition.

[0097] In some embodiments, an antibiotic comprises a penicillin, tetracycline, cephalosporin, quinolones, lincomycins, macrolides, sulfonamids, glycopeptides, aminoglycosides, carbapenems, or any combination thereof.Biomarker / identifying susceptibility

[0098] Alkylating agents, antimetabolites, topoisomerase inhibitors, mitotic inhibitors and cytotoxic antibiotics are chemotherapies with different mechanisms of action. Their anticancer activities rely on disrupting DNA integrity, enzymes for DNA repair and synthesis. However, chemotherapy also damages normal cells due to its non-specificity. Recent findings have suggested that targeting microbiota could be promising to improve chemotherapeutic efficacy and reduce toxicity. Generally, commensal microbes interact with chemotherapeutics mainly by modulating drug metabolism (ie, pharmacokinetics) and host immunity (ie, pharmacodynamics).

[0099] Having millions of protein-coding genes, gut bacteria can not only process ingested nutrients but also alter drug pharmacokinetics. Microbes-mediated drug metabolism can be33167 / 70549 / PCdivided into direct or indirect interactions. Microbes can directly convert drugs into active, inactive or even toxic metabolites. Alternatively, this process can be indirectly mediated by microbes-derived metabolites.

[0100] Beyond drug toxicity, bacterial metabolism is essential to drug effects. Recent studies using Caenorhabditis elegans models have identified bacterial genes mediating chemotherapeutic efficacy especially those involved in ribonucleotide and vitamin B6 and B9 metabolism. Mechanistically, bacterial ribonucleotide metabolism is required to activate 5-fluorouracil (5-FU) into cytotoxic 5-fluorou ridine triphosphate for exhibiting its RNA-damaging effects. Disruption of bacterial vitamin B6 and B9 production, which is linked to ribonucleotide metabolism, diminishes 5-FU efficacy, thus implicating the influence of bacterial metabolites on chemotherapeutics. Notably, the antidiabetic drug metformin could inhibit the bacterial one-carbon metabolism which is needed for 5-FU to exhibit its anticancer effects, causing reduction of 5-FU efficacy. Therefore, special caution is necessary when using chemotherapy in patients with cancer with comorbidity (Scott et al., Cell 2017;169:442-56.).

[0101] In some embodiments, a method for identifying susceptibility to fluoropyrimidine toxicity in a subject, comprises: (a) measuring the level of dihydropyrimidine dehydrogenase expression in the gastrointestinal microbiome of the subject; (b) administering a dosage of capecitabine to the subject, wherein the dosage of capecitabine is adjusted based on the level of dihydropyrimidine dehydrogenase expression; and (c) optionally administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

[0102] In some embodiments, a level of dihydropyrimidine dehydrogenase expression is measured using sequencing of 16s ribosomal RNA from the gastrointestinal microbiome. In some embodiments, the gastrointestinal microbiome comprises Escherichia coli, Agathobacter rectalis, Anaerosalibacter massiliensis, Anaerostipes caccae, Anaerostipes hadrus Citrobacter braakii, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter pasteurii, Citrobacter werkmanii, Escherichia fergusonii, Eubacterium hallii, Eubacterium siraeum, Kluyvera ascorbate, Lactobacillus oris, Lactobacillus reuteri, Pseudomonas aeruginosa, Pseudomonas extremaustralis, Ruminiclostridium siraeum, Ruminococcus bromii, Salmonella enterica, or any combination thereof.

[0103] Before the present disclosure is further described, it is to be understood that this disclosure is not limited to particular embodiments described, as such may, of course, vary. It is33167 / 70549 / PCalso to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

[0104] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

[0105] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited.

[0106] It must be noted that as used herein and in the appended claims, the singular forms "a," "and," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a conformation switching probe" includes a plurality of such conformation switching probes and reference to "the microfluidic device" includes reference to one or more microfluidic devices and equivalents thereof known to those skilled in the art, and so forth. It is further noted that the claims may be drafted to exclude any element, e.g., any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as "solely," "only" and the like in connection with the recitation of claim elements, or use of a "negative" limitation.

[0107] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated33167 / 70549 / PCrange includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

[0108] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order which is logically possible. This is intended to provide support for all such combinations.EXAMPLESExample 1 - Expansion of a bacterial operon during cancer treatment ameliorates drug toxicityResults

[0109] Oral fluoropyrimidines perturb gut microbial community structure The Gut microbiome and Oral fluoropyrimidine (GO) clinical study (ClinicalTrials.gov ID NCT04054908), a prospective longitudinal study investigating the impact of oral fluoropyrimidines on the gut microbiome was conducted. Stool was collected at 7 timepoints spanning pre- treatment to postcycle 3 of chemotherapy (Fig. 1 A). Of the 52 patients enrolled, 40 submitted at least one stool sample. Patients were distributed among three sub-cohorts: (A) CAP as monotherapy or part of a standard-of-care regimen (n = 21); (B) TAS-102 (trifluridine / tipiracil, n = 9); (C), CAP with bevacizumab and pembrolizumab immunotherapy (patients participating on ClinicalTrials.gov ID NCT03396926, n = 10). The 40 participants had a mean age of 52.5±10.5 y; 50% were male, 78% identified as white, and 7.5% identified as Hispanic or Latino ethnicity. 83% of patients were TNM stage lll-IV and 68% had prior surgery.

[0110] 16S rRNA gene sequencing was utilized to assess longitudinal shifts in the gut microbiota during cancer therapy. The overall longitudinal trends across all patients in all subcohorts were captured to better power the analysis. The combined dataset represented 217 samples from 40 patients with 67,915±3,744 high-quality reads per sample. Treatment was significantly associated with microbiota composition (Fig. 7A). For linear modeling, we truncated the data at 50 days post-Cycle 1 treatment initiation to avoid the influence of long- duration outliers and patient drop-out on model results (Fig. 7B). Alpha diversity and amplicon sequence variants (ASVs) were analyzed using a mixed-effects model, with time as a fixed effect and Patient ID as a random effect (see Methods). We observed a significant decrease in Shannon33167 / 70549 / PCdiversity index on treatment (Fig. 1 B). Alternative markers of diversity trended lower but did not reach statistical significance (Figs. 7B, 7C). We identified 43 differentially abundant ASVs, including 16 that increased and 27 that decreased over time (Fig. 1 C, Fig. 7F). The shift in ASV abundance during Cycle 1 and Trough relative to baseline were correlated (Fig. 7D), with similar ASV shifts observed in each sub-cohort individually (Fig. 7E). Treatment-associated ASVs were from 11 bacterial orders (Fig. 1 D). The depleted ASVs included a cluster of Oscillospirales, whereas the enriched ASVs were more phylogenetically dispersed (Fig. 1 E). Remarkably, our list of treatment-associated ASVs was significantly associated with sensitivity to CAP and 5-FU in mono-culture (Fig. 1 F), suggesting that at least some observed changes were due to the direct impact of fluoropyrimidines on the gut microbiota.

