Background: Annual low dose CT (LDCT) screening of individuals at high demographic risk reduces lung cancer mortality by more than 20%. However, subjects selected for screening based on demographic criteria typically have less than a 10% lifetime risk for lung cancer. Thus, there is need for a biomarker that better stratifies subjects for LDCT screening. Toward this goal, we previously reported a lung cancer risk test (LCRT) biomarker comprising 14 genome-maintenance (GM) pathway genes measured in normal bronchial epithelial cells (NBEC) that accurately classified cancer (CA) from non-cancer (NC) subjects. The primary goal of the studies reported here was to optimize the LCRT biomarker for high specificity and ease of clinical implementation.

Methods: Targeted competitive multiplex PCR amplicon libraries were prepared for next generation sequencing (NGS) analysis of transcript abundance at 68 sites among 33 GM target genes in NBEC specimens collected from a retrospective cohort of 120 subjects, including 60 CA cases and 60 NC controls. Genes were selected for analysis based on contribution to the previously reported LCRT biomarker and/or prior evidence for association with lung cancer risk. Linear discriminant analysis was used to identify the most accurate classifier suitable to stratify subjects for screening.

Results: The best classifier comprised 11 features including age and 10 transcript abundance assays representing nine genes; specifically, CDKN1A, E2F1, ERCC5, GPX1, GSTP1, KEAP1, TP63, XPA, and XRCC1. After cross-validation, the Receiver Operator Characteristic area under the curve was 0.94 (95% CI: 0.92-0.96) and overall classification accuracy was 88.3% (95% CI 82.4%-94.1%). Using a threshold set at 95% sensitivity, specificity was > 70%.

Conclusions: The LCRT biomarker reported here displayed high specificity and ease of implementation on a high throughput, quality-controlled targeted NGS platform. As such, it is optimized for clinical validation in specimens from the ongoing LCRT blinded prospective cohort study. Following validation, the biomarker is expected to have clinical utility by better stratifying subjects for annual lung cancer screening compared to current demographic criteria alone.

Citation Format: Jiyoun Yeo, Erin Crawford, Xiaolu Zhang, Sadik Khuder, Tian Chen, Albert Levin, Thomas Blomquist, James Willey. A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4737. doi:10.1158/1538-7445.AM2017-4737