Background Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. Methods In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. Results In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). Conclusions The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Background Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular “field of injury” from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient’s risk stratification. Methods The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories. Results Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale). Conclusions The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

Accurate assessment of the risk of malignancy (ROM) is critical in the management of a screen-detected or incidental pulmonary nodule (PN) to minimize procedures for benign disease and for timely diagnosis and treatment of patients with lung cancer. We recently demonstrated that a clinical-genomic classifier using RNA whole-transcriptome sequencing of cells from the nasal epithelium in ever-smokers with a PN can accurately classify cancer risk. 1 Now unblinded after clinical validation, we show that the classifier has excellent performance in PN independent of size or stage.

8551 Background: The goal of lung nodule management is to make an early diagnosis in patients with lung cancer while avoiding unnecessary, costly and potentially harmful procedures in patients with benign lesions. Increased implementation of low dose CT screening will lead to increased numbers of both benign and malignant nodules that will require effective management. We have previously described the feasibility of detecting gene expression changes associated with lung cancer (“field of injury”) in nasal epithelium utilizing whole transcriptome RNA sequencing from non-invasive nasal brush samples. Using this approach, we now report the performance of candidate nasal classifiers that combine both genomic and clinical features to risk stratify lung nodules from ever-smokers to aid in the diagnosis of lung cancer. Methods: Patients from several clinical cohorts who were ever-smokers with a lung nodule < 30 mm and without a history of prior cancer underwent nasal epithelium sampling. All patients had at least one year of follow up or until a final diagnosis of benign or malignant nodule was made. Candidate classifiers were developed using whole-transcriptome RNA sequencing and machine learning. Training of these classifiers included genomic and clinical information (age, sex, pack-years, years-since-quit, nodule size and nodule spiculation). Two decision boundaries were chosen to maximize sensitivity and specificity for low and high-risk nodules, respectively. The performance of these classifiers was evaluated using cross-validation (CV). Results: All candidate nasal classifiers underwent CV assessment on over 700 patients with lung nodules. All candidate classifiers achieved CV performance of > 40% specificity at 95% sensitivity in low- risk nodules and all candidate classifiers achieved CV performance of > 60% sensitivity at 90% specificity in high-risk nodules. The classifiers stratified benign nodules as low- risk with > 95% negative predictive value (NPV), intermediate risk nodules were stratified as 5-65% risk and malignant nodules were stratified as high- risk with > 65% positive predictive value (PPV) in a population with estimated 25% prevalence. This performance was robust across subgroups of age ( < 65 year vs. >65 year), sex, and current versus former smokers. Conclusions: The nasal genomic-clinical candidate classifiers have a high NPV effectively identifying benign nodules when calling them low- risk, thereby, informing decisions to more safely avoid a diagnostic workup. Additionally, the high PPV of these classifiers identifies malignant nodules when calling them high- risk and informs decisions regarding the urgency of a further diagnostic work-up. A nasal genomic-clinical classifier has the potential to serve as a non-invasive tool for lung cancer risk-stratification to help inform decision making in patients with lung nodules.