Development of a Tumor-Selective Approach to Treat Metastatic Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patients diagnosed with metastatic cancer have almost uniformly poor prognoses. The treatments available for patients with disseminated disease are usually not curative and have side effects that limit the therapy that can be given. A treatment that is selectively toxic to tumors would maximize the beneficial effects of therapy and minimize side effects, potentially enabling effective treatment to be administered. METHODS AND FINDINGS: We postulated that the tumor-tropic property of stem cells or progenitor cells could be exploited to selectively deliver a therapeutic gene to metastatic solid tumors, and that expression of an appropriate transgene at tumor loci might mediate cures of metastatic disease. To test this hypothesis, we injected HB1.F3.C1 cells transduced to express an enzyme that efficiently activates the anti-cancer prodrug CPT-11 intravenously into mice bearing disseminated neuroblastoma tumors. The HB1.F3.C1 cells migrated selectively to tumor sites regardless of the size or anatomical location of the tumors. Mice were then treated systemically with CPT-11, and the efficacy of treatment was monitored. Mice treated with the combination of HB1.F3.C1 cells expressing the CPT-11-activating enzyme and this prodrug produced tumor-free survival of 100% of the mice for >6 months (P<0.001 compared to control groups). CONCLUSIONS: The novel and significant finding of this study is that it may be possible to exploit the tumor-tropic property of stem or progenitor cells to mediate effective, tumor-selective therapy for metastatic tumors, for which no tolerated curative treatments are currently available.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it