[0111] Previously published (16) 16S rRNA gene sequencing data from 33 CRC patients treated with CAP in the Netherlands were obtained. We observed a decline in ASV number, Inverse Simpson index, and Shannon index on treatment. A single significantly decreased ASV was detected and mapped to the Anaerovoracaceae. Consistent with the GO dataset, the shift in ASV abundance in Cycle 3 was similar at Trough relative to Baseline. ASV-level shifts with respect to treatment were significantly associated between the GO and NE studies. Thus, it is possible to identify consistent shifts in the gut microbiota in CRC patients treated with oral fluoropyrimidines.

[0112] Chemotherapy selects for gut microbial pyrimidine metabolism genes Given the numerous potential mechanisms through which the gut microbiome can influence cancer progression and drug response, metagenomic sequencing was used to develop a mechanistic hypothesis as to the consequences of fluoropyrimidine-induced shifts in the gut microbiome for drug response. The metagenomic GO dataset represents 229 stool samples from 40 patients, with 26.02±10.87 million high quality reads per sample (7.68±0.22 Gbp). The distance to baseline based on metagenomic KEGG Orthologous group (KO) abundances (central log ratio (CLR)-Euclidean ordination) spiked during Cycle 1 with subsequent partial recovery (Fig. 8A); therefore, we opted to focus on KO abundances in Cycle 1 versus Baseline. Consistent shifts in KO abundance were detectable by principal coordinate analysis (Fig. 2A), reflecting 152 KOs with significantly altered relative abundance (Fig. 2B). Notably, nearly all (149) of these KOs increased in abundance, with the exception of 3 KOs that decreased: K06989 (aspartate dehydrogenase), K20626 (lactoyl-CoA dehydratase subunit alpha), and K20461 (lantibiotic transport system permease protein). Gene set enrichment analysis of the 149 enriched genes33167 / 70549 / PChighlighted multiple pathways relevant to fluoropyrimidine metabolism, including pyrimidine metabolism, pantothenate and CoA biosynthesis, and [3-alanine metabolism (Fig. 2C).

[0113] Inspection of the KOs within these pathways (Fig. 2D, Fig. 8B) revealed a significant increase in the abundance of uracil phosphoribosyltransferase (upp, K00757), which is a well-established contributor to bacterial drug sensitivity (9, 17, 18). Consistent with this trend, levels of pyrimidine- nucleoside phosphorylase (pdp, K00756) increased following the initiation of treatment. In turn, multiple genes in the bacterial pathway for 5-Fll inactivation were enriched, including the bacterial dihydropyrimidine dehydrogenase (preT, K17722 and preA, K17723) responsible for the reduction of 5-FU to DHFU and dihydropyrimidinase (hydA, K01464) that further processes DHFU to a- Fluoro- -ureidopropionic acid (FUPA). We further validated enrichment of pyrimidine- metabolizing pathways and genes using UniRef90 and enzyme class (EC) annotation schemes (Figs. 8C-F).

[0114] The bacterial taxa were further analyzed to explain the observed treatment-associated enrichment in preTA levels. Analysis of stratified gene abundance data revealed that Anaerostipes hadrus, Escherichia spp., Eubacterium siraeum, and Citrobacter spp. accounted for >99% of preTA (Fig. 2E). Consistent with the observed inter-individual heterogeneity in the dominant preTA+ species, no single ASV from these species was significantly enriched during Cycle 1 (false discovery rate > 0.2, mixed-effects model with Treatment Group as a fixed effect and Patient as a random effect). In contrast, the aggregated abundance of all preTA+ ASVs significantly increased following fluoropyrimidine treatment in both the GO (Fig. 2F) and NE cohorts. Our aggregate metric for preTA+ ASV abundance by 16S rRNA gene sequencing was significantly associated with preTA gene abundance by metagenomics (Fig. 2G).

[0115] These findings were further validated by testing the functional relevance of shifts in the abundance of preTA for 5-FU metabolism. Baseline stool samples from 3 patients with variable baseline preTA were cultured ex vivo in the presence of vehicle, 5-FU, or CAP for 48 hours. Both 5-FU and its oral prodrug, CAP, significantly inhibited overall bacterial growth, consistent with prior evidence for CAP bioactivation by human gut bacteria (5, 9). 16S rRNA gene sequencing of the endpoint microbiotas revealed decreased diversity in response to CAP and 5-FU and significantly altered community structure. The shifts in ASV abundance in response to CAP and 5-FU were significantly associated . Quantification of 5-FU levels at the endpoint by LC-MS / MS revealed marked differences between each ex vivo community that were significantly associated with preTA abundance (Fig. 2H). The source of preTA was primarily E. co / / (Fig. 2I).33167 / 70549 / PC

[0116] Microbiota depletion exacerbates CAP toxicity These data raised the questions about whether an expansion of preTA+ bacteria following the initiation of fluoropyrimidine treatment could potentially mitigate gastrointestinal (Gl) side effects. However, there was no established mouse model of oral fluoropyrimidine toxicity, necessitating extensive experimentation to establish such a model (data not shown). A daily dose of 1 ,500 mg / kg CAP, which is equivalent to a daily human dose of 4500 mg / m2 (19) was selected as being slightly higher than total daily GO study dose (1700-2500 mg / m2). CAP was administered by oral gavage to 8-week old female C57BL / 6J mice (n = 8-12 mice / group; Fig. 3A). CAP led to significant weight loss relative to vehicle controls (Figs. 3B-C) without any significant differences in body composition, small intestine length, or spleen weight. Clear evidence of colonic inflammation was detected, including decreased colon length (Fig. 3D) and increased stool levels of lipocalin-2 (Fig. 3E). Hind paw thermal hyperalgesia was significantly worsened (Fig.3F), indicating that this mouse model mimics aspects of the CAP-induced hand-foot syndrome (20), a common reason for CAP dose modifications (7). The severity of these phenotypes were significantly associated (Fig. 3G), motivating the use of weight loss as a proxy in subsequent experiments.

[0117] CAP significantly altered the gut microbiota in the mouse model. A significant decrease was observed in Shannon diversity index (Fig. 9A). CAP treatment was significantly associated with microbiota composition (Fig. 9B). We identified 64 differentially abundant genera in CAP- treated but not vehicle-treated mice, including 37 that increased and 27 that decreased over time (Fig. 9C). Treatment associated-genera were from multiple bacterial orders, including many Clostridiales members (Fig. 9D).

[0118] Having established the mouse model, the overall gut microbiota was tested to measure the impact of CAP toxicity. To deplete the gut microbiota, 6-8-week-old female C57BL / 6J mice were treated with a cocktail of broad-spectrum antibiotics (ampicillin, vancomycin, neomycin, and metronidazole; AVNM) or vehicle for one week prior to CAP administration (Fig. 4A). AVNM significantly decreased total colonization level and increased cecal weight. Weight loss was significantly accelerated in AVNM-treated mice (Fig. 4B-C), resulting in significantly decreased survival relative to vehicle controls (Fig. 4D).

