<sup>18</sup>F-5-Fluoroaminosuberic Acid as a Potential Tracer to Gauge Oxidative Stress in Breast Cancer Models
Bibliographic record
Abstract
The cystine transporter (system xC−) is an antiporter of cystine and glutamate. It has relatively low basal expression in most tissues and becomes upregulated in cells under oxidative stress (OS) as one of the genes expressed in response to the antioxidant response element promoter. We have developed 18F-5-fluoroaminosuberic acid (FASu), a PET tracer that targets system xC−. The goal of this study was to evaluate 18F-FASu as a specific gauge for system xC− activity in vivo and its potential for breast cancer imaging. Methods:18F-FASu specificity toward system xC− was studied by cell inhibition assay, cellular uptake after OS induction with diethyl maleate, with and without anti-xCT small interfering RNA knockdown, in vitro uptake studies, and in vivo uptake in a system xC−–transduced xenograft model. In addition, radiotracer uptake was evaluated in 3 breast cancer models: MDA-MB-231, MCF-7, and ZR-75-1. Results: Reactive oxygen species–inducing diethyl maleate increased glutathione levels and 18F-FASu uptake, whereas gene knockdown with anti-xCT small interfering RNA led to decreased tracer uptake. 18F-FASu uptake was robustly inhibited by system xC− inhibitors or substrates, whereas uptake was significantly higher in transduced cells and tumors expressing xCT than in wild-type HEK293T cells and tumors (P < 0.0001 for cells, P = 0.0086 for tumors). 18F-FASu demonstrated tumor uptake in all 3 breast cancer cell lines studied. Among them, triple-negative breast cancer MDA-MB-231, which has the highest xCT messenger RNA level, had the highest tracer uptake (P = 0.0058 when compared with MCF-7; P < 0.0001 when compared with ZR-75-1). Conclusion:18F-FASu as a system xC− substrate is a specific PET tracer for functional monitoring of system xC− and OS imaging. By enabling noninvasive analysis of xC− responses in vivo, this biomarker may serve as a valuable target for the diagnosis and treatment monitoring of certain breast cancers.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".