Background: Pancreatic cancer is the third leading cause of cancer death in the United States, with a 5-year survival rate of only 12.8%. If detected early enough the cancer is still localized and the survival rate for pancreatic cancer patients increases to 44%. Unfortunately, only 14% of pancreatic cancer patients are diagnosed with localized pancreatic cancer according to NCI Cancer Statistics. Therefore, there is a tremendous need for early screening and detection of pancreatic cancer. Objective: The analysis of volatile organic compounds (VOCs) in exhaled breath has a great potential to be developed as a rapid and non-invasive screening tool for detection of early pancreatic cancer. The objective of this work is to develop a microreactor technology for quantitative analysis of carbonyl compounds in exhaled breath for detection of pancreatic cancer. Certain elevated levels of these compounds in exhaled breath are related to cancer dysfunction. Method: In this exploratory study, we used a newly developed breath analysis technology that combines a microfabricated silicon microreactor with an enhanced carbonyl trapping agent to capture carbonyl compounds via oximation reactions for analysis by Ultra high-performance liquid chromatography – mass spectrometry (UHPLC-MS). Results: We analyzed exhaled breath samples from 25 pancreatic cancer patients and 23 controls. A panel of aldehydes and ketones demonstrated significantly higher concentrations for the pancreatic cancer patients. 13 of the 34 VOCs are significantly different between cancer patient group and control group, with the best linear discriminant analysis performance being the differences between hexanone, acetaldehyde, and MDA concentrations. Conclusion: This panel of carbonyl compounds is promising for detection of early pancreatic cancer. Larger numbers of pancreatic cancer patients and controls without cancer will be required for future development of the technology and establishment of machine learning algorithms for increasing diagnostic sensitivity and specificity for clinical applications.
