Evidence map›Paper›PMID 41318392›Full record

ArticleBMC cancer2025

Multi-omics analysis reveals the role of tumor-infiltrating CD4

Zizhong Yang, Lupeng Qiu, Guhe Jia, Zhuoya Sun, Yixin Gong, Yin Chen, Yu Wang, Lai Song, Xiao Zhao, Shunchang Jiao

Abstract read
In one paragraph

Article in BMC cancer, 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

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

10 authors.

Zizhong Yang *School of Medicine, Nankai University, Tianjin, China.
Lupeng Qiu *Department of Medical Oncology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Guhe Jia *School of Medicine, Nankai University, Tianjin, China.
Zhuoya SunDepartment of Medical Oncology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Yixin GongBeijing DCTY Biotech Co., Ltd, Beijing, China.
Yin ChenBeijing DCTY Biotech Co., Ltd, Beijing, China.
Yu WangDepartment of Medical Oncology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Lai SongBeijing DCTY Biotech Co., Ltd, Beijing, China.
Xiao ZhaoDepartment of Medical Oncology, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China. dr.zx@163.com.
Shunchang JiaoSchool of Medicine, Nankai University, Tianjin, China. scjiao163@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the crucial involvement of the EGFR pathway in hepatocellular carcinoma (HCC), the clinical efficacy of EGFR antibodies in HCC remains uncertain. While existing evidence suggests that immune dysfunction and tumor microenvironment alterations may contribute to treatment resistance, the precise markers underlying this phenomenon in HCC warrant further investigation.

methodsIn this study, we employed patient-derived xenograft (PDX) models generated from 14 HCC patients enrolled in the REHOPE301 cohort to evaluate the sensitivity to nimotuzumab, a humanized anti-EGFR monoclonal antibody. Whole-exome sequencing (WES) and single-cell RNA sequencing (scRNA) were performed on tumor tissues and tumor-infiltrating lymphocytes (TILs) to elucidate the association between TIL characteristics and EGFR antibody response. In addition, immunofluorescence (IF) staining and flow cytometry were used to validate the findings from scRNA. A predictive risk score and nomogram were subsequently developed using LASSO regression analysis. The prognostic performance of this model was evaluated using 2 external datasets (ICGC-JP and GSE141202) through receiver operator characteristic (ROC) curves and calibration curves analyses.

resultsNimotuzumab demonstrated a 50% response rate (7/14) in PDX models. Immune profiling revealed distinct TIL patterns between responders and non-responders. Notably, CD4+CCR7+ T cells were significantly enriched in resistant tumors (p < 0.001) and negatively correlated with the nimotuzumab response (r = -0.767 p = 0.02). IF analysis revealed a higher proportion of CD4+CCR7+ double-positive T cells in the non-responder group compared to responders (p = 0.012). In non-responsive tumors, CD4+CCR7+ T cells exhibited interactions with of macrophages and CD8+PDCD1+ T subsets. The proportion of CD4+CCR7+ T showed negative correlations with active CD8 T infiltrations. A reduced infiltration of CD4+CCR7+ T cells was associated with improved prognosis and enhanced EGFR antibody efficacy across multiple cancer types. Furthermore, a nine-gene signature related to CD4+CCR7+ T cells was identified as a strong prognostic factor in HCC (HR = 5.19, 95% CI: 3.18–8.46, P < 0.001), and was used to construct a nomogram. WES confirmed prognostic gene mutations (VCAN, CAMK4, and CD226) potentially influencing nimotuzumab response.

conclusionsOur findings demonstrate that elevated infiltration of central memory CD4+CCR7+ T cells in HCC correlates with an immunosuppressive tumor microenvironment, which may contribute to impaired efficacy of EGFR-targeted antibodies and worse clinical outcomes.

Indexed as

Antibodies, Monoclonal, HumanizedCarcinoma, HepatocellularCD4-Positive T-LymphocytesDrug Resistance, NeoplasmLiver NeoplasmsLymphocytes, Tumor-InfiltratingAnimalsAntineoplastic Agents, ImmunologicalErbB ReceptorsExome SequencingFemaleHumansMaleMiceMiddle AgedPrognosisAntibodies, Monoclonal, HumanizedAntineoplastic Agents, ImmunologicalEGFR protein, humanErbB ReceptorsnimotuzumabCC-chemokine receptor 7EGFR-antibodyHepatocellular carcinomaNimotuzumabPDX-model

Identifiers

PMID41318392
PMCPMC12772023

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.