Don D Sin1, C Martin Tammemagi, Stephen Lam, Matt J Barnett, Xiaobo Duan, Anthony Tam, Heidi Auman, Ziding Feng, Gary E Goodman, Samir Hanash, Ayumu Taguchi. 1. Don D. Sin, Stephen Lam, and Anthony Tam, University of British Columbia; Don D. Sin and Anthony Tam, Institute of Heart and Lung Health, James Hogg Research Center, St. Paul's Hospital; Stephen Lam and Xiaobo Duan, British Columbia Cancer Agency, Vancouver, British Columbia; C. Martin Tammemagi, Brock University, St. Catharines, Ontario, Canada; Matt J. Barnett, Ziding Feng, and Gary E. Goodman, Fred Hutchinson Cancer Research Center, Seattle, WA; Heidi Auman, Canary Foundation, Palo Alto, CA; and Ziding Feng, Samir Hanash, and Ayumu Taguchi, University of Texas MD Anderson Cancer Center, Houston, TX.
Abstract
PURPOSE: Preliminary studies have identified pro-surfactant protein B (pro-SFTPB) to be a promising blood biomarker for non-small-cell lung cancer. We conducted a study to determine the independent predictive potential of pro-SFTPB in identifying individuals who are subsequently diagnosed with lung cancer. PATIENTS AND METHODS: Pro-SFTPB levels were measured in 2,485 individuals, who enrolled onto the Pan-Canadian Early Detection of Lung Cancer Study by using plasma sample collected at the baseline visit. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB in addition to known lung cancer risk factors. Calibration and discrimination were evaluated, the latter by an area under the receiver operating characteristic curve (AUC). External validation was performed with samples collected in the Carotene and Retinol Efficacy Trial (CARET) participants using a case-control study design. RESULTS: Adjusted for age, sex, body mass index, personal history of cancer, family history of lung cancer, forced expiratory volume in one second percent predicted, average number of cigarettes smoked per day, and smoking duration, pro-SFTPB (log transformed) had an odds ratio of 2.220 (95% CI, 1.727 to 2.853; P < .001). The AUCs of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95% CI, 0.620 to 0.717; difference in AUC P < .001). In the CARET Study, the use of pro-SFPTB yielded an AUC of 0.683 (95% CI, 0.604 to 0.761). CONCLUSION: Pro-SFTPB in plasma is an independent predictor of lung cancer and may be a valuable addition to existing lung cancer risk prediction models.
PURPOSE: Preliminary studies have identified pro-surfactant protein B (pro-SFTPB) to be a promising blood biomarker for non-small-cell lung cancer. We conducted a study to determine the independent predictive potential of pro-SFTPB in identifying individuals who are subsequently diagnosed with lung cancer. PATIENTS AND METHODS: Pro-SFTPB levels were measured in 2,485 individuals, who enrolled onto the Pan-Canadian Early Detection of Lung Cancer Study by using plasma sample collected at the baseline visit. Multivariable logistic regression models were used to evaluate the predictive ability of pro-SFTPB in addition to known lung cancer risk factors. Calibration and discrimination were evaluated, the latter by an area under the receiver operating characteristic curve (AUC). External validation was performed with samples collected in the Carotene and Retinol Efficacy Trial (CARET) participants using a case-control study design. RESULTS: Adjusted for age, sex, body mass index, personal history of cancer, family history of lung cancer, forced expiratory volume in one second percent predicted, average number of cigarettes smoked per day, and smoking duration, pro-SFTPB (log transformed) had an odds ratio of 2.220 (95% CI, 1.727 to 2.853; P < .001). The AUCs of the full model with and without pro-SFTPB were 0.741 (95% CI, 0.696 to 0.783) and 0.669 (95% CI, 0.620 to 0.717; difference in AUC P < .001). In the CARET Study, the use of pro-SFPTB yielded an AUC of 0.683 (95% CI, 0.604 to 0.761). CONCLUSION: Pro-SFTPB in plasma is an independent predictor of lung cancer and may be a valuable addition to existing lung cancer risk prediction models.
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