Evidence mapPaperPMID 41817586Full record

ArticleJCI insight2026

RAS signaling in lung adenocarcinoma is defined by lineage context and DUSP4 loss.

Minjeong Kim, Wisut Lamlertthon, Heejoon Jo, Yan Cui, Miyeon Yeon, Hyo Young Choi, Katherine A Hoadley, Matthew P Smeltzer, Michele C Hayward, Matthew D Wilkerson and 2 more

Abstract read
In one paragraph

Article in JCI insight, 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

12 authors.

Minjeong KimDepartment of Medicine, Division of Hematology and Oncology, College of Medicine, and.
Wisut LamlertthonPrincess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Lak Si, Bangkok, Thailand.
Heejoon JoDepartment of Medicine, Division of Hematology and Oncology, College of Medicine, and.
Yan CuiDepartment of Genetics, Genomics, and Informatics, and.
Miyeon YeonDepartment of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Hyo Young ChoiDepartment of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Katherine A HoadleyDepartment of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Matthew P SmeltzerDivision of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, Tennessee, USA.
Michele C HaywardLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Matthew D WilkersonDepartment of Anatomy, Physiology, and Genetics, Center for Military Precision Health, Uniformed Services University, Bethesda, Maryland, USA.
Liza MakowskiDepartment of Medicine, Division of Hematology and Oncology, College of Medicine, and.
D Neil HayesDepartment of Medicine, Division of Hematology and Oncology, College of Medicine, and.

Funding

Algorithm-based prevention and reduction of differences in cancer outcomes arising from data imbalanceR01CA262296 · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2025 to 2025
$352k
NCI NIH HHS R01 CA262296
6 · The paper itself

Abstract

BACKGROUNDThe molecular landscape of lung adenocarcinoma (LUAD) is often illustrated as a driver-oncogene pie chart, but identical mutations exhibit heterogeneous signaling shaped by comutations, transcriptional programs, and lineage context. We propose a lineage-integrated signaling framework using an EGFR mutation signature (mSig).METHODSWe defined EGFR mSig using differentially expressed genes in EGFR-mutant (EGFR-mt) LUADs. Semisupervised clustering and machine learning models were used to test reproducibility in different combinations of datasets. We analyzed molecular subtypes, lineage markers, co-occurring mutations, and EGFR copy number alterations in EGFR mSig-defined subtypes of LUAD.RESULTSEGFR mSig showed robust classification performance (area under receiver operating characteristic curve = 0.83-0.95; mean negative predictive value = 96.3%). Validated gene expression subtypes and lung lineage markers were closely aligned with EGFR mSig status. Most EGFR mSig+ tumors, including many without EGFR mutations, belonged to the bronchioid subtype. A subset of canonical RAS mutations were mSig+ and mirrored the EGFR mutation pattern. EGFR WT/mSig- tumors were enriched for nonbronchioid subtypes and had comutations in TP53 or RAS/RAF/RTKs. We highlight a parsimonious collection of coordinated mutations, including RAS, KEAP1, STK11, TP53, and CDKN2A, that taken together suggest coordination of tumor signaling previously suggested but now reproduced and expanded.CONCLUSIONA potentially novel EGFR mSig that captures the transcriptional footprint of EGFR activation revealed a subset of EGFR WT LUADs with mt-like features. mSig refines LUAD taxonomy beyond mutation-only pie-chart models by incorporating lineage and comutation context. Lineage-directed stratification with coalteration identifies clinically relevant groups across EGFR and RAS states and highlights treatment opportunities for patients currently considered oncogene-negative.FUNDINGNational Cancer Institute (NCI) U01CA272541, R01CA262296, U24CA264021, UG1CA233333, R01CA211939.

Indexed as

Adenocarcinoma of LungDual-Specificity PhosphatasesLung NeoplasmsMitogen-Activated Protein Kinase Phosphatasesras ProteinsErbB ReceptorsGene Expression Regulation, NeoplasticHumansMutationSignal TransductionDual-Specificity PhosphatasesDUSP4 protein, humanEGFR protein, humanErbB ReceptorsMitogen-Activated Protein Kinase Phosphatasesras ProteinsBiomarkersCell biologyClinical ResearchGenetic variationLung cancerOncology

Identifiers

PMID41817586
PMCPMC13135389

What Socratic 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 Socratic graph.