[0119] These effects were validated in gnotobiotic mice. CAP toxicity was compared in germ-free (GF), conventionalized (ex-GF, CONV-D), and conventionally-raised (CONV-R) 6-8 week-old female C57BL / 6J mice (Fig. 4E). Because GF mice experience higher serum 5-Fll levels following CAP administration (9), the dose of CAP was reduced to 1,100 mg / kg by daily33167 / 70549 / PCoral gavage. Consistent with the antibiotic depletion data, CAP led to significantly enhanced weight loss (Fig. 4F-G) and decreased survival (Fig. 4H) in GF mice relative to CONV-R or CONV-D mice. Taken together, these results indicate that an intact microbiota at the time of CAP treatment is critical to avoid increased drug toxicity.

[0120] Microbial preTA rescues oral fluoropyrimidine toxicity The toxicity model was leveraged to test the impact of bacterial preTA on CAP toxicity alone and in the context of a complex human gut microbiota. First, 6-8 week-old female C57BL / 6J CONV- R mice were provided 1 g / L streptomycin in drinking water to enable the stable engraftment of isogenic preTA or preTA overexpressing (preTA++) E. coli MG1655 as described previously (9) (Fig. 5A). E. co / / colonization levels were similar between groups (Fig. 5B). Remarkably, the preTA++strain was sufficient to rescue CAP-induced weight loss (Fig. 5C-D) and survival relative to the Aprs TA strain (Fig. 5E). These results were validated in mono-colonized mice. Next, GF 6-8 week-old male C57BL / 6J mice were colonized with baseline stool samples collected from CRC patients in the GO study with varying preTA abundance (Fig. 5F). preTA levels were quantified at baseline via metagenomic sequencing, finding measurable baseline preTA in 18 / 31 mice with evaluable stool (Fig. 5G). Consistent with our results with E. coli, mice with measurable baseline preTA experienced lower weight loss (Fig. 5H-J) and increased colon length (Fig. 5K).

[0121] Finally, to assess the translational relevance of the findings in this mouse model of toxicity, a retrospective chart review of oral fluoropyrimidine-associated side effects reported on the GO study was performed. The majority (21 / 40) of GO subjects had at least one documented toxicity-related event, including 6 subjects with documented CAP dose reductions during the study (Fig. 6A). Consistent with the results in mice, preTA was significantly more abundant in patients without reported evidence of clinically significant toxicity (Fig. 6B). Using preTA level to predict host toxicity with univariate receiver operating characteristic (ROC) curve analysis, an area under the curve (AUG) was achieved AUG = 0.68 in patients and AUG = 0.85 in humanized mice (Fig. 10A-B). In addition to links to host drug toxicity, we noted a correlation between the magnitude of microbiota disruption and preTA abundance. Patients with high preTA maintained higher microbial diversity (Fig. 6C) and less deviation in microbial community structure relative to baseline (Fig. 6D).Discussion

[0122] This study underscores the complex and clinically meaningful interplay between the gut microbiome and oral fluoropyrimidines, a commonly used chemotherapy. Longitudinal analyses of patient stool showed that chemotherapy has broad impacts on human gut microbial33167 / 70549 / PCcommunity structure, gene, and pathway abundance. This includes an enrichment for the bacterial preTA operon, which enables the inactivation of 5-FU to DHFU. A mouse model of CAP toxicity was established and implemented, allowing a demonstration that CAP toxicity is microbiome-dependent and that high levels of preTA expression are sufficient to control CAP toxicity. Together, these findings provide a proof-of-concept for the “reverse translation” of observations from cancer patients to gain mechanistic insight from follow-on mouse and cell culture experiments.

[0123] Remarkably, consistent oral fluoropyrimidine-associated shifts were detected in the gut microbiota in distinct patient cohorts from the United States and the Netherlands. This is particularly surprising given the geographical differences in the human gut microbiota (21) and prior literature emphasizing the difficulties in finding consistent differences across cohorts for cancer immunotherapy (22). The longitudinal nature of frequent sampling is a strength of the present approach, allowing evaluation of changes in the microbiome over time by controlling for within-subject differences that confound cross-sectional studies. These results suggest that oral chemotherapy has a more robust and reproducible impact on the human gut microbiota due to its ability to directly alter bacterial growth, as opposed to more indirect mechanisms at play for immunotherapy.

[0124] While the present example focused primarily on preTA, the results highlight numerous mechanisms through which chemotherapy-induced microbiome shifts could influence treatment outcomes. For example, an overall decrease in microbial diversity following treatment was identified and has been associated with fluoropyrimidine-induced diarrhea in prior studies (23). Multiple bacterial taxa associated with immunotherapy response were depleted following chemotherapy, including the positively associated Faecalibacterium prausnitzii and negatively associated Oscillospiraceae species (24, 25). Notably, the most significantly enriched gene family was multidrug / hemolysin ABC transporter component cylA (K11050), a known virulence factor (26) that has also been implicated in inflammatory bowel disease (27). Thus, shifts in the microbiome following oral fluoropyrimidine treatment likely results in a complex combination of beneficial, neutral, and deleterious shifts in gut microbial ecology and host-microbiome interactions.

[0125] The results emphasize the complex role the gut microbiome can play in chemotherapy response. In the present example, a novel mouse model was utilized to demonstrate that CAP toxicity is microbiome-dependent, consistent with data in human subjects showing heightened toxicity of CAP-containing regimens in antibiotic-pretreated patients (28).33167 / 70549 / PCOn the other hand, extensive prior literature has shown that antibiotics alleviate the Gl toxicity of the anti-cancer drug irinotecan (SN-38) in rats and humans by preventing bacterial reactivation of inactive SN-38G (29, 30). Since treatment regimens such as XELIRI and FOLFIRI combine both fluoropyrimidines and irinotecan (31), use of antibiotics may worsen CAP toxicity while alleviating irinotecan toxicity, highlighting a clinical need to develop targeted microbial manipulations rather than relying on broad-spectrum antibiotics.

[0126] The observation that bacterial preTA is sufficient to control CAP toxicity in mice has clear translational relevance, raising the potential to utilize current or next-generation probiotics to ameliorate Gl toxicity in cancer patients. Gl toxicities usually present early during CAP treatment (32-34). This observation is consistent with decreased toxicity following preTA expansion during treatment. A critical next step is to assess the trade-offs between decreased toxicity and efficacy, especially given prior data suggesting that high levels of preTA can interfere with the anti-tumor effects of CAP (9). More broadly, probiotic strains that express preTA could be a valuable tool to treat pyrimidine-related toxicity in other conditions, potentially depriving cancer cells of uracil in pancreatic ductal adenocarcinoma (37) and clearing excess uracil in patients with clinical DPYD deficiency (38).

