Evidence map›Paper›PMID 33637086›Full record

ArticleCancer cell international2021

Immune subtyping for pancreatic cancer with implication in clinical outcomes and improving immunotherapy.

Jingkai Liu, Qiaofei Liu, Xiang Zhang, Ming Cui, Tong Li, Yalu Zhang, Quan Liao

Open access · goldAbstract read
In one paragraph

Article in Cancer cell international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.4field-weighted citation impact, top 9% of its field
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

22 citing papers in PubMed, 37 citations in OpenAlex.

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  10. The Role of Transglutaminase 2 in Cancer: An Update.International journal of molecular sciences · 2024
    Review
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  15. The immune landscape of solid pediatric tumors.Journal of experimental & clinical cancer research : CR · 2022
    Article
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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

7 authors at 1 institution in 1 country.

Jingkai LiuDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Qiaofei LiuDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Xiang ZhangDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Ming CuiDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Tong LiDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yalu ZhangDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Quan LiaoDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China. lqpumc@126.com.ORCID http://orcid.org/0000-0003-1324-6213
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

National Natural Science Foundation of China 81502068National Natural Science Foundation of China 81673023National Natural Science Foundation of China 81872501
6 · The paper itself

Abstract

backgroundEmerging evidence has shown that intra-tumor immune features are associated with response to immune checkpoint blockade (ICB) therapy. Accordingly, patient stratification is needed for identifying target patients and designing strategies to improve the efficacy of ICB therapy. We aimed to depict the specific immune features of patients with pancreatic cancer and explore the implication of immune diversity in prognostic prediction and individualized immunotherapy.

methodsFrom transcriptional profiles of 383 tumor samples in TCGA, ICGC, and GEO database, robust immune subtypes which had different response immunotherapy, including ICB therapy, were identified by consensus clustering with five gene modules. DEGs analysis and tumor microarray were used to screen and demonstrate potential targets for improving ICB therapy.

resultsThree subtypes of pancreatic cancer, namely cluster 1-3 (C1-C3), characterized with distinct immune features and prognosis, were generated. Of that, subtype C1 was an immune-cold type in lack of immune regulators, subtype C2, with an immunosuppression-dominated phenotype characterized by robust TGFβ signaling and stromal reaction, showed the worst prognosis, subtype C3 was an immune-hot type, with massive immune cell infiltration and in abundance of immune regulators. The disparity of immune features uncovered the discrepant applicability of anti-PD-1/PD-L1 therapy and potential sensitivity to other alternative immunotherapy for each subtype. Patients in C3 were more suitable for anti-PD-1/PD-L1 therapy, while patients in the other two clusters may need combined strategies targeted on other immune checkpoints or oncogenic pathways. A promising target for improving anti-PD-1/PD-L1 treatment, TGM2, was screened out and its role in the regulation of PD-L1 was investigated for the first time.

conclusionCollectively, immune features of pancreatic cancer contribute to distinct immunosuppressive mechanisms that are responsible for individualized immunotherapy. Despite pancreatic cancer being considered as a poor immunogenic cancer type, the derived immune subtypes may have implications in tailored designing of immunotherapy for the patients. TGM2 has potential synergistic roles with ICB therapy.

Indexed as

HeterogeneityImmune cellImmune checkpointsImmunotherapyPancreatic cancerTransglutaminase 2

Identifiers

PMID33637086
PMCPMC7908647
OpenAlexW3113472020

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Registered trials

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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.