Evidence map›Paper›PMID 40463198›Full record

ArticlebioRxiv : the preprint server for biology2025

Low-Strength Type I Interferon Signaling Promotes CAR T-Cell Treatment Efficacy.

Erting Tang, Yifei Hu, Guoshuai Cao, Nicholas W Asby, Duy-Thuc Nguyen, Nada S Aboelella, Hanna Ruiz, Yu Zhao, Lishi Xie, Xiufen Chen and 5 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Erting TangPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.ORCID 0000-0001-5348-799X
Yifei HuPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.ORCID 0000-0003-2870-7772
Guoshuai CaoPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Nicholas W AsbyPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Duy-Thuc NguyenPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Nada S AboelellaSection on Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
Hanna RuizPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Yu ZhaoPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.
Lishi XieCommittee on Immunology, University of Chicago, Chicago, IL 60637, USA.
Xiufen ChenCommittee on Immunology, University of Chicago, Chicago, IL 60637, USA.
Michael R BishopSection on Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
Peter A RiedellSection on Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
James L LaBelleSection on Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
Justin P KlineCommittee on Immunology, University of Chicago, Chicago, IL 60637, USA.
Jun HuangPritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.ORCID 0000-0003-0271-4384

Funding

VIRAL ONCOLOGY CORE FACILITYP30CA014599 · NCI · UNIVERSITY OF CHICAGO · PI KUNLE ODUNSI · 1985 to 2026
$122.1M
MEDICAL SCIENTIST TRAININGT32GM007281 · NIGMS · UNIVERSITY OF CHICAGO · PI CLARK, MARCUS RAMSAY · 1985 to 2022
$32.1M
Multi-Disciplinary Training Grant in Cancer ResearchT32CA009594 · NCI · UNIVERSITY OF CHICAGO · PI KAY F MACLEOD, Scott A. Oakes · 1989 to 2026
$11.5M
Molecular mechanism of natural killer cell recognitionDP2AI144245 · NIAID · UNIVERSITY OF CHICAGO · PI HUANG, JUN · 2018 to 2022
$2.9M
Development of antigen multimers for CAR T cell detection and functional profilingR21AI169159 · NIAID · UNIVERSITY OF CHICAGO · PI HUANG, JUN, KLINE, JUSTIN P. · 2023 to 2024
$437k
NCI NIH HHS P30 CA014599NCI NIH HHS T32 CA009594NIAID NIH HHS DP2 AI144245NIAID NIH HHS R21 AI169159NIGMS NIH HHS T32 GM007281
6 · The paper itself

Abstract

CD19-directed chimeric antigen receptor (CAR) T-cell therapy has significantly advanced the treatment landscape for relapsed/refractory diffuse large B-cell lymphoma (r/r DLBCL). However, up to 60% of patients do not achieve a complete response. To uncover determinants of therapeutic efficacy, we analyzed the infusion products of eight r/r DLBCL patients with distinct clinical responses to axicabtagene ciloleucel using single-cell transcriptomics. Compared to patients who exhibited progressive disease, infusion products of complete responders demonstrated enriched signatures of type I interferon (IFN-I) signaling. Based on these findings, we developed a novel strategy to improve CD19-directed CAR T-cell treatment efficacy by incorporating IFN-I as an enhancer during the ex vivo manufacturing process. For both CD28- and 4-1BB-costimulated second-generation CARs, we found that low-strength IFN-I signaling enhanced CAR T-cell cytotoxicity and in vivo efficacy. On the other hand, high-strength IFN-I signaling compromised cell viability and in vivo efficacy. Our low-strength IFN-I signaling approach leverages an existing FDA-approved pharmacologic agent and is compatible with current CAR constructs and manufacturing workflows. Together, our results establish IFN-I as a potent and costimulation-independent enhancer of CAR T-cell efficacy and provide a translationally feasible approach to enhance CAR T-cell therapies for r/r DLBCL.

Indexed as

axicabtagene ciloleucel (axi-cel)chimeric antigen receptor (CAR)diffuse large B-cell lymphoma (DLBCL)lisocabtagene maraleucel (liso-cel)type I interferon (IFN-I)

Identifiers

PMID40463198
PMCPMC12132530

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.