PET-Based Radiogenomics Supports KEAP1/NFE2L2 Pathway Targeting for Non-Small Cell Lung Cancer Treated with Curative Radiotherapy

Abstract

Radiotherapy (RT) is one of the major treatment options for localized lung cancer. Either delivered in normo- or moderate/highly hypofractionated regimens, use of RT is increasing especially thanks to the development of stereotactic radiotherapy (SBRT). RT is associated with a higher risk of local relapse when compared with surgery but with a preferable toxicity profile. The KEAP1/NFE2L2 mutational status has been proven as significantly correlated with local relapse in patients treated with RT. Several (33) transcriptomic features were previously identified as dependent from the KEAP1/NFE2L2 mutational status. However, these genetic and transcriptomic tests are rarely performed because of their cost and lack of availability. Prediction of the KEAP1/NFE2L2 mutational status on non-invasive modalities such as imaging could help in further personalizing each therapeutic strategy. Due to the small size of patients with both mutation status (MutKEAP1-NFE2L2) and PET/CT, a first model (RNASeq) predicting the mutation status (MutRNASeq) using the 33 previously identified transcriptomic features was developed on patients from the TCGA-LUSC, TCGA-LUAD, CPTAC-LSCC and CPTAC-LUAD cohorts (770 patients) and externally validated on the NSCLC-Radiogenomics cohort (117 patients). Narrowing the patients to those with an available PET/CT, a second model (RNAPET) was then built and internally validated to predict the previously MutRNASeq probability using PET/CT-extracted radiomics features. The RNAPET model was then validated on an external cohort of 151 patients treated with curative radiotherapy for a localized non-small cell lung cancer (VMAT cohort). For each model, features were combined using a neural network approach (Multilayer Perceptron) within a statistical software modeler. Performances were evaluated based on the ROC-features as well as decision curve analysis. The RNASeq model resulted in a C-Index of 0.82, Sensitivity (Se) of 70.3% and Specificity (Sp) of 93.4% in the validation cohort. Regarding the PET/CT-based prediction on a training cohort of 101 patients, the retained RNAPET model resulted in an AUC of 0.90 (p < 0.001). With a probability threshold of 20% and applied to the testing cohort, the RNAPET model achieved a C-Index of 0.7 with respective Se/Sp of 60.0% and 80.9% for the prediction of the MutRNASeq. The same radiomics model was validated on the VMAT cohort as patients were significantly stratified based on their risk of locoregional (LR) relapse with a hazard ratio of 2.61 (p = 0.02). Our three-step approach enables the prediction of the MutKEAP1-NFE2L2 using PET/CT-extracted radiomics features and efficiently classified patient at risk of LR relapse in an external cohort treated with radiotherapy. This first evidence should be further evaluated on larger cohorts, and implemented in LR risk prediction models.