Evidence map›Paper›PMID 35533264›Full record

ArticleMolecular cancer research : MCR2022

Pan-Cancer HLA Gene-Mediated Tumor Immunogenicity and Immune Evasion.

Xutong Gong, Rachel Karchin

Open access · bronzeAbstract read
In one paragraph

Article in Molecular cancer research : MCR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

24 citing papers in PubMed, 39 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Xutong GongDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
Rachel KarchinDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0002-5069-1239
Johns Hopkins University · US

Funding

Large-Scale Genetic Analyses of Human CancerR01CA121113 · NCI · JOHNS HOPKINS UNIVERSITY · PI ANAGNOSTOU, VALSAMO, VELCULESCU, VICTOR E. · 2006 to 2022
$4.3M
NCI NIH HHS R01 CA121113
6 · The paper itself

Abstract

Human leukocyte antigen (HLA) expression contributes to the activation of antitumor immunity through interactions with T-cell receptors. Pan-cancer HLA-mediated immunogenicity and immunoediting mechanisms have not been systematically studied previously. In a retrospective analysis of 33 tumor types from the Cancer Genome Atlas (TCGA), we characterized the differential expression of HLA class I and class II genes across various oncogenic pathways and immune subtypes. While HLA I genes were upregulated in all immunogenically hot tumors, HLA II genes were upregulated in an inflammatory immune subtype associated with best prognosis and were systematically downregulated in specific oncogenic pathways. A subset of immunogenically hot tumors which upregulated HLA class I but not class II genes exploited HLA-mediated escape strategies. Furthermore, with a machine learning model, we demonstrated that HLA gene expression data can be used to predict the immune subtypes of patients receiving immune checkpoint blockade and stratify patient survival. Interestingly, tumors with the highest immune infiltration did not have the best prognosis but showed significantly higher immune exhaustion. IMPLICATIONS: Taken together, we highlight the prognostic potential of HLA genes in immunotherapies and suggest that higher tumor immunogenicity mediated by HLA expression may sometimes lead to tumor escape under strong selective pressure.

Indexed as

Histocompatibility Antigens Class INeoplasmsTumor EscapeHumansImmunotherapyRetrospective StudiesHistocompatibility Antigens Class I

Identifiers

PMID35533264
PMCPMC9357147
OpenAlexW4229439361

What OpenQuestion holds

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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.