Effectiveness of chimeric antigen receptor-T therapy on prostate cancer: A preclinical and clinical systematic review
Bibliographic record
Abstract
Chimeric antigen receptor-T (CAR-T) therapy has been an effective treatment for leukemia and lymphoma. Unlike hematological cancers, solid tumors like prostate cancer utilize a dynamic microenvironment to evade the host immune defenses. We aimed to systematically review preclinical and clinical studies to evaluate how CAR-T therapies in prostate cancer modify the tumor microenvironment and influence patient outcomes. PubMed, Embase, and Scopus were screened for published, peer reviewed preclinical and clinical studies in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The CAR-T antigen, tumor eradication rates, change in prostate–specific antigen (PSA) expression, and tumor tissue infiltration were compared across studies. Nineteen preclinical trials examining xenograft mice models and 3 phase I clinical trials with 32 total patients were included in this review. Tumor eradication rates in mice treated with armored CAR-T therapy were significantly greater than that of mice treated with unarmored CAR-T cells (p–value < .05). Ten of 32 clinical trial patients had a minimum of 30% PSA decline. Patients receiving higher doses of lymphocyte depletion (LD) therapy had higher peaks of CAR-T expansion, and those receiving LD therapy before CAR-T infusion experienced reduced dose–limiting toxicities. Immunohistochemistry staining of biopsied tumor tissue suggests CAR-T increased T cell proliferation markers and upregulated cytokines. CAR-T cells can modify the tumor microenvironment when armored or paired with LD therapy. Future studies should include expanded clinical investigations, particularly using armored CAR-T cells with LD regimens, to determine its safety and efficacy profiles in prostate cancer.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".