Mazzone, Peter J., Wang, Xiao-Feng, Xu, Yaomin, Mekhail, Tarek, Beukemann, Mary C., Na, Jie, Kemling, Jonathan W., Suslick, Kenneth S., Sasidhar, Madhu
funding text
Disclosure: Peter J. Mazzone, MD, MPH, reports that his employing organization has received a grant from a company developing the technology presented in this manuscript for continued research. Kenneth S. Suslick, PhD, co-founded and is a consultant for iSense LLC, which is commercializing the technology under licensing from the University of Illinois. The University of Illinois holds several patents on which he is a co-inventor. iSense did not provide any funding for the research presented in this manuscripts. The other authors declare no conflicts of interest. Supported by the State of Ohio Third Frontier Program, ODOD TECH 06-55 and in part (K. S. S. and J. W. K.) through the NIH Genes, Environment and Health Initiative (U01ES016011).
abstract
Introduction: The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature. Objectives: To develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. Methods: The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models. Results: The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825-0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.864 for adenocarcinoma versus squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively). Conclusions: A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.