The Metabolism of Fluoropyrimidine Anticancer Drugs by the Human Gut Microbiome
Bibliographic record
Abstract
The treatment of cancer is notoriously difficult due to the high toxicity of the current medications and unpredictable variations between patients. Genetic polymorphisms within the human genome are certainly important, but they often fail to explain most of the observed variation in treatment outcomes in patients. We propose that the human gut microbiome is a major contributor to the inter‐individual variation in anticancer drug response and hypothesize that this microbial reservoir harbors determinants of resistance towards anticancer drugs, including genes responsible for drug metabolism. We are focusing on the oral cancer drug capecitabine (CAP), a prodrug of 5‐fluorouracil (5FU), due to its widespread administration for colorectal and other cancers, the unexplained differences in drug efficacy and toxicity, and studies suggesting that the mechanism of action and metabolism of this class of compounds (fluoropyrimidines) is conserved in bacteria. Here, we assessed the reciprocal interactions between CAP and 5FU and the human gut microbiome. Ex vivo incubations of human gut microbial communities demonstrated that CAP and 5FU impacts the cellular integrity and growth of gut bacteria. We determined the minimum inhibitory concentration of 5FU versus a diverse collection of 50 individual human gut bacterial type strains. Interestingly, gut bacteria showed a remarkable high variation of sensitivity towards 5FU, ranging over four orders of magnitude. Using a bioassay, we screened 5FU‐resistant gut bacteria for their ability to inactivate 5FU. Several gut bacteria, including Escherichia coli , were capable of 5FU inactivation. Mass spectrometry analysis showed that E. coli converts 5FU to the less active metabolite dihydrofluorouracil (DHFU). This activity is dependent on the preTA operon, which encodes the enzyme dihydropyrimidine dehydrogenase (DPD), as a preTA ‐knockout strain of E. coli fails to metabolize 5FU. Orthologs of DPD are found in other gut bacterial species and their ability to metabolize 5FU is being investigated. Such microbial enzymes from the gut microbiome may influence the pharmacokinetics and toxicity of anticancer drugs. Additional studies in animal models and human cohorts are needed to evaluate the clinical relevance of these findings and their utility in improving clinical medicine. Support or Funding Information Peter Spanogiannopoulos is supported by a Canadian Institutes of Health Research Postdoctoral Fellowship. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".