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A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer.

ABSTRACT: Bronchoscopy is frequently nondiagnostic in patients with pulmonary lesions suspected to be lung cancer. This often results in additional invasive testing, although many lesions are benign. We sought to validate a bronchial-airway gene-expression classifier that could improve the diagnostic performance of bronchoscopy.Current or former smokers undergoing bronchoscopy for suspected lung cancer were enrolled at 28 centers in two multicenter prospective studies (AEGIS-1 and AEGIS-2). A gene-expression classifier was measured in epithelial cells collected from the normal-appearing mainstem bronchus to assess the probability of lung cancer.A total of 639 patients in AEGIS-1 (298 patients) and AEGIS-2 (341 patients) met the criteria for inclusion. A total of 43% of bronchoscopic examinations were nondiagnostic for lung cancer, and invasive procedures were performed after bronchoscopy in 35% of patients with benign lesions. In AEGIS-1, the classifier had an area under the receiver-operating-characteristic curve (AUC) of 0.78 (95% confidence interval [CI], 0.73 to 0.83), a sensitivity of 88% (95% CI, 83 to 92), and a specificity of 47% (95% CI, 37 to 58). In AEGIS-2, the classifier had an AUC of 0.74 (95% CI, 0.68 to 0.80), a sensitivity of 89% (95% CI, 84 to 92), and a specificity of 47% (95% CI, 36 to 59). The combination of the classifier plus bronchoscopy had a sensitivity of 96% (95% CI, 93 to 98) in AEGIS-1 and 98% (95% CI, 96 to 99) in AEGIS-2, independent of lesion size and location. In 101 patients with an intermediate pretest probability of cancer, the negative predictive value of the classifier was 91% (95% CI, 75 to 98) among patients with a nondiagnostic bronchoscopic examination.The gene-expression classifier improved the diagnostic performance of bronchoscopy for the detection of lung cancer. In intermediate-risk patients with a nondiagnostic bronchoscopic examination, a negative classifier score provides support for a more conservative diagnostic approach. (Funded by Allegro Diagnostics and others; AEGIS-1 and AEGIS-2 ClinicalTrials.gov numbers, NCT01309087 and NCT00746759.).

SUBMITTER: Silvestri GA

PROVIDER: S-EPMC4838273 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Project description:BackgroundBronchoscopy is frequently used for the evaluation of suspicious pulmonary lesions found on computed tomography, but its sensitivity for detecting lung cancer is limited. Recently, a bronchial genomic classifier was validated to improve the sensitivity of bronchoscopy for lung cancer detection, demonstrating a high sensitivity and negative predictive value among patients at intermediate risk (10-60 %) for lung cancer with an inconclusive bronchoscopy. Our objective for this study was to determine if a negative genomic classifier result that down-classifies a patient from intermediate risk to low risk (<10 %) for lung cancer would reduce the rate that physicians recommend more invasive testing among patients with an inconclusive bronchoscopy.MethodsWe conducted a randomized, prospective, decision impact survey study assessing pulmonologist recommendations in patients undergoing workup for lung cancer who had an inconclusive bronchoscopy. Cases with an intermediate pretest risk for lung cancer were selected from the AEGIS trials and presented in a randomized fashion to pulmonologists either with or without the patient's bronchial genomic classifier result to determine how the classifier results impacted physician decisions.ResultsTwo hundred two physicians provided 1523 case evaluations on 36 patients. Invasive procedure recommendations were reduced from 57 % without the classifier result to 18 % with a negative (low risk) classifier result (p < 0.001). Invasive procedure recommendations increased from 50 to 65 % with a positive (intermediate risk) classifier result (p < 0.001). When stratifying by ultimate disease diagnosis, there was an overall reduction in invasive procedure recommendations in patients with benign disease when classifier results were reported (54 to 41 %, p < 0.001). For patients ultimately diagnosed with malignant disease, there was an overall increase in invasive procedure recommendations when the classifier results were reported (50 to 64 %, p = 0.003).ConclusionsOur findings suggest that a negative (low risk) bronchial genomic classifier result reduces invasive procedure recommendations following an inconclusive bronchoscopy and that the classifier overall reduces invasive procedure recommendations among patients ultimately diagnosed with benign disease. These results support the potential clinical utility of the classifier to improve management of patients undergoing bronchoscopy for suspect lung cancer by reducing additional invasive procedures in the setting of benign disease.

Project description:BackgroundThe gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort.MethodsWe collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study.ResultsWe found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.ConclusionWe developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.

Project description:Objective: With the rapid development of cancer genomics and immunomics, some new treatments of small cell lung cancer (SCLC) are emerging. However, there are limitations to the clinical use of tumor tissue. Our study aimed to evaluate the potential use of bronchial washing fluid (BWF) in the liquid biopsy of SCLC. Methods: Twenty-one extensive SCLC (ES-SCLC) patients were enrolled in this study. For all patients, four sample types, BWF supernatant (BWFs), BWF precipitate (BWFp), plasma and tumor tissue, were collected before receiving chemotherapy, and one type, plasma, was collected after chemotherapy. All samples were conducted to NGS using the 1021-gene panel. The concordance rates of genomic profiling using NGS in the four types of samples were evaluated. Multiple clinical information was analyzed for correlation. Results: We successfully tested 20 BWFs samples, 21 BWFp samples, 21 tumor tissue samples, 20 pre-treatment plasma, and 13 post-treatment plasma of these 21 patients. The detectability of somatic mutations was 100% for BWFs, BWFp, tumor tissues, and post-treatment plasma, and only one pre-treatment plasma was absent with any mutation. Matched tumor tissue, BWFs, BWFp, and pre-treatment plasma samples were subsistent for 19 patients. For these patients, 204 genomic alterations were identified in tissue samples, while 189 (92.6%), 175 (85.5%), and 163 (79.9%) alterations were detected in the matched BWFs, BWFp, and pre-treatment plasma, respectively. Moreover, we found that the three tumor markers associated with SCLC have a lower sensitivity than genomic alterations. The endocrine resistance pathway was found enriched in hyponatremia patients which may be related to the hyponatremia. The TMBs of BWF, BWFp, and pre-treatment plasma samples all had a strong correlation with that of tissue samples. Both the VAF and the MVAF of mutations in post-treatment plasma were less than those in pre-treatment plasma, which was in accordance with the evaluation of curative effect. Conclusions: For ES-SCLC patients, the liquid biopsy of BWF showed a highly potential advantage to identify DNA alterations, which suggested that genomic analysis of BWF liquid biopsy may have clinical value as a supplement for tissue and blood detection. Through the restricted validation, it can be widely used in routine clinical practice.

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A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer.

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