CD70-Targeting CAR NK Cells Overcome BCMA Downregulation and Improve Survival in High-risk Multiple Myeloma Models
Bibliographic record
Abstract
CD70 is highly expressed in many cancers, including multiple myeloma. We show in two cohorts of patients with multiple myeloma that CD70 is elevated in several high-risk disease categories and correlates with poor survival. These findings were validated using single-cell RNA sequencing, flow cytometry, and IHC. Moreover, we demonstrate the feasibility of targeting CD70 in myeloma using NK cells engineered with a chimeric antigen receptor (CAR) incorporating the CD70 cognate receptor CD27 and IL-15 (CAR27/IL-15). CAR27/IL-15 NK cells exerted potent in vitro and in vivo cytotoxicity against CD70+ multiple myeloma cells, comparable with CAR27/IL-15 T cells, and remained effective in BCMA knockout models. Collectively, these results establish CD70 as a promising therapeutic target for high-risk multiple myeloma, particularly for patients who relapse after BCMA-directed therapy, providing preclinical support for the ongoing phase I/II clinical trial of CD70-targeting CAR NK cells (NCT05092451). SIGNIFICANCE: We demonstrate that CD70 expression is elevated in patients with high-risk multiple myeloma and in patients with t(4;14) translocation. CD70-targeting CAR NK cells exhibit potent cytotoxicity against CD70+ multiple myeloma cells and significantly improve survival in xenograft mouse models of multiple myeloma, even in the absence of BCMA expression. See related commentary by Benson Jr and Caligiuri, p. 166.
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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 | 0.001 | 0.002 |
| 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".