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A Blood-Based Multi-Gene Expression Classifier to Distinguish Benign From Malignant Pulmonary Nodules
Lung Cancer SESSION TITLE: Advances in Lung Cancer SESSION TYPE: Original Investigation Slide PRESENTED ON: Wednesday, November 1, 2017 at 08:45 AM - 10:00 AM
A Blood-Based Multi-Gene Expression Classifier to Distinguish Benign From Malignant Pulmonary Nodules Anil Vachani* Michael Atalay Ross Bremner Brad Broussard Karen Copeland Katarine Egressy J Ferguson Lyssa Friedman Randall Harris Joseph Leach Philip McQuary Thomas O’Brien Saiyad Sarkar Nadia Sheibani Jaime Shuff Thomas Siler Clyde Southwell and Lyndal Hesterberg University of Pennsylvania, Philadelphia, PA PURPOSE: Pulmonary nodules are commonly encountered in clinical practice as a result of increased use of CT imaging. The risk of lung cancer for any nodule is largely determined by size, appearance, patient age and smoking history. Patients with pulmonary nodules frequently undergo invasive diagnostic testing; however, these approaches are potentially limited by inconclusive results and complications and are often performed in patients with benign disease. We have previously reported on a blood gene expression classifier for lung cancer that may identify patients with low risk nodules, allowing these patients to be triaged to surveillance imaging, sparing the use of more invasive procedures. We now report results from an expanded analysis of this biomarker-based test. METHODS: A total of 299 previous or current smokers with nodules ranging from 5-30mm were enrolled in a multicenter prospective observational study. At baseline, a blood sample and data on various lung cancer risk factors were obtained. RNA was isolated from PAXgene tubes and analyzed using the NanoString nCounter gene expression platform. A multivariate gene expression classifier was derived using 6-fold cross validation for marker selection and internal model validation. For each fold, bootstrap forest partitioning and regression models utilizing a lasso-fitting algorithm were used to generate 6 individual models. Further algorithm refinement used markers identified in the initial analysis training on all samples. Nodule size was included as a variable in all models. The performance of the classifier was evaluated with the use of receiver-operating-characteristic curves and calculation of area under the curve (AUC).
CONCLUSIONS: A blood-based gene expression classifier in current and former smokers with a pulmonary nodule allows identification of benign and malignant nodules with high accuracy. CLINICAL IMPLICATIONS: This classifier demonstrates feasibility for improved assessment of cancer risk in former and current smokers with lung nodules ranging from 5-30 mm in diameter. This classifier warrants further development and validation of its performance. DISCLOSURE: Anil Vachani: Consultant fee, speaker bureau, advisory committee, etc.: Consultant to OncoCyte, Other: Payment for enrollment to current study via Clinical Trial Agreement Michael Atalay: Other: Payment for enrollment to current study via Clinical Trial Agreement Ross Bremner: Other: Payment for enrollment to current study via Clinical Trial Agreement Brad Broussard: Other: Payment for enrollment to current study via Clinical Trial Agreement Karen Copeland: Consultant fee, speaker bureau, advisory committee, etc.: Consultant to OncoCyte Katarine Egressy: Other: Payment for enrollment to current study via Clinical Trial Agreement J Ferguson: Other: Payment for enrollment to current study via Clinical Trial Agreement Lyssa Friedman: Shareholder: OncoCyte shareholder, Consultant fee, speaker bureau, advisory committee, etc.: OncoCyte consultant Randall Harris: Other: Payment for enrollment to current study via Clinical Trial Agreement Joseph Leach: Other: Payment for enrollment to current study via Clinical Trial Agreement Philip McQuary: Employee: Employee of OncoCyte, Shareholder: Shareholder of OnocCyte Thomas O’Brien: Other: Payment for enrollment to current study via Clinical Trial Agreement Saiyad Sarkar: Other: Payment for enrollment to current study via Clinical Trial Agreement Nadia Sheibani: Employee: Employee of
chestjournal.org
629A
LUNG CANCER
RESULTS: A total 158 benign and 141 malignant pulmonary nodules were analyzed (overall cancer prevalence 47%). Malignant nodules included NSCLC (n=124), SCLC (n=8), carcinoid (n=4), metastatic colorectal (n=1), lymphoma (n=1) and cancer type unknown (n=3). The average performance of the classifier in the test sets across all 6 folds yielded a sensitivity of 0.90 (95% CI 0.73-0.96), specificity of 0.62 (95% CI, 0.48-0.72), and AUC of 0.86. There were 18 markers selected in three or more of the 6-fold models. These 18 markers were then used to train a classifier on all 299 samples. The final classifier used 15 markers and nodule size, yielding an overall AUC 0.92. In this study population, nodule size alone and the VA risk model, which also includes nodule size, yielded AUC of 0.85 and 0.82 respectively.
