Novel low‐avidity glypican‐3 specific CARTs resist exhaustion and mediate durable antitumor effects against HCC
Bibliographic record
Abstract
Abstract Background and Aims Chimeric antigen receptor engineered T cells (CARTs) for HCC and other solid tumors are not as effective as they are for blood cancers. CARTs may lose function inside tumors due to persistent antigen engagement. The aims of this study are to develop low‐affinity monoclonal antibodies (mAbs) and low‐avidity CARTs for HCC and to test the hypothesis that low‐avidity CARTs can resist exhaustion and maintain functions in solid tumors, generating durable antitumor effects. Methods and Results New human glypican‐3 (hGPC3) mAbs were developed from immunized mice. We obtained three hGPC3‐specific mAbs that stained HCC tumors, but not the adjacent normal liver tissues. One of them, 8F8, bound an epitope close to that of GC33, the frequently used high‐affinity mAb, but with approximately 17‐fold lower affinity. We then compared the 8F8 CARTs to GC33 CARTs for their in vitro function and in vivo antitumor effects. In vitro, low‐avidity 8F8 CARTs killed both hGPC3 high and hGPC3 low HCC tumor cells to the same extent as high‐avidity GC33 CARTs. 8F8 CARTs expanded and persisted to a greater extent than GC33 CARTs, resulting in durable responses against HCC xenografts. Importantly, compared with GC33 CARTs, there were 5‐fold more of 8F8‐BBz CARTs in the tumor mass for a longer period of time. Remarkably, the tumor‐infiltrating 8F8 CARTs were less exhausted and apoptotic, and more functional than GC33 CARTs. Conclusion The low‐avidity 8F8‐BBz CART resists exhaustion and apoptosis inside tumor lesions, demonstrating a greater therapeutic potential than high‐avidity CARTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".