Engineered CD47 protects T cells for enhanced antitumor immunity
Bibliographic record
Abstract
Abstract Adoptively transferred T cells and agents designed to block the CD47/SIRPα axis are promising antitumor therapeutics, which activate distinct arms of the immune system. We administered anti-CD47 (αCD47) with adoptively transferred T cells with the goal of enhancing antitumor efficacy but observed rapid macrophage-mediated clearance of T cells expressing chimeric antigen receptors (CARs) or engineered T cell receptors, which blunted therapeutic benefit. αCD47 mediated CAR T clearance was potent and rapid enough to serve as an effective safety switch. To overcome this challenge, we engineered a CD47 variant (47 E ) that engaged SIRPα and provided a “don’t-eat-me” signal that was not blocked by αCD47 antibodies. TCR or CAR T cells expressing 47 E were resistant to clearance by macrophages following αCD47, and mediated significant, sustained macrophage recruitment into the TME. Although many of the recruited macrophages manifested an M2-like profile, the combined therapy resulted in synergistic enhancement in antitumor efficacy. This work identifies macrophages as major regulators of T cell persistence and illustrates the fundamental challenge of combining T cell directed therapeutics with those designed to activate macrophages. It further delivers a therapeutic approach capable of simultaneously harnessing the antitumor effects of T cells and macrophages that manifests markedly enhanced potency against 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) | 0.002 | 0.001 |
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".