Modeling Spontaneous Metastatic Renal Cell Carcinoma (mRCC) in Mice Following Nephrectomy
Bibliographic record
Abstract
One of the key challenges to improved testing of new experimental therapeutics in renal cell carcinoma (RCC) is the development of models that faithfully recapitulate early- and late-stage metastatic disease progression. Typical tumor implantation models utilize ectopic or orthotopic primary tumor implantation, but few include systemic spontaneous metastatic disease that mimics the clinical setting. This protocol describes the key steps to develop RCC disease progression stages similar to patients. First, it uses a highly metastatic mouse tumor cell line in a syngeneic model to show orthotopic tumor cell implantation. Methods include superficial and internal implantation into the sub-capsular space with cells combined with matrigel to prevent leakage and early spread. Next it describes the procedures for excision of tumor-bearing kidney (nephrectomy), with critical pre- and post- surgical mouse care. Finally, it outlines the steps necessary to monitor and assess micro-and macro-metastatic disease progression, including bioluminescent imaging as well provides a detailed visual necropsy guide to score systemic disease distribution. The goal of this protocol description is to facilitate the widespread use of clinically relevant metastatic RCC models to improve the predictive value of future therapeutic testing.
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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) | 0.000 | 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 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".