Obesity-associated checkpoint blockade sensitivity is regulated by the diet-microbiota-immunity axis 4468
Bibliographic record
Abstract
Abstract Description Globally, more people are overweight/obese than underweight, and obesity is associated with increased risk and mortality of several types of cancer. Paradoxically, 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 degrees of metabolic and immunological dysfunction over 15 weeks. We found that tumor growth and anti-PD-1 sensitivity are diet-dependent and vary significantly between obesity-promoting diets. Characterization of the gut microbiota by 16s rRNA uncovered bacterial families associated with ICI sensitivity that differed between obesity-inducing diets. To uncouple the effects of diet and adiposity, we showed that changing diet just 48 hours prior to the onset of treatment significantly alters ICI response independent of weight changes, which was also achieved with fecal microbiota transplants. 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. Funding Sources Canadian Cancer Society (707387) La Vie en Rose Canadian Institutes of Health Research (PJT-178306) Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".