Molecular Pathogenesis of Penile Squamous Cell Carcinoma: Current Understanding and Potential Treatment Implications
Bibliographic record
Abstract
CONTEXT.—: Penile squamous cell carcinomas (PSCCs) are divided into tumors that are human papillomavirus (HPV) associated and those that are non-HPV associated. HPV and non-HPV PSCCs each display unique pathogenic mechanisms, histologic subtypes, and clinical behaviors. Treatment of localized PSCC tumors is linked to significant physical and psychological morbidity, and management of advanced disease is often treatment refractory. The identification of novel actionable mutations is of critical importance so that translational scientists and clinicians alike can pursue additional therapeutic options. OBJECTIVE.—: To provide an update on the molecular pathogenesis associated with PSCC. A special emphasis is placed on next-generation sequencing data and its role in identifying potential therapeutic targets. DATA SOURCES.—: A literature review using the PubMed search engine to access peer-reviewed literature published on PSCC. CONCLUSIONS.—: Our understanding of the genetic and molecular mechanisms that underlie PSCC pathogenesis continues to evolve. PSCC tumorigenesis is mediated by multiple pathways, and mutations of oncogenic significance have been identified that may represent targets for personalized therapy. Preliminary results of treatment with immune checkpoint inhibition and tyrosine kinase inhibitors have produced variable clinical results. Further insight into the pathogenesis of PSCC will help guide clinical trials and develop additional precision medicine approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".