Targeting Quiescent Tumor Cells via Oxygen and IGF-I Supplementation
Bibliographic record
Abstract
Conventional chemotherapy targets proliferating cancer cells, but most cells in solid tumors are not in a proliferative state. Thus, strategies to enable conventional chemotherapy to target noncycling cells may greatly increase tumor responsiveness. In this study, we used a 3-dimensional tissue culture system to assay diffusible factors that can limit proliferation in the context of the tumor microenvironment, with the goal of identifying targets to heighten proliferative capacity in this setting. We found that supraphysiologic levels of insulin or insulin-like growth factor I (IGF-I) in combination with oxygen supplementation were sufficient to initiate proliferation of quiescence cells in this system. At maximal induction with IGF-I, net tissue proliferation increased 3- to 4-fold in the system such that chemotherapy could trigger a 3- to 6-fold increase in cytotoxicity, compared with control conditions. These effects were confirmed in vivo in colon cancer xenograft models with demonstrations that IGF-I receptor stimulation was sufficient to generate a 45% increase in tumor cell proliferation, along with a 25% to 50% increase in chemotherapy-induced tumor growth delay. Although oxygen was a dominant factor limiting in vitro tumor cell proliferation, we found that oxygen supplementation via pure oxygen breathing at 1 or 2 atmospheres pressure (mimicking hyperbaric therapy) did not decrease hypoxia in the tumor xenograft mouse model and was insufficient to increase tumor proliferation. Thus, our findings pointed to IGF-I receptor stimulation as a rational strategy to successfully increase tumor responsiveness to cytotoxic chemotherapy.
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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 | 0.000 | 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 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".