Stephen Baylin

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Defining a gene promoter methylation signature in sputum for lung cancer risk assessment.

Shuguang Leng; Kieu Do; Christin M Yingling; Maria A Picchi; Holly J Wolf; Timothy C Kennedy; William J Feser; Anna E Baron; Wilbur A Franklin; Malcolm V Brock; et al. (Profiled Authors: James Herman; Stephen Baylin; Malcolm Brock)

Lung Cancer Program, Lovelace Respiratory Research Institute, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico 87108, USA.
Clinical cancer research : an official journal of the American Association for Cancer Research 2012;18(12):3387-95.

PURPOSE: To evaluate the methylation state of 31 genes in sputum as biomarkers in an expanded nested, case-control study from the Colorado cohort, and to assess the replication of results from the most promising genes in an independent case-control study of asymptomatic patients with stage I lung cancer from New Mexico. EXPERIMENTAL DESIGN: Cases and controls from Colorado and New Mexico were interrogated for methylation of up to 31 genes using nested, methylation-specific PCR. Individual genes and methylation indices were used to assess the association between methylation and lung cancer with logistic regression modeling. RESULTS: Seventeen genes with ORs of 1.4 to 3.6 were identified and selected for replication in the New Mexico study. Overall, the direction of effects seen in New Mexico was similar to Colorado with the largest increase in case discrimination (ORs, 3.2-4.2) seen for the PAX5α, GATA5, and SULF2 genes. Receiver operating characteristic (ROC) curves generated from seven-gene panels from Colorado and New Mexico studies showed prediction accuracy of 71% and 77%, respectively. A 22-fold increase in lung cancer risk was seen for a subset of New Mexico cases with five or more genes methylated. Sequence variants associated with lung cancer did not improve the accuracy of this gene methylation panel. CONCLUSIONS: These studies have identified and replicated a panel of methylated genes whose integration with other promising biomarkers could initially identify the highest risk smokers for computed tomographic screening for early detection of lung cancer.

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