FOXM1 Inhibitors as Potential Diagnostic Agents: First Generation of a PET Probe Targeting FOXM1 To Detect Triple‐Negative Breast Cancer <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Abstract The FOXM1 protein controls the expression of essential genes related to cancer cell cycle progression, metastasis, and chemoresistance. We hypothesize that FOXM1 inhibitors could represent a novel approach to develop 18F‐based radiotracers for Positron Emission Tomography (PET). Therefore, in this report we describe the first attempt to use 18F‐labeled FOXM1 inhibitors to detect triple‐negative breast cancer (TNBC). Briefly, we replaced the original amide group in the parent drug FDI‐6 for a ketone group in the novel AF‐FDI molecule, to carry out an aromatic nucleophilic (18F)‐fluorination. AF‐FDI dissociated the FOXM1‐DNA complex, decreased FOXM1 levels, and inhibited cell proliferation in a TNBC cell line (MDA‐MB‐231). [18F]AF‐FDI was internalized in MDA‐MB‐231 cells. Cell uptake inhibition experiments showed that AF‐FDI and FDI‐6 significantly decreased the maximum uptake of [18F]AF‐FDI, suggesting specificity towards FOXM1. [18F]AF‐FDI reached a tumor uptake of SUV=0.31 in MDA‐MB‐231 tumor‐bearing mice and was metabolically stable 60 min post‐injection. These results provide preliminary evidence supporting the potential role of FOXM1 to develop PET radiotracers.
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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".