[0127] Low preTA levels were predictive of increased adverse effects in humanized mice and patients, providing a proof-of-concept for the use of microbial “genotyping” as a tool for predicting toxicity. Baseline preTA serves as a mechanistic microbial predictor of toxicity with near-perfect class separation in humanized DPYD+I+ mice and an AUG of 0.68 in the GO patient cohort. Meanwhile, clinical predictors in DPYD+ / + patients combining age, body surface area, type of treatment regimen, and creatinine levels achieve an AUG of 0.68 when predicting global Grade 3+ toxicity (39), and comprehensive DPYD genotyping alone has an AUG < 0.5 for predicting global Grade 2+ toxicity (40). With refinement and integration with known clinical variables and host genetics, our microbiome-based prediction algorithm could help address long-standing challenges in avoiding the severe toxicities associated with fluoropyrimidine drugs.

[0128] While this work emphasizes the crucial role of drug-microbiome interactions in cancer therapy, key translational questions remain. Heterogeneous patient populations, combinatorial treatment regimens, and small sub-cohort sample sizes limited our ability to test the impact of immunotherapy, radiation, and biologies on the microbiome. Since host DPYD deficiency is associated with life-threatening side effects (14), future work seeking to understand toxicity should integrate genetics of both host DPYD and microbial preTA. Data in human33167 / 70549 / PCsubjects suggests that orally delivered fluoropyrimidines have ten times less neutropenia but comparable Gl toxicity to infusional 5-FU (41 , 42). The degree to which bacterial preTA is better able to mitigate toxicities following oral versus intravenous delivery is an important area for future studies.

[0129] Overall, the results highlight the complex and bidirectional relationship between the gut microbiome and oral fluoropyrimidine chemotherapy and the consequences of this relationship for host drug toxicity. Integrating longitudinal microbiome analysis with in vivo and ex vivo experiments, we show that fluoropyrimidine metabolism gene preTA is a critical human gut bacterial mediator of oral fluoropyrimidine toxicity. This microbial operon serves as a biomarker for predicting and a novel target for controlling dose-limiting toxicities. Our findings underscore the necessity of considering the microbiome as an integral component in the pharmacological landscape, paving the way for microbiota-based precision therapy.Materials and Methods

[0130] Study Design The Gut microbiome and Oral fluoropyrimidines (GO) study was an observational clinical study registered at ClinicalTrials.gov under the identifier NCT04054908. All participants provided informed consent. The study was approved by the LICSF Institutional Review Board. Participants were recruited and sampled, without any compensation, at UGSF from the study start date (2018 / 04 / 13) to end date (2022 / 06 / 30). Inclusion criteria included: (1) 18 years or older, (2) histologically confirmed colorectal adenocarcinoma, and (3) expected to receive oral fluoropyrimidine therapy. Patients who met any of the following criteria were excluded: (1) known HIV positive diagnosis, (2) chemotherapy, biologic or immunotherapy in the previous 2 weeks, (3) exposure to >2 weeks antibiotics in the last 6 months, (4) exposure to antibiotics in the past 4 weeks. Patients were enrolled to one of three cohorts: Cohort A received oral CAP as part of standard-of-care therapy; Cohort B received TAS-102 (trifluridine / tipiracil) ± Y-90 radioembolization (43); Cohort C received CAP + immunotherapy (pembrolizumab) + bevacizumab as part of a separate clinical trial (NCT03396926). Patients with concurrent rectal radiation therapy (RT) were originally excluded, but the eligibility criteria was amended to include patients with concurrent rectal RT to increase enrollment. CAP and TAS-102 tablets were prescribed according to FDA labels for oral dosing. CAP tablets were taken twice daily on days 1-14 of a 21 -day Cycle (no RT) or on days of radiation only (RT); TAS-102 tablets were taken twice daily on days 1-5 and 8-12 of a 28-day Cycle. Stool collection occurred at home before chemotherapy initiation and on day 1 of Cycles 1 , 2, 3. During Cycle 1 , stool was also collected at day 3 and midpoint (day 7 for Cohorts A, C; day 1033167 / 70549 / PCfor cohort B). Stool samples were collected on fecal occult blood test (FOBT) cards for all time points. Additional bulk scoop samples were collected at baseline for culturing. Stool was stored at -80 °C upon receipt at LICSF. 229 stool samples from 40 participants on FOBT cards were ultimately evaluable; 4 samples were not evaluable due to insufficient extracted DNA quantity.Table 1 : Resources Table33167 / 70549 / PC33167 / 70549 / PC

[0131] DNA extraction ZymoBIOMICs 96 MagBead DNA Kit was used for DNA extractions from mouse and human stool. 750 pL of lysis solution was added to fecal aliquots (20-50 mg) in lysis tubes. Samples were homogenized with 5 min bead beating (Mini-Beadbeater-96, BioSpec), followed by 5 min room temperature (RT) incubation, and repeat 5 min bead beating. Samples were centrifuged for 1 min at 15,000 ref, with 200 pL supernatant transferred into 1 mL deep-well plates and purified according to the manufacturer’s instructions.

[0132] 16S rRNA gene sequencing The V4 region of the 16S rRNA gene was amplified using primers targeting 515F / 806R regions. The reaction mix was 0.45 pL DMSO, 0.0045 pL SYBR Green I 10x diluted in DMSO to 1000x, KAPA HiFi PCR kit (1.8 pL 5x KAPA HiFi Buffer, 0.27 pL 10 mM dNTPs, 0.18 pL KAPA HiFi polymerase), 0.045 pL of each amplification primer (final concentration 1 pM), 6.2055 pL nuclease-free water, and 1 pL DNA. A BioRad CFX 384 real-time PCR instrument amplified four 10-fold serial dilutions of DNA with the following parameters: 5 min 95°C, 20x (20 sec 98°C, 15 sec 55°C, 60 sec 72°C), hold 4°C. Non-plateaued individual sample dilutions were selected for indexing PCR. KAPA HiFi PCR kit was used with: 4 pL 5x KAPA HiFi Buffer, 0.6 pL 10 mM dNTPs, 1 pL DMSO, 0.4 pL KAPA HiFi polymerase, 4 pL indexing primer, 10 pL of 100-fold diluted primary PCR reaction. Secondary PCR amplification parameters were identical to Primary PCR. Amplicons were quantified with PicoGreen according to manufacturer’s instructions, equimolar pooled, and gel purified (QIAquick Gel Extraction Kit). Libraries were quantified with KAPA Library Quantification Kit for33167 / 70549 / PCIllumina Platforms according to the manufacturer's instructions, spiked with 15% PhiX, and sequenced on an Illumina MiSeqV3 instrument. Primers and adapters were removed using the cutadapt trim-paired command in QIIME2 (v2020.11) (44). Sequences underwent trimming to 220 bp (forward) or 150 bp (reverse), quality filtering, denoising, and chimera filtering using dada2 (v1.18.0) with QIIME2 command denoise-paired (45). Sequence length was filtered to 250-255 bp with QIIME2 command feature- table filter-seqs. Taxonomy was assigned to amplicon sequence variants (ASVs) using the SILVA v138 database (46). Sequence variants not present in >3 samples with >10 reads were removed from downstream analysis.

