CD70-Targeted Allogeneic CAR T-Cell Therapy for Advanced Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
Therapeutic approaches for clear cell renal cell carcinoma (ccRCC) remain limited; however, chimeric antigen receptor (CAR) T-cell therapies may offer novel treatment options. CTX130, an allogeneic CD70-targeting CAR T-cell product, was developed for the treatment of advanced or refractory ccRCC. We report that CTX130 showed favorable preclinical proliferation and cytotoxicity profiles and completely regressed RCC xenograft tumors. We also report results from 16 patients with relapsed/refractory ccRCC who received CTX130 in a phase I, multicenter, first-in-human clinical trial. No patients encountered dose-limiting toxicity, and disease control was achieved in 81.3% of patients. One patient remains in a durable complete response at 3 years. Finally, we report on a next-generation CAR T construct, CTX131, in which synergistic potency edits to CTX130 confer improved expansion and efficacy in preclinical studies. These data represent a proof of concept for the treatment of ccRCC and other CD70+ malignancies with CD70- targeted allogeneic CAR T cells. Significance: Although the role of CAR T cells is well established in hematologic malignancies, the clinical experience in solid tumors has been disappointing. This clinical trial demonstrates the first complete response in a patient with RCC, reinforcing the potential benefit of CAR T cells in the treatment of solid tumors.
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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".