Autologous Bone Marrow Stromal Cells Genetically Engineered to Secrete an IGF-I Receptor Decoy Prevent the Growth of Liver Metastases
Bibliographic record
Abstract
Liver metastases respond poorly to current therapy and remain a frequent cause of cancer-related mortality. We reported previously that tumor cells expressing a soluble form of the insulin-like growth factor-I receptor (sIGFIR) lost the ability to metastasize to the liver. Here, we sought to develop a novel therapeutic approach for prevention of hepatic metastasis based on sustained in vivo delivery of the soluble receptor by genetically engineered autologous bone marrow stromal cells. We found that when implanted into mice, these cells secreted high plasma levels of sIGFIR and inhibited experimental hepatic metastases of colon and lung carcinoma cells. In hepatic micrometastases, a reduction in intralesional angiogenesis and increased tumor cell apoptosis were observed. The results show that the soluble receptor acted as a decoy to abort insulin-like growth factor-I receptor (IGF-IR) functions during the early stages of metastasis and identify sustained sIGFIR delivery by cell-based vehicles as a potential approach for prevention of hepatic metastasis.
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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 | 0.001 |
| 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".