Long-term progression and therapeutic response of visceral metastatic disease non-invasively monitored in mouse urine using β-human choriogonadotropin secreting tumor cell lines
Bibliographic record
Abstract
Historically, the use of mouse models of metastatic disease to evaluate anticancer therapies has been hampered because of difficulties in detection and quantification of such lesions without sacrificing the mice, which in turn may also be dictated by institutional or ethical guidelines. Advancements in imaging technologies have begun to change this situation. A new method to non-invasively measure tumor burden, as yet untested to monitor spontaneous metastases, is the use of transplanted tumors expressing secretable human beta-chorionic gonadotropin (beta-hCG) that can be measured in urine. We describe examples of beta-hCG-transfected tumor cell lines for evaluating the effect of different therapies on metastatic disease, which in some cases involved monitoring tumor growth for >100 days. We used beta-hCG-tagged mouse B16 melanoma and erbB-2/Her-2-expressing human breast cancer MDA-MB-231 models, and drug treatments included metronomic low-dose cyclophosphamide chemotherapy with or without a vascular endothelial growth factor receptor 2-targeting antibody (DC101) or trastuzumab, the erbB-2/Her-2-targeting antibody. Both experimental and spontaneous metastasis models were studied; in the latter case, an increase in urine beta-hCG always foreshadowed the development of lung, liver, brain, and kidney metastases. Metastatic disease was unresponsive to DC101 or trastuzumab monotherapy treatment, as assessed by beta-hCG levels. Our results also suggest that beta-hCG levels may be set as an end point for metastasis studies, circumventing guidelines, which have often hampered the use of advanced disease models. Collectively, our data indicates that beta-hCG is an effective noninvasive preclinical marker for the long term monitoring of untreated or treated metastatic disease.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".