Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies

Rachel J. DeHoog; Jialing Zhang; Elizabeth Alore; John Q. Lin; Wendong Yu; Spencer Woody; Christopher Almendariz; Monica Lin; Anton F. Engelsman; Stan B. Sidhu; Robert Tibshirani; James Suliburk; Livia S. Eberlin

doi:10.1073/pnas.1911333116

New Research In

Contributed by Robert Tibshirani, August 22, 2019 (sent for review July 3, 2019; reviewed by Herbert Chen and Nicholas Winograd)

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Significance

Fine-needle aspiration (FNA) biopsy is a well-established technique for diagnosis of suspicious thyroid lesions. However, histologic discrimination between malignant and benign thyroid nodules from FNA can be challenging. Patients with an indeterminate FNA diagnosis often require diagnostic surgery, with the majority ultimately receiving a benign diagnosis. Here, we employ desorption electrospray ionization mass spectrometry (DESI-MS) imaging to diagnose thyroid lesions based on the molecular profiles obtained from FNA biopsy samples. Based on the molecular profiles obtained from malignant thyroid carcinomas and benign thyroid tissues, classification models were generated and used to predict on DESI-MSI data from FNA material with high performance. Our results demonstrate the potential for DESI-MSI to reduce the number of unnecessary diagnostic thyroid surgeries.

Abstract

Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.

Footnotes

↵¹R.J.D. and J.Z. contributed equally to this work.
↵²E.A. and J.Q.L. contributed equally to this work.
↵³To whom correspondence may be addressed. Email: tibs{at}stanford.edu, suliburk{at}bcm.edu, or liviase{at}utexas.edu.

Author contributions: R.T., J.S., and L.S.E. designed research; R.J.D., J.Z., E.A., J.Q.L., W.Y., S.W., C.A., M.L., R.T., J.S., and L.S.E. performed research; A.F.E. and S.B.S. contributed new reagents/analytic tools; R.J.D., J.Z., E.A., J.Q.L., W.Y., S.W., R.T., J.S., and L.S.E. analyzed data; and R.J.D., E.A., J.S., and L.S.E. wrote the paper.
Reviewers: H.C., The University of Alabama at Birmingham School of Medicine; and N.W., The Pennsylvania State University.
Competing interest statement: R.J.D., J.Z., E.A., W.Y., J.S., and L.S.E. are inventors on a provisional patent application owned by the Board of Regents of the University of Texas System and Baylor College of Medicine that relates to the use of mass spectrometry to diagnose thyroid cancer. L.S.E. and Nicholas Winograd are coauthors on a 2019 Q&A article.
Data deposition: Data reported in this paper have been deposited in Dataverse, https://doi.org/10.7910/DVN/3B6NSQ.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1911333116/-/DCSupplemental.

Published under the PNAS license.

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