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Updated: Jul 3, 2025
Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
PET/CT-Based Radiogenomics Supports KEAP1/NFE2L2 Pathway Targeting for Non-Small Cell Lung Cancer Treated with
Vincent Bourbonne1,2, Moncef Morjani3, Olivier Pradier3,2
1Department of Radiation Oncology, University Hospital, Brest, France; vincent.bourbonne@chu-brest.fr.
Abstract:
In lung cancer patients, radiotherapy is associated with a increased risk of local relapse (LR) when compared with surgery but with a preferable toxicity profile. The KEAP1/NFE2L2 mutational status (MutKEAP1/NFE2L2) is significantly correlated with LR in patients treated with radiotherapy but is rarely available. Prediction of MutKEAP1/NFE2L2 with noninvasive modalities could help to further personalize each therapeutic strategy. Methods: Based on a public cohort of 770 patients, model RNA (M-RNA) was first developed using continuous gene expression levels to predict MutKEAP1/NFE2L2, resulting in a binary output. The model PET/CT (M-PET/CT) was then built to predict M-RNA binary output using PET/CT-extracted radiomics features. M-PET/CT was validated on an external cohort of 151 patients treated with curative volumetric modulated arc radiotherapy. Each model was built, internally validated, and evaluated on a separate cohort using a multilayer perceptron network approach. Results: The M-RNA resulted in a C statistic of 0.82 in the testing cohort. With a training cohort of 101 patients, the retained M-PET/CT resulted in an area under the curve of 0.90 (P < 0.001). With a probability threshold of 20% applied to the testing cohort, M-PET/CT achieved a C statistic of 0.7. The same radiomics model was validated on the volumetric modulated arc radiotherapy cohort as patients were significantly stratified on the basis of their risk of LR with a hazard ratio of 2.61 (P = 0.02). Conclusion: Our approach enables the prediction of MutKEAP1/NFE2L2 using PET/CT-extracted radiomics features and efficiently classifies patients at risk of LR in an external cohort treated with radiotherapy.
Insights
Predicting KEAP1/NFE2L2 mutational status using radiomics from PET/CT scans can identify lung cancer patients at high risk of local relapse after radiotherapy, aiding personalized treatment strategies.
Area of Science:
- Oncology
- Radiology
- Genetics
Background:
- Radiotherapy for lung cancer offers a favorable toxicity profile but carries a higher risk of local relapse (LR) compared to surgery.
- KEAP1/NFE2L2 mutational status (MutKEAP1/NFE2L2) is a key predictor of LR in radiotherapy patients, but this information is often unavailable.
- Noninvasive prediction of MutKEAP1/NFE2L2 is crucial for personalizing lung cancer treatment strategies.
Purpose of the Study:
- To develop and validate a noninvasive method for predicting KEAP1/NFE2L2 mutational status in lung cancer patients.
- To assess the ability of radiomics features from PET/CT scans to predict MutKEAP1/NFE2L2 and subsequent local relapse risk.
- To personalize radiotherapy strategies by identifying patients at high risk for local relapse.
Main Methods:
- A model RNA (M-RNA) was developed using gene expression data to predict MutKEAP1/NFE2L2 status in a public cohort (n=770).
- A model PET/CT (M-PET/CT) was subsequently built using radiomics features from PET/CT scans to predict the M-RNA binary output.
- Both models were internally validated and evaluated on separate cohorts, including an external cohort (n=151) treated with volumetric modulated arc radiotherapy.
Main Results:
- The M-RNA model achieved a C statistic of 0.82 in the testing cohort.
- The M-PET/CT model demonstrated strong predictive performance with an area under the curve of 0.90 (P < 0.001) in the training cohort.
- In the external validation cohort, M-PET/CT (C statistic of 0.7) significantly stratified patients based on LR risk (hazard ratio = 2.61, P = 0.02).
Conclusions:
- PET/CT-derived radiomics features can effectively predict KEAP1/NFE2L2 mutational status in lung cancer patients.
- This noninvasive approach enables efficient classification of patients at risk for local relapse following radiotherapy.
- The findings support the integration of radiomics analysis for personalized lung cancer treatment planning.
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