TY - JOUR
T1 - Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies
AU - DeHoog, Rachel J.
AU - Zhang, Jialing
AU - Alore, Elizabeth
AU - Lin, John Q.
AU - Yu, Wendong
AU - Woody, Spencer
AU - Almendariz, Christopher
AU - Lin, Monica
AU - Engelsman, Anton F.
AU - Sidhu, Stan B.
AU - Tibshirani, Robert
AU - Suliburk, James
AU - Eberlin, Livia S.
N1 - Funding Information:
This work was supported by the Cancer Prevention and Research Institute of Texas (CPRIT), Grant RP170427. We thank the Baylor College of Medicine Tissue Bank; the Kolling Institute of Medical Research Tumour Bank; and the Cooperative Human Tissue Network, which is funded by the National Cancer Institute, for providing tissue samples.
Funding Information:
ACKNOWLEDGMENTS. This work was supported by the Cancer Prevention and Research Institute of Texas (CPRIT), Grant RP170427. We thank the Baylor College of Medicine Tissue Bank; the Kolling Institute of Medical Research Tumour Bank; and the Cooperative Human Tissue Network, which is funded by the National Cancer Institute, for providing tissue samples.
Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019/10/22
Y1 - 2019/10/22
N2 - 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.
AB - 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.
KW - Ambient mass spectrometry
KW - Cancer diagnosis
KW - Metabolic profiles
KW - Molecular biomarkers
KW - Thyroid nodule diagnosis
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U2 - 10.1073/pnas.1911333116
DO - 10.1073/pnas.1911333116
M3 - Article
C2 - 31591199
AN - SCOPUS:85073714939
SN - 0027-8424
VL - 116
SP - 21401
EP - 21408
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 43
ER -