Evidence mapPaperPMID 38862995Full record

ArticleMolecular cancer2024

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

Syuzo Kaneko, Ken Takasawa, Ken Asada, Kouya Shiraishi, Noriko Ikawa, Hidenori Machino, Norio Shinkai, Maiko Matsuda, Mari Masuda, Shungo Adachi and 21 more

Abstract read
In one paragraph

Article in Molecular cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

31 authors.

Syuzo Kaneko *Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan. sykaneko@ncc.go.jp.ORCID http://orcid.org/0000-0003-4558-9800
Ken Takasawa *Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0001-8699-0858
Ken Asada *Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0003-0548-4449
Kouya ShiraishiDivision of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0002-5821-7400
Noriko IkawaDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Hidenori MachinoDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0001-6843-7696
Norio ShinkaiDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0001-7998-8493
Maiko MatsudaDivision of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Mari MasudaDepartment of Proteomics, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0003-2311-8696
Shungo AdachiDepartment of Proteomics, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Satoshi TakahashiDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Kazuma KobayashiDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Nobuji KounoDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Amina BolatkanDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
Masaaki KomatsuDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0003-0421-8085
Masayoshi YamadaEndoscopy Division, National Cancer Center Hospital, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0003-3979-5560
Mototaka MiyakeDepartment of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0009-0003-6287-7932
Hirokazu WatanabeDepartment of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0009-0007-2914-6651
Akiko TateishiDepartment of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Takaaki MizunoDivision of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0003-4642-0345
Yu OkuboDepartment of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Masami MukaiDivision of Medical Informatics, National Cancer Center Hospital, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0001-6315-6078
Tatsuya YoshidaDepartment of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Yukihiro YoshidaDepartment of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Hidehito HorinouchiDepartment of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0001-9090-801X
Shun-Ichi WatanabeDepartment of Thoracic Surgery, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Yuichiro OheDepartment of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Yasushi YatabeDepartment of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Vassiliki SalouraCenter for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.
Takashi KohnoDivision of Genome Biology, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0002-5371-706X
Ryuji HamamotoDivision of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan. rhamamot@ncc.go.jp.ORCID http://orcid.org/0000-0002-2632-1334

Funding

AMED Innovative Cancer Medical Practice Research Project JP22ck0106643JSPS Grant-in-Aid for Scientific Research JP21H03550JSPS Grant-in-Aid for Scientific Research on Innovative Areas JP18H04908JST AIP-PRISM JPMJCR18Y4JST CREST JPMJCR1689The Princess Takamatsu Cancer Research Fund 19-25108
6 · The paper itself

Abstract

backgroundIn an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD.

methodsWe performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases.

resultsWe demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations.

conclusionsOur findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Indexed as

Adenocarcinoma of LungEnhancer Elements, GeneticErb-b2 Receptor Tyrosine KinasesGene Expression Regulation, NeoplasticLung NeoplasmsAgedBiomarkers, TumorFemaleGenomicsGenomic Structural VariationHumansMaleMiddle AgedMutationPrognosisBiomarkers, TumorERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesDriver mutationsIntegrated analysisLung adenocarcinomaPrecision medicineStructural variationsSuper-enhancersTargeted therapy

Identifiers

PMID38862995
PMCPMC11165761

What Socratic holds

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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 Socratic graph.