Novel low-avidity glypican-3 specific CARTs resist exhaustion and mediate durable antitumor effects against hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Chimeric antigen receptor engineered T cells (CARTs) are being developed to treat solid tumors including hepatocellular carcinoma (HCC). However, thus far, CARTs have not been as effective against solid tumors as compared to blood cancers. A main reason is that, once infiltrating into a solid tumor mass, CARTs are surrounded and chronically stimulated by persistent target antigens, which may drive them to exhaustion. We hypothesize that, due to weak engagement, low-avidity CARTs will resist the antigen-driven exhaustion and apoptosis and maintain effector functions in solid tumors, generating durable antitumor effects. To test this idea, we developed a novel human glypican-3 (hGPC3) specific antibody (8F8) that binds an epitope close to that of GC33 (the frequently used high-affinity antibody), but with ~17 folds lower affinity. In vitro , the low-avidity 8F8 CART killed tumor cells and produced effector cytokines to the same extent as high-avidity GC33 CART. Remarkably, however, 8F8 CART expanded and persisted to a greater extent than GC33 CART in vivo , resulting in durable responses against HCC xenografts. Compared to GC33 CARTs, there were significantly more (5 times) 8F8-BBz CART detected in the tumor mass. Importantly, the tumor infiltrating 8F8 CARTs were less apoptotic and more resistant to exhaustion, revealed by their enhanced and durable effector functions overtime. We predict that this novel low-avidity 8F8-BBz CART has a greater potential than mainstream high-avidity CARTs in effectively treating patients with HCC or other hGPC3+ 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 | 0.000 |
| 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 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".