Effect of hypoxia on human equilibrative nucleoside transporters hENT1 and hENT2 in breast cancer
Bibliographic record
Abstract
Elevated proliferation rates in cancer can be visualized with positron emission tomography (PET) using 3‘‐deoxy‐3‘‐ l ‐[ 18 F]fluorothymidine ([ 18 F]FLT). This study investigates whether [ 18 F]FLT transport proteins are regulated through hypoxia. Expression and function of human equilibrative nucleoside transporter (hENT)‐1, hENT2, and thymidine kinase 1 (TK1) were studied under normoxic and hypoxic conditions, and assessed with [ 18 F] FLT‐PET in estrogen receptor positive (ER+)‐MCF7, triple‐negative MDA‐MB231 breast cancer (BC) cells, and MCF10A cells (human mammary epithelial cells). Functional involvement of hENT2 [ 18 F]FLT transport was demonstrated in all cell lines. In vitro [ 18 F]FLT uptake was higher in MDA‐MB231 than in MCF7:242 ± 9 vs. 147 ± 18% radioactivity/mg protein after 60 min under normoxia. Hypoxia showed no significant change in radiotracer uptake. Protein analysis revealed increased hENT1 ( P < 0.0963) in MDA‐MB231. Hypoxia did not change expression of either hENT1, hENT2, or TK1. In vitro inhibition experiments suggested involvement of hENT1, hENT2, and human concentrative nucleoside transporters during [ 18 F]FLT uptake into all cell lines. In vivo PET imaging revealed comparable tumor uptake in MCF7 and MDA‐MB231 tumors over 60 min, reaching standardized uptake values of 0.96 ± 0.05 vs. 0.89 ± 0.08 ( n = 3). Higher hENT1 expression in MDA‐MB231 seems to drive nucleoside transport, whereas TK1 expression in MCF7 seems responsible for comparable [ 18 F]FLT retention in ER + tumors. Our study demonstrates that hypoxia does not significantly affect nucleoside transport as tested with [ 18 F]FLT in BC.—Krys, D., Hamann, I., Wuest, M., Wuest, F. Effect of hypoxia on human equilibrative nucleoside transporters hENT1 and hENT2 in breast cancer. FASEB J. 33, 13837‐13851 (2019). www.fasebj.org
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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".