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A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer
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  • Published: 06 March 2026

A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer

  • Mitchell Chen1,2,5,
  • Susan J. Copley2,
  • Kristofer Linton-Reid1,
  • Patrizia Viola3,
  • Yidong Han2,
  • Alessio Cortellini1,4,
  • Haonan Lu1,
  • Aleksander Mani2,
  • Marize Bahket2,
  • David J. Pinato1,
  • Danielle Power2,
  • Andrea G. Rockall1,2 &
  • …
  • Eric O. Aboagye1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Cancer
  • Computational biology and bioinformatics
  • Genetics
  • Oncology

Abstract

Newer-generation tyrosine kinase inhibitors (TKIs) have shown increasing efficacy in cancers driven by specific mutations, with epidermal growth factor receptor (EGFR) alterations remaining the most common actionable targets in non-small cell lung cancer (NSCLC). Treatment decisions are currently guided by tissue sampling and genetic testing, which are limited by procedural risks, patient tolerance, tumour heterogeneity and mutation evolution. Because co-mutations involving EGFR and other targetable genes can diminish treatment response, identifying exclusive EGFR mutation, defined by the absence of other actionable alterations, represents a clinically favourable scenario for first-line EGFR-TKI therapy. We developed a CT-based radiomics signature, EGFR-RPV, to predict exclusive EGFR mutational status using NSCLC patients (n = 304) from a multi-centre cohort with paired imaging and genomics data, and validated performance in an independent testing set (n = 51), alongside transcriptomics enrichment analysis. EGFR-RPV predicted exclusive EGFR mutation with accuracies of 0.77 (95% CI 0.66–0.88) and 0.71 (95% CI 0.54–0.89) in internal and external testing, respectively, and stratified patient prognosis (hazard ratio 2.15, 95% CI 1.50–3.08). FAM190A and CBMO1 were enriched in exclusive EGFR-positive cases, consistent with their roles in cell division regulation and vitamin A biosynthesis, respectively. EGFR-RPV thus offers a non-invasive approach to identify exclusive EGFR mutations, with a potential role in guiding first-line EGFR-TKI use.

Data availability

Our institutional study data (clinical, genetics and imaging) are retrospective in nature and protected through institutional compliance; and can be shared as per specific institutional review board (IRB) requirements. Upon reasonable request, a data sharing agreement can be initiated between the interested parties and the clinical institution following institution-specific guidelines. The gene expression dataset analysed during the study are from the NSCLC Radiogenomics public domain dataset, available in the Gene Expression Omnibus (GEO) repository, accessed via accession number GSE103584. Its paired imaging and clinical data are deposited in The Cancer Imaging Archive (TCIA), which can be accessed through the NSCLC Radiogenomics collection at: (https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics).

Code availability

All R scripts and guidance for use can be found on GitHub: https://github.com/scat2801/egfr-rpv/.

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Funding

MC is funded by Medical Research Council (MRC) Clinician Scientist Fellowship WSCC_PB1626 and a North West London Pathology E&R grant; EOA received funding from the Imperial College London Biomedical Research Centre (ICL-BRC), Experimental Cancer Medicines Centre (ECMC) and National Cancer Imaging Translational Accelerator consortium (NCITA). EOA also acknowledges the MRC.

Author information

Authors and Affiliations

  1. Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK

    Mitchell Chen, Kristofer Linton-Reid, Alessio Cortellini, Haonan Lu, David J. Pinato, Andrea G. Rockall & Eric O. Aboagye

  2. Imperial College Healthcare NHS Trust, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK

    Mitchell Chen, Susan J. Copley, Yidong Han, Aleksander Mani, Marize Bahket, Danielle Power & Andrea G. Rockall

  3. North West London Pathology, Charing Cross Hospital, London, W6 8RF, UK

    Patrizia Viola

  4. Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, Rome, 00128, Italy

    Alessio Cortellini

  5. Imperial Centre for Translational and Experimental Medicine (ICTEM), Hammersmith Campus Du Cane Road, London, UK

    Mitchell Chen

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Contributions

M.C: Conceptualisation, data curation, formal analysis, funding acquisition, project administration, resources and software, manuscript – original draft; S.J.C: Data curation, funding acquisition, resources, supervision; K.L: Software; P.V: Investigation, resources; Y.H: Data curation; A.C: Resources; H.L: Investigation; A.M: Data curation; M.B: Data curation; D.J.P: Resources; D.P: Resources; A.G.R: Resources; E.O.A: Conceptualisation, funding acquisition, funding acquisition, resources, supervision; All authors contributed to Manuscript – review & editing.

Corresponding author

Correspondence to Mitchell Chen.

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The authors declare no competing interests in the publication of this paper. MC sits on the Royal College of Radiologists AI steering committee; SJC and MC sit on the North West London Imaging Network AI Diagnostic Fund panel.

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Chen, M., Copley, S.J., Linton-Reid, K. et al. A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42948-4

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  • Received: 19 November 2025

  • Accepted: 28 February 2026

  • Published: 06 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-42948-4

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Keywords

  • Non-small cell lung cancer
  • Imaging biomarker
  • Radiogenomics
  • EGFR mutation
  • Tyrosine kinase inhibitor
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