[0133] 16S rRNA gene analysis Alpha diversity metrics were computed with Phyloseq command estimate_richness (47). Differential alpha diversity was calculated with mixed effects model Diversity ~ Day + 1 |Patient with the nlme (v3.1-164) command Ime. ASV relative abundances were central log ratio (CLR)- transformed prior to further analysis. Principal coordinate analysis (PCoA) was performed with command prcomp (CLR-Euclidean ordination). PERMANOVA was performed using vegan (v2.6-4) commands vegdist (CLR-Euclidean ordination) and adonis2 (48), with Patient as a strata. Differential abundance was calculated using mixed effects model Abundance ~ Day + 1 |Patient, followed by false discovery rate (FDR) correction with R command p. adjust, with FDR < 0.2 called as significant. ASV phylogenetic tree construction was performed with QIIME2 command phylogeny align-to-tree-mafft-fasttree. Line plots were plotted using ggplot2 (v3.5.1) command geom smooth with LOESS regression (49).

[0134] Metagenomic sequencing Shotgun libraries were prepared using the Illumina DNA Prep Tagmentation kit according to the manufacturer protocol. Libraries were assessed with PicoGreen and TapeStation 4200 (Agilent) for quantity and quality checks. Paired end libraries were sequenced using S1 flow cells on Illumina NovaSeq 6000 platforms. Demultiplexed reads underwent adapter trimming and quality filtering with FastP (vO.23.2) (50) and host read removal by mapping to human genome (GRCh38) with BMTagger (v3.101) (51). Genome equivalents were quantified with microbeCensus (v1.1.1 ) (52). Taxonomy was annotated with MetaPhlAn 4 (53). Genes were annotated with HUMAnN 3.0 (54), with UniRef90 families further mapped to KEGG Ortholog and Enzyme Class.

[0135] Metagenomic analysis Gene abundances were normalized to reads per kilobase per genome equivalent using microbeCensus values prior to downstream analysis (52). PCA was performed with command prcomp (CLR-Euclidean ordination). PERMANOVA was performed using vegan commands vegdist (CLR-Euclidean ordination) and adonis2 (48), with Patient as a strata. Differential abundance was calculated with mixed effects model Abundance33167 / 70549 / PC~ Day + 11 Patient using the nlme command Ime, followed by FDR correction, with FDR < 0.2 called as significant. Gene set enrichment analysis was performed on significant genes using clusterProfiler (v4.6.2) command enrichKEGG with a q-value cutoff of 0.2 (55). Line plots were plotted using ggplot2 command geom smooth with LOESS regression (49).

[0136] Generation of ex vivo communities Three patients from Cohort A with variable Baseline vs C1 D7 diversity were selected for ex vivo communities. Brain Heart Infusion supplemented with L-cysteine hydrochloride (0.05% w / v), hemin (5 pg / ml), and vitamin K (1 pg / ml), termed BHI+, was used as media. 100 mg baseline stool from each patient was thawed and added anaerobically to 1 mL BHI+, vortexed for 1 min, and incubated 5 min at RT to allow debris to settle. 500 pL supernatant (“fecal slurry”) was transferred to a new tube. 5-FU was dissolved in dimethylsulfoxide, supplemented at 1% (v / v) in BHI+ media and assayed at 50 ug / mL. 1% DMSO was added to BHI+ for CAP and vehicle groups; CAP was directly resuspended in this media and assayed at 5 mg / mL. 5 pL fecal slurry was inoculated in sextuplicate into 195 pL of media ± drug in a 96-well plate, with negative control wells to confirm media sterility. Plates were covered with BreathEasy covers and incubated at 37°C for 48 hr in a Gen5 plate reader, with 1 min linear shake prior to OD6oo readings every 15 min. Plates were removed from plate reader and spun at 3000 rpm for 30 min, followed by supernatant (“spent media”) transfer to a new plate.

[0137] Liquid chromatography-triple quadrupole mass spectrometry (LC-MS / MS) quantification of 5-FU from ex vivo communities Quantification was performed using a validated 5-FU quantification protocol (9). Briefly, spent media was thawed on ice. 50 pL spent media was dissolved in 150 pL water and 800 pL organic phase (50% ACN, 50% MeOH). A standard curve was generated by dissolving serial dilutions of 5-FU in DMSO into BHI+. 10 pL of 10 pg / ml 5-fluorouracil-13C,15N2 internal standard was spiked into all samples. Samples were vortexed 5 min and incubated on ice for 30 min. Extraction mixture was spun down at 15000 ref for 20 min. 250 pL extraction supernatant was dried in speed vac and resuspended in 200 pL of 10% ACN in water for injection. Samples were loaded into a Synergi column on a SCIEX Triple Quad 7500 instrument with a linear ion QTRAP. Chromatographic separation was achieved using a Phenomenex Synergi column 4 pM Fusion RP-80 (50 x 2 mm) at 35 °C. The mobile phase was methanol + 0.1% formic acid (A) and HPLC-grade water + 0.1% formic acid (B). A flow rate of 0.4 ml / min was used for the following gradient elution profile: 0% B at 0-2 min, gradient to 100% B from 2 to 5.9 min, gradient to 0% B at 5.9-6 min. The autosampler was maintained at 4 °C. Eluate from the column was ionized in the LC-MS / MS using an electrospray33167 / 70549 / PCionization source in positive polarity. Peak areas were calculated using built-in SCIEX OS software. 5-FU peak area was normalized to the internal standard, with concentration calculated based on the 5-FU standard curve (Pearson R2 = 1.00).

[0138] Mouse studies All animal experiments were conducted under protocol AN200526 approved by the UCSF Institutional Animal Care and Use Committee. GF C57BL / 6J mice were born at UCSF; specific pathogen free (SPF) C57BL6 / J mice were purchased from Jackson Laboratory. Diets were provided ad libitum, with standard chow diet (LabDiet 5058) in the SPF facility and standard autoclaved chow diet (LabDiet 5021 ) in the gnotobiotic facility. Mice were housed at temperatures ranging from 67 to 74 °F and humidity ranging from 30 to 70% in light / dark cycle 12h / 12h. GF mice were maintained within the UCSF Gnotobiotic Core Facility. Stool pellets from the Gnotobiotic Core Facility were screened every 2 weeks by culture and qPCR, whenever mice were transferred between isolators, and at the beginning and end of each experiment using V4_515F_Gnoto / V4_806R_Gnoto universal primers.

[0139] CAP dosing in murine models Buffer was prepared by dissolving 964 mg sodium citrate dihydrate and 139 mg of citric acid into 90 mL deionized water (DI), adjusting pH to 6.0 with HCI or NaOH, adding 5g gum arabic, bringing final volume to 100 mL, and autoclaving for 20 min. CAP solution was prepared fresh daily (SPF experiments) or weekly (gnotobiotic experiments) by dissolving CAP into the buffer with continuous agitation and heating. Mice were given a single CAP bolus daily by oral gavage. CAP was dosed by body weight, with CAP quantity determined such that 200 pL solution contained the total CAP dose (1100 or 1500 mg / kg) for the largest mouse. Gavage needles were rinsed with DI in between each mouse, with different needles for each experimental group.

