Human Neural Stem Cell Tropism to Metastatic Breast Cancer
Bibliographic record
Abstract
Metastasis to multiple organs is the primary cause of mortality in breast cancer patients. The poor prognosis for patients with metastatic breast cancer and toxic side effects of currently available treatments necessitate the development of effective tumor-selective therapies. Neural stem cells (NSCs) possess inherent tumor tropic properties that enable them to overcome many obstacles of drug delivery that limit effective chemotherapy strategies for breast cancer. We report that increased NSC tropism to breast tumor cell lines is strongly correlated with the invasiveness of cancer cells. Interleukin 6 (IL-6) was identified as a major cytokine mediating NSC tropism to invasive breast cancer cells. We show for the first time in a preclinical mouse model of metastatic human breast cancer that NSCs preferentially target tumor metastases in multiple organs, including liver, lung, lymph nodes, and femur, versus the primary intramammary fat pad tumor. For proof-of-concept of stem cell-mediated breast cancer therapy, NSCs were genetically modified to secrete rabbit carboxylesterase (rCE), an enzyme that activates the CPT-11 prodrug to SN-38, a potent topoisomerase I inhibitor, to effect tumor-localized chemotherapy. In vitro data demonstrate that exposure of breast cancer cells to conditioned media from rCE-secreting NSCs (NSC.rCE) increased their sensitivity to CPT-11 by 200-fold. In vivo, treatment of tumor-bearing mice with NSC.rCE cells in combination with CPT-11 resulted in reduction of metastatic tumor burden in lung and lymph nodes. These data suggest that NSC-mediated enzyme/prodrug therapy may be more effective and less toxic than currently available chemotherapy strategies for breast cancer metastases.
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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) | 0.003 | 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".