86P Regulation of cancer progression through the gut microbiome and immuno-nutrition
Bibliographic record
Abstract
Obesity rivals smoking as a leading modifiable risk factor for cancer mortality, accounting for up to 20% of adult cancer-related deaths. Diet is a crucial factor in obesity development and significantly influences cancer growth. Paradoxically, high BMI has been linked to improved immune checkpoint inhibitor (ICI) efficacy in various cancers, challenging the notion that obesity is universally detrimental in cancer contexts. To address this paradox, we devised a panel of 12 diets that mimic human dietary patterns in mouse models, observing vastly different rates of cancer growth and ICI response. Surprisingly, not all obesity-inducing diets were beneficial for ICI, prompting an investigation into the cause of these disparities. The microbiome's pivotal role in regulating cancer and therapy through its profound influence on the immune system is well-supported, with gut dysbiosis and antibiotic use increasing cancer risk and blunting ICI response in patients and fecal microbial transplant from ICI-responsive patients shown to enhance ICI therapy. We propose that the interplay between diet and systemic inflammatory responses to gut microbiota contributes to the variations in ICI response across obesity-inducing diet models. To track the gut microbial composition changes in mice during the development of our 12-diet model, we collected stool samples and performed 16s rRNA sequencing. The analyses focused on four obesity-inducing diets: American, High Fat, Ketogenic and Mediterranean. Analysis unveiled differences in gut bacterial composition at the phylum level across all four obesity inducing diets. However, family-level changes were associated with ICI response in a diet-specific manner. To delve deeper into these findings, we conducted metagenomic sequencing, revealing specific species associated with ICI response in preliminary analyses. These findings suggest that diet-induced gut microbial modifications, in the context of obesity, may influence ICI efficacy, and offers promising avenues for the development of new therapeutic approaches that utilize the microbiome to enhance cancer treatment outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".