[0140] Establishment of CAP toxicity experiment 20 mice were gavaged with 1500 mg / kg CAP (n = 12) or 200 pL buffer (n = 8) once daily. Half the mice were sacrificed at Day 4, with remaining mice sacrificed at Day 12. Endpoint small intestine, cecum, and colon contents were collected. 5 metrics were used to assess toxicity. (1) Weight loss and survival time. Mice were weighed daily and sacrificed at 15% weight loss (“survival time”) or at experimental endpoint, whichever came first. (2) Anatomic measurements. Mouse colon length, small intestine length, spleen weight, and gonadal fat pad weight were measured. (3) Body composition. Mouse body composition was measured at Days 0, 4, and 12 by EchoMRI with 1 primary accumulation. (4) Lipocalin-2 ELISA. Enzyme-linked immunosorbent assay was performed with Mouse Lipocalin-2 / NGAL DuoSet ELISA kit using the manufacturer’s protocol with the following modifications: (a) preweighed colon contents (50-100 mg) were combined with33167 / 70549 / PC1 mL PBS + 0.1% Tween 20 and vortexed for 20 min, then centrifuged for 10 min at 4°C and 12,000 ref. For sample wells, 20 pL of extracted sample was added to 100 pL of reagent diluent and 6-fold serially diluted, (b) For standard wells, 125 pg / mL mouse lipocalin-2 standard with 2-fold serial dilutions was used, with two blank wells, (c) Absolute concentrations were calculated with linear fit to a log-log plot of mouse Lcn-2 concentration vs OD. (5) Hand-foot syndrome. Thermal hind paw hyperalgesia was measured as described in (20). Briefly, mice were placed on a 52°C hotplate and latency to rear paw lick, rear paw flick, or jump was recorded.

[0141] Broad-spectrum antibiotic CAP toxicity experiment Antibiotic (AVNM) or Vehicle (Veh) water was prepared fresh weekly from autoclaved tap water supplemented with sucrose (0.5 g / L sucrose) ± antibiotics (1 g / L ampicillin, 0.5g / L vancomycin, 1 g / L neomycin, 0.5 g / L metronidazole), followed by 0.2 pm filter sterilization. 24 mice were treated ad libitum with AVNM (n = 12) or Veh (n = 12) water for the duration of the experiment. After 1 week of antibiotics, mice were treated with 1500 mg / kg CAP daily for 2 weeks. Weight was measured daily and stool was taken at Days -7, 0, 3, 5 (relative to CAP start). Endpoint small intestine, cecum, and colon contents were collected. Endpoint cecal contents weight and gonadal fat pad weight were measured. To assess bacterial load, fecal slurries were generated by adding 100 mg Day 0 stool into 1 mL BHI+, vortexing for 1 min, and allowing fecal debris to settle for 5 min.10 pL of supernatant was 10x serially diluted in BHI+, with 2.5 pL of each dilution spotted on BHI+ agar plates and incubated at 37°C for 5 days to quantify colony forming units (CPUs). All stool work was performed anaerobically.

[0142] Germ-free vs conventional microbiota CAP toxicity experiment To generate CONV-D mice, cecal contents from a single SPF donor was resuspended in 10% BHI+ aerobically and passed through a 100 pm filter, with 200 pL administered to GF mice followed by a 1 week engraftment period. CONV-R (n = 8), CONV-D (n = 8), and GF (n = 8) mice were treated with 1100 mg / kg CAP daily for 2 weeks. Weight was measured daily. Endpoint small intestine contents, cecal contents, colon contents, and large intestine length were collected for all mice, with endpoint small intestine length, spleen weight, cecal contents weight, and gonadal fat pad weight collected for CONV-D and GF mice.

[0143] preTA CAP rescue experiment Streptomycin water was prepared fresh weekly from autoclaved tap water supplemented with 5 g / L streptomycin, followed by 0.2 pm filter sterilization. 32 mice were treated ad libitum with streptomycin water for the duration of the experiment. Overnight cultures of engineered E. co / / strains (E coli preTA and E. coli preTA++) were pelleted by centrifugation, washed with an equal volume of sterile 0.85% saline, pelleted33167 / 70549 / PCby centrifugation, and resuspended in 1 :10 sterile saline. Mice were gavaged with 200 pl bacterial suspension (n = 16 E. coli preTA, n = 16 E. coli preTA++) by gavage following 1 day on strep water. One day post-colonization (“Day 0”), mice began 2 weeks of treatment with 1500 mg / kg CAP daily. Weight was measured daily and stool was taken at Days -1 , 0, 4, 7. Endpoint small intestine, cecum, and colon contents were collected. Fecal slurries were generated by anaerobically adding 100 mg Day 0 stool into 1 mL LB + strep, vortexing 1 min, and allowing fecal debris to settle for 5 min. 10 pL of supernatant was 10-fold serially diluted in LB + strep, with 2.5 pL of each dilution spotted on EMB agar plates and incubated anaerobically at 37°C for 2 days to quantify E. co / / CPUs.

[0144] Gnotobiotic preTA CAP rescue experiment Overnight cultures of engineered E. co / / strains ( coli preTA and E. coli preTA++) were pelleted by centrifugation, washed with an equal volume of sterile 0.85% saline, pelleted by centrifugation again and resuspended in 1 :10 sterile saline. Mice were gavaged with 200 pl bacterial suspension or saline only (n = 6 £ coli preTA, n= Q E. coli preTA++, n = 6 “mock”). One week post-colonization, stool was collected and mice began 2 weeks of treatment with 1500 mg / kg CAP daily, with weight measured daily. Endpoint small intestine length and colon length were measured. Endpoint E. co / / CPUs from Day 0 stool were quantified by culturing on MacConkey Agar as described previously (9). For group comparisons, preTA and mock colonized mice were binned together into “mock / ApreTA” (both contain no preTA).

[0145] Gnotobiotic humanization CAP toxicity experiments Two independent experiments were conducted. For each experiment, two donors with variable baseline preTA were selected to humanize 16 gnotobiotic mice (n = 8 per donor; 1 isolator per donor). Fecal slurries were generated anaerobically by adding 100 mg stool to 2 mL PBS, vortexing, and letting the mixture settle for 5 min. 1.5 mL of supernatant (“fecal slurry”) was transferred to a new tube on ice. 100 pL fecal slurry was orally gavaged into each mouse, with the researcher blinded to colonization group. One week post-colonization, baseline stool was collected and mice began 2 weeks of treatment with 1500 mg / kg CAP daily. Weight was measured daily. Endpoint stool, small intestine contents, cecal contents, colon contents, colon length, spleen weight, and gonadal fat pad weights were collected. Baseline stool was subjected to metagenomic sequencing to assess preTA status. For group comparisons, mice with no detected K17722 / K17723 were called “preTA low”, with others called “preTA high.” Where stool was not evaluable (1 / 32 mice), mice were called as the preTA level of their cagemates.33167 / 70549 / PC

