Abstract IA015: Obesity and immunometabolism in cancer immunotherapy
Bibliographic record
Abstract
Abstract Obesity is increasing in prevalence and contributes to approximately 1 million new cancer cases annually. The effects of obesity and altered metabolism on cancers and anti-tumor immunity, remain poorly understood. The metabolism of T cells and other immune cells is dynamically regulated through growth signals and microenvironmental cues to influence biosynthesis, signaling, and cell fate. We have shown that CD4 T cell subsets are metabolically distinct and that each requires a specific metabolic program for their function. To address how cell-extrinsic factors in obesity regulate immunotherapy responses we have studied obesity and fever temperatures as regulators of immune cell metabolism and fate. Obesity is associated with increased risk of cancer but can paradoxically enhance the efficacy of immune therapy in some cases. We found this effect is in part mediated by the adipokine Leptin, which is elevated in obesity and can stimulate T cells. In addition, tumors in obese individuals and animals have increased frequencies of PD-1+ macrophages. We tested the role of PD-1 on macrophages and show with inhibitors and genetic knockouts that it is a direct regulator of macrophage metabolism and suppresses ability to of macrophages to phagocytose and present antigen and stimulate T cells. Macrophages induced PD-1 in response to multiple inflammatory cytokines, but also upon exposure to lipids or with Leptin or Insulin. PD-1-deficient macrophages had increased glycolysis and ability to stimulate T cells. These data show that obesity both promotes inflammation through adipokines such as Leptin, while simultaneously inhibiting anti-tumor immunity through induction of PD-1 on macrophages. Citation Format: Jeffrey Rathmell. Obesity and immunometabolism in cancer immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr IA015.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".