Data from Pharmacologically Increased Tumor Hypoxia Can Be Measured by <sup>18</sup>F-Fluoroazomycin Arabinoside Positron Emission Tomography and Enhances Tumor Response to Hypoxic Cytotoxin PR-104
Bibliographic record
Abstract
Abstract Purpose: Solid tumors contain microenvironmental regions of hypoxia that present a barrier to traditional radiotherapy and chemotherapy, and this work describes a novel approach to circumvent hypoxia. We propose to overcome hypoxia by augmenting the effectiveness of drugs that are designed to specifically kill hypoxic tumor cells. Experimental Design: We have constructed RKO colorectal tumor cells that express a small RNA hairpin that specifically knocks down the hypoxia-inducible factor 1a (HIF1a) transcription factor. We have used these cells in vitro to determine the effect of HIF1 on cellular sensitivity to the hypoxic cytotoxin PR-104, and its role in cellular oxygen consumption in response to the pyruvate dehydrogenase kinase inhibitor dichloroacetate (DCA). We have further used these cells in vivo in xenografted tumors to determine the role of HIF1 in regulating tumor hypoxia in response to DCA using 18F-fluoroazomycin arabinoside positron emission tomography, and its role in regulating tumor sensitivity to the combination of DCA and PR-104. Results: HIF1 does not affect cellular sensitivity to PR-104 in vitro. DCA transiently increases cellular oxygen consumption in vitro and increases the extent of tumor hypoxia in vivo as measured with 18F-fluoroazomycin arabinoside positron emission tomography. Furthermore, we show that DCA-dependent alterations in hypoxia increase the antitumor activity of the next-generation hypoxic cytotoxin PR-104. Conclusions: DCA interferes with the HIF-dependent “adaptive response,” which limits mitochondrial oxygen consumption. This approach transiently increases tumor hypoxia and represents an important method to improve antitumor efficacy of hypoxia-targeted agents, without increasing toxicity to oxygenated normal tissue. (Clin Cancer Res 2009;15(23):7170–4)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".