Sunitinib induces early histomolecular changes in a subset of renal cancer cells that contribute to resistance
Bibliographic record
Abstract
Sunitinib is the standard-of-care, first-line treatment for advanced renal cell carcinoma (RCC). Characteristics of treatment-resistant RCC have been described; however, complex tumor adaptation mechanisms obstruct the identification of significant operators in resistance. We hypothesized that resistance is a late manifestation of early, treatment-induced histomolecular alterations; therefore, studying early drug response may identify drivers of resistance. We describe an epithelioid RCC growth pattern in RCC xenografts, which emerges in sunitinib-sensitive tumors and is augmented during resistance. This growth modality is molecularly and morphologically related to the RCC spheroids that advance during in vitro treatment. Based on time-lapse microscopy, mRNA and microRNA screening, and tumor behavior-related characteristics, we propose that the spheroid and adherent RCC growth patterns differentially respond to sunitinib. Gene expression analysis indicated that sunitinib promoted spheroid formation, which provided a selective survival advantage under treatment. Functional studies confirm that E-cadherin is a key contributor to the survival of RCC cells under sunitinib treatment. In summary, we suggest that sunitinib-resistant RCC cells exist in treatment-sensitive tumors and are histologically identifiable.-Lichner, Z., Saleeb, R., Butz, H., Ding, Q., Nofech-Mozes, R., Riad, S., Farag, M., Varkouhi, A. K., dos Santos, C. C., Kapus, A., Yousef, G. M. Sunitinib induces early histomolecular changes in a subset of renal cancer cells that contribute to resistance.
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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) | 0.002 | 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".