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A. Vachani



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    ORAL 39 - Potential Biomarkers for CT Screening (ID 149)

    • Event: WCLC 2015
    • Type: Oral Session
    • Track: Screening and Early Detection
    • Presentations: 3
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      ORAL39.04 - Discussant for ORAL39.01, ORAL39.02, ORAL39.03 (ID 3437)

      16:45 - 18:15  |  Author(s): A. Vachani

      • Abstract
      • Presentation

      Abstract not provided

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      ORAL39.06 - Whole Blood microRNA Expression May Not Be Useful for Screening Non-Small Cell Lung Cancer (ID 2547)

      16:45 - 18:15  |  Author(s): A. Vachani

      • Abstract
      • Presentation
      • Slides

      Background:
      Five studies have shown that microRNA levels in whole blood can be used to diagnose lung cancer. We conducted a large bi-institutional study to validate this finding.

      Methods:
      PAXgene[TM] Blood miRNA System (Qiagen®) was used for peripheral venous blood collection and total RNA isolation for 85 pathologic stage IA-IIIB non-small cell lung cancer cases and 76 clinically-relevant controls who either had a high risk of developing lung cancer because of smoking and age >50 y, or had a benign pulmonary nodule. Cases and controls were accrued at two institutions in the United States, Roswell Park Cancer Institute, Buffalo and University of Pennsylvania, Philadelphia. MiRCURY™ microarrays (Exiqon®) with locked nucleic acid hybridization probes were used to quantify microRNAs in RNA isolates. Quantification was also performed using Taqman™ microRNA reverse transcription (RT)-PCR assays (ABI®) for five microRNAs whose lung cancer-diagnostic biomarker utility had been suggested by the five published studies.

      Results:
      Cases (n=85) and controls (n=76) were similar for age, gender, race, and blood hemoglobin and leukocyte but not platelet levels (Table 1). Of the 1936 human mature microRNAs detectable with the microarray platform, 586 (30%) were identified as expressed and reliably quantified among the study's subjects. However, none of the microRNAs was differentially expressed between cases and controls (P >0.05 in test using empirical Bayes-moderated t statistics and false discovery rate <5%). In classification analysis using the whole blood microRNA profiles with leave-one-out internal cross-validation, accuracy was 48% and 50% with the support vector machines and top-scoring pair methods, respectively. With RT-PCR assays, cases and controls did not differ for any of the five microRNAs whose biomarker potential had been suggested by previous studies.

      Table 1. Characteristics of study groups; *Fisher's exact test for categorical variables, and t test for others; #blood values for 84 cases and 30 controls.
      Cases Controls P*
      85 76
      Mean age, y (range, SD) 64 (41-83, 8) 61 (45-83, 9) 0.07
      %male 49 51 0.87
      %white 90 93 0.57
      RPCI 42 32 0.43
      U. Pennsylvania 43 44
      Adenocarcinoma 43
      Squamous cell 33
      Other non-small cell 9
      High-risk control 58
      Nodule control 18
      Leukocytes (x1000/µl; mean, SD)# 8.2 (2.6) 7.8 (2.1) 0.37
      Platelets (x1000/µl; mean, SD)# 291.8 (114.3) 238.2 (50.2) 0.01
      Hemoglobin (g/dl; mean, SD)# 13.4 (1.8) 13.9 (1.4) 0.15


      Conclusion:
      This study suggests that whole blood microRNA expression profiles may not be useful for developing biomarkers for use in non-invasive blood-based assays for generic screening of non-small lung cancer. Further studies are required to examine if whole blood microRNA diagnostic biomarkers may exist for use with specific types of lung cancer or non-cancer control conditions.

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      ORAL39.07 - A Bronchial Genomic Classifier Measured in Airway Epithelial Cells Improves Diagnostic Sensitivity of Bronchoscopy for Lung Cancer (ID 2215)

      16:45 - 18:15  |  Author(s): A. Vachani

      • Abstract
      • Presentation
      • Slides

      Background:
      Bronchoscopy is often used for the diagnosis of lung cancer however its sensitivity is imperfect, especially for small and peripheral lesions. Adjunctive methods to improve the sensitivity of cancer detection would reduce the need for more invasive follow-up procedures when bronchoscopy is non-diagnostic. It has previously been shown that gene expression of cytologically-normal bronchial airway epithelial cells is altered in smokers with lung cancer. In this study we evaluated the performance of a bronchial genomic classifier to predict malignancy in an independent cohort of suspect lung cancer patients.

      Methods:
      A bronchial genomic classifier consisting of the expression of 23 genes measured in the airway epithelium was evaluated in a previously published, independent cohort (n=163) of current and former undergoing bronchoscopy for suspect lung cancer. In cases where bronchoscopy was non-diagnostic for malignancy, the performance of the classifier was evaluated using ROC-AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

      Results:
      In the test set, bronchoscopy led to a diagnosis in 40 of 78 patients with cancer (sensitivity=51%, 95% CI 40-63%). The combination of the classifier with bronchoscopy improved the sensitivity to 96% (95% CI 89-99%; p <0.001); see Table. The prediction accuracy of the classifier was similar in lesions <3cm, as well as across cancer stage and histology. Among the 123 patients with a non-diagnostic bronchoscopy, 38 were ultimately diagnosed with lung cancer (prevalence of 31%). In this group of patients, the classifier had an AUC of 0.81 (95% CI, 0.73-0.88), accurately identifying 35 of the 38 lung cancer patients (sensitivity=92%; 95% CI, 78-98%), and 45 of 85 patients with benign lesions (specificity=53%; 95% CI, 42-63%). Of the 48 patients with a negative classifier result, 45 were diagnosed with benign lesions (NPV=94%, 95% CI 83-99%).

      Table. Performance of bronchoscopy, classifier, and the combined procedures in the test set
      Category Bronchoscopy Classifier[a] Combined
      Total, N 163 123 163
      Lung Cancer, N 78 38 78
      Benign Lesion, N 85 85 85
      Sens. (95% CI) 51% (40-62%) 92% (78-98%) 96% (89-99%)
      Spec. (95% CI) 100% (95-100%) 53% (42-63%) 53% (42-63%)
      NPV (95% CI) 69% (60-77%) 94% (83-99%) 94% (83-98%)
      PPV (95% CI) 100% (90-100%) 47% (36-58%) 65% (56-73%)
      a) The performance of the classifier was evaluated for patients in whom bronchoscopy did not result in a finding of lung cancer (n=123).

      Conclusion:
      A gene expression classifier measured in bronchial epithelial cells is able to accurately identify those at low risk for lung cancer in patients who have undergone bronchoscopy with non-diagnostic results. Due to the high sensitivity and NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing for lung cancer in patients whose bronchoscopy is non diagnostic.

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