A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells

Abstract

BackgroundAnnual 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.MethodsTargeted 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 61 CA cases and 59 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.ResultsAfter cross-validation, a model comprising expression values from 12 genes (CDKN1A, E2F1, ERCC1, ERCC4, ERCC5, GPX1, GSTP1, KEAP1, RB1, TP53, TP63, and XRCC1) and demographic factors age, gender, and pack-years smoking, had Receiver Operator Characteristic area under the curve (ROC AUC) of 0.975 (95% CI: 0.96–0.99). The overall classification accuracy was 93% (95% CI 88%–98%) with sensitivity 93.1%, specificity 92.9%, positive predictive value 93.1% and negative predictive value 93%. The ROC AUC for this classifier was significantly better (p < 0.0001) than the best model comprising demographic features alone.ConclusionsThe LCRT biomarker reported here displayed high accuracy 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-017-3287-4) contains supplementary material, which is available to authorized users.

Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells

“…Together, these observations support the hypothesis that lung cancer predisposition is due, in part, to hereditary and/or acquired sub-optimal protection of AEC DNA from damage associated with cigarette smoking. For example, there is large inter-individual variation in regulation of key DNA repair, antioxidant, and cell-cycle control genes in AEC [11, 20, 38–40], and the lung cancer risk test (LCRT) based on this variation has high accuracy to identify lung cancer subjects [11]. The LCRT prospective validation study is currently in progress (NCT01130285) [22].…”

“…The LCRT prospective validation study is currently in progress (NCT01130285) [22]. One of the variables in the LCRT biomarker is TP53 transcript abundance, and there is a 100-fold variation in TP53 expression in AEC [11, 20]. TP53 plays a key role in upregulating DNA repair genes in response to DNA damage [41], and TP53 protein directly regulates the key nucleotide excision repair (NER) gene, ERCC5, in AEC [42].…”

“…One approach toward this goal is to characterize differences in the prevalence and characteristics of somatic cell genetic damage in histologically normal airway epithelium of lung cancer cases compared to controls matched for smoking and age [7–9]. This idea is supported by the presence of extensive morphologic and molecular changes in the airway epithelium of lung tissue from heavy smokers, including large chromosomal changes and point mutations, and higher prevalence of these changes in subjects with lung cancer than in non-cancer subjects matched for smoking and age [7, 10, 11].…”

“…As implemented here, SNAQ-SEQ was able to measure variants at VAF as low as 5 × 10 − 4 (0.05%). We also demonstrated the value of including IS in targeted NGS for both RNA-Seq and mutation analysis [11, 19, 20].…”

Germline mutations and age at onset of lung adenocarcinoma

“…Individuals with an affected relative have been shown to have a higher risk of lung cancer 4 . Screening for an inherited predisposition may prove useful for identifying individuals who are at elevated risk of lung cancer 5 and thus may be appropriate candidates for lung cancer screening. Recent studies have shown that a minority of patients with lung adenocarcinoma carry germline pathogenic variants (PVs) in cancer‐associated genes, especially those in the Fanconi anemia (FA) or DNA repair pathways 6 .…”