OncoCyte, Shareholder: Shareholder of OncoCyte Jaime Shuff: Other: Payment for enrollment to current study via Clinical Trial Agreement Thomas Siler: Other: Payment for enrollment to current study via Clinical Trial Agreement Clyde Southwell: Other: Payment for enrollment to current study via Clinical Trial Agreement Lyndal Hesterberg: Employee: Employee of OncoCyte, Shareholder: Shareholder of OncoCyte Results of development of a blood-based gene expression classifier that is not yet available for clinical use. DOI:
http://dx.doi.org/10.1016/j.chest.2017.08.661
LUNG CANCER
Copyright ª 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
630A
[
152#4S CHEST OCTOBER 2017
]
A Blood-Based Multi-Gene Expression Classifier to Distinguish Benign From Malignant Pulmonary Nodules Anil Vachani* Michael Atalay Ross Bremner Brad Broussard Karen Copeland Katarine Egressy J Ferguson Lyssa Friedman Randall Harris Joseph Leach Philip McQuary Thomas O’Brien Saiyad Sarkar Nadia Sheibani Jaime Shuff Thomas Siler Clyde Southwell and Lyndal Hesterberg University of Pennsylvania, Philadelphia, PA PURPOSE: Pulmonary nodules are commonly encountered in clinical practice as a result of increased use of CT imaging. The risk of lung cancer for any nodule is largely determined by size, appearance, patient age and smoking history. Patients with pulmonary nodules frequently undergo invasive diagnostic testing; however, these approaches are potentially limited by inconclusive results and complications and are often performed in patients with benign disease. We have previously reported on a blood gene expression classifier for lung cancer that may identify patients with low risk nodules, allowing these patients to be triaged to surveillance imaging, sparing the use of more invasive procedures. We now report results from an expanded analysis of this biomarker-based test. METHODS: A total of 299 previous or current smokers with nodules ranging from 5-30mm were enrolled in a multicenter prospective observational study. At baseline, a blood sample and data on various lung cancer risk factors were obtained. RNA was isolated from PAXgene tubes and analyzed using the NanoString nCounter gene expression platform. A multivariate gene expression classifier was derived using 6-fold cross validation for marker selection and internal model validation. For each fold, bootstrap forest partitioning and regression models utilizing a lasso-fitting algorithm were used to generate 6 individual models. Further algorithm refinement used markers identified in the initial analysis training on all samples. Nodule size was included as a variable in all models. The performance of the classifier was evaluated with the use of receiver-operating-characteristic curves and calculation of area under the curve (AUC).
CONCLUSIONS: A blood-based gene expression classifier in current and former smokers with a pulmonary nodule allows identification of benign and malignant nodules with high accuracy. CLINICAL IMPLICATIONS: This classifier demonstrates feasibility for improved assessment of cancer risk in former and current smokers with lung nodules ranging from 5-30 mm in diameter. This classifier warrants further development and validation of its performance. DISCLOSURE: Anil Vachani: Consultant fee, speaker bureau, advisory committee, etc.: Consultant to OncoCyte, Other: Payment for enrollment to current study via Clinical Trial Agreement Michael Atalay: Other: Payment for enrollment to current study via Clinical Trial Agreement Ross Bremner: Other: Payment for enrollment to current study via Clinical Trial Agreement Brad Broussard: Other: Payment for enrollment to current study via Clinical Trial Agreement Karen Copeland: Consultant fee, speaker bureau, advisory committee, etc.: Consultant to OncoCyte Katarine Egressy: Other: Payment for enrollment to current study via Clinical Trial Agreement J Ferguson: Other: Payment for enrollment to current study via Clinical Trial Agreement Lyssa Friedman: Shareholder: OncoCyte shareholder, Consultant fee, speaker bureau, advisory committee, etc.: OncoCyte consultant Randall Harris: Other: Payment for enrollment to current study via Clinical Trial Agreement Joseph Leach: Other: Payment for enrollment to current study via Clinical Trial Agreement Philip McQuary: Employee: Employee of OncoCyte, Shareholder: Shareholder of OnocCyte Thomas O’Brien: Other: Payment for enrollment to current study via Clinical Trial Agreement Saiyad Sarkar: Other: Payment for enrollment to current study via Clinical Trial Agreement Nadia Sheibani: Employee: Employee of
chestjournal.org
629A
LUNG CANCER
RESULTS: A total 158 benign and 141 malignant pulmonary nodules were analyzed (overall cancer prevalence 47%). Malignant nodules included NSCLC (n=124), SCLC (n=8), carcinoid (n=4), metastatic colorectal (n=1), lymphoma (n=1) and cancer type unknown (n=3). The average performance of the classifier in the test sets across all 6 folds yielded a sensitivity of 0.90 (95% CI 0.73-0.96), specificity of 0.62 (95% CI, 0.48-0.72), and AUC of 0.86. There were 18 markers selected in three or more of the 6-fold models. These 18 markers were then used to train a classifier on all 299 samples. The final classifier used 15 markers and nodule size, yielding an overall AUC 0.92. In this study population, nodule size alone and the VA risk model, which also includes nodule size, yielded AUC of 0.85 and 0.82 respectively.
OncoCyte, Shareholder: Shareholder of OncoCyte Jaime Shuff: Other: Payment for enrollment to current study via Clinical Trial Agreement Thomas Siler: Other: Payment for enrollment to current study via Clinical Trial Agreement Clyde Southwell: Other: Payment for enrollment to current study via Clinical Trial Agreement Lyndal Hesterberg: Employee: Employee of OncoCyte, Shareholder: Shareholder of OncoCyte Results of development of a blood-based gene expression classifier that is not yet available for clinical use. DOI:
http://dx.doi.org/10.1016/j.chest.2017.08.661
LUNG CANCER
Copyright ª 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
630A
[
152#4S CHEST OCTOBER 2017
]