TY - JOUR
T1 - Performance of the MasSpec pen for rapid diagnosis of Ovarian cancer
AU - Sans, Marta
AU - Zhang, Jialing
AU - Lin, John Q.
AU - Feider, Clara L.
AU - Giese, Noah
AU - Breen, Michael T.
AU - Sebastian, Katherine
AU - Liu, Jinsong
AU - Sood, Anil K.
AU - Eberlin, Livia S.
N1 - Funding Information:
Employment or Leadership: None declared. Consultant or Advisory Role: A.K. Sood, Kiyatec. Stock Ownership: A.K. Sood, Biopath. Honoraria: L.S. Eberlin, UT Health San Antonio. Research Funding: A.K. Sood, MTrap; L.S. Eberlin, the National Institutes of Health (Grant R00CA190783), the Cancer Prevention Research Institute of Texas (CPRIT, Grant RP180381), the Marion Milligan Mason Award for Women in the Chemical Sciences. Tissue samples were provided by the Cooperative Human Tissue Network (supported by the NCI), the MD Anderson Tissue Bank, and Seton Medical Center. Expert Testimony: None declared. Patents: J. Zhang, J.Q. Lin, and L.S. Eberlin are inventors in patent applications submitted by The University of Texas at Austin related to the MasSpec Pen technology. J. Zhang, US 15/692,167, US 62/ 591,179, US 62/640,385; J.Q. Lin, US Patent App. 15/692,167; N. Giese, US 62/591,179, US 62/640,385; A.K. Sood, SiRNA delivery (patents on DOPC, rHDL); L.S. Eberlin, US 15/692,167, US 62/ 591,179, US 62/640,385.
Funding Information:
A.K. Sood, MTrap; L.S. Eberlin, the National Institutes of Health (Grant R00CA190783), the Cancer Prevention Research Institute of Texas (CPRIT, Grant RP180381), the Marion Milligan Mason Award for Women in the Chemical Sciences. Tissue samples were provided by the Cooperative Human Tissue Network (supported by the NCI), the MD Anderson Tissue Bank, and Seton Medical Center.
Publisher Copyright:
© 2019 American Association for Clinical Chemistry.
PY - 2019/5
Y1 - 2019/5
N2 - BACKGROUND: Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. METHODS: MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. RESULTS: High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. CONCLUSIONS: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.
AB - BACKGROUND: Accurate tissue diagnosis during ovarian cancer surgery is critical to maximize cancer excision and define treatment options. Yet, current methods for intraoperative tissue evaluation can be time intensive and subjective. We have developed a handheld and biocompatible device coupled to a mass spectrometer, the MasSpec Pen, which uses a discrete water droplet for molecular extraction and rapid tissue diagnosis. Here we evaluated the performance of this technology for ovarian cancer diagnosis across different sample sets, tissue types, and mass spectrometry systems. METHODS: MasSpec Pen analyses were performed on 192 ovarian, fallopian tube, and peritoneum tissue samples. Samples were evaluated by expert pathologists to confirm diagnosis. Performance using an Orbitrap and a linear ion trap mass spectrometer was tested. Statistical models were generated using machine learning and evaluated using validation and test sets. RESULTS: High performance for high-grade serous carcinoma (n = 131; clinical sensitivity, 96.7%; specificity, 95.7%) and overall cancer (n = 138; clinical sensitivity, 94.0%; specificity, 94.4%) diagnoses was achieved using Orbitrap data. Variations in the mass spectra from normal tissue, low-grade, and high-grade serous ovarian cancers were observed. Discrimination between cancer and fallopian tube or peritoneum tissues was also achieved with accuracies of 92.6% and 87.9%, respectively, and 100% clinical specificity for both. Using ion trap data, excellent results for high-grade serous cancer vs normal ovarian differentiation (n = 40; clinical sensitivity, 100%; specificity, 100%) were obtained. CONCLUSIONS: The MasSpec Pen, together with machine learning, provides robust molecular models for ovarian serous cancer prediction and thus has potential for clinical use for rapid and accurate ovarian cancer diagnosis.
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U2 - 10.1373/clinchem.2018.299289
DO - 10.1373/clinchem.2018.299289
M3 - Article
C2 - 30770374
AN - SCOPUS:85065481990
SN - 0009-9147
VL - 65
SP - 674
EP - 683
JO - Clinical chemistry
JF - Clinical chemistry
IS - 5
ER -