Modulation of intratumoral hypoxia by the epidermal growth factor receptor inhibitor gefitinib detected using small animal PET imaging
Bibliographic record
Abstract
Blockade of signaling through the epidermal growth factor receptor (EGFR) tyrosine kinase by inhibitors such as gefitinib (Iressa) can inhibit tumor angiogenesis and enhance responses to ionizing radiation. In this study, the ability of gefitinib to modulate intratumoral oxygenation was evaluated in human EGFR-expressing A431 squamous cell carcinoma xenografts using in vivo small animal positron emission tomography (PET) imaging with the hypoxia marker [(18)F]fluoroazomycin arabinoside (FAZA) and by the immunohistochemical detection of hypoxia-induced adducts of the 2-nitroimidazole, pimonidazole. Serial noninvasive PET imaging of A431 xenografts showed a significant reduction in FAZA uptake following treatment with 75 mg/kg/d of gefitinib [tumor to background ratio, 6.1 +/- 1.0 (pretreatment) versus 2.3 +/- 0.6 (posttreatment); P = 0.0004]. Similarly, ex vivo quantitation of FAZA uptake showed significantly reduced FAZA uptake in established A431 xenografts treated with gefitinib compared with vehicle control (tumor to blood ratio for controls versus gefitinib, 8.0 +/- 3.0 versus 2.7 +/- 0.8; P = 0.007; or tumor to muscle ratio controls versus gefitinib, 8.6 +/- 2.8 versus 2.6 +/- 1.0; P = 0.002). The effect of gefitinib treatment seemed to be independent of tumor size. In addition, gefitinib treatment reduced pimonidazole-binding in A431 xenografts measured after 5 and 8 days of gefitinib treatment compared with baseline and with tumors treated with vehicle alone. A strong correlation was observed between pimonidazole binding and FAZA uptake. Together, these findings show that gefitinib reduces intratumoral hypoxia.
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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) | 0.001 | 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 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".