[0146] Statistical analysis Statistical analysis was performed in R (v4.2.1) (56), with plots generated using ggplot2 (v3.5.1) and ggpubr (v0.6.0) (49, 57). All statistical tests are specified in the text or figure legends where used, with core tests summarized here. Linear mixed-effects models with day as a fixed effect and patient as a random effect were used to identify statistically significant changes in alpha diversity, beta diversity, taxa, and genes with respect to time. For differential mixed-effects analyses, samples prior to Day 0 (1st day of treatment) were set to Day 0. Diversity, taxa, and genes were plotted with respect to time by LOESS regression with standard error shaded; patients where the taxa / gene of interest was not detected in any samples were excluded from these plots. PERMANOVA testing with treatment and patient as effects and patient as a strata was used to test for compositional differences in taxa and genes (CLR-Euclidean ordination). Comparisons between two groups were performed with Mann-Whitney U test or ANOVA. Comparisons between three groups were performed with Kruskal-Wallis testing. Two-way ANOVA was used to identify significant group differences in mouse weight with respect to time. Mouse weights are plotted as mean with standard error bars; days after the first mouse sacrifice due to 15% weight loss are excluded from these plots. Spearman correlation was used to identify significant relationships between continuous variables. Survival distributions were compared with the Mantel-Cox test. pROC (v1.18.5) command roc was for calculating sensitivity, specificity, and AUG (58). Significance was determined as p-value < 0.05 (individual tests) or Benjamini- Hochberg FDR < 0.2 (multiple hypothesis correction of taxa / genes).References1. Muller et al. Nat Rev Drug Discov. 2012 Oct;11 (10):751 -61 .2. Ma et al. Pharmacol Rev. 2011 Jun;63(2):437-59.3. Tamraz et al. JCO Oncol Pract. 2024 Aug;20(8):1009-1011 .4. Zimmermann et al. Nature. 2019 Jun;570(7762):462-467.5. Javdan et al. Cell. 2020 Jun 25;181(7):1661-1679.e22.6. Miller et al. CA Cancer J Clin. 2022 Sep;72(5):409-436.7. Saif et al. Anticancer Drugs. 2008 Jun;19(5):447-64.8. Budman. Invest New Drugs. 2000 Nov;18(4):355-63.9. Spanogiannopoulos et al. Nat Microbiol. 20220ct;7(10):1605-1620.10. Liu et al. mSphere. 2024 Apr 23;9(4):e0081623.11. Lunenburg et al. Eur J Cancer. 2016 Feb;54:40-48.12. Rosmarin et al. J Clin Oncol. 2014 Apr 1 ;32(10):1031 -9.13. Jacobs et al. CPT Pharmacometrics Syst Pharmacol. 2019 Dec;8(12):940-950.14. Jennings et al. PLoS One. 2013 Oct 22;8(10):e78053.15. Piawah et al. Cancers (Basel). 2023 Sep 13;15(18):4546.16. Aarnoutse et al. Clin Colorectal Cancer. 2022 Jun;21(2):e87-e97.17. O'Donovan et al. Bacteriol Rev. 1970 Sep;34(3):278-343.18. Rosener et al. Elife. 2020 Nov 30;9:e59831.33167 / 70549 / PCNair et al. J Basic Clin Pharm. 2016 Mar;7(2):27-31.Yang et al. Br J Cancer. 2023 Jan;128(2):219-231.Suzuki et al. Science. 2022 Sep 16;377(6612):1328-1332.Lee et al. Nat Med. 2022 Mar;28(3):535-544.Kawasaki et al. Digestion. 2023; 104(5) :357-369.Gao et al. Cancer Res. 2023 Nov 15;83(22):3710-3725.Gunjur et al. Nat Med. 2024 Mar;30(3):797-809.Lewis et al. Protoplasma. 2012 Oct;249(4):919-42.Jovel et al. Front Microbiol. 2022 Feb 3;13:829378.Imai et al. J Oncol. 2020 Jun 17;2020:1701326.Takasuna et al. Cancer Chemother Pharmacol. 1998;42(4):280-6.Flieger et al. Oncology. 2007;72(1-2):10-6.Xu et al. Lancet Oncol. 2018 May; 19(5) :660-671.Role of dihydropyrimidine dehydrogenase deficiency in systemic fluoropyrimidine-related toxicity (available on the world wide web atmedsafe. govt. nz / profs / PUArticles / March2023 / Role-of-DPD-deficiency-in-systemic-fluoropyrimidine-related-toxicity.html).Gmeiner. Precis Cancer Med. 2021 Dec;4:38.Seltzer et al. Clin Drug Investig. 2023 May;43(5):359-363.Chang et al. Int J Mol Sci. 2020 Jan 8;21 (2):386.Osterlund et al. Br J Cancer. 2007 Oct 22;97(8): 1028-34.Zeng et al. Pancreas. 2011 Jul;40(5):657-63.Van Kuilenburg et al. Hum Genet. 1999 Jan;104(1):1-9.Knikman et al. Clin Pharmacol Ther. 2024 Feb;115(2):269-277.Palles et al. Cancers (Basel). 2021 Mar 24;13(7):1497.Ota et al. Scand J Gastroenterol. 2019 Feb;54(2):227-232.Van Cutsem et al. Br J Cancer. 2004 Mar 22;90(6):1190-7.Fidelman et al. BMC Cancer. 2022 Dec 13 ;22( 1 ):1307.Hall et al., in Microbiome Analysis: Methods and Protocols, R. G. Beiko, W. Hsiao, J. Parkinson, Eds. (Springer New York, New York, NY, 2018), pp. 113-129.Callahan et al. Nat Methods. 2016 Jul; 13(7):581 -3.Quast et al. Nucleic Acids Res. 2013 Jan ;41( Database issue):D590-6.McMurdie et al. PLoS One. 2013 Apr 22;8(4):e61217.Dixon. J Veg Sci. 2003 Apr 9; 14(6)927-930.Wilkinson. Biometrics. 2011 Jun 21 ;67(2):678-679Chen et al. Bioinformatics. 2018 Sep 1 ;34(17):i884-i890.K. Rotmistrovsky, R. Agarwala, BMTagger: Best Match Tagger for removing human reads from metagenomics datasets. NCBI / NLM, National Institutes of Health .Nayfach et al. Genome Biol. 2015 Mar 25; 16(1 ):51.Blanco-Miguez et al. Nat Biotechnol. 2023 Nov;41 (11 ): 1633-1644.Beghini et al. Elife. 2021 May 4;10:e65088.Yu et al. OMICS. 2012 May;16(5):284-7.Ihaka et al. J. Comput Graph Stat.1996 Sept;5(3):299-314A. Kassambara, Ggpubr: “ggplot2” based publication ready plots. R package version 0, 2 (2018).Robin et al. BMC Bioinformatics. 2011 Mar 17;12:77.33167 / 70549 / PCSEQUENCESTable 2: Nucleic Acid Sequences33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PCTable 3: Amino Acid Sequences33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC33167 / 70549 / PC

Claims

33167 / 70549 / PCCLAIMS1. A method for preventing fluoropyrimidine toxicity in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

2. A method of treating fluoropyrimidine toxicity in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

3. A method for preventing fluoropyrimidine-associated microbiome shifts in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

4. A method of treating fluoropyrimidine-associated microbiome shifts in a subject, the method comprising administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

5. The method of claim 1 or 2, wherein the fluoropyrimidine toxicity results from accumulation of 5-fluorouracil (5-FU).

6. The method of claim 1 or 2, wherein the fluoropyrimidine toxicity is associated with capecitabine treatment.

7. The method of claim 5, wherein the dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or the microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof metabolizes 5-FU to dihydrofluorouracil (DHFU).

