Mixed Adenosquamous Cell Carcinoma of the Prostate with Paired Sequencing on the Primary and Liver Metastasis
Bibliographic record
Abstract
This report aims to shed light on the intricate challenges encountered during the diagnosis and treatment of an uncommon variant of prostate cancer-mixed adenosquamous cell carcinoma of the prostate. Prostate cancers of this nature pose distinctive diagnostic and therapeutic dilemmas due to their rarity and complex histological composition. We present a case of a 63-year-old man with metastatic prostate cancer, featuring adenocarcinoma with squamous cell differentiation, who underwent a multimodal treatment approach. The patient responded to first-line carboplatin, docetaxel, and androgen deprivation therapy, followed by androgen receptor pathway inhibitor (ARPI) maintenance. However, disease progression led to radiation therapy and a subsequent switch to Lutetium (177Lu) vipivotide tetraxetan after chemotherapy challenges. Comprehensive genetic profiling revealed shared mutations in the prostate and liver lesions, emphasizing the role of targeted therapies. Prostate-specific membrane antigen (PSMA)-targeted therapy resulted in a notable PSA decline. This case highlights the evolving treatment landscape for rare prostate cancers, integrating genetic insights for tailored interventions. In conclusion, squamous cell carcinoma (SCC) of the prostate is rare, emphasizing the imperative for enhanced comprehension in diagnosis and management. Our case suggests the potential efficacy of ARPI and PSMA-targeted therapies. Our findings advocate for a more nuanced approach to the management of this rare prostate cancer variant, leveraging genomic insights for personalized treatment strategies. This exploration serves as a foundation for further research and clinical considerations in addressing the challenges posed by mixed adenosquamous cell carcinoma of the prostate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".