Abstract 5162: Tumor transcriptional identity as a key predictor of clinical outcome in EGFR-Mutant non-small cell lung cancer

Abstract

Abstract Background: EGFR tyrosine-kinase inhibitors (TKIs) produce significant clinical benefit in patients (pts) harboring EGFR-activating mutations, however, patient outcomes remain heterogeneous, highlighting the need to identify novel biomarkers for treatment response prediction. We aim to investigate the association of tumor transcriptional identity with clinical outcomes in EGFR-mutant NSCLC. Methods: We used consensus classification to derive unsupervised transcriptionally distinct clusters in EGFR-mutant NSCLC from the TCGA (N= 44). Then, we built a predictive model using extreme-gradient boosting to (1) classify an additional 88 surgically resected tumors from the LCCS study and analyzed for overall survival (OS) prediction; (2) classify metastatic EGFR-mutant NSCLC from the RELAY trial with available RNAseq data (N = 106) and analyzed for progression-free survival (PFS) with erlotinib treatment. Results: Consensus clustering revealed two distinct clusters, one associated with more lineage-preserving features and one with more lineage-independent features that could transcriptionally distinguish EGFR-mutant lung cancer. No differences in primary EGFR mutations were detected across the two clusters. Ingenuity Pathway Analysis revealed TGFB1 as the most significantly enriched pathway in the lineage-independent cluster which was also significantly more mesenchymal (p = 0.0028). For surgically resectioned EGFR-mutant NSCLC, OS was consistently shorter in the lineage-independent cluster for TCGA (23.6 vs 58.3 mo; p = 0.049; HR = 0.4) and the LCCS cohort (55.7 vs not-reached; p = 0.0073; HR = 0.27). In the metastatic setting with erlotinib treatment, there was a trend for PFS to be shorter in the lineage-independent cluster (15.2 vs 19.4 mo; p = 0.12; HR = 0.61). Conclusions: Transcriptional identity in EGFR-mutant lung cancer are prognostic of clinical outcome. The lineage-independent tumors have inferior benefit to EGFR TKI as well as inferior OS, indicating that transcriptional biomarkers can be independent of oncogene biomarkers. These transcriptional biomarkers may help identify pts with poor outcome for future intensification of therapies. Citation Format: Simon Heeke, Monique Nilsson, Don Gibbons, Jianjun Zhang, Ignatio I. Wistuba, Keunchil Park, John V. Heymach, Xiuning Le. Tumor transcriptional identity as a key predictor of clinical outcome in EGFR-Mutant non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5162.