Contributed by Richard N. Zare, October 11, 2019 (sent for review June 4, 2019; reviewed by Nathalie Y. R. Agar, Alan K. Jarmusch, and Bineet Sharma)
Early diagnosis and characterization of the severity of CFTR mutations carried in cystic fibrosis (CF) impacts life expectancy and quality of life for patients. We demonstrate a testing platform that combines analysis of perspiration samples by desorption electrospray ionization mass spectrometry and a machine-learning method of gradient boosted decision trees, with an accuracy for the correct identification of CF cases of 98 ± 2%. Our sampling method is minimally invasive; it only requires swiping a standard microscope slide across the patient’s forehead, with no sample processing. The whole collection and testing process takes less than 2 min, which suggests a faster alternative with comparable accuracy to the conventional sweat chloride test, which takes up to 3 h.
The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.
Author contributions: Z.Z. and R.N.Z. designed research; Z.Z. and D.A. performed research; Z.Z., D.A., C.M., and R.N.Z. analyzed data; and Z.Z., C.M., and R.N.Z. wrote the paper.
Reviewers: N.Y.R.A., Harvard Medical School; A.K.J., University of California, San Diego; and B.S., Renji Hospital.
The authors declare no competing interest.
Data deposition: Raw data can be accessed through the Open Science Framework at https://osf.io/j59h2/?view_only=c0212307a2714d909559550a65db0213, DOI:10.17605/OSF.IO/J59H2.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1909630116/-/DCSupplemental.
Published under the PNAS license.
