CAR-T cell therapy in treating advanced prostate cancer
Bibliographic record
Abstract
Prostate cancer (PCa), a common cancer among older men, poses unique issues due to its slow progression and limited therapeutic choices for advanced stages. Since standard medicines such as chemotherapy and radiation have limited efficacy, it is in urgent need to develop novel therapies. CAR-T cell treatment modifies patients' T cells to produce chimeric antigen receptors that target certain antigens expressed on cancer cells, thereby enhancing the immune system's ability to identify and eliminate malignant cells. Targets for CAR-T cell therapy in prostate cancer include PSCA, PSMA, and EpCAM. Numerous studies have been conducted on PSMA, which is overexpressed in PCa cells. CAR-T cells that target PSMA have shown encouraging results in preclinical and clinical trials. Similarly, PSCA and EpCAM are promising targets for CAR-T cell therapy, with continuing research focused on improving their efficacy and safety profiles. The evolution of CAR-T cell therapy, spanning multiple generations, reflects continual efforts to enhance therapeutic outcomes. From first-generation CAR-T cells lacking co-stimulatory signals to advanced fourth and fifth-generation CAR-T cells equipped with additional functionalities like cytokine secretion, each iteration represents a progression towards improved efficacy and safety. However, optimizing CAR-T cell design, managing side effects, and identifying appropriate antigen targets still need to be addressed to realize the full promise of CAR-T cell therapy in PCa treatment. This review introduces the mechanisms, structures, evolution, and uses of CAR-T cell therapy in the treatment of PCa and discusses its great potential as a transformative cancer treatment strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".