Engineered CD47 protects T cells for enhanced antitumor immunity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it