Targeted Elimination of Prostate Cancer by Genetically Directed Human T Lymphocytes
Bibliographic record
Abstract
The genetic transfer of antigen receptors is a powerful approach to rapidly generate tumor-specific T lymphocytes. Unlike the physiologic T-cell receptor, chimeric antigen receptors (CARs) encompass immunoglobulin variable regions or receptor ligands as their antigen recognition moiety, thus permitting T cells to recognize tumor antigens in the absence of human leukocyte antigen expression. CARs encompassing the CD3zeta chain as their activating domain induce T-cell proliferation in vitro, but limited survival. The requirements for genetically targeted T cells to function in vivo are less well understood. We have, therefore, established animal models to assess the therapeutic efficacy of human peripheral blood T lymphocytes targeted to prostate-specific membrane antigen (PSMA), an antigen expressed in prostate cancer cells and the neovasculature of various solid tumors. In vivo specificity and antitumor activity were assessed in mice bearing established prostate adenocarcinomas, using serum prostate-secreted antigen, magnetic resonance, computed tomography, and bioluminescence imaging to investigate the response to therapy. In three tumor models, orthotopic, s.c., and pulmonary, we show that PSMA-targeted T cells effectively eliminate prostate cancer. Tumor eradication was directly proportional to the in vivo effector-to-tumor cell ratio. Serial imaging further reveals that the T cells must survive for at least 1 week to induce durable remissions. The eradication of xenogeneic tumors in a murine environment shows that the adoptively transferred T cells do not absolutely require in vivo costimulation to function. These results thus provide a strong rationale for undertaking phase I clinical studies to assess PSMA-targeted T cells in patients with metastatic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".