74P Obesity regulates tumor progression and sensitivity to checkpoint blockade through the diet-microbiota-immunity axis
Bibliographic record
Abstract
Globally, more people are overweight/obese than underweight, and obesity is associated with increased risk and mortality of at least 13 types of cancer. Paradoxically, obesity is not detrimental in all cancer contexts. For example, obesity is associated with improved immune checkpoint inhibitor (ICI) efficacy in a variety of cancer types. While there is a body of literature demonstrating that the gut microbiome impacts ICI efficacy in preclinical models and human clinical trials, it is unknown how these observations relate to dietary habits and/or body weight. To investigate how diet influences cancer progression, we exposed our preclinical mouse model of lung cancer to 12 unique diets that lead to varying amounts of weight gain and metabolic dysfunction over 15 weeks. To identify biological mechanisms driving ICI sensitivity, we characterized peripheral blood and tumor-infiltrating immune cells by spectral flow cytometry. To identify diet-induced intestinal bacteria signatures, we performed 16s rRNA sequencing to profile the gut microbiota. We found that tumor growth and anti-PD-1 sensitivity are diet-dependent and vary significantly between obesity-promoting diets. Flow cytometric analysis of the peripheral blood revealed an inverse correlation between T cells and weight gain, and positive correlation with monocyte populations. However, these immune changes at-steady state were not associated with tumor growth or ICI sensitivity. Interestingly, the gut microbiome stabilized after only 3 weeks following diet enrollment, independent of significant weight gain over the diet enrollment period. Further, 3 weeks on diet was sufficient to phenocopy tumor growth kinetics observed after 15 weeks of diet, independent of any major bodyweight changes. These findings suggest that diet-induced changes to the gut microbiome may be driving differences in tumor growth and ICI sensitivity, and that diet and nutrition can be optimized to maximize the patient population that can benefit from ICI therapy.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".