8. The method of any one of claims 1-7, wherein the subject is administered a human dihydropyrimidine dehydrogenase enzyme or a microbial dihydropyrimidine dehydrogenase enzyme.33167 / 70549 / PC9. The method of any one of claims 1 -7, wherein the subject is administered a microorganism that expresses a human dihydropyrimidine dehydrogenase enzyme or a microbial dihydropyrimidine dehydrogenase enzyme.

10. The method of any one of claims 8 or 9, wherein the human dihydropyrimidine dehydrogenase enzyme is encoded by a human DPYD gene.

11. The method of any one of claims 8 or 9, wherein the microbial dihydropyrimidine dehydrogenase enzyme is encoded by a microbial gene or operon with 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% similarity to the nucleotide sequence of any one of SEQ ID NOs: 1-14.

12. The method of any one of claims 1-11, wherein the microorganism is genetically-modified to overexpresses the dihydropyrimidine dehydrogenase enzyme.

13. The method of claim 2, wherein the treating fluoropyrimidine toxicity comprises a reduction in the severity and / or duration of fluoropyrimidine toxicity symptoms.

14. The method of claim 3 or 4, wherein the gastrointestinal microbiome of the subject comprises Escherichia coli, Agathobacter rectalis, Anaerosalibacter massiliensis, Anaerostipes caccae, Anaerostipes hadrus Citrobacter braakii, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter pasteurii, Citrobacter werkmanii, Escherichia fergusonii, Eubacterium hallii, Eubacterium siraeum, Kluyvera ascorbate, Lactobacillus oris, Lactobacillus reuteri, Pseudomonas aeruginosa, Pseudomonas extremaustralis, Ruminiclostridium siraeum, Puminococcus bromii, Salmonella enterica, or any combination thereof.

15. The method of claim 3 or 4, wherein the fluoropyrimidine-associated gastrointestinal microbiome shift is measured relative to a reference gastrointestinal microbiome.

16. The method of any one of claims 1 -15, further comprising administering capecitabine to the subject.

16. The method of any one of claims 1 -2 or 5-16, wherein fluoropyrimidine toxicity comprises central neurotoxicity, acute cardiac toxicity, or any combination thereof.

17. The method of any one of claims 1 -2 or 5-17, wherein fluoropyrimidine toxicity comprises vomiting, diarrhea, hand-foot syndrome, or any combination thereof.

18. The method of any one of claims 1 -17, wherein the subject is a human subject.33167 / 70549 / PC19. The method of any one of claims 1 -18, wherein the subject has a cancer.

20. The method of claim 19, wherein the cancer comprises breast cancer, colon cancer, gastric cancer, or any combination thereof.

21. The method of any one of claims 1 -20, wherein the subject has been treated with capecitabine previously.

22. The method of any one of claims 1 -21 , wherein the composition is administered prior to administering capecitabine.

23. The method of any one of claims 1-22, wherein the composition is administered concurrently with capecitabine.

24. The method of any one of claims 1-23, wherein the composition is administered between administrations of capecitabine.

25. The method of any one of claims 21-24, wherein capecitabine is administered orally.

26. The method of any one of claims 1-25, wherein the composition is administered orally.

27. The method of any one of claims 1-26, wherein the microorganism comprises a bacteria.

28. The method of claim 27, wherein the microorganism comprises a genetically modified version of a microorganism naturally occurring the gastrointestinal microbiome.

29. The method claim 28, wherein the microorganism comprises Escherichia coli, Agathobacter rectalis, Anaerosalibacter massiliensis, Anaerostipes caccae, Anaerostipes hadrus Citrobacter braakii, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter pasteurii, Citrobacter werkmanii, Escherichia fergusonii, Eubacterium hallii, Eubacterium siraeum, Kluyvera ascorbate, Lactobacillus oris, Lactobacillus reuteri, Pseudomonas aeruginosa, Pseudomonas extremaustralis, Ruminiclostridium siraeum, Ruminococcus bromii, Salmonella enterica, or any combination thereof..

30. The method of claims 29, wherein the microorganism is E. coli.

31. The method of any one of claims 1 -30, wherein the composition is formulated for administration as an oral probiotic or fecal transfer.

32. The method of any one of claims 1 -31 , further comprising administering an antibiotic to the subject prior to administering the composition.33167 / 70549 / PC33. The method of claim 32, wherein the antibiotic comprises a penicillin, tetracycline, cephalosporin, quinolones, lincomycins, macrolides, sulfonamids, glycopeptides, aminoglycosides, carbapenems, or any combination thereof.

34. A method of treating a cancer in a subject, the method comprising:(a) administering capecitabine to the subject; and(b) administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

35. The method of claim 34, wherein the cancer comprises breast cancer, colon cancer, gastric cancer, or any combination thereof.

36. A method for identifying susceptibility to fluoropyrimidine toxicity in a subject, the method comprising:(a) measuring the level of dihydropyrimidine dehydrogenase expression in the gastrointestinal microbiome of the subject;(b) administering a dosage of capecitabine to the subject, wherein the dosage of capecitabine is adjusted based on the level of dihydropyrimidine dehydrogenase expression; and(c) optionally administering a composition, the composition comprising (i) a dihydropyrimidine dehydrogenase enzyme or functional fragment thereof, and / or (ii) a microorganism that expresses a dihydropyrimidine dehydrogenase or functional fragment thereof.

37. The method of claim 36, wherein the level of dihydropyrimidine dehydrogenase expression is measured using sequencing of 16s ribosomal RNA from the gastrointestinal microbiome.

38. The method of claim 36 or 37, wherein the gastrointestinal microbiome comprises Escherichia coli, Agathobacter rectalis, Anaerosalibacter massiliensis, Anaerostipes caccae, Anaerostipes hadrus, Citrobacter braakii, Citrobacter amalonaticus, Citrobacter freundii, Citrobacter pasteurii, Citrobacter werkmanii, Escherichia fergusonii, Eubacterium hallii, Eubacterium siraeum, Kluyvera ascorbate, Lactobacillus oris, Lactobacillus reuteri, Pseudomonas aeruginosa, Pseudomonas extremaustralis, Ruminiclostridium siraeum, Ruminococcus bromii, Salmonella enterica, or any combination thereof.

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