Treatment-Induced Gene Expression Changes in Metastatic Renal Cell Carcinoma: Insights from a Syngeneic Mouse Model
Bibliographic record
Abstract
This study aimed to clarify the alterations in gene expression in metastatic renal cell carcinoma (mRCC) during disease progression and in response to treatment with immune checkpoint inhibitors using a syngeneic mouse mRCC model. RENCA cells were orthotopically implanted in BALB/c mice. Mice received first-line treatment with cabozantinib, anti-PD-1 antibody, or a combination. Tumor progression was monitored using serial micro-computed tomography. Lung metastasis samples were collected, and RNA sequencing was performed. Mice with apparent disease progression received second-line treatment with axitinib, everolimus, or lenvatinib after combination therapy. The median overall survival was 28, 34, 34, and 49 days in untreated mice and those treated with cabozantinib, anti-PD-1, or their combination, respectively (p < 0.05). RNA sequencing revealed upregulation of the fibroblast growth factor pathway in lung metastases after monotherapy, whereas mTOR pathway activation was observed only after combination therapy. Treatment-specific gene expression changes occur in mRCC, suggesting that the optimal target for sequential therapy in mRCC varies depending on prior treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".