Evidence map›Paper›PMID 41858121›Full record

Observational studyThoracic cancer2026

TP53 CpG Site Mutations Predict the Survival of First-Line Chemoimmunotherapy in Advanced Lung Adenocarcinoma.

Lige Wu, Zihua Zou, Yan Li, Xuezhi Hao, Jie Zhang, Jianming Ying, Puyuan Xing, Junling Li

Abstract readObservational Study
In one paragraph

Observational study in Thoracic cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

8 authors.

Lige WuDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0002-6785-2430
Zihua ZouDepartment of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, People's Republic of China.ORCID https://orcid.org/0000-0003-0677-3998
Yan LiDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Xuezhi HaoDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Jie ZhangDepartment of Medical Oncology, Beijing Chaoyang District Sanhuan Cancer Hospital, Beijing, People's Republic of China.
Jianming YingDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Puyuan XingDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0003-4415-9892
Junling LiDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0002-7361-325X

Funding

Medical Oncology Innovation Team of Cancer Hospital Chinese Academy of Medical Sciences
6 · The paper itself

Abstract

backgroundChemoimmunotherapy is the first-line standard for advanced lung adenocarcinoma (LUAD) without driver mutations, but predictive biomarkers are still limited. TP53 is the most frequently mutated gene in LUAD, and its mutation subtypes affect treatment efficacy differently.

methodsThis is a retrospective observational study. We retrospectively analyzed 133 patients with advanced LUAD who received first-line chemoimmunotherapy in our hospital. TP53 mutations were classified using a multidimensional approach. Key subtypes were identified through elastic-net and multivariate Cox regression analyses in clinical cohorts. The underlying mechanisms were further explored by transcriptomic and pathway analyses, including gene set variation analysis (GSVA) and immune cell deconvolution, using TCGA data.

resultsAn elastic-net Cox model within the TP53-mutant cohort identified three key features: TP53 variant allele frequency (VAF), CpG site mutation status, and dominant-negative effects (DNE)-loss-of-function (LOF) classification. Subsequent multivariate Cox regression in the full cohort confirmed that TP53 mutations at CpG sites were an independent favorable prognostic factor. Patients harboring CpG site mutations had significantly longer median progression-free survival (PFS) for first-line chemoimmunotherapy than both non-CpG-mutant and wild-type patients. Exploratory transcriptomic analyses revealed that CpG-mutant tumors were associated with an immune-activated tumor microenvironment (TME) characterized by enhanced T-Cell receptor (TCR) signaling, antigen presentation, IFN-γ signaling, and CD8

conclusionsIn conclusion, this study demonstrates that TP53 mutations at CpG sites define a distinct subgroup of patients with advanced LUAD who derive significantly greater PFS benefit from first-line chemoimmunotherapy.

Indexed as

Adenocarcinoma of LungCpG IslandsImmunotherapyLung NeoplasmsMutationTumor Suppressor Protein p53AgedBiomarkers, TumorFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesBiomarkers, TumorTP53 protein, humanTumor Suppressor Protein p53chemoimmunotherapyCpG sitelung adenocarcinomapredictive biomarkerTP53 mutation

Identifiers

PMID41858121
PMCPMC13097391

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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.