Generation and Screening of Various CD70 CAR NK Cells Identify the Most Effective Construct Against Hematologic Malignancies
Bibliographic record
Abstract
The ability to re-direct the intrinsic power of the immune system against cancer has resulted in unprecedented outcomes in certain patients with otherwise incurable diseases. Natural killer (NK) cells, similar to T cells, are powerful immune effectors that possess intrinsic anti-tumor properties and are capable of generating a strong cytotoxic response upon engaging tumor cells. However, unlike T cells, NK cells do not identify specific antigens and do so in a way that is HLA-independent, posing minimal risk of GvHD. Thus, NK cells have are being explored as a possible allogeneic source for over-the-counter cellular immunotherapy. CD70, the ligand for the CD27 receptor, is an attractive “pan-cancer antigen”, since in addition to being expressed in hematologic malignancies such as acute myeloid leukemia (AML) and lymphoma, it is also expressed on many solid tumors, including bladder, lung, triple negative breast cancer, renal cell carcinoma, pancreatic cancer, and melanoma. Moreover, CD70 is only transiently found on activated T and B lymphocytes and on dendritic cells. To target CD70-expressing cancers, we designed and tested a number of different chimeric antigen receptors (CAR). Briefly, we used the natural interaction between CD70 and CD27 to make a CAR construct that incorporated the extracellular domain of human CD27, which is the natural receptor for CD70. We generated various second-generation CAR constructs that differed in their transmembrane domains (CD27 vs. CD28) and costimulatory domains. We included co-stimulatory domains that are NK-centric (DAP10 and DAP12), T-cell-centric (CD28), or bi-centric (4-1BB). The CD3ζ chain was used as a signaling endodomain, and all the CAR constructs also included IL-15 to enhance in vivo proliferation and persistence. Cord blood (CB)-derived NK cells were efficiently retrovirally transduced with the different CAR constructs and subjected to an in vitro functional screen against various cancer cells to evaluate the best constructs for in vivo validation (Figure 1). The top constructs from our in vitro screen were further evaluated in multiple in vivo models, including a Raji lymphoma model and a THP-1 AML model. The best CAR construct in terms of superior tumor control and prolongation of survival was selected for GMP manufacturing. Based on these preclinical findings, a Phase I/II clinical study is now being conducted at our center to assess the safety and effectiveness of CD70 CAR NK cells against CD70-expressing hematologic malignancies (NCT05092451). Figure 1: Flow chart showing the screening process to identify the most effective CAR construct.
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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) | 0.002 | 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".