Evidence map›Paper›PMID 42258951›Full record

ArticleEBioMedicine2026

An immunogenomic classification of solid tumours reveals subtype-specific therapeutic vulnerabilities for immunotherapy.

Yiming Zhao, Pei Wang, Zhiren Han, Zixuan Qiu, Xin Du, Qingliang Wen, Ziwei Zhou, Xiaorong Lin, Jiaxin Zhong, Beinan Han and 10 more

Abstract read
In one paragraph

Article in EBioMedicine, 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

20 authors.

Yiming ZhaoDepartment of Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Pei WangDepartment of Oncology, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, 710004, China.
Zhiren HanDepartment of Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Zixuan QiuBreast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, 510120, China.
Xin DuBreast Cancer Center, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
Qingliang WenBreast Cancer Center, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
Ziwei ZhouDepartment of Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Xiaorong LinDiagnosis and Treatment Center of Breast Diseases, Shantou Central Hospital, Shantou, 515031, China.
Jiaxin ZhongDepartment of Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Beinan HanDepartment of Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Wenkui FuMedical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Keyi SunSecond Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, 310053, China.
Herui YaoDepartment of Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Zhenkun NaHangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China.
Canming WangHangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China; Department of Pathology, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
Taobo LuoHangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China; Department of pulmonary surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China; Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China.
Dan SuHangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China; Department of Pathology, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
Jian ZengHangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China; Department of pulmonary surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China; Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China.
Hai HuBreast Cancer Center, Zhejiang Cancer Hospital, Hangzhou, 310022, China; Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, 310018, China.
Man-Li LuoGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. Electronic address: luomli@mail.sysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe efficacy of immune checkpoint blockade (ICB) is heterogeneous across patients. Tumour immune phenotype classification (immune-inflamed, -excluded, and -desert) represents a foundational but inadequate framework for predicting ICB efficacy. Here we aimed to develop an integrated immunogenomic classification to improve ICB response prediction and identify subtype-specific therapeutic vulnerabilities.

methodsWe analysed 13 public ICB cohorts and an in-house cohort. Using RNA-seq data, we developed ImmPred, a seven-gene classifier trained on IHC-defined immune phenotypes, and integrated it with TMB to define immunogenomic subtypes. Subtype-specific resistance mechanisms were investigated via pathway analysis and validated in syngeneic mouse models.

findingsPatients with cancers can be stratified into five immunogenomic subtypes with divergent responses to ICB, which are TMB-High (H) inflamed, TMB-Low (L) inflamed, TMB-H excluded, TMB-L excluded, and desert phenotypes. In immune-excluded tumours, MTAP deficiency contributes to ICB resistance in TMB-H excluded subtype and PRMT5 inhibitors enhances ICB efficacy in MTAP-KO B16-F10 and CT26 syngeneic mouse model, whereas TGF-β hyperactivation drives intrinsic resistance of TMB-L excluded subtype and TGF-β blockade potentiates anti-tumour immunity in MB49 and EMT6 mouse model. In TMB-H inflamed tumours, IFN-γ is a critical determinant of ICB efficacy, and TLR7 agonist, via enhancing IFN-γ signalling, improves anti-PD-L1 efficacy in MC38 mouse model. In TMB-L inflamed tumours, targeting COX-2-PGE2 axis with celecoxib sensitises ICB in LLC1 mouse model.

interpretationLeveraging clinical feasible RNA-seq and TMB analysis, our model exhibits robust predictive efficacy of ICB response in multiple cancers, enabling subtype-tailored therapeutic combinations to improve immunotherapy response.

fundingThis work was supported by grants from National Key Research and Development Program of China (2021YFA1300602), National Natural Science Foundation of China (82025026, 82230091, 82472775), Guang Dong Basic and Applied Basic Research Foundation (2023A1515012412 and 2023A1515011214), Guangdong Science and Technology Department (2023B1212060013, 2023B1111030006), Key R&D Program of Zhejiang (2024C03160), Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang Province (2024R01005 and 2025R01009).

Indexed as

ImmunotherapyNeoplasmsAnimalsBiomarkers, TumorDisease Models, AnimalDrug Resistance, NeoplasmGene Expression Regulation, NeoplasticHumansImmune Checkpoint InhibitorsImmunoinformaticsMiceBiomarkers, TumorImmune Checkpoint InhibitorsImmune checkpoint blockadeImmunogenomic classificationTargeted therapyTumour immune phenotypesTumour mutational burden

Identifiers

PMID42258951
PMCPMC13264204

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.