Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging. (28th October 2020)

• Record Type: Journal Article
• Authors: Bensussan, Alena V; Lin, John; Guo, Chunxiao; Katz, Ruth; Krishnamurthy, Savitri; Cressman, Erik; Eberlin, Livia S
• Is Part Of: Clinical chemistry. Volume 66:Number 11(2020)
• Page Start: 1424
• Page End: 1433
• Publication Date: 2020-10-28
• Subjects: lung -- subtypes -- adenocarcinoma -- squamous cell carcinoma -- desorption electrospray ionization -- imaging -- mass spectrometry; Clinical chemistry -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; Biochimie -- Périodiques; Diagnostics biologiques -- Périodiques; Biochemistry; Clinical chemistry; Pharmaceutical chemistry; Biochemistry; Laboratory Techniques and Procedures; Klinische chemie; Periodicals; 616.075605
• DOI: 10.1093/clinchem/hvaa207
• Languages: English
• ISSNs: 0009-9147

Abstract

Abstract: BACKGROUND: Distinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples. METHODS: DESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA. RESULTS: Various metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide. Conclusions: DESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.