Evidence map›Paper›PMID 42488656›Full record

ReviewFrontiers in immunology2026

CAR-T cell signaling dynamics, rational design principles and artificial intelligence for next-generation chimeric antigen receptors.

Xin Liu, Fangjia Tong, Jiayi Zhang, William Gradishar, Huiping Liu, Rongfu Wang

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2026. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xin Liu *Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Fangjia Tong *Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
Jiayi ZhangDepartment of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
William GradisharDivision of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
Huiping LiuDepartment of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
Rongfu WangDepartment of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.

Funding

Novel Strategies for Intervention of Inflammatory Diseases and Cancer - Research Supplement to R01-CA101795R01CA101795 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, RONGFU · 2004 to 2022
$4.6M
CD4+ T cells and neoantigens in melanoma immunotherapy.R01CA246547 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, RONGFU · 2021 to 2025
$3.1M
NCI NIH HHS R01 CA101795NCI NIH HHS R01 CA246547
6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T cell therapy has transformed the treatment of hematologic malignancies, yet its efficacy in solid tumors and durability across broader application remain limited. A central challenge lies in how CAR signaling is initiated, amplified, and regulated over time. Unlike the native T cell receptor (TCR), CARs are synthetic, modular receptors whose signaling output is dictated by the composition and spatial organization of their extracellular, transmembrane, and intracellular domains. Emerging evidence suggests that CAR signaling requirements are not static: insufficient signaling at early time points can impair activation and tumor clearance, whereas excessive or prolonged signaling promotes exhaustion, toxicity, and loss of persistence. More recent CAR designs therefore emphasize fine-tuned signaling, embracing a "less-is-more" paradigm to balance potency with durability. In this review, we summarized recent advances in CAR signaling biology, focusing on temporal signaling thresholds, modular design principles, and emerging strategies to precisely control signal strength and quality. Finally, we discuss how high-throughput screening, computational modeling, and machine learning approaches may enable disease-specific, personalized CAR designs in the future.

Indexed as

Artificial IntelligenceImmunotherapy, AdoptiveNeoplasmsReceptors, Antigen, T-CellReceptors, Chimeric AntigenSignal TransductionT-LymphocytesAnimalsHumansReceptors, Antigen, T-CellReceptors, Chimeric Antigenartificial intelligence (AI)cancer immunotherapyCAR-Tmachine learningT cell signaling

Identifiers

PMID42488656
PMCPMC13388281

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.