Modulating the microbiome as an approach to anticancer drug development
Bibliographic record
Abstract
Recent studies have suggested that the commensal microbiome positively affects cancer prognosis and treatment outcomes. However, only a few strategies for regulating the microbiome composition have been reported. In this study, we identified gold(I) complexes that selectively inhibited nonbeneficial bacterial strains in vitro without affecting commensal Lactobacillus strains. In contrast, clinically used drugs demonstrated comparable effects on both commensal and noncommensal strains. Consistent with the in vitro results, the selected gold(I) complex induced favorable changes in the intratumoral and gastrointestinal microbiomes in vivo. Furthermore, its anticancer efficacy was found to be dependent on the composition of microbiome and correlated with the production of short-chain fatty acid bacterial metabolites. Structure–activity relationship studies have dissected the contribution of each structural component in both the in vivo efficacy and microbiome-modulating properties. Transcriptomic analysis of tumors revealed microbiome-associated gene signaling pathways. These findings provide valuable insights for future research on microbiome-modulating anticancer drugs, presenting potential avenues to optimize cancer treatment outcomes and mitigate side effects such as gastrointestinal dysbiosis. Furthermore, our study provides insights into the involvement of microbiome in the mechanism of action of metal-based chemotherapeutics.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".