173P Overcoming monocyte imbalance and T cell exhaustion in obesity-associated colorectal cancer liver metastasis
Bibliographic record
Abstract
Obesity is associated with chronic inflammation and elevated incidence and mortality from multiple cancer types, including colorectal cancer (CRC). We found that CRC progression is enhanced in diet induced obesity (DIO) mouse models. Obesity-induced metastasis showed significant enrichment in myeloid cells coinciding with a relative decrease in lymphoid populations. However, the functional contribution of these associations to CRC progression remains unknown. Here we define how obesity changes the tumor immune microenvironment (TIME) to alter disease outcome. To model obesity-driven CRC progression, 5-week old wild-type male mice were enrolled on low fat (LF) or high fat (HF) isocaloric diet and injected with syngeneic CRC cell lines. To test the effect of myeloid populations, genetic and antibody-based approaches were used to deplete specific myeloid subsets. Primary and metastatic tumors were analyzed by histology and spectral flow cytometry to evaluate disease burden and accompanying immunological changes. Obesity was associated with a ∼20% reduction in lymphocytes in the TIME. T cells in particular were significantly excluded from the microenvironment and exhibited an exhausted phenotype. This effect was paired with a ∼50% increase in intratumoral myeloid cells in obese mice compared to lean mice. Targeting specific myeloid subsets in obese mice improved lymphocyte infiltration by ∼40%, rescued their exhaustion status, and reduced disease burden. These data suggest the immunological consequences of obesity contribute to cancer progression through a bias toward myeloid populations, which suppress functional T cells to impair immune surveillance. Our findings provide insight into myeloid-targeted immunotherapies in obesity-associated CRC.
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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 | 0.001 |
| 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".