An 19F Magnetic Resonance–Based <i>In Vivo</i> Assay of Solid Tumor Methotrexate Resistance: Proof of Principle
Bibliographic record
Abstract
PURPOSE: Studies in oncology have implicated multiple molecular mechanisms as contributors to intrinsic and acquired tumor resistance to antifolate therapy. Here we show the utility of an (19)F-labeled methotrexate (FMTX) with (19)F magnetic resonance to differentiate between sensitive and resistant tumors in vivo and thus predict therapeutic response. EXPERIMENTAL DESIGN: Human sarcoma xenografts in nude mice were used in this study. The sarcoma cell lines chosen for this study (HT-1080, HS-16, and M-805) are well characterized in terms of their methotrexate sensitivity and molecular mechanisms of resistance. The pharmacokinetics of tumor uptake/washout of FMTX were monitored via in vivo (19)F magnetic resonance spectroscopy (pulse/acquire with surface coil localization) following an i.v. bolus injection. Response post-therapy, following leucovorin rescue, was monitored via tumor growth. RESULTS: The three tumor models show differences in both the peak concentrations of tumor FMTX and the dynamics of uptake/retention. These differences are most pronounced for time points late in the magnetic resonance observation period (225-279 minutes post-injection). A statistically significant linear correlation between tumor tissue concentrations of FMTX at these late time points and therapeutic response in the days/weeks post-treatment is shown (R = 0.81, F = 9.27, P < 0.001). Interestingly, a 400 mg/kg i.v. bolus injection of FMTX is a more potent cytotoxic agent in vivo against methotrexate-sensitive tumors than is the parent compound (P = 0.011). CONCLUSIONS: In principle, the assay method described herein could be implemented in the clinic as a diagnostic tool to make decisions regarding therapeutic protocol for the treatment of osteosarcoma on a case-by-case basis.
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 | 0.001 | 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 | 0.001 |
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".