Penile Cancer-Derived Cells Molecularly Characterized as Models to Guide Targeted Therapies
Bibliographic record
Abstract
Penile cancer (PeCa) is a common disease in poor and developing countries, showing high morbidity rates. Despite the recent progress in understanding the molecular events involved in PeCa, the lack of well-characterized in vitro models precludes new advances in anticancer drug development. Here we describe the establishment of five human primary penile cancer-derived cell cultures, including two epithelial and three cancer-associated fibroblast (CAF) cells. Using high-throughput genomic approaches, we found that the epithelial PeCa derived- cells recapitulate the molecular alterations of their primary tumors and present the same deregulated signaling pathways. The differentially expressed genes and proteins identified are components of key oncogenic pathways, including EGFR and PI3K/AKT/mTOR. We showed that epithelial PeCa derived cells presented a good response to cisplatin, a common therapeutic approach used in PeCa patients. The growth of a PeCa-derived cell overexpressing EGFR was inhibited by EGFR inhibitors (cetuximab, gefitinib, and erlotinib). We also identified CAF signature markers in three PeCa-derived cells with fibroblast-like morphology, indicating that those cells are suitable models for PeCa microenvironment studies. We thus demonstrate the utility of PeCa cell models to dissect mechanisms that promote penile carcinogenesis, which are useful models to evaluate therapeutic approaches for the disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".