Metabolic crossroads: unravelling immune cell dynamics in gastrointestinal cancer drug resistance
Bibliographic record
Abstract
Metabolic reprogramming within the tumor microenvironment (TME) plays a critical role in driving drug resistance in gastrointestinal cancers (GI), particularly through the pathways of fatty acid oxidation and glycolysis. Cancer cells often rewire their metabolism to sustain growth and reshape the TME, creating conditions such as nutrient depletion, hypoxia, and acidity that impair antitumor immune responses. Immune cells within the TME also undergo metabolic alterations, frequently adopting immunosuppressive phenotypes that promote tumor progression and reduce the efficacy of therapies. The competition for essential nutrients, particularly glucose, between cancer and immune cells compromises the antitumor functions of effector immune cells, such as T cells. Additionally, metabolic by-products like lactate and kynurenine further suppress immune activity and promote immunosuppressive populations, including regulatory T cells and M2 macrophages. Targeting metabolic pathways such as fatty acid oxidation and glycolysis presents new opportunities to overcome drug resistance and improve therapeutic outcomes in GI cancers. Modulating these key pathways has the potential to reinvigorate exhausted immune cells, shift immunosuppressive cells toward antitumor phenotypes, and enhance the effectiveness of immunotherapies and other treatments. Future strategies will require continued research into TME metabolism, the development of novel metabolic inhibitors, and clinical trials evaluating combination therapies. Identifying and validating metabolic biomarkers will also be crucial for patient stratification and treatment monitoring. Insights into metabolic reprogramming in GI cancers may have broader implications across multiple cancer types, offering new avenues for improving cancer treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".