Abstract 1090: Multiomic analysis of Taiwanese NSCLC: insights from a predominantly non-smoking cohort

Abstract Introduction: We curated multiomic data including whole exome sequencing (WES) and high-resolution chest CT from patients diagnosed with NSCLC, and evaluated the potential for a multitask deep learning algorithm to predict tumor mutational status and WES signatures from CT. Materials and Methods: Taipei Veterans General Hospital (TVGH) initiated a multi-omic databank integrating clinical, radiological, pathological, and genomic data from NSCLC patients. A predefined pipeline identified INDEL and SNP mutations with high or moderate impact using Ensembl Variant Effect Predictor (VEP). PCA reduced dimensionality of the WES binary data, and t-SNE was used for clustering and visualization. Chest CT was performed using scanners with ≥ 20 detector rows, at end-inspiration with 120 kV, automated exposure control, and a 50 cm field of view. Patients were split 80% for training and 20% for validation. 3D CT image data centered on lesions were rotated, resampled and cropped. A 3D ResNet-18 network, pre-trained on Kinetics-400, was used for predicting common NSCLC mutations (EGFR, TP53), and the first and second principal component coefficients. Results: A total of 317 patients were included, with 74% non-smokers.(Table 1) Of 109 LDCT detected nodules, only 27 were from smokers. PCA of 12, 023 gene mutations identified five principal components. t-SNE visualized the clustered distribution of the top three genes with the highest weights: EGFR, TP53, and RBM10, along with tumor mutation burden (TMB) and EGFR co-mutation status. Performance of the deep learning algorithm in the validation set (n=63) showed: EGFR mutation prediction: AUROC 71%, PPV 71%; TP53 mutation prediction: AUROC 76%, NPV 94%, sensitivity 0.84, specificity 0.72.Pearson correlation for prediction of the coefficients of the genetic signature principal components from CT images were 0.37 and 0.47. Conclusion: Deep learning algorithms can infer genetic mutation signatures from CT radiologic features. Further optimization is needed. Citation Format: Hsu-Ching Huang, Samira Masoudi, Daniel Halmos, Chien-jung Huang, Yu-Chao Wang, Yi-Chen Yeh, Yung-Hung Luo, Han-Shui Hsu, Yuh-Min Chen, Chun-Ku Chen, Sandip Patel, Albert Hsiao. Multiomic analysis of Taiwanese NSCLC: insights from a predominantly non-